E D 130 -Informatique

Virgilio Kmetzsch

Encadrée Par

Emmanuelle Becker

Olivier Colliot

Anaïs Baudot

Professeur Blaise Hanczar

Professeure Alessandra Carbone

Yann Le Cunff

SORBONNE UNIVERSITÉ

Keywords: data: macro-average ROC AUC and Spearman correlation . 6.6 Real data: latent space, computed trajectory, and scores . . . . . . . . . . . . . . . . . . . . comparison . . . . . . . . . . . histogram of computed DPS . . . . . . . . . . . . . . . . . . . . . . . Apathy evaluation scale

Multimodal analysis of neuroimaging and transcriptomic data in genetic frontotemporal dementia

Frontotemporal dementia (FTD) represents the second most common type of dementia in adults under the age of 65. Currently, there are no treatments that can cure this condition. In this context, it is essential that biomarkers capable of assessing disease progression are identified. Asymptomatic individuals who carry a genetic mutation that causes FTD are the ideal population for research. This thesis has two objectives. First, to analyze the expression patterns of mi-croRNAs taken from blood samples of patients, asymptomatic individuals who have certain genetic mutations causing FTD, and controls, to identify whether the expressions of some microRNAs correlate with mutation status and disease progression.

Second, this work aims at proposing methods for integrating cross-sectional data from microRNAs and neuroimaging to estimate disease progression.

We conducted three studies. Initially, we focused on plasma samples from C9orf72 expansion carriers. We identified four microRNAs whose expressions correlated with the clinical status of the participants. This study suggested that some microR-NAs may be dysregulated even before the onset of symptoms. Next, we tested all microRNA signatures identified in the literature as potential biomarkers of FTD or amyotrophic lateral sclerosis (ALS), in two groups of individuals (with C9orf72 expansion or with GRN mutation). The results of this study showed that microRNAs previously identified in sporadic or mixed cohorts may be useful to follow the progression of C9orf72-associated disease, but not of GRN-associated disease. Finally, in our third work, we proposed a new approach, using a supervised multimodal variational autoencoder, that estimates a disease progression score from cross-sectional microRNA expression and neuroimaging datasets with small sample sizes.

The work conducted in this interdisciplinary thesis showed that it is possible to use non-invasive biomarkers, such as circulating microRNAs and magnetic resonance imaging, to assess the progression of rare neurodegenerative diseases such as FTD and ALS. The main challenge in the future is to gather larger cohorts, with longitudinal data, to precisely assess the accuracy of these biomarkers in estimating disease progression of individual patients. I would like to express my sincere gratitude to both my supervisors Emmanuelle Becker and Olivier Colliot. First, for proposing such a fascinating subject and for recruiting me to carry out this thesis. Second, for their invaluable guidance, feedback and support during this work. Last, for their understanding, flexibility and kindness during a period of personal difficulties. It has

Cette thèse a deux objectifs. Premièrement, analyser les profils d'expression des microARNs circulants prélévés dans le plasma sanguin de participants, afin d'identifier si l'expression de certains microARNs est corrélée au statut mutationnel et à la progression de la maladie. Deuxièmement, proposer des méthodes pour intégrer des données transversales de type microARN et de neuroimagerie pour estimer la progression de la maladie.

Nous avons mené trois études. D'abord, nous avons analysé des échantillons de plasma provenant de porteurs d'une expansion dans le gène C9orf72. Nous avons identifié quatre microARNs dont l'expression était corrélée avec l'état clinique. Cette étude a suggéré que certains microARNs peuvent être dérégulés avant même l'apparition des symptômes. Ensuite, nous avons testé toutes les signatures de microARNs identifiées dans la littérature comme biomarqueurs potentiels de la DFT ou de la sclérose latérale amyotrophique (SLA), dans deux cohortes indépendantes, avec une mutation dans le gène C9orf72 ou GRN. Les résultats de cette étude ont montré que les microARNs précédemment identifiés dans des cohortes sporadiques ou mixtes peuvent être utiles pour suivre la progression de la maladie associée au gène C9orf72, mais pas celle de la maladie associée au gène GRN. Enfin, dans notre troisième étude, nous avons proposé une nouvelle méthode, utilisant un autoencodeur variationnel multimodal supervisé, qui estime à partir d'échantillons de petite taille un score de progression de la maladie en fonction de données transversales d'expression de microARNs et de neuroimagerie.

Les travaux menés dans cette thèse interdisciplinaire ont montré qu'il est possible d'utiliser des biomarqueurs non invasifs, tels que les microARNs circulants et l'imagerie par résonance magnétique, pour évaluer la progression de maladies neurodégénératives rares telles que la DFT et la SLA. Le principal défi à l'avenir est de rassembler des cohortes plus importantes, avec des données longitudinales, pour mieux déterminer la précision de ces biomarqueurs dans l'estimation de la progression de la maladie à l'échelle du patient. vii Chapter 1

Introduction 1.1 Context

Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disease, characterized by significant alterations in personality and social behavior, and associated with brain atrophy in the frontal and temporal lobes [START_REF] Neary | Frontotemporal dementia[END_REF]. Although a rare condition, FTD is the second most frequent early-onset dementia, after Alzheimer's disease, with an average age at onset being around 50 to 60 years [START_REF] Seelaar | Clinical, genetic and pathological heterogeneity of frontotemporal dementia: a review[END_REF]. Currently, there are no effective treatments for this fatal disorder, which has terrible personal, familial, and social consequences.

Genetic factors have an important contribution to FTD, since a positive family history is observed in up to 50% of patients [START_REF] Dejesus Hernandez | Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS[END_REF]. The most common causes of familial FTD are repeat expansions in the chromosome 9 open reading frame 72 (C9orf72) gene and mutations in the progranulin gene (GRN) [START_REF] Dejesus Hernandez | Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS[END_REF][START_REF] Renton | A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD[END_REF].

FTD shares disease mechanisms with amyotrophic lateral sclerosis (ALS), a devastating disorder caused by motor neuron degeneration, leading to progressive muscle atrophy and eventually complete paralysis [START_REF] Pasinelli | Molecular biology of amyotrophic lateral sclerosis: insights from genetics[END_REF]. The hexanucleotide repeat expansion in the C9orf72 gene is the most frequent cause of both familial FTD and familial ALS identified to date. Both diseases may occur within the same family, or even the same patient [START_REF] Dejesus Hernandez | Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS[END_REF].

Most validated biomarkers in these diseases are used to distinguish patients from neurologically healthy controls. However, future therapeutic trials also need to monitor treatment responses, so research on potential progression biomarkers is becoming increasingly important [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF]. Particularly, the study of genetic forms in the presymptomatic phase may provide valuable information about the early disease stages, before any irreversible neuronal loss has occurred (Rohrer et al., 2015).

In this context, the group led by Isabelle Le Ber at the Institut du Cerveau -Paris Brain Institute -ICM and the Institute for Memory and Alzheimer's Disease at the Pitié-Salpêtrière Hospital has put together some of the world's largest cohorts on familial variants of FTD/ALS. In each cohort, participants were divided into three groups: symptomatic mutation carriers (patients), neurologically healthy mutation Chapter 1. Introduction carriers (in the presymptomatic phase), and non-carriers (control group). Each subject underwent various examinations, including but not limited to structural magnetic resonance imaging (MRI) brain scans and blood samples.

Before the beginning of this PhD, the structural MRI scans were used to compute the volumes of specific brain regions of interest, an extensively applied technique for the study of neurodegenerative diseases. Statistical analyses of neuroimaging data have led to the identification of potential biomarkers of preclinical disease progression [START_REF] Bertrand | Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years[END_REF]. However, neuroimaging alone cannot totally explain such complex disorders and reliably measure treatment response [START_REF] Carreiro | Integrative biomarker discovery in neurodegenerative diseases[END_REF].

Blood samples were extracted to allow the quantification of circulating microRNA (miRNA) expression levels. MicroRNAs are a class of small ribonucleic acids (RNA) that regulate gene expression by degrading certain messenger RNAs or inhibiting their translation into proteins [START_REF] Huntzinger | Gene silencing by microRNAs: contributions of translational repression and mRNA decay[END_REF]. Circulating miR-NAs have been recently investigated as potential biomarkers for neurodegenerative diseases, with promising but so far conflicting results [START_REF] Grasso | Circulating miR-NAs as biomarkers for neurodegenerative disorders[END_REF].

From a clinical standpoint, the main goal of these cohort studies is to use multimodal data to identify robust biomarkers of FTD/ALS, in order to evaluate disease progression in future therapeutic trials.

Objectives

This thesis has two main objectives.

First, we aim to analyze miRNA expression levels in blood samples from presymptomatic mutation carriers, FTD/ALS patients and controls, to identify potential noninvasive biomarkers of disease progression. In particular, clinical trials would greatly benefit from robust preclinical biomarkers, that could assess the effect of treatments prior to the onset of symptoms and permanent brain damage.

Second, we intend to design a new approach to fuse multimodal data from miRNA expression and neuroimaging and evaluate disease progression using a cross-sectional study design. Indeed, research studies on rare neurodegenerative conditions, such as FTD and ALS, lack sufficient longitudinal data to apply existing disease progression models. Specifically, our work seeks to elucidate the following research questions regarding genetic forms of frontotemporal dementia and amyotrophic lateral sclerosis:

• Are circulating miRNAs promising disease progression biomarkers?

• Could we estimate disease progression using only cross-sectional miRNA and neuroimaging data?

Contributions

The contributions of this thesis are three-fold.

First, we investigate the expression levels of plasma miRNAs in a cohort of C9orf72 mutation carriers, and we identify four miRNAs likely to play a role in neurodegeneration and C9orf72-associated pathogenesis. Our study suggests that the expression levels of certain miRNAs are dysregulated during disease progression, and can be detected even before neurological symptoms appear.

Second, we analyze two homogeneous cohorts of C9orf72 and GRN mutation carriers, to assess all circulating miRNA signatures identified in previous conflicting studies about FTD or ALS. Our findings indicate that miRNA expression is most likely mutation specific, since miRNAs previously revealed in sporadic/mixed cohorts can potentially serve as biomarkers in C9orf72-associated FTD or ALS, but not in GRN-associated FTD.

Finally, we propose a novel approach for assessing disease progression scores (DPS) using cross-sectional neuroimaging and miRNA data, which may be used in small samples like those observed in rare disorders. The technique is developed and tested using data from C9orf72-associated FTD and ALS, but it might be applied to other disorders as well. The method's capacity to reliably predict the DPS is demonstrated on synthetic data, while tests on a real-world dataset, in the absence of ground truth scores, reveal the classification of distinct diagnostic groups.

The results of this work support that a single disease progression score, computed from cross-sectional data, might be used to represent a neurodegenerative disease progression.

Outline of this manuscript

Following this Introduction (Chapter 1), the manuscript consists of six more chapters:

• Chapter 2 describes the background related to this thesis, covering (1) genetic FTD and ALS, (2) miRNA data, (3) neuroimaging data, [START_REF] Gascon | Alterations in microRNA-124 and AMPA receptors contribute to social behavioral deficits in frontotemporal dementia[END_REF] integrative data analysis in neurodegenerative diseases, and ( 5) disease progression modeling and disease progression scores.

• Chapter 3 shows the miRNA and neuroimaging datasets used in this work.

• Chapter 4 presents a study analysing the expression of circulating miRNAs in a cohort of C9orf72 carriers and their first-degree relatives, aiming at identifying potential plasma biomarkers in this genetic form of FTD and ALS.

• Chapter 5 extends the previous study by selecting all published articles that identify miRNA signatures associated with FTD and/or ALS, and testing them in an independent cohort focused on the C9orf72 mutation and another cohort focused on the GRN mutation.

Chapter 1. Introduction

• Chapter 6 details the implementation of a novel framework, based on a supervised multimodal variational autoencoder, that infers disease progression scores from cross-sectional miRNA and neuroimaging data, evaluated on synthetic datasets and on a real-world dataset.

• Finally, Chapter 7 summarizes our contributions and outlines perspectives for future research.

In addition, the supplementary materials associated with the studies described in Chapters 4, 5 and 6 are presented in, respectively, Appendices A, B and C.

Chapter 2

Background

This chapter provides the background knowledge regarding the main concepts involved in this thesis. We initially present frontotemporal dementia and amyotrophic lateral sclerosis, focusing on their familial forms (Section 2.1). Next, we describe the usual preprocessing and feature extraction steps for microRNA (miRNA) expression data (Section 2.2) and structural neuroimaging data (Section 2.3). Then, we give an overview about integrative data analysis in neurodegenerative diseases, in particular the integration of transcriptomic and neuroimaging data (Section 2.4). Last, we briefly review the literature on disease progression modeling and disease progression scores (Section 2.5).

Frontotemporal dementia & amyotrophic lateral sclerosis

In this section, we present the two neurodegenerative diseases discussed in this thesis. We begin with a brief definition of frontotemporal dementia (section 2.1.1), followed by an explanation about its different familial forms (section 2.1.2), its overlap with amyotrophic lateral sclerosis (section 2. 1.3), and finally an overview about the neuroimaging and fluid biomarkers associated with these diseases (section 2.1.4).

Frontotemporal dementia definition

Frontotemporal dementia (FTD) designates a group of clinically, pathologically and genetically heterogeneous disorders, that have in common the degeneration of the frontal and temporal lobes of the brain [START_REF] Lashley | Review: An update on clinical, genetic and pathological aspects of frontotemporal lobar degenerations[END_REF]. The annual incidence of these devastating conditions is estimated to be approximately 4 new cases per 100,000 people [START_REF] Ratnavalli | The prevalence of frontotemporal dementia[END_REF]. Although rare, these disorders represent the second most common form of dementia in adults aged under 65 years, after Alzheimer's disease (AD) [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF].

FTD has two main clinical manifestations. First, behavioural variant FTD (bvFTD) is characterized by a progressive decline in cognition and social relationships, leading to impulsive actions, loss of empathy, compulsive behaviours, and deficits in executive functions [START_REF] Rascovsky | Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia[END_REF]. Second, primary progression aphasia (PPA) is marked by an increasing language deficit that impairs daily activities, and is divided into semantic variant PPA (svPPA), nonfluent variant PPA (nfvPPA) and Chapter 2. Background lopogenic variant PPA (lvPPA) [START_REF] Gorno-Tempini | Classification of primary progressive aphasia and its variants[END_REF]. Depending on the variant, PPA may lead to fluent speech with impaired comprehension of single words (svPPA), nonfluent speech with inability to use fundamental grammar and syntax (nfvPPA), or nonfluent speech characterized by compromised word retrieval (lvPPA) [START_REF] Gorno-Tempini | Classification of primary progressive aphasia and its variants[END_REF]. Other clinical phenotypes include concomitant motor neuron disease (MND) in the disease course, that may manifest as amyotrophic lateral sclerois (ALS), corticobasal syndrome (CBD), or progressive supranuclear palsy (PSP) [START_REF] Lashley | Review: An update on clinical, genetic and pathological aspects of frontotemporal lobar degenerations[END_REF].

Pathological heterogeneity is revealed by analyzing postmortem brain tissues collected from FTD patients. Different protein inclusions are observed, either of transactive response DNA-binding protein 43 (TDP-43), RNA-binding protein FUS, or microtubule associated protein tau [START_REF] Lashley | Review: An update on clinical, genetic and pathological aspects of frontotemporal lobar degenerations[END_REF]. The underlying pathology cannot be accurately predicted from the clinical phenotype [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF].

Familial forms

FTD may occur in sporadic or familial forms. However, a clear distinction between familial and sporadic disease is sometimes challenging, due to the difficulty to establish a reliable family history of cognitive disorders [START_REF] Turner | Genetic screening in sporadic ALS and FTD[END_REF]. Patients frequently have a family history of a comparable condition, usually in a pattern suggesting dominant inheritance [START_REF] Lashley | Review: An update on clinical, genetic and pathological aspects of frontotemporal lobar degenerations[END_REF]. Most familial forms of FTD are caused by mutations in one of three genes: C9orf72 (encoding protein C9orf72), GRN (encoding progranulin), or MAPT (encoding microtubule associated protein tau) [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF]. Other genes rarely associated with FTD include VCP, TARDP, and TBK1 [START_REF] Pottier | Genetics of FTLD: overview and what else we can expect from genetic studies[END_REF]. We observe that genetic forms have a predictable pathology: GRN mutations and C9orf72 repeat expansions result in TDP-43 pathology, while the MAPT mutation causes tau pathology. However, a particular genetic mutation may result in distinct phenotypes.

Overlap with amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal, progressive, degenerative disorder characterized by the deterioration of motor neurons in the brain and spinal chord, leading to the complete paralysis of voluntary muscles, and eventually death induced by respiratory failure [START_REF] Pasinelli | Molecular biology of amyotrophic lateral sclerosis: insights from genetics[END_REF]. ALS is estimated to have an incidence of 2.1 new cases per 100,000 people per year [START_REF] Chiò | Global epidemiology of amyotrophic lateral sclerosis: a systematic review of the published literature[END_REF].

It is recognized that FTD and ALS form a disease spectrum, with remarkable pathological and genetic overlap, both conditions occurring sometimes within the same family, or even the same individual [START_REF] Abramzon | The Overlapping Genetics of Amyotrophic Lateral Sclerosis and Frontotemporal Dementia[END_REF]. According to cross-sectional studies, up to 50% of ALS patients also show cognitive impairment FIGURE 2.1: Clinical, pathological and genetic spectrum of FTD (reproduced from [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF], with permission from the publisher). bvFTD: behavioural variant FTD; CBD: corticobasal degeneration; FUS: RNA-binding protein FUS; MND: motor neuron disease; nfvPPA: nonfluent variant primary progressive aphasia; PSP: progressive supranuclear palsy; svPPA: semantic variant primary progressive aphasia; TDP-43: transactive response DNA-binding protein 43.

as a consequence of FTD, and motor dysfunction affects up to 30% of FTD patients [START_REF] Burrell | Motor neuron dysfunction in frontotemporal dementia[END_REF].

Pathological overlap between these disorders is supported by the fact that TDP-43 represents the most frequent pathological protein aggregation in both FTD and ALS patients [START_REF] Dejesus Hernandez | Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS[END_REF]. Genetic overlap is observed mainly because the C9orf72 repeat expansion is the most common genetic abnormality observed in familial forms of both FTD and ALS [START_REF] Dejesus Hernandez | Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS[END_REF], with some other rarer genetic mutations, such as in VCP, TARDP, and TBK1 also playing a role in this overlap [START_REF] Abramzon | The Overlapping Genetics of Amyotrophic Lateral Sclerosis and Frontotemporal Dementia[END_REF]. On the other hand, mutations in GRN and MAPT are linked only to FTD.

Biomarkers

The Biomarkers Definitions Working Group defines a biomarker as "a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention" (Biomarkers Definitions Working Group., 2001). The authors highlight the main applications of biomarkers, including their use as a diagnostic tool, indicator of disease staging, or measure of clinical response to a treatment (Biomarkers Definitions Working Group., 2001).

Since frontotemporal dementia and amyotrophic lateral sclerosis are heterogeneous disorders with no disease-modifying treatments to date, robust biomarkers Chapter 2. Background are crucial for selecting individuals to clinical trials and evaluating treatment results [START_REF] Bertrand | Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years[END_REF][START_REF] Rohrer | Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis[END_REF]. Over the last two decades, a lot of effort went into identifying these biomarkers, with a particular focus on fluid biomaterial and neuroimaging features [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF]. Ideally, biomarkers should be noninvasive, reliable, and inexpensive, and ought to enable diagnosis, monitoring of disease progression, and treatment response [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF].

Evidence suggests that alterations in certain biomarkers occur several years before symptoms appear, implying that the best time to treat FTD and ALS is before clinical presentation, when the least amount of irreversible neuronal loss has happened and cognitive function is still preserved [START_REF] Bertrand | Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years[END_REF][START_REF] Rohrer | Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis[END_REF]. Studies focusing on familial forms of FTD and ALS are particularly important, since mutation carriers in the presymptomatic stage may give insight into the diseases' initial phases and improve therapy options [START_REF] Rohrer | Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis[END_REF]. Below, we explore the main biomarkers associated with FTD and ALS in the literature, as well as their limitations.

Neuroimaging biomarkers

Most imaging studies in FTD have used volumetric T1-weighted MRI to investigate changes in gray matter structure, either to determine the volumes of specific brain regions of interest (ROI) or to evaluate the rate of atrophy in longitudinal studies [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF]. Findings from investigations of FTD presymptomatic mutation carriers have shown gray matter atrophy at least ten years before expected disease onset [START_REF] Bertrand | Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years[END_REF][START_REF] Rohrer | Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis[END_REF].

To some degree, genetic forms can be distinguished by different patterns of gray matter atrophy at a group level, as shown by Figure 2.2, displaying the characteristic patterns of gray matter atrophy in FTD patients with different underlying mutations [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF].

Asymmetrical frontotemporoparietal atrophy is commonly seen in patients with GRN mutations, while most patients with a C9orf72 repeat expansion exhibit a generalized symmetrical atrophy, and MAPT mutations cause symmetrical temporal atrophy [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF]. Atrophy rates also vary between different genetic forms, since patients carrying the GRN mutation present significantly faster brain atrophy patterns than C9orf72 expansion carriers [START_REF] Whitwell | Brain atrophy over time in genetic and sporadic frontotemporal dementia: a study of 198 serial magnetic resonance images[END_REF]. Even though these distinct patterns are visible in group-level studies, it is not possible to distinguish between patients with different underlying pathologies by analyzing gray matter atrophy alone [START_REF] Whitwell | Neuroimaging in frontotemporal lobar degeneration-predicting molecular pathology[END_REF].

Recently, FTD imaging studies have also focused on white matter integrity using diffusion tensor imaging (DTI), which are more sensitive to detect the earliest changes in the brain, in comparison to gray matter atrophy [START_REF] Bertrand | Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years[END_REF]. Neurite orientation dispersion and density imaging (NODDI) has also been explored, and seems to provide even higher sensitivity to changes in the presymptomatic phase than DTI [START_REF] Wen | Predict to Prevent Frontotemporal Lobar Degeneration and Amyotrophic Lateral Sclerosis (PREV-DEMALS) Study Group[END_REF]. Other neuroimaging modalities used in FIGURE 2.2: Gray matter atrophy in different genetic subtypes of frontotemporal dementia (reproduced from [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF], with permission from the publisher). The characteristic patterns of gray matter atrophy in patients carrying each mutation are highlighted in red.

FTD studies include positron emission tomography with 18F-fluorodeoxyglucose as the tracer (FDG-PET) to visualize alterations in brain metabolism, functional connectivity between brain regions measured with resting-state functional MRI (RS-fMRI), and arterial spin labelling (ASL) to create a tracer of cerebral blood flow [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF].

Even though there is converging evidence that neuroimaging modalities are useful to assess disease progression, their use in clinical practice is until now limited to classification between FTD, AD and controls [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF]. Larger studies are necessary to validate robust biomarkers, before these modalities can be used at an individual level to evaluate the outcomes of clinical trials [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF].

Fluid biomarkers

The main idea behind fluid biomarkers is that changes in specific protein concentrations in different human fluid compartments, such as cerebrospinal fluid (CSF) or blood, can indicate pathophysiological changes in disease processes [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF].

CSF amyloid-β and tau have been extensively validated to distinguish between Alzheimer's disease and frontotemporal dementia [START_REF] Rivero-Santana | Cerebrospinal Fluid Biomarkers for the Differential Diagnosis between Alzheimer's Disease and Frontotemporal Lobar Degeneration: Systematic Review, HSROC Analysis, and Confounding Factors[END_REF]. These biomarkers have also demonstrated potential to differentiate individuals with different genetic or pathological FTD subtypes, but additional studies are necessary to validate these applications [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF].

Chapter 2. Background

Moreover, increased levels of neurofilaments reflect axonal damage, and it has been established that blood and CSF levels of neurofilaments are increased in FTD patients when compared to controls, and could potentially be used as biomarkers of disease progression [START_REF] Rohrer | Serum neurofilament light chain protein is a measure of disease intensity in frontotemporal dementia[END_REF][START_REF] Saracino | Plasma NfL levels and longitudinal change rates in C9orf72 and GRN-associated diseases: from tailored references to clinical applications[END_REF]. However, these proteins are also overexpressed in other neurodegenerative diseases, so they must be combined with other disease-specific biomarkers to increase their clinical utility [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF].

Additionally, some gene-specific fluid biomarkers, such as progranulin levels in the CSF of GRN mutation carriers [START_REF] Feneberg | Progranulin as a candidate biomarker for therapeutic trial in patients with ALS and FTLD[END_REF] and dipeptide-repeat proteins in the CSF of C9orf72 expansion carriers [START_REF] Su | Discovery of a biomarker and lead small molecules to target r(GGGGCC)-associated defects in c9FTD/ALS[END_REF], have been identified

as potential progression biomarkers of FTD and FTD/ALS respectively, but need to be combined with other biomarkers to have clinical value [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF].

Finally, recent studies have been focusing on miRNAs as potential biomarkers for several neurodegenerative diseases [START_REF] Grasso | Circulating miR-NAs as biomarkers for neurodegenerative disorders[END_REF], including FTD and ALS [START_REF] Eitan | Vulnerability of microRNA biogenesis in FTD-ALS[END_REF]Gascon and Gao, 2014). MicroRNAs act as regulators of key biological functions in the central nervous system, such as synaptic plasticity and neurogenesis, and are preserved in biofluids such as CSF and blood [START_REF] Grasso | Circulating miR-NAs as biomarkers for neurodegenerative disorders[END_REF]. Since miRNAs have an essential regulatory function in the brain, changes in their concentration levels could indicate physiological changes that precede neuronal cell loss [START_REF] Watson | Small Non-coding RNAs: New Class of Biomarkers and Potential Therapeutic Targets in Neurodegenerative Disease[END_REF].

The reasoning behind the link between miRNAs and FTD/ALS is that TDP-43 and FUS, two of the proteins frequently accumulated in the brains of FTD/ALS patients, are RNA-binding proteins that participate in DNA replication, mRNA translation, and miRNA synthesis [START_REF] Buratti | The multiple roles of TDP-43 in pre-mRNA processing and gene expression regulation[END_REF]. Therefore, the dysregulation of these proteins associated with FTD and ALS pathogenesis could lead to alterations in miRNAs expression levels (Gascon and Gao, 2014).

It is highly unlikely that a single miRNA could be a useful biomarker of a neurodegenerative disease, but a combination of miRNA biomarkers (a miRNA signature) could enable diagnosis and disease monitoring [START_REF] Watson | Small Non-coding RNAs: New Class of Biomarkers and Potential Therapeutic Targets in Neurodegenerative Disease[END_REF]. Numerous studies have recently identified specific miRNAs as potential biomarkers of FTD [START_REF] Denk | Specific serum and CSF mi-croRNA profiles distinguish sporadic behavioural variant of frontotemporal dementia compared with Alzheimer patients and cognitively healthy controls[END_REF][START_REF] Grasso | Plasma microRNA profiling distinguishes patients with frontotemporal dementia from healthy subjects[END_REF][START_REF] Piscopo | Circulating miR-127-3p as a Potential Biomarker for Differential Diagnosis in Frontotemporal Dementia[END_REF][START_REF] Sheinerman | Circulating brain-enriched microR-NAs as novel biomarkers for detection and differentiation of neurodegenerative diseases[END_REF] and ALS [START_REF] De Felice | miR-338-3p is over-expressed in blood, CFS, serum and spinal cord from sporadic amyotrophic lateral sclerosis patients[END_REF][START_REF] Dobrowolny | A longitudinal study defined circulating microRNAs as reliable biomarkers for disease prognosis and progression in ALS human patients[END_REF][START_REF] Freischmidt | Serum microRNAs in sporadic amyotrophic lateral sclerosis[END_REF][START_REF] Freischmidt | Serum microRNAs in patients with genetic amyotrophic lateral sclerosis and pre-manifest mutation carriers[END_REF][START_REF] Magen | Circulating miR-181 is a prognostic biomarker for amyotrophic lateral sclerosis[END_REF][START_REF] Raheja | Correlating serum microRNAs and clinical parameters in Amyotrophic lateral sclerosis[END_REF][START_REF] Sheinerman | Circulating brain-enriched microR-NAs as novel biomarkers for detection and differentiation of neurodegenerative diseases[END_REF][START_REF] Soliman | Assessment of diagnostic potential of some circulating microRNAs in Amyotrophic Lateral Sclerosis Patients, an Egyptian study[END_REF][START_REF] Takahashi | Identification of plasma microRNAs as a biomarker of sporadic Amyotrophic Lateral Sclerosis[END_REF][START_REF] Tasca | Circulating microR-NAs as biomarkers of muscle differentiation and atrophy in ALS[END_REF][START_REF] Waller | Serum miR-NAs miR-206, 143-3p and 374b-5p as potential biomarkers for amyotrophic lateral sclerosis (ALS)[END_REF]. Although promising, results among these different works are conflicting, for three main reasons: heterogeneous cohorts (genetic, sporadic or mixed), lack of independent validation cohorts in most cases, and study designs with different assumptions about which miRNAs to investigate. We tackle these issues by conducting a discovery study without a priori assumptions in an homogeneous cohort of C9orf72 expansion carriers (Chapter 4), and a comprehensive validation study using two independent homogeneous cohorts of C9orf72 and GRN mutation carriers (Chapter 5).

MicroRNA data

In this section, we begin by defining what is a miRNA (section 2.2.1). Then, we describe the main steps of a typical RNA sequencing (RNA-seq) experiment (section 2.2.2), and finally we explain how to apply RNA-seq to assess miRNA expression levels (section 2.2.3).

MicroRNA definition

The central dogma of molecular biology explains the flow of information contained in genes: DNA is transcribed into messenger RNA (mRNA), which is translated into proteins [START_REF] Crick | Central Dogma of Molecular Biology[END_REF]. As a consequence of encoding proteins via the genetic code, mRNA molecules were historically the most investigated RNA species [START_REF] Kukurba | RNA Sequencing and Analysis[END_REF].

Recently, novel classes of noncoding RNA have been identified, including miR-NAs, which are small RNA molecules (average length of 22 nucleotides) that have a regulatory role: they repress the expression of certain mRNA targets by inducing translational inhibition, mRNA degradation, or both [START_REF] Huntzinger | Gene silencing by microRNAs: contributions of translational repression and mRNA decay[END_REF]. One miRNA can target up to hundreds of mRNA transcripts, thus regulating the levels of several genes (Gascon and Gao, 2014). Likewise, a particular mRNA may be targeted by many different miRNAs [START_REF] Watson | Small Non-coding RNAs: New Class of Biomarkers and Potential Therapeutic Targets in Neurodegenerative Disease[END_REF].

Next-generation sequencing technologies, that allow the identification and quantification of RNA molecules in biological samples, have enabled increased rates of miRNA discovery in recent years [START_REF] Kozomara | miRBase: integrating microRNA annotation and deep-sequencing data[END_REF]. The most used of these technologies, called RNA sequencing, is described below.

RNA sequencing

The first studies measuring RNA expression levels relied on low-throughput technologies such as polymerase chain reaction (PCR), which are limited to quantifying individual transcripts [START_REF] Becker-André | Absolute mRNA quantification using the polymerase chain reaction (PCR). A novel approach by a PCR aided transcript titration assay (PATTY)[END_REF]). In the mid-1990s, methods evolved to high-throughput techniques such as microarrays, that quantify the abundance of large sets of transcripts, and allowed the first genome-wide analyses of gene expression patterns [START_REF] Brown | Exploring the new world of the genome with DNA microarrays[END_REF]. Nevertheless, these technologies have important limitations: they rely upon existing knowledge about the sequences to be analyzed, and they have a narrow dynamic range of detection of expression levels.

To overcome these limitations, a high-throughput next-generation sequencing (NGS) technology called RNA sequencing (RNA-Seq) was developed, and began to grow in popularity after 2008 [START_REF] Lowe | Transcriptomics technologies[END_REF][START_REF] Wang | RNA-Seq: a revolutionary tool for transcriptomics[END_REF]. RNA-Seq has clear advantages compared to other approaches: it does not require prior knowledge about the target sequences, it has a high accuracy even for very lowly expressed and very highly expressed transcripts, and it has high levels of reproducibility (Wang Chapter 2. Background et al., 2009). Since 2015, RNA-Seq surpassed microarrays as the most used technique for measuring RNA expression [START_REF] Lowe | Transcriptomics technologies[END_REF].

Specific RNA-Seq protocols may differ in several aspects, such as transcript enrichment, fragmentation, and amplification [START_REF] Lowe | Transcriptomics technologies[END_REF]. Almost 100 different methods have been created based on the standard RNA-Seq protocol [START_REF] Stark | RNA sequencing: the teenage years[END_REF]. Usually, the main steps of a typical RNA-Seq experiment are [START_REF] Kocerha | Altered microRNA expression in frontotemporal lobar degeneration with TDP-43 pathology caused by progranulin mutations[END_REF] extraction and isolation of RNA from the organism, (2) sequencing library preparation, (3) sequencing on a NGS platform, and ( 4) alignment of sequencing reads and quantification of transcripts [START_REF] Kukurba | RNA Sequencing and Analysis[END_REF][START_REF] Lowe | Transcriptomics technologies[END_REF]. These typical steps are displayed in Figure 2.3 and explained below, while more specific procedures concerning miRNA sequencing are detailed in section 2.2.3.

RNA extraction and isolation

The first step for a successful RNA-Seq experiment is to isolate high-quality RNA from the analyzed biological samples (e.g. tissue, plasma) using a specific protocol, which is a critical factor to avoid incorrect conclusions in downstream analyses [START_REF] Kukurba | RNA Sequencing and Analysis[END_REF]. Within organisms, the transcription process copies segments of DNA into precursor mRNA molecules, then the splicing process removes introns (noncoding sequences) and joins exons (coding sequences), creating mature mRNA molecules to be extracted [START_REF] Lowe | Transcriptomics technologies[END_REF].

Sequencing library preparation

The next step is to construct RNA-Seq libraries. An appropriate library preparation protocol must be chosen, each with its own biases and limitations, in order to enrich or deplete the RNA sample to target specific RNA species [START_REF] Kukurba | RNA Sequencing and Analysis[END_REF]. Usually, RNA molecules have to be fragmented, because messenger RNA molecules are longer than the typical read-lengths supported by nextgeneration sequencing methods [START_REF] Lowe | Transcriptomics technologies[END_REF]. Moreover, since most sequencing technologies require DNA libraries, it is necessary to convert RNA into stable double-stranded complementary DNA (ds-cDNA) [START_REF] Kukurba | RNA Sequencing and Analysis[END_REF]. Next, DNA adaptors are attached to the ends of the complementary DNA fragments [START_REF] Kukurba | RNA Sequencing and Analysis[END_REF]. Finally, before sequencing, PCR amplification may be applied to allow sequencing of low-input amounts of RNA [START_REF] Lowe | Transcriptomics technologies[END_REF]. In that case, to control for PCR amplification biases, unique molecular identifiers (UMIs) may be used to tag cDNA fragments before amplification, allowing the identification and removal of PCR duplicates [START_REF] Stark | RNA sequencing: the teenage years[END_REF].

Sequencing on a NGS platform

In recent years, although several next-generation sequencing platforms have been developed and are now commercially available, the Illumina platform is the most frequently applied in RNA-Seq experiments and has clearly dominated the industry [START_REF] Kukurba | RNA Sequencing and Analysis[END_REF]. Released in 2006, Illumina technology provides a highly accurate tool for measuring RNA expression (single read accuracy of 99.9%), with typical read lengths from 50 to 300 base pairs [START_REF] Lowe | Transcriptomics technologies[END_REF], and usual read depths of 10 to 30 million reads per sample [START_REF] Stark | RNA sequencing: the teenage years[END_REF]. A recent survey indicated that more than 95% of the published RNA-Seq data has been generated using the Illumina sequencing technology [START_REF] Stark | RNA sequencing: the teenage years[END_REF]. In order to output high-quality and biologically meaningful data, sequencing parameters such as read depth and read length must be carefully chosen, depending on the study characteristics [START_REF] Stark | RNA sequencing: the teenage years[END_REF]. The standard RNA-Seq pipeline outputs the raw sequence data in FASTQ-format files. FASTQ is a standard text-based file format for storing sequencing read data, that combines both the nucleotide sequences and the Chapter 2. Background corresponding quality scores [START_REF] Cock | The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants[END_REF]. The following steps are all computational.

Alignment and quantification of transcripts

Once the raw reads are available, the next step is to align them to an annotated reference genome, a task for which several approaches exist [START_REF] Kukurba | RNA Sequencing and Analysis[END_REF]. Each alignment tool has advantages concerning performance and resource utilisation, therefore selecting the best method depends on the peculiarities of each RNA-Seq study [START_REF] Kukurba | RNA Sequencing and Analysis[END_REF]. Regardless of the chosen alignment tool, a quality control step is essential to remove any abnormalities from the data, since sequence reads are not perfect [START_REF] Lowe | Transcriptomics technologies[END_REF]. After reads have been mapped to genomic locations, a quantification step is performed to obtain an expression matrix, where the rows represent features (genes or transcripts), the columns represent samples, and the values are read counts [START_REF] Stark | RNA sequencing: the teenage years[END_REF]. More detailed quality assessment, alignment and quantification steps, specific to miRNA sequencing, are described in the next subsection.

MicroRNA sequencing

In addition to quantifying messenger RNA transcripts, RNA-Seq can be applied to analyze different populations of RNA, including miRNAs [START_REF] Kukurba | RNA Sequencing and Analysis[END_REF]. Since miRNAs are shorter and less abundant than messenger RNA, specific protocols focused on miRNA sequencing have been developed and are commercially available as extraction kits [START_REF] Kukurba | RNA Sequencing and Analysis[END_REF].

From a computational point of view, multiple tools are available for miRNA sequencing analysis, which may cause uncertainty in how to define the most appropriate computation pipeline [START_REF] Potla | A bioinformatics approach to microRNAsequencing analysis[END_REF]. The vast majority of RNA-Seq studies performed to date are focused on messenger RNAs, and the few studies that perform miRNA sequencing do not provide enough details about particular choices of their bioinformatics analysis [START_REF] Potla | A bioinformatics approach to microRNAsequencing analysis[END_REF].

Recently, a generic miRNA bioinformatics pipeline was proposed, using exclusively open source software, with the goal to improve clarity and reproducibility among miRNA sequencing studies [START_REF] Potla | A bioinformatics approach to microRNAsequencing analysis[END_REF]. We thus describe below this computation pipeline, which has five major steps: (1) quality assessment of raw reads, ( 2) UMI extraction and adapter trimming, (3) alignment of reads to the mature miRNA sequences, (4) removal of PCR duplicates, and (5) creation of miRNA count tables.

Quality assessment of raw reads

It is critical to analyze if the raw reads are of sufficient quality for downstream analyses. Tools like FastQC (Andrews S. 2010 1 ) may be applied to FASTQ format files, generating summary graphs and tables that allow assessing the overall quality of each sample by checking for unusual patterns. For instance, one may observe the number of total reads, the quality scores of individual base sequences, the distribution of sequence lengths, the amount of duplicate sequences, and other criteria [START_REF] Potla | A bioinformatics approach to microRNAsequencing analysis[END_REF].

UMI extraction and adapter trimming

If in the previous step the raw reads are considered to be of sufficient quality, the next step is to extract the UMIs from the reads, and save them for later use. For this task, UMI-tools [START_REF] Smith | UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy[END_REF] removes UMIs from FASTQ files and appends them to each read name. Then, Cutadapt [START_REF] Martin | Cutadapt removes adapter sequences from high-throughput sequencing reads[END_REF] finds and eliminates all adapter sequences, as well as discards reads that are either shorter (less than 18 nucleotides) or longer (more than 30 nucleotides) than the expected read lengths with some tolerance. After UMI extraction, adapter trimming, and read filtering, the remaining reads are of good quality and ready for alignment.

Alignment of reads to the mature miRNA sequences

Before describing the alignment of reads, it is useful to mention the importance of miRBase2 . Next-generation sequencing technologies have contributed to an increasing rate of novel miRNA discovery, and the community maintained miRBase database is the most important online repository for all published miRNA sequences, curating a consistent nomenclature scheme and providing miRNA target predictions and validations [START_REF] Kozomara | miRBase: integrating microRNA annotation and deep-sequencing data[END_REF]. It is recommended to use the latest version of miRBase as the reference database. Alignment must be performed with strict criteria, to avoid matches of the same read across multiple mature miRNA sequences [START_REF] Potla | A bioinformatics approach to microRNAsequencing analysis[END_REF], so it is recommended to align reads using Bowtie [START_REF] Langmead | Ultrafast and memory-efficient alignment of short DNA sequences to the human genome[END_REF], allowing no mismatches. After the alignment of reads to the reference database, a second major quality-check must performed, since samples with too few aligned reads must be discarded to avoid biasing the results [START_REF] Potla | A bioinformatics approach to microRNAsequencing analysis[END_REF]. To give an idea about the order of magnitude of a successful alignment, on average, a sequencing depth of 10 million reads per sample will yield at least 3 million aligned reads per sample [START_REF] Potla | A bioinformatics approach to microRNAsequencing analysis[END_REF].

Removal of PCR duplicates

Once the alignment step is complete for all samples, UMI-tools [START_REF] Smith | UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy[END_REF] is used again, this time to collapse read counts with the same UMIs, in order to count the number of reads corresponding to the original biological sample, before PCR amplification.

Chapter 2. Background

Creation of miRNA count tables

Finally, Samtools idxstats [START_REF] Li | Bibliography 165 "The Sequence Alignment/Map format and SAMtools[END_REF] counts each miRNA in every sample, and records the counts in a tab-separated values (TSV) file, where each row corresponds to a mature miRNA, and each column represents a sample. These raw counts are then ready to be used in downstream differential expression analyses.

Neuroimaging data

In the last decades, analyses of neuroimaging data have been extensively performed to reveal neuroanatomical and functional differences between patients with neurodegenerative diseases and healthy controls [START_REF] Orrù | Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review[END_REF]. One of the most noticeable changes in the brains of patients with dementia is gray matter atrophy resulting from the loss of neuronal cells, which is visualized by structural imaging techniques such as structural MRI (Cedazo-Minguez and Winblad, 2010). Among the existing structural MRI sequences, T1-weighted (T1w) MRI is one of the most frequently used to assess brain abnormalities caused by neurodegenerative diseases [START_REF] Popuri | Gray matter changes in asymptomatic C9orf72 and GRN mutation carriers[END_REF][START_REF] Tartaglia | Neuroimaging in Dementia[END_REF].

Indeed, in the clinical research cohorts that we had access, T1w MRI is the most commonly available sequence, and with the most reliable harmonization across centers. Furthermore, it is recommended to be systematically included in the clinical follow-up of patients with suspected dementia. This explains our focus on this imaging modality, even though other MRI sequences (including diffusion MRI and functional MRI) or imaging modalities (including PET) have the potential to provide more sensitive biomarkers.

In this section, we briefly describe the two main steps usually carried out prior to T1-weighted MRI data analysis: image acquisition (section 2.3.1) and image preprocessing (section 2.3.2) for feature extraction. For more details on T1w MRI, or on other MRI modalities unrelated to this thesis, one can refer to [START_REF] Bernstein | Handbook of MRI Pulse Sequences[END_REF][START_REF] Haacke | Magnetic Resonance Imaging: Physical Principles and Sequence Design[END_REF][START_REF] Mcrobbie | MRI from Picture to Proton[END_REF].

T1-weighted MRI acquisition

MRI scanners apply a uniform magnetic field to align the hydrogen nuclei within the tissues being examined, then generate radiofrequency (RF) pulses to perturb this alignment [START_REF] Bitar | MR Pulse Sequences: What Every Radiologist Wants to Know but Is Afraid to Ask[END_REF]. After a perturbation, the scanner measures the RF signals emitted from the realignment of the nuclei from different tissues, each with their own characteristic relaxation times, allowing the detection of locations with distinct concentrations of fat and water [START_REF] Bitar | MR Pulse Sequences: What Every Radiologist Wants to Know but Is Afraid to Ask[END_REF].

To create tissue contrast on MRI, there are two key parameters: repetition time (TR) is the time between the application of two RF pulses, and echo time (TE) is the time between the application of a RF pulse and the echo detected [START_REF] Bitar | MR Pulse Sequences: What Every Radiologist Wants to Know but Is Afraid to Ask[END_REF].

Neuroimaging data

These parameters may be tuned to emphasize a particular type of contrast: in T1weighted MRI, short TR and TE are used [START_REF] Brown | MR Imaging Abbreviations, Definitions, and Descriptions: A Review[END_REF]. When exposed to a T1-weighted sequence, fluids generate a very low signal intensity, corresponding to darker regions in the resulting images [START_REF] Brown | MR Imaging Abbreviations, Definitions, and Descriptions: A Review[END_REF]. Therefore, the cerebrospinal fluid appears in dark, gray matter in dark gray, and white matter in light gray (due to the high lipid fat content of the myelin) 3 .

T1-weighted MR images clearly depict the anatomy of the brain, and for that reason have been extensively used to reveal patterns of brain atrophy associated with several neurodegenerative diseases [START_REF] Tartaglia | Neuroimaging in Dementia[END_REF].

T1-weighted MRI preprocessing

The quality of MR images is mainly affected by spatial resolution, contrast, and signal to noise ratio, the latter being mainly determined by scan time and substantially influenced by the patients' ability to remain still [START_REF] Symms | A review of structural magnetic resonance neuroimaging[END_REF]. Any head motion during the MRI exam, including the respiratory cycle and eye movements, can result in motion artifacts that degrade image quality [START_REF] Symms | A review of structural magnetic resonance neuroimaging[END_REF]. Artifacts may also be caused by the MR scanner itself, from the complex interactions between the main magnet, RF transmitter and receiver, and computer systems for image reconstruction [START_REF] Zhuo | MR Artifacts, Safety, and Quality Control[END_REF].

Therefore, preprocessing is an essential step to improve image quality for successful downstream analyses. The essential steps for T1w MRI preprocessing in the Chapter 2. Background context of brain atrophy associated with neurodegenerative diseases are: (1) intensity non-uniformity correction, (2) intensity rescaling and standardization, (3) skull stripping, (4) image registration, and ( 5) feature extraction [START_REF] Bertrand | Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years[END_REF].

Each of these steps is described below, and can be performed, for example, using the software packages FreeSurfer4 [START_REF] Fischl | FreeSurfer[END_REF], the FMRIB Software Library (FSL)5 [START_REF] Jenkinson | FSL[END_REF], or Statistical Parametric Mapping (SPM)6 [START_REF] Ashburner | SPM: A history[END_REF], three of the most popular software packages for processing human brain MRI scans.

Intensity non-uniformity correction

MR images can suffer from a phenomenon usually referred to as intensity nonuniformity, or bias field, characterized by a smooth intensity variation across the image [START_REF] Vovk | A review of methods for correction of intensity inhomogeneity in MRI[END_REF]. These artifacts arise from imperfections in the image acquisition process, causing the intensity of the same tissue to vary depending on its location within the image, and therefore degrading the performance of automatic segmentation algorithms [START_REF] Vovk | A review of methods for correction of intensity inhomogeneity in MRI[END_REF]. Several approaches exist to reduce or eliminate this intensity non-uniformity, among which the popular nonparametric nonuniform intensity normalization (N3) method [START_REF] Sled | A nonparametric method for automatic correction of intensity nonuniformity in MRI data[END_REF], available in the FreeSurfer software package, or its variant N4ITK [START_REF] Tustison | N4ITK: Improved N3 Bias Correction[END_REF], available in the Insight Toolkit (ITK) 7 (McCormick et al., 2014).

Intensity rescaling and standardization

There is no canonical scale for MR images intensities (except for quantitative MRI which are outside of our scope). Even considering images of the same body region from a particular patient, obtained with the same scanner within the same MRI protocol, absolute intensity values have no fixed numeric meaning, and only differences between intensities are relevant [START_REF] Madabhushi | Interplay between intensity standardization and inhomogeneity correction in MR image processing[END_REF]. This lack of standard for interpreting image intensities may compromise the quality of post-processing applications, therefore methods like histogram matching [START_REF] Nyul | New variants of a method of MRI scale standardization[END_REF] should be applied to transform images so that similar intensities have similar tissue meaning.

Skull stripping

MR brain images also contain extra-cranial or non-brain tissues from head scans, which pose a major obstacle for automatic segmentation algorithms [START_REF] Kalavathi | Methods on Skull Stripping of MRI Head Scan Images-a Review[END_REF]. A preprocessing step, usually referred to as skull stripping, consists in isolating the brain from these non-brain tissues, a task for which several methods have been proposed [START_REF] Kalavathi | Methods on Skull Stripping of MRI Head Scan Images-a Review[END_REF]. The approach used in the FreeSurfer preprocessing pipeline uses three groups of operations: intensity thresholding to obtain a preliminary mask, removal of false connections between brain and non-brain structures using graph cuts, and post-processing to smooth the final result [START_REF] Sadananthan | Skull stripping using graph cuts[END_REF].

Image registration

MR brain image registration is a process that aligns multiple images to guarantee the spatial correspondence of brain regions across these images [START_REF] Zhang | Linear Registration of Brain MRI Using Knowledge-Based Multiple Intermediator Libraries[END_REF].

The adequate transformation (linear or non-linear) must be identified to ensure that corresponding image features are spatially aligned [START_REF] Despotović | MRI Segmentation of the Human Brain: Challenges, Methods, and Applications[END_REF]. Linear registration may be rigid (6-parameter transformation corresponding to rotation and translation in three dimensions) or affine (12-parameter transformation corresponding to rotation, translation, scaling and skewing in three dimensions), and is commonly applied for registering images from the same subject [START_REF] Despotović | MRI Segmentation of the Human Brain: Challenges, Methods, and Applications[END_REF]. To register images from different subjects, a non-linear algorithm is usually required [START_REF] Despotović | MRI Segmentation of the Human Brain: Challenges, Methods, and Applications[END_REF]. Several methods have been developed for MRI registration [START_REF] Oliveira | Medical image registration: a review[END_REF], among which the FSL tools FLIRT8 [START_REF] Greve | Accurate and robust brain image alignment using boundary-based registration[END_REF] and FNIRT9 [START_REF] Andersson | Non-linear registration, aka spatial normalisation[END_REF], dedicated respectively to linear and non-linear registration. Frequently, all MR images from a given study are registered into a standard space such as the Montreal Neurological Institute (MNI space) [START_REF] Evans | 3D statistical neuroanatomical models from 305 MRI volumes[END_REF], which enables the comparison of results across different studies [START_REF] Despotović | MRI Segmentation of the Human Brain: Challenges, Methods, and Applications[END_REF].

Feature extraction

After the preprocessing steps described above, different feature extraction pipelines may be implemented, according to the study design. For instance, volumetric segmentation pipelines may be applied to T1-weighted MR images to extract voxelbased anatomical features such as maps of tissue density (gray matter, white matter, and cerebrospinal fluid), and to compute the volumes of cortical and subcortical gray matter regions of interest (ROI) [START_REF] Bertrand | Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years[END_REF][START_REF] Routier | Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies[END_REF]. Alternatively, cortical reconstruction pipelines may be used to obtain surface-based cortical thickness maps for each image [START_REF] Popuri | Gray matter changes in asymptomatic C9orf72 and GRN mutation carriers[END_REF][START_REF] Routier | Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies[END_REF].

Integrative data analysis in neurodegenerative diseases

Neurodegenerative disorders such as frontotemporal dementia and amyotrophic lateral sclerosis are extremely complex processes. Data from single sources are unable to explain the intricate pathways involved in these diseases, therefore it is crucial to integrate data from different modalities, that provide complementary views, 20

Chapter 2. Background to obtain more powerful models [START_REF] Carreiro | Integrative biomarker discovery in neurodegenerative diseases[END_REF].

In this section, we begin by describing the strategies of data and model integration most commonly applied in multimodal data from neurodegenerative diseases (section 2.4.1). Next, we give an overview about the integration of neuroimaging and different types of omics data (section 2.4.2). Then, we focus on the emerging field of imaging transcriptomics, that integrate brain imaging and gene expression data (section 2.4.3). Finally, we narrow our attention to the few published studies that jointly analyzed miRNA and neuroimaging data (section 2.4.4).

Categories of data and model integration

Among the categories of data and model integration defined in [START_REF] Azuaje | Integrative Data Analysis for Biomarker Discovery[END_REF], three are frequently used in multimodal studies dealing with neurodegenerative diseases:

integration at the input level, heterogeneous data and model integration, and serial integration. These categories are described below, along with examples.

Integration at the input level

Data integration at the input level happens when features from different modalities are concatenated together into a single input vector, before being processed by the model [START_REF] Azuaje | Integrative Data Analysis for Biomarker Discovery[END_REF]. Synonyms for this method are early integration or featurebased integration [START_REF] Behrad | An overview of deep learning methods for multimodal medical data mining[END_REF]. This is the most frequently used data integration strategy, notably to build models using already established biomarkers [START_REF] Carreiro | Integrative biomarker discovery in neurodegenerative diseases[END_REF]. For instance, one study on Alzheimer's disease investigated multimodal features including cortical volumes from several brain regions extracted from structural MRI scans, concentrations of certain proteins measured in the cerebrospinal fluid, and results from cognitive and neuropsychological assessments [START_REF] Cui | Identification of conversion from mild cognitive impairment to Alzheimer's disease using multivariate predictors[END_REF]. The authors performed feature selection to identify the optimal subset of features from each modality that independently achieved a high performance in classifying individuals with mild cognitive impairment from those with Alzheimer's disease. Then, they compared the results of support vector machine classifiers trained with single modalities (MRI, CSF or tests scores) and a model trained with input vectors concatenating all modalities. Their conclusion was that the simple concatenation of multimodal features at the input level was able to leverage complementary information and outperform all individual modalities.

Heterogeneous data and model integration

In this category, each modality is used to train a specific model, then the outputs of all models are fused together to give the final prediction [START_REF] Azuaje | Integrative Data Analysis for Biomarker Discovery[END_REF]. An ensemble of models built with data from different sources fits into this category [START_REF] Carreiro | Integrative biomarker discovery in neurodegenerative diseases[END_REF]. This method is also known as decision-level, model-based, or late integration [START_REF] Behrad | An overview of deep learning methods for multimodal medical data mining[END_REF]. One example of model within this category is the bi-level multi-source feature learning approach presented in [START_REF] Xiang | Bilevel Multi-Source Learning for Heterogeneous Block-wise Missing Data[END_REF], applied to features extracted from imaging scans (MRI, PET), CSF and plasma of Alzheimer's disease patients, mild cognitive impairment patients, and controls. In this study, the authors proposed to learn individual models for each data modality, then learn the appropriate weights for their combination, while extending their framework to handle missing data without the need for imputation. They explored the fact that, in medical applications, missing data is frequently block-wise, meaning that for each patient, a given modality is either available or non existent. They partitioned the dataset into non-disjoint subsets, according to the available data sources, in order to have complete data within each subset. This approach allowed the use of the whole dataset, even of samples with just one available data source, and the classification performance of the final model was greatly improved by including incomplete samples in the training set.

Serial integration

In a serial integration approach, the output of one model is fed as input to the next model, and so forth [START_REF] Azuaje | Integrative Data Analysis for Biomarker Discovery[END_REF]. This strategy may be used when subgroups of features or subjects are initially identified and then used by the subsequent models [START_REF] Carreiro | Integrative biomarker discovery in neurodegenerative diseases[END_REF]. For instance, one study on sporadic ALS consisted of a serial integration approach using first animal models, then human tissue, with the goal of identifying disease-relevant differentially expressed genes [START_REF] Kudo | Integrative genetissue microarray-based approach for identification of human disease biomarkers: application to amyotrophic lateral sclerosis[END_REF]. The authors performed a combination of microarray methods, initially identifying candidate genes in spinal cord motor neurons of transgenic mice, then evaluated the relevance of these genes as clinical biomarkers by testing their expression in the blood of mouse models, and finally provided a follow-up confirmation in human postmortem spinal cord tissues. Their serial integration approach to study a highly complex disease allowed the identification of 13 genes, encouraging their use as clinically useful biomarkers.

Integration of neuroimaging and omics data

The definition of omics imaging, according to [START_REF] Antonelli | Integrating imaging and omics data: A review[END_REF], is "an emerging interdisciplinary field concerned with the integration of data collected from biomedical images and omics analyses". In this context, omics refers mainly to three popular fields, as illustrated by [START_REF] Antonelli | Integrating imaging and omics data: A review[END_REF]: genomics addresses the study of DNA sequences, often in comparison to a reference genome, focusing on the association between DNA mutations and observed phenotypes; transcriptomics studies the patterns of gene expression, either in the form of messenger RNAs or non-coding RNAs;

and proteomics refers to the study of expressed proteins in different tissues.

Regarding neurological diseases, the vast majority of studies integrating imaging and omics data leverage data from Single Nucleotide Polymorphisms (SNPs) on the human genome, and thus fall into the category of radiogenomics (Kuo and Jamshidi, Chapter 2. Background 2014) or imaging genomics [START_REF] Thompson | Imaging genomics[END_REF]. In particular, the Alzheimer's Disease Neuroimaging Initiative (ADNI)10 dataset contains multimodal data from MRI, PET, CSF biomarkers and SNPs from hundreds of participants, which has motivated several imaging genomics research initiatives. A comprehensive review of these studies is provided in [START_REF] Antonelli | Integrating imaging and omics data: A review[END_REF].

However, nomenclature describing the landscape of imaging and omics data integration is not homogeneous. For example, one of the first studies combining neuroimaging and large-scale transcriptomic data was also framed in the field of radiogenomics by the authors, even though they did not examine DNA sequences and mutations [START_REF] Zinn | Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme[END_REF]. Their research focused on correlating MRI features and gene expression patterns in patients with glioblastoma, using data obtained from

The Cancer Genome Atlas (TCGA)11 , a publicly available database containing imaging and omics data from patients with several types of cancer.

Eventually, a more appropriate nomenclature for the combination of imaging and transcriptomic data to better understand disease mechanisms was proposed in [START_REF] Katrib | RADIOTRANSCRIPTOMICS": A synergy of imaging and transcriptomics in clinical assessment[END_REF]: radiotranscriptomics. In this review, the authors illustrate the potential applications of radiotranscriptomics in precision medicine, notably for increasing the knowledge about pathological processes in complex, multifactorial disorders such as cancer and neurodegeneration [START_REF] Katrib | RADIOTRANSCRIPTOMICS": A synergy of imaging and transcriptomics in clinical assessment[END_REF].

More recently, the narrower field of imaging transcriptomics has emerged to study the correlation between neuroimaging features and regional variability in gene expression in brain tissues [START_REF] Arnatkeviciute | A practical guide to linking brain-wide gene expression and neuroimaging data[END_REF][START_REF] Martins | Imaging transcriptomics: Convergent cellular, transcriptomic, and molecular neuroimaging signatures in the healthy adult human brain[END_REF]. This discipline is described in the next section.

Imaging transcriptomics

Recent advances in high-throughput sequencing technologies allowed to multiply the number of large-scale transcriptomic projects, leading to the creation of comprehensive datasets of gene expression across brain regions, many of them publicly available [START_REF] Keil | Brain Transcriptome Databases: A User's Guide[END_REF]. For instance, one of the largest brain-wide gene expression atlases is the Allan Human Brain Atlas (AHBA), which contains expression levels from more than 20,000 genes observed in 3702 tissue samples covering nearly the entire brain [START_REF] Arnatkeviciute | A practical guide to linking brain-wide gene expression and neuroimaging data[END_REF][START_REF] Sunkin | Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system[END_REF].

Public databases such as the AHBA have motivated several studies integrating transcriptomic and neuroimaging data, aiming at understanding the spatial correlations of gene expression levels and neuroimaging-derived phenotypes [START_REF] Martins | Imaging transcriptomics: Convergent cellular, transcriptomic, and molecular neuroimaging signatures in the healthy adult human brain[END_REF]. This emerging field of research, known as imaging transcriptomics, has already started to reveal how variations of gene expression in different parts of the brain relate to structural and functional characteristics, and vary during brain disease [START_REF] Arnatkeviciute | A practical guide to linking brain-wide gene expression and neuroimaging data[END_REF][START_REF] Martins | Imaging transcriptomics: Convergent cellular, transcriptomic, and molecular neuroimaging signatures in the healthy adult human brain[END_REF]. Several examples of imaging transcriptomics studies are described in [START_REF] Arnatkeviciute | A practical guide to linking brain-wide gene expression and neuroimaging data[END_REF], where the authors propose a standardized data processing pipeline to enable the comparison of results from different investigations.

It is important to note that the aforementioned studies focus on identifying correlations between transcriptomic and neuroimaging features, not to combine these features to improve prediction performance.

Integration of microRNA and neuroimaging data

In this section, we present the few published studies jointly analyzing miRNA and neuroimaging data.

One of the first works that simultaneously investigated miRNA expression and brain MRI focused on the association between these modalities, to better understand multiple sclerosis (MS) [START_REF] Regev | Association Between Serum MicroRNAs and Magnetic Resonance Imaging Measures of Multiple Sclerosis Severity[END_REF]. The authors computed the Spearman correlation coefficients between the expression of serum miRNAs and measures of MS severity extracted from quantitative MRI (lesions and atrophy). Although several miRNAs were identified as associated with MRI measures, the associations were not significant after correcting for multiple comparisons using the false discovery rate [START_REF] Regev | Association Between Serum MicroRNAs and Magnetic Resonance Imaging Measures of Multiple Sclerosis Severity[END_REF]. In a follow up work from the same research team, the authors defined four groups of patients with MS, based on MRI phenotypes characterizing lesion volume and brain atrophy, then identified a signature of 16 serum miRNAs differentially expressed among these groups. [START_REF] Hemond | MRI phenotypes in MS[END_REF].

Another study investigated the Pearson correlation between gray matter volumes (extracted from T1w MRI) and miRNA expression (measured in blood) in attention-deficit/hyperactivity disorder (ADHD) patients [START_REF] Wang | Gray matter volume and mi-croRNA levels in patients with attention-deficit/hyperactivity disorder[END_REF]. The authors identified that gray matter volumes in some regions of the cingulate cortex were negatively correlated with the expressions of three miRNAs found in blood, and thus concluded that miRNA dysregulation likely participates in the pathophysiology of ADHD.

Similarly, the expression of certain miRNAs in serum was found to be significantly correlated (Pearson's correlation test) with brainstem volume in patients with

Wolfram syndrome [START_REF] Zmyslowska | Serum microRNA as indicators of Wolfram syndrome's progression in neuroimaging studies[END_REF]. The findings of this study demonstrated that a miRNA signature could be used as a non-invasive and easily accessible indicator of neurodegeneration in subjects suffering from this rare syndrome.

Another example of association study aiming at replacing MRI scans with blood tests was focused on cerebral malaria, an acute and often fatal neurological complication caused by severe malaria [START_REF] Gupta | Plasma levels of hsa-miR-3158-3p microRNA on admission correlate with MRI findings and predict outcome in cerebral malaria[END_REF]. Since MRI scanners are rarely accessible in some malaria-endemic countries, the authors investigated if plasma miRNAs could be used, instead of expensive neuroimaging, to assess the progression of cerebral malaria. They used Spearman's correlation test to assess the correlation between MRI features (brain volume and apparent diffusion coefficient) and miRNA expression in plasma samples from patients with cerebral malaria. This

Chapter 2. Background study identified miR-3158-3p as a promising biomarker for measuring cerebral malaria progression.

Recently, PET-derived features and miRNA expression levels in blood were jointly analyzed in cognitively normal individuals with subjective memory complaints, who have an increased risk to develop Alzheimer's disease [START_REF] Vergallo | MiRNA-15b and miRNA-125b are associated with regional Abeta-PET and FDG-PET uptake in cognitively normal individuals with subjective memory complaints[END_REF].

The authors initially selected brain-enriched miRNAs in a pilot study, then investigated their longitudinal association with brain metabolic uptake computed in 12 cortical regions of interest using FDG-PET. To this aim, they fitted linear mixed models, then tested the interaction effect between miRNA expression and time on metabolic uptake. The results of this study, which reported significant associations between miRNA expression levels in plasma and neuroimaging biomarkers, supported the role of miRNAs as candidate biomarkers of AD.

Remarkably, none of the previously described works leveraged the joint analysis of miRNA and neuroimaging data to enhance prediction performance: all of them were based on association studies of these modalities. To the best of our knowledge, the only published study that integrated miRNA data and MR imaging features to improve prediction is [START_REF] Gallivanone | In Silico Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis[END_REF], which did not focus on brain imaging, but on breast cancer differential diagnosis. The authors trained support vector machine classifiers integrating miRNA expression and imaging features at the input level, with the goal of distinguishing different breast cancer subtypes. They showed that the combination of miRNA and MRI data resulted in better classification performance than using any of these modalities alone.

Disease progression modeling and disease progression scores

To introduce the concepts of disease progression modeling and disease progression scores (DPS), we will turn to one of the most studied neurodegenerative disorders:

Alzheimer's disease.

In a seminal work, Jack and colleagues proposed a hypothetical model of the temporal evolution of AD biomarkers, and their relation to disease staging [START_REF] Jack | Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade[END_REF]. The authors explained that certain AD biomarkers begin to vary decades before any visible clinical symptoms, and that dementia is the end result of the accumulation of several pathological changes. Moreover, they showed evidence suggesting that biomarkers become abnormal in an ordered manner, and that understanding this time-dependent order is crucial for estimating disease stage.

Figure 2.5 depicts the hypothetical model presented in [START_REF] Jack | Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade[END_REF], which followed two main assumptions: (1) as the disease progresses, more biomarkers become abnormal, and (2) rates of change vary in a non-linear fashion, hypothesised to be in a sigmoid shape. Notably, no single biomarker can cover the full spectrum of AD progression, so a combination of multiple biomarkers is essential to estimate disease stage for a particular individual [START_REF] Jack | Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade[END_REF]. A few years later, an update on this model supported its main assumptions, while changing the definition of the horizontal axis [START_REF] Jack | Update on hypothetical model of Alzheimer's disease biomarkers[END_REF]. Indeed, in empirical testing, the authors concluded that it was problematic to index subjects on the horizontal axis by a continuous measure of cognitive impairment. The main reason was that measures of cognitive decline are imprecise during the preclinical phase, which usually corresponds to more than half of the total disease duration. Jack and colleagues then proposed that the horizontal axis should represent the "distance traveled along the AD pathophysiological pathway". They explained that, ideally, this axis should be constructed using a large longitudinal dataset containing all relevant biomarkers, measured for decades at multiple time points, in several subjects.

Since it is very difficult to build a dataset of this magnitude, the authors argued that the model should instead rely on piece-wise data from several subjects in different stages of the disease.

In order of increasing power, disease progression models could be built using purely cross-sectional data, cross-sectional imaging data with longitudinal followup of non-imaging data, or short-term longitudinal data contemplating all modalities [START_REF] Jack | Update on hypothetical model of Alzheimer's disease biomarkers[END_REF]. In particular, Jack and colleagues mentioned that multiple biomarkers could be non-linearly combined to produce a single horizontal-axis metric: a latent trait to represent the entire disease spectrum.

Although hypothetical, [START_REF] Jack | Update on hypothetical model of Alzheimer's disease biomarkers[END_REF][START_REF] Jack | Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade[END_REF] have served as a foundation for future data-driven models for neurodegenerative diseases progression. Since then, several disease progression modeling approaches have been proposed, most of them Chapter 2. Background depending on large amounts of longitudinal data. Some examples include fitting logistic functions to biomarker trajectories [START_REF] Jedynak | A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort[END_REF][START_REF] Mehdipour Ghazi | Robust parametric modeling of Alzheimer's disease progression[END_REF], non-linear mixed-effects models [START_REF] Koval | AD Course Map charts Alzheimer's disease progression[END_REF][START_REF] Schiratti | Learning spatiotemporal trajectories from manifold-valued longitudinal data[END_REF]Schiratti et al., , 2017)), recurrent neural networks [START_REF] Mehdipour Ghazi | Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling[END_REF], and Gaussian processes [START_REF] Lorenzi | Probabilistic disease progression modeling to characterize diagnostic uncertainty: Application to staging and prediction in Alzheimer's disease[END_REF]. To the best of our knowledge, the only published disease progression models that work with cross-sectional data are event-based models [START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF][START_REF] Venkatraghavan | Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling[END_REF]. In the next sections, we briefly describe all these approaches.

Logistic functions and biomarker trajectories

One of the first studies that proposed a method for estimating a disease progression score (DPS) combining multiple biomarkers was [START_REF] Jedynak | A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort[END_REF]. The motivation behind this work was to estimate an accurate measure of Alzheimer's disease progression in the early stages of the disease, when symptoms are not yet present and there is a higher chance of a successful therapeutic intervention. The authors developed a generic computational approach, and evaluated their models using the largest freely available longitudinal dataset of biomarkers of a neurodegenerative disease: the ADNI dataset.

In a nutshell, the main contribution of [START_REF] Jedynak | A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort[END_REF] is a method to compute a common temporal scale representing the disease's underlying biological and cognitive changes, and to assign to each subject, at each time point, a DPS within this scale. Their method relies on three main assumptions: (1) individuals follow a common disease progression, possibly with different rates of progression; (2) the evolution of all biomarkers is sigmoid-shaped; and (3) the rate of disease progression for a given individual is constant. Within this framework, the DPS of a given subject i is obtained by linearly transforming age:

DPS i = α i × age + β i (2.1)
where α i and β i are coefficients to be fitted for each subject. Regarding the biomarkers, each one of them is considered to be a sigmoidal function of the DPS, with the same dynamic across the population. The parameters defining each biomarker's sigmoid function (four parameters per function) and the parameters defining the linear transformations for each subject's DPS are fitted using alternating least squares. Details of the fitting algorithm are available in [START_REF] Jedynak | A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort[END_REF].

To evaluate their method, Jedynak and colleagues computed the DPS for 687 subjects from the ADNI database, using data collected from seven biomarkers measured over 3658 visits in total (average of 5.3 visits per subject). Figure 2.6 displays the normalized sigmoid functions for each of the seven biomarkers, plotted as a function of the DPS, as well as the DPS probability densities conditioned on the clinical status of each individual. Notably, this figure demonstrates that the DPS correlates with the clinical status, since Alzheimer's patients tend to have a higher DPS than individuals with mild cognitive impairment, who tend to have a higher DPS than healthy controls. Moreover, these results support the hypothetical plot from [START_REF] Jack | Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade[END_REF] depicted in Figure 2.5.

The authors concluded that their data-driven approach, which creates a composite biomarker, could be used to stage patients and presymptomatic individuals of other neurodegenerative diseases, as long as enough longitudinal data is available [START_REF] Jedynak | A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort[END_REF]. More recently, an extension to the aforementioned approach was proposed, with the main goal to address outliers and obtain more robust estimations [START_REF] Mehdipour Ghazi | Robust parametric modeling of Alzheimer's disease progression[END_REF]. Mehdipour and colleagues argued that the use of a new logistic function (modified Stannard), along with M-estimation to fit the parameters of the logistic functions to the dynamics of each biomarker, would better fit the biomarker trajectories and reduce the influence of outliers. Additionally, the authors presented an end-to-end approach that estimates the trajectory of each biomarker, Chapter 2. Background orders events based on inflection points, and classifies the clinical status of each subject using the computed DPS.

Tests with 16 ADNI biomarkers were conducted, using data from 1518 subjects collected in a total of 9098 visits (on average, 6 visits per subject) [START_REF] Mehdipour Ghazi | Robust parametric modeling of Alzheimer's disease progression[END_REF]. The diagnostic performance of the models was assessed by computing the multiclass area under the receiver operating characteristic curve (ROC-AUC)

when classifying the clinical status of individuals from the test set with a Bayesian classifier, based exclusively on the DPS. Results showed state-of-the-art performance (average ROC-AUC of 0.937 on the Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) 12 challenge). The authors also showed the generalizability of their approach by training with ADNI data and testing with an independent test set, for which a ROC-AUC of 0.929 was obtained.

Non-linear mixed-effects models

A generic Bayesian mixed-effects model to estimate progression of biological phenomena from longitudinal observations was presented in [START_REF] Schiratti | Learning spatiotemporal trajectories from manifold-valued longitudinal data[END_REF] and further extended in (Schiratti et al., 2017). Schiratti and colleagues argued that the temporal evolution of biomarkers could be modeled as spatiotemporal trajectories, since trajectories vary across individuals for two reasons: (1) intrinsic phenotypic differences (spatial variability), and ( 2) different ages of onset and paces of progression (temporal variability). Here, the term spatial is to be taken in a broad sense, since it does not refer to physical space within the brain, but to a more general abstract space of biomarkers. Thus, this framework is generic and has been applied to various types of data, from cognitive scores to brain images.

The authors proposed a data-driven approach to estimate the typical trajectory of biomarker changes within a population, as well as the spatiotemporal variability across individuals, using longitudinal datasets (Schiratti et al., 2017). They extended nonlinear mixed-effects models to deal with longitudinal observations lying on Riemannian manifolds, in order to decompose spatial and temporal variability. The fixed effects captured the typical group-average trajectory, while the random effects accounted for subject-specific variability of the trajectories (Schiratti et al., 2017).

This generic framework was later applied to build a spatiotemporal atlas of Alzheimer's disease progression, called AD Course Map [START_REF] Koval | AD Course Map charts Alzheimer's disease progression[END_REF]. Koval and colleagues leveraged longitudinal data from ADNI to build typical progression curves for each studied biomarker, where the x-axis representing disease stage was named Alzheimer's Age (AA). The model's core idea was to compute three parameters per subject, in order to consider individual variability: (1) a time-shift translating the curves along the x-axis to account for different ages and disease onsets; [START_REF] Chen-Plotkin | TMEM106B, the Risk Gene for Frontotemporal Dementia, Is Regulated by the microRNA-132/212 Cluster and Affects Progranulin Pathways[END_REF] an acceleration factor scaling the x-axis to reflect different paces of disease progression; and (3) space-shifts for each biomarker, to change their ordering and account for different phenotypes [START_REF] Koval | AD Course Map charts Alzheimer's disease progression[END_REF].

AD Course Map was evaluated using data from the TADPOLE challenge, and was able to predict cognitive decline with a better accuracy than other 56 competing approaches, while achieving the same prediction error as the best performing method in the prediction of ventricular volume [START_REF] Koval | AD Course Map charts Alzheimer's disease progression[END_REF].

Recurrent neural networks

A nonparametric approach to disease progression modeling, based on recurrent neural networks (RNN), was presented in [START_REF] Mehdipour Ghazi | Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling[END_REF]. Mehdipour

Ghazi and colleagues argued that standard RNNs cannot be directly used in most longitudinal cohorts, because of frequent missing values, so they proposed an alternative formulation of backpropagation to handle incomplete data. In short, to minimize the influence of missing values, the authors modified the batch gradient descent algorithm used to train long short-term memory (LSTM) networks, introducing a weighted update rule depending on the number of available time points per biomarker.

Contrary to [START_REF] Jedynak | A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort[END_REF][START_REF] Mehdipour Ghazi | Robust parametric modeling of Alzheimer's disease progression[END_REF], the goal of this approach was not to compute disease progression scores, but instead to focus on predicting future biomarker values [START_REF] Mehdipour Ghazi | Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling[END_REF]. The authors evaluated their end-to-end method by modeling the longitudinal dynamics of six imaging biomarkers from the ADNI dataset, collected from 742 individuals that had at least three visits. They computed the mean absolute values between estimated and actual biomarker values using their proposed approach, two standard LSTM networks with different data imputation techniques, and the parametric regressionbased model from [START_REF] Jedynak | A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort[END_REF]. Moreover, tests were conducted for different amounts of missing values. Results demonstrated the superiority of the proposed robust RNN to predict biomarker values, in comparison to all other tested models, up until 74% of the data were missing [START_REF] Mehdipour Ghazi | Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling[END_REF].

Finally, the authors concluded that using RNNs to model disease progression imposes fewer assumptions on the data and thus brings more flexibility, for instance supporting biomarkers with a non-monotonic behaviour [START_REF] Mehdipour Ghazi | Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling[END_REF].

Gaussian processes

A different modeling paradigm was proposed in [START_REF] Lorenzi | Probabilistic disease progression modeling to characterize diagnostic uncertainty: Application to staging and prediction in Alzheimer's disease[END_REF], with the main goal of quantifying uncertainty in the estimation of biomarker trajectories and diagnostic predictions. The authors reformulated disease progression modeling within a probabilistic setting, based on Gaussian process regression, and assessed uncertainty with respect to missing observations, biomarkers, and follow-up visits.

Chapter 2. Background

To evaluate the proposed framework, the authors used longitudinal training data from 15 biomarkers measured in 200 subjects from ADNI, while the test set contained information from 582 subjects. The estimated AD biomarker progression was compatible with the hypothetical model from [START_REF] Jack | Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade[END_REF], and classification between controls and AD patients achieved an area under the ROC curve of 0.99.

The authors also demonstrated their framework's ability to quantify the uncertainty of single biomarkers in characterizing disease progression, thus allowing to compare the usefulness of different biomarkers (Lorenzi et al., 2019a). Finally, their findings also confirmed the intuition that the greater the amount of longitudinal information used to train the model, the lower the uncertainty in predicting the diagnosis of a given test subject.

Event-based models

Even though analyzing longitudinal data is in principle the best way to assess disease progression, building disease progression models from longitudinal data can be challenging. With the exception of common disorders such as AD and Parkinson's disease, large longitudinal datasets of neurodegenerative disorders are rare [START_REF] Venkatraghavan | Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling[END_REF]. To overcome this limitation, Fonteijn and colleagues proposed event-based models (EBM), which describe disease progression as a sequence of discrete events that can be estimated from cross-sectional data [START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF]. In this context, an event may be defined as the appearance of a symptom [START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF], or more generally as a biomarker transition from a normal to an abnormal value [START_REF] Venkatraghavan | Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling[END_REF].

Figure 2.7 illustrates hypothetical biomarker trajectories throughout a neurodegenerative disease progression, with each biomarker sequentially transitioning from a normal to an abnormal state. The inspiration from Jack's hypothetical model [START_REF] Jack | Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade[END_REF] is noteworthy. An event-based model aims at computing the sequence of biomarker transitions that maximizes the likelihood of the data given that sequence [START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF].

The two main assumptions of an EBM are (1) biomarker values change monotonically throughout disease progression, and (2) all subjects follow the same ordering of events [START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF]. Without these assumptions, it would not be possible to infer the sequence of events in a particular cohort only from cross-sectional data [START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF].

To put it formally, EBM consist of a set of N events (E 1 , . . . , E N ) and a sequence S = (s(1), . . . , s(N)), which determines the event ordering (E s(1) , . . . , E s(N) ), estimated from the cross-sectional dataset X containing biomarker values X j for each subject j = (1, . . . , J) [START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF]. The model is fitted by identifying the sequence that maximizes p(S|X), which can be written using Bayes' theorem:

p(S|X) = p(S)p(X|S) p(X) (2.2) FIGURE 2
.7: Hypothetical biomarker trajectories from a normal to an abnormal state (reproduced from [START_REF] Venkatraghavan | Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling[END_REF], CC BY-NC-ND). The red dots represent events, as defined in the EBM context.

Since the authors assume that all sequences S are equally likely, the problem of inferring S is equivalent to the maximum likelihood problem of maximizing p(X|S).

However, Fonteijn and colleagues instead use a Markov Chain Monte Carlo (MCMC) algorithm to obtain 1,000,000 samples from the posterior p(S|X), and then compute the average position of events S in the samples [START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF]. Uncertainty in the ordering may be visualized in a positional variance diagram, as illustrated in Once the event-based model is fitted, the estimated ordering of events can be used to infer a disease stage for a new subject, by finding the stage that maximizes the subject's data likelihood, given the estimated sequence of events [START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF]. Concretely, the estimated disease stage k for subject j is the one that maximizes p(X j | S, k) [START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF]. This approach results in a discrete set of stages, with N + 1 possible values (corresponding to 0 to N abnormal biomarkers).

Recently, a new EBM framework called discriminative event-based model (DEBM)

was proposed in order to scale the algorithm to a larger number of biomarkers, as well as to relax the assumption that all subjects in a dataset follow the same sequence of events [START_REF] Venkatraghavan | Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling[END_REF]. The authors also improved disease staging by proposing the concept of relative distance between events.

In summary, the DEBM framework has three main steps: (1) Bayesian classifiers are independently trained for each biomarker to differentiate between normal and abnormal values; (2) orderings of biomarker abnormalities are estimated initially for each subject, then a central ordering is computed as the mean of subject-specific estimates by minimizing the probabilistic Kendall's Tau distances between the central ordering and the individual orderings; and (3) a patient staging algorithm uses FIGURE 2.8: Event sequence computed by an EBM using regional atrophy values from T1-weighted MRI scans from a familial AD cohort (reproduced from [START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF], with permission from the publisher). The regions are ordered according to the average position of events S. Positional variance is depicted according to the number of times a region occupies each position over the samples obtained with MCMC. MCI: mild cognitive impairment; AD: Alzheimer's disease. the central ordering of events and the relative distances between event-centers to compute continuous patient stages [START_REF] Venkatraghavan | Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling[END_REF].

To evaluate their framework, Venkatraghavan and colleagues used 88 features measured in 1737 subjects from the ADNI dataset at baseline, comprising gray matter volumes of 83 regions of interest, values from three CSF biomarkers, and two cognitive scores [START_REF] Venkatraghavan | Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling[END_REF]. Since there was no ground-truth data for the event ordering, the authors relied on the classification performance of controls versus AD patients, using only the computed disease stages, as an indirect evaluation of the estimated event ordering.

Results demonstrated that DEBM distinguishes controls from patients consistently better than previously published EBM models [START_REF] Venkatraghavan | Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling[END_REF].

Additionally, the authors were able to better classify MCI converters from nonconverters, illustrating their framework's ability to identify individuals with a high risk of developing AD. Finally, experiments with simulated data supported that the continuous patient staging mechanism was more accurate than the traditional discrete staging approach from [START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF].

Variations of event-based models have been successfully applied to model the progression of several neurodegenerative diseases, such as AD [START_REF] Archetti | Multi-study validation of data-driven disease progression models to characterize evolution of biomarkers in Alzheimer's disease[END_REF][START_REF] Firth | Sequences of cognitive decline in typical Alzheimer's disease and posterior cortical atrophy estimated using a novel event-based model of disease progression[END_REF][START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF][START_REF] Oxtoby | Data-driven models of dominantly-inherited Alzheimer's disease progression[END_REF][START_REF] Venkatraghavan | Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling[END_REF]Young et al., 2014), multiple sclerosis [START_REF] Dekker | The sequence of structural, functional and cognitive changes in multiple sclerosis[END_REF][START_REF] Eshaghi | Progression of regional grey matter atrophy in multiple sclerosis[END_REF]),

Parkinson's disease [START_REF] Oxtoby | Sequence of clinical and neurodegeneration events in Parkinson's disease progression[END_REF]), Huntington's disease [START_REF] Wijeratne | A Multi-Study Model-Based Evaluation of the Sequence of Imaging and Clinical Biomarker Changes in Huntington's Disease[END_REF], FTD [START_REF] Ende | A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia[END_REF][START_REF] Panman | Modelling the cascade of biomarker changes in GRN-related frontotemporal dementia[END_REF], and ALS [START_REF] Gabel | Evolution of white matter damage in amyotrophic lateral sclerosis[END_REF].

These are currently the state-of-the-art approaches to model disease progression from cross-sectional data.

Chapter 3

Datasets

In this chapter, we describe the datasets used in each study conducted during this PhD project. We present the three microRNA datasets in Section 3.1 and the neuroimaging dataset in Section 3.2.

MicroRNA datasets

The first miRNA dataset presented below (section 3.1.1) was used in the miRNA discovery study detailed in Chapter 4, and to assess the estimation of disease progression scores from multimodal neuroimaging and miRNA expression data in Chapter 6. The other two miRNA datasets (described in section 3.1.2 and section 3.1.3) were used in the comprehensive validation study in Chapter 5.

C9orf72 carriers from PREV-DEMALS

The Predict to Prevent Frontotemporal Lobar Degeneration and Amyotrophic Lateral Sclerosis (PREV-DEMALS) 1 project is a French multicentric, observational study focused on C9orf72 expansion carriers and their first-degree relatives, who have a 50% chance to carry the mutation. The main objective of the PREV-DEMALS study is to identify biomarkers that allow an early diagnosis of C9orf72-associated disease, and to follow disease progression.

Subjects from this cohort were recruited and evaluated between 2015 and 2017.

Participants underwent T1-weighted MRI, diffusion MRI, functional MRI, and FDG-PET scans, as well as blood sampling and neuropsychological tests. Not all subjects underwent all assessments, and in this thesis we used only features extracted from T1-weighted MRI and blood plasma miRNA expression. • Patient group: 22 C9orf72 expansion carriers with a diagnosis of FTD, ALS, or both.

C9orf72 carriers from clinical practice

In addition to the subjects studied in the PREV-DEMALS cohort, C9orf72 expansion carriers and controls were recruited through the French research network on FTD/ALS (Inserm RBM02-59) between 2011 and 2021.

These individuals underwent blood sampling, from which plasma miRNAs were extracted and sequenced in January 2022. The resulting dataset contained normalized log2 expression levels of 2656 miRNAs (corresponding to all miRNAs listed in mirBase version 22.1) from 77 subjects divided into three groups:

• Control group: 31 non-carriers (this group is shared with the GRN cohort from Predict-PGRN described in section 3.1.3).

• Presymptomatic group: 17 C9orf72 expansion carriers without neurological symptoms.

• Patient group: 29 C9orf72 expansion carriers with a diagnosis of FTD, ALS, or both.

GRN carriers from Predict-PGRN

The Natural History Characterization in Symptomatic and Asymptomatic Progranuline Gene Mutation Carriers (Predict-PGRN) 3 study is focused on the GRN mutation. The main goal of the Predict-PGRN study is to investigate whether biomarkers in presymptomatic GRN mutation carriers may be used for early diagnosis of GRNassociated FTD and to assess disease progression.

Individuals from this cohort were recruited and evaluated between 2011 and 2021, and underwent the same assessments as individuals from the PREV-DEMALS cohort.

From the Predict-PGRN cohort, we used in this thesis only miRNA expression data, extracted from blood plasma. Samples were stored in the Paris Brain Institute DNA and cell bank, and miRNA sequencing was performed in January 2022.

The resulting dataset consisted of normalized log2 expression levels of 2656 miR-NAs (corresponding to all miRNAs listed in mirBase version 22.1) from 89 subjects divided into three groups:

• Control group: 31 non-carriers (this group is shared with the C9orf72 cohort from clinical practice described in section 3.1.2).

• Presymptomatic group: 30 GRN mutation carriers without neurological symptoms.

• Patient group: 28 GRN mutation carriers with a diagnosis of FTD.

Neuroimaging dataset

The neuroimaging dataset described below (section 3.2.1) was used to evaluate the model presented in Chapter 6, which estimates disease progression scores from multimodal neuroimaging and miRNA expression data.

C9orf72 carriers from PREV-DEMALS

Neuroimaging features consisted of gray matter volumes extracted from T1-weighted MR images, including cortical structures (68 regions of interest), subcortical structures (18 regions of interest) and the estimated total intracranial volume (TIV), thus resulting in 87 neuroimaging features. Of the 110 individuals recruited in the PREV-DEMALS cohort, only 91 underwent T1-weighted MRI scans. The composition of each group is as follows:

• Control group: 37 non-carriers of the C9orf72 expansion.

• Presymptomatic group: 40 C9orf72 expansion carriers without neurological symptoms.

• Patient group: 14 C9orf72 expansion carriers with a diagnosis of FTD, ALS, or both.

Neuroimaging features were extracted as described in [START_REF] Bertrand | Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years[END_REF], notably using the Desikan atlas [START_REF] Desikan | An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest[END_REF] for the cortical ROIs and the Aseg nomenclature [START_REF] Fischl | Whole Brain Segmentation[END_REF] for the subcortical ROIs.

Abstract

Objective. To identify potential biomarkers of preclinical and clinical progression in C9orf72-associated disease by assessing the expression levels of plasma microRNAs (miRNAs) in C9orf72 patients and presymptomatic carriers.

Methods.

The PREV-DEMALS study is a prospective study including 22 C9orf72 patients, 45 presymptomatic C9orf72 mutation carriers and 43 controls. We assessed the expression levels of 2576 miRNAs, among which 589 were above noise level, in plasma samples of all participants using RNA sequencing (RNA-seq). The expression levels of the differentially expressed miRNAs between patients, presymptomatic carriers and controls were further used to build logistic regression classifiers.

Introduction

Frontotemporal dementia (FTD) designates neurodegenerative dementias characterized by progressive behavioral, executive and language impairments (Rascovsky et al., 2011). Amyotrophic lateral sclerosis (ALS) is a degenerative disease of motor neurons that leads to progressive muscle atrophy and motor deficit. FTD and ALS form a clinical continuum, as these two diseases may be associated in the same patients (FTD-ALS) or within families. They also share common pathophysiological mechanisms and genetic causes [START_REF] Mackenzie | TDP-43 and FUS in amyotrophic lateral sclerosis and frontotemporal dementia[END_REF]. The most frequent genetic cause of familial FTD and ALS is a hexanucleotide (GGGGCC) repeat expansion in the chromosome 9 open reading frame 72 (C9orf72) gene [START_REF] Dejesus Hernandez | Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS[END_REF][START_REF] Renton | A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD[END_REF]. This autosomal dominant mutation may cause neurodegeneration through C9orf72 loss of function, aggregation of mutant RNA in nuclear foci and of dipeptide repeats generated by repeat-associated non-AUG (RAN) translation, ultimately leading to pathological inclusions of TAR-DNA binding protein 43 (TDP-43) [START_REF] Mackenzie | The neuropathology associated with repeat expansions in the C9ORF72 gene[END_REF].

There are no effective treatments available in C9orf72 disease to date, but several promising trials including antisense therapies are being developed. Presymptomatic C9orf72 carriers represent an optimal target population for the development of new therapeutic interventions for FTD and ALS [START_REF] Bertrand | Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years[END_REF][START_REF] Eisen | Amyotrophic lateral sclerosis: a long preclinical period?[END_REF]. Therefore, it is of paramount importance to identify biomarkers of preclinical progression for FTD and ALS, which could be used to initiate and monitor potential disease-modifying treatments before any irreversible brain damage has occurred.

There is increasing evidence that microRNA (miRNA) expression in body fluids, such as plasma/serum [START_REF] Grasso | Circulating miR-NAs as biomarkers for neurodegenerative disorders[END_REF] or CSF [START_REF] Schneider | Downregulation of exosomal miR-204-5p and miR-632 as a biomarker for FTD: a GENFI study[END_REF], correlates with the diagnosis and progression of many neurodegenerative diseases, including FTD [START_REF] Denk | Specific serum and CSF mi-croRNA profiles distinguish sporadic behavioural variant of frontotemporal dementia compared with Alzheimer patients and cognitively healthy controls[END_REF] and ALS [START_REF] Freischmidt | Serum microRNAs in patients with genetic amyotrophic lateral sclerosis and pre-manifest mutation carriers[END_REF]. MicroRNAs are a class of small noncoding RNAs that negatively regulate gene expression by promoting translational repression and messenger RNA degradation [START_REF] Huntzinger | Gene silencing by microRNAs: contributions of translational repression and mRNA decay[END_REF]. Since TDP-43 promotes miRNA biogenesis [START_REF] Buratti | The multiple roles of TDP-43 in pre-mRNA processing and gene expression regulation[END_REF], the dysregulation of TDP-43 activity associated with FTD and ALS pathogenesis could impact miRNA expression levels (Gascon and Gao, 2014). Notably, miRNAs originating from neurons and glial cells are released through extracellular vesicles, especially exosomes, and can be measured in different body fluids, including CSF and plasma [START_REF] Li | Roles of extracellular microRNAs in central nervous system[END_REF]. Aberrant expression of miRNAs can be thus noninvasively detected in easily accessible body compartments, such as blood plasma, and potentially serve as biomarkers [START_REF] Sohel | Extracellular/Circulating MicroRNAs: Release Mechanisms, Functions and Challenges[END_REF].

Previous studies have explored selected plasma miRNAs as biomarkers for FTD and ALS [START_REF] Sheinerman | Circulating brain-enriched microR-NAs as novel biomarkers for detection and differentiation of neurodegenerative diseases[END_REF] or FTD [START_REF] Grasso | Plasma microRNA profiling distinguishes patients with frontotemporal dementia from healthy subjects[END_REF][START_REF] Piscopo | Circulating miR-127-3p as a Potential Biomarker for Differential Diagnosis in Frontotemporal Dementia[END_REF] using quantitative real-time PCR. Two of them have analyzed the expression of a limited number of candidate miRNAs: nine miRNAs linked with apoptosis [START_REF] Piscopo | Circulating miR-127-3p as a Potential Biomarker for Differential Diagnosis in Frontotemporal Dementia[END_REF] or 37 brain-enriched miRNAs [START_REF] Sheinerman | Circulating brain-enriched microR-NAs as novel biomarkers for detection and differentiation of neurodegenerative diseases[END_REF]. A wider miRNA profiling study [START_REF] Grasso | Plasma microRNA profiling distinguishes patients with frontotemporal dementia from healthy subjects[END_REF] analyzed 752 miRNAs, as a first attempt to perform an unbiased assessment of circulating miRNAs in patients with FTD. In addition, a more recent study [START_REF] Magen | Classification and prediction of frontotemporal dementia based on plasma microRNAs[END_REF] assessed the expression levels of 2313 miRNAs in a merged cohort of FTD patients with different genetic forms (C9orf72, MAPT, GRN, TBK1) or with sporadic forms, by next generation RNA sequencing (RNA-seq). However, results among different studies have been conflicting so far, probably due to the heterogeneity of cohorts with respect to the underlying pathology (genetic or sporadic). Besides, these studies only compared healthy controls and symptomatic patients, focusing on evaluating potential diagnostic biomarkers.

To date, no studies have evaluated plasma miRNAs as progression biomarkers for FTD or ALS in presymptomatic individuals.

The present work aims at investigating expression levels of plasma miRNAs in a large homogeneous genetic cohort of C9orf72 mutation carriers, both in the presymptomatic and in the clinical phases, to identify potential non-invasive biomarkers of preclinical and clinical progression in C9orf72-associated FTD and ALS. We hypothesize that performing large scale RNA-seq analyses in plasma samples, without a priori assumptions, will reveal significant differences in miRNA expression levels between healthy controls, presymptomatic and symptomatic mutation carriers.

Material and Methods

Participants

PREV-DEMALS (https://clinicaltrials.gov/ Identifier: NCT02590276) is a national multicentric study focused on C9orf72 mutation carriers. Between 2015 and 2017, hospitals (Paris, Limoges, Lille, Rouen), as previously described [START_REF] Bertrand | Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years[END_REF][START_REF] Montembeault | Cognitive inhibition impairments in presymptomatic C9orf72 carriers[END_REF]. Written informed consents were obtained from all participants, after approval obtained from the Comité de Protection des Personnes CPP Ile-De-France VI (CPP 68-15 and ID RCB 2015-A00856-43). This cohort included 22 patients (15 FTD, 4 FTD/ALS, 3 ALS) carrying a C9orf72 expansion and 89 asymptomatic first-degree relatives of C9orf72 patients (who have 50%-risk to carry the mutation), out of 64 families. A pathogenic expansion was detected in 46 of them, denoted as the presymptomatic group. The control group was formed by the 43 asymptomatic individuals that did not carry an expansion.

At inclusion, each participant's cognitive and behavioral clinical status was assessed based on standardized interview with relatives, comprehensive neurological examination, an extensive neuropsychological battery assessing all cognitive domains (including, notably, MMSE, FAB, MDRS, Ekman faces tests) and behavioral scales (including Frontal Behavioral Inventory and Apathy Evaluation scale) (Table 4.1). The cognitive and behavioral evaluations and their scores have been described in more detail elsewhere [START_REF] Bertrand | Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years[END_REF][START_REF] Montembeault | Cognitive inhibition impairments in presymptomatic C9orf72 carriers[END_REF] and in Appendix A Method A1. Neuromuscular function was thoroughly evaluated by means of quantitative motor testing according to Medical Research Council (MRC) muscle scale, assessment of upper and lower motor neuron signs, and administration of ALS-FRS (ALS functional rating scale), evaluating the degree of functional impairment. All participants underwent a systematic standardized interview to investigate the presence of cramps, fatigue, muscle pain, muscle weakness, muscle stiffness or fasciculations. Electromyography was proposed to the participants with even subtle motor signs or complaints.

One participant was excluded because mild cerebellar syndrome was detected at a neurological examination, after inclusion. Thus, the present study comprises 110 individuals (22 patients, 45 presymptomatic carriers, 43 healthy controls), all of which underwent plasma sampling at their inclusion. The demographic and clinical characteristics of the studied population are shown in Table 4.1.

The participants have then been clinically followed after their inclusion during a 3-year period, from 2017 to 2020. Four out of the 45 C9orf72 presymptomatic carriers have developed subtle frontal cognitive and/or behavioral changes and/or motor signs/symptoms during this period, without fitting diagnostic criteria for FTD or ALS, suggesting they were in the transitional prodromal phase at the moment of or just after their inclusion visit. These cases are described in Appendix A Method A2.

All analyses in the presymptomatic group were performed with (n=45) and without (n=41) the four prodromal subjects. We also analyzed these cases separately in an additional complementary approach.

Plasma collection and preparation

Blood samples were collected on EDTA using the same standardized collection and handling procedures for all participants across the centers. The mean disease duration at sampling was 6.2 ± 4.0 years in the patients' group. All were in fasted state. All samples were centralized at the ICM DNA and cell bank, and processed using the same protocol. Plasma was extracted at room temperature after centrifugation of blood samples at 2500 rpm for 10 minutes. Aliquots of 1 mL were stored in polypropylene tubes at -80°C.

MiRNA extraction and sequencing

MiRNA extraction was performed with a miRNeasy Serum/Plasma Kit (Qiagen) following the manufacturer's instructions. We used 200 µL of plasma quickly melted Chapter 4. Plasma microRNA signature in C9orf72-associated FTD and ALS and directly added to 1 mL of Qiazol solution. MiRNAs were eluted in 14 µL of water; 5 µL were used for miRNA sequencing library preparation with QIAseq miRNA Library Kit (Qiagen) according to the manufacturer's protocol.

MiRNA sequencing was performed on Illumina NovaSeq 6000 in three independent batches, targeting a minimum of 10 million mapped reads per sample. Since batch effects may have a critical impact in high-throughput experiments, we randomly assigned each individual to one batch, equally distributing clinical status (control, presymptomatic, patient) and centers (Paris, Limoges, Lille, Rouen), to allow adjusting for batch effects during data analysis. Appendix A Table A1 describes the distribution of subjects across batches.

Raw reads to miRNA counts computation pipeline

Quality control of raw reads was performed with FastQC (Andrews S. 2010) 1 . UMItools [START_REF] Smith | UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy[END_REF] and Cutadapt [START_REF] Martin | Cutadapt removes adapter sequences from high-throughput sequencing reads[END_REF] were used respectively to extract UMIs and suppress adapting sequences as well as polyA tails. The resulting sequences were aligned with Bowtie [START_REF] Langmead | Ultrafast and memory-efficient alignment of short DNA sequences to the human genome[END_REF] and sorted by genomic location with Samtools sort [START_REF] Li | Bibliography 165 "The Sequence Alignment/Map format and SAMtools[END_REF]. PCR bias was corrected with UMI-tools, its efficacy was assessed per chromosome with Samtools idxstats. After controlling for the overlap/ambiguity between miRNAs enrichment and Gencode annotation with FeatureCounts [START_REF] Liao | featureCounts: an efficient general purpose program for assigning sequence reads to genomic features[END_REF], miRNAs were counted with miRDeep2 [START_REF] Friedländer | miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades[END_REF].

Statistical analysis

Statistical analyses were performed using R version 3.6.1 (R Foundation for Statistical Computing, Vienna, Austria). The differential expression of miRNAs between clinical groups was assessed with the R package EdgeR (Robinson et al., 2010). The analysis began with a count matrix with 2576 rows (one per miRNA i) and 110 columns (one per individual j). Only miRNAs considered above noise level (minimum count of 50 reads for at least one sample and a minimum total count of 1000)

were retained for statistical analyses, reducing the count matrix to 589 rows. We assumed that miRNA counts followed a negative binomial distribution with mean µ ij and dispersion ϕ i and used generalized linear models to fit a log-linear model

log 2 µ ij = x T j β i (4.1)
for each miRNA, where x j is the vector of covariates that describes sample j and β i is the vector of coefficients to be fitted for miRNA i. To control for possible batch, center, age and gender effects, we added these variables as covariates in the model, in addition of clinical status. Raw counts were normalized using a trimmed mean of M-values [START_REF] Robinson | A scaling normalization method for differential expression analysis of RNA-seq data[END_REF]. Once the models were fitted, quasilikelihood (QL) F-test was employed to determine the subset of miRNAs differentially expressed between clinical conditions (miRNA signature). Statistical significance was set at level α = 0.05 and p-values were adjusted for multiple testing using the Benjamini-Hochberg method.

Machine learning for binary classification

After the differentially expressed miRNAs were identified, we implemented logistic regression classifiers with L2 regularization in Python 3.8.0 using scikit-learn (Pedregosa et al., 2011) version 0.22.1. We used the expression levels of the miRNA signature as features to train binary classification models for each pairwise comparison between clinical status: controls versus presymptomatic individuals, controls versus patients and presymptomatic individuals versus patients. A stratified nested cross-validation strategy (Appendix A Figure A1) was chosen to find the optimal hyperparameter (L2 regularization coefficient) and to assess model performance using the area under the receiver operating characteristic curve (ROC AUC). We computed 90% confidence intervals (CIs) for the ROC AUC scores from 2000 bootstrap samples, by taking the 5 th and 95 th percentiles of the bootstrap distribution. Stratification with respect to clinical status was performed to preserve the proportion of healthy controls, presymptomatic subjects and patients in each fold.

Generalization analysis

Since the differentially expressed miRNAs were computed with the entire dataset, the test folds of the cross-validation were also used in the feature selection for our classification models, which could inflate prediction performance. To estimate this possible bias, we then incorporated feature selection in the nested 5-fold cross validation process: differentially expressed miRNAs were computed using only the outer cross-validation loop training data (four out of five folds) at each iteration.

The nested cross-validation was repeated 100 times with different fold splits to assess the generalization performance of our classifiers.

Analysis of the transitional stage to clinical FTD/ALS disease

Since we hypothesized that the expression levels of differentially expressed miRNAs might provide information relevant to C9orf72 disease progression, we designed an experiment to evaluate prediction performance of clinical conversion to FTD/ALS in presymptomatic carriers. A logistic regression classifier was fitted with the expression levels of differentially expressed miRNAs from controls and patients. We used a regular 5-fold cross-validation to determine the optimal hyperparameter (L2 regularization coefficient). Subsequently, this model was tested with the expression levels from the four known presymptomatic carriers who were in their transitional Chapter 4. Plasma microRNA signature in C9orf72-associated FTD and ALS stage to the clinical disease. Scores from 0 to 1 were provided for each subject, indicating proximity with the expression levels of controls (scores near 0) or patients (scores closer to 1).

Target prediction and pathway analysis

A target-gene based miRNA enrichment analysis was performed, to discover potential biological functions regulated by the differentially expressed miRNAs. We used the publicly available tool DIANA-miRPath v.3 [START_REF] Vlachos | DIANA-miRPath v3.0: deciphering microRNA function with experimental support[END_REF], which implements an in silico miRNA target prediction algorithm (DIANA-microT-CDS) as well as an experimentally validated miRNA:gene interaction dataset (DIANA-TarBase v7.0). Both approaches were carried out to identify target genes and the associated Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, using the set of differentially expressed miRNAs as input. The enrichment analysis method consisted of Fisher's exact test (hypergeometric distribution) with Benjamini-Hochberg adjusted p-value threshold of 0.05, giving as output a union set of associated KEGG pathways.

Results

Differentially expressed miRNAs computed with the entire dataset

Table 4.2 displays all miRNAs identified as differentially expressed, for each pairwise comparison between clinical status, after correction for multiple comparisons.

Four miRNAs were computed as differentially expressed between healthy controls and patients: miR-34a-5p and miR-345-5p were overexpressed, while miR-200c-3p

and miR-10a-3p were underexpressed in symptomatic mutation carriers. Interestingly, miR-34a-5p was identified as significantly overexpressed also in presymptomatic mutation carriers compared to healthy controls, suggesting that miR-34a-5p expression is associated with C9orf72 mutation status. Additionally, miR-345-5p was also significantly overexpressed in patients when compared to presymptomatic carriers. When removing the four prodromal subjects from the presymptomatic group, the same miRNAs were identified as differentially expressed, indicating that the differences between the presymptomatic and other groups were not mainly driven by the four prodromal subjects.

We considered these four miRNAs (miR-34a-5p, miR-345-5p, miR-200c-3p, miR-10a-3p) as our miRNA signature for further analyses. The complete output from EdgeR is available in Appendix A Table A2.

47

MiRNA signature to classify between clinical groups

To assess whether the identified miRNA signature could distinguish between clinical groups, we implemented logistic regression models using as features the expression levels of the four differentially expressed miRNAs (miR-34a-5p, miR-345-5p, miR-200c-3p and miR-10a-3p). The area under the ROC curve (ROC AUC) for the classification of healthy controls and presymptomatic mutation carriers was 0.90 (90% CI 0.83 to 0.95), for controls and patients was 0.90 (90% CI 0.82 to 0.97) and to distinguish presymptomatic carriers and patients was 0.80 (90% CI 0.67 to 0.90)

(

Generalization analysis

Since we used the entire dataset to identify the miRNA signature, including test data, classification performance could be inflated. In order to assess the generality of our classification scores, we then incorporated feature selection in the nested cross- and miR-10a-3p (respectively 497, 335, 259 and 196 occurrences) for controls and patients; miR-345-5p (157 occurrences) when analyzing presymptomatic subjects and patients.

Regarding prediction performance, the average ROC AUC when classifying controls versus presymptomatic subjects was 0.88 (90% CI 0.83 to 0.91), for controls versus patients was 0.89 (90% CI 0.83 to 0.94) and for presymptomatic individuals versus patients was 0.67 (90% CI 0.52 to 0.77). The distributions of the ROC AUC scores computed with 100 different fold splits are displayed in Appendix A Figure A4.

Analysis of the transitional stage to clinical FTD/ALS disease

We evaluated the performance to predict the transitional stage to FTD/ALS disease by training a logistic regression classifier with the expression levels from patients and controls and testing with the expression levels of presymptomatic individuals.

The probability scores computed for the four subjects in their transitional stage were

Target prediction and pathway analysis

Using the four differentially expressed miRNAs (miR-34a-5p, miR-345-5p, miR-200c-3p and miR-10a-3p) as input, we performed target prediction and pathway analysis with two methods available in DIANA-miRPath v. Appendix A Figure A6 shows miRNA versus KEGG pathways heatmaps, which depict the level of enrichment in significant KEGG pathways for the four differentially expressed miRNAs as computed by the two approaches.

Discussion

The present study aimed to identify fluid biomarkers by analyzing expression levels of plasma miRNAs without a priori knowledge in a large cohort of healthy controls, presymptomatic and symptomatic C9orf72 carriers. We identified four miR-NAs differentially expressed between clinical conditions: miR-34a-5p, miR-345-5p, miR-200c-3p and miR-10a-3p. Significantly higher expression of miR-34a-5p was found in mutation carriers when compared with healthy controls, which suggests 4.5. Discussion 51 that miR-34a-5p expression is deregulated in cases with C9orf72 mutation. Additionally, we observed miR-345-5p expression to be significantly increased in patients when compared with presymptomatic carriers, which supports the correlation of miR-345-5p expression with the progression of C9orf72-associated disease. Finally, our results also suggest that miR-200c-3p and miR-10a-3p underexpression might be associated with full-blown disease as decreased expression levels were significant only between patients and healthy controls.

We used the expression levels of the miRNA signature to train logistic regression classifiers, which were able to differentiate individuals from different clinical groups with good predictive performance (Figure 4.2). Notably, presymptomatic and symptomatic C9orf72 carriers were distinguished with ROC AUC of 0.80 (90% CI 0.67 to 0.90), which suggests the suitability of plasma miRNAs for following preclinical progression and determining disease onset. We believe that this score was lower in our generalization analysis (0.67, 90% CI 0.52 to 0.77) because the limited number of patients ( 22) led to a higher variability in the differentially expressed miR-NAs in each step of the cross-validation loop (Figure 4.3). Furthermore, we have obtained promising results regarding prediction performance of conversion from the presymptomatic to the clinical stage of FTD/ALS. The four presymptomatic subjects in transitional stage exhibited scores above 0.50, denoting a stronger similarity with the expression levels of patients. Although preliminary, these results suggest that the expression levels of our miRNA signature might be used as early predictors of the C9orf72 disease conversion.

Previous studies have shown the potential of miRNAs in serum, plasma or CSF as diagnostic biomarkers for FTD and ALS [START_REF] Grasso | Plasma microRNA profiling distinguishes patients with frontotemporal dementia from healthy subjects[END_REF][START_REF] Magen | Classification and prediction of frontotemporal dementia based on plasma microRNAs[END_REF][START_REF] Piscopo | Circulating miR-127-3p as a Potential Biomarker for Differential Diagnosis in Frontotemporal Dementia[END_REF][START_REF] Schneider | Downregulation of exosomal miR-204-5p and miR-632 as a biomarker for FTD: a GENFI study[END_REF][START_REF] Sheinerman | Circulating brain-enriched microR-NAs as novel biomarkers for detection and differentiation of neurodegenerative diseases[END_REF], focusing on comparing healthy controls and patients. However, our findings differ from preceding results: only two miRNAs from our signature (miR-345-5p, miR-200c-3p) were identified as differentially expressed in one of these studies [START_REF] Magen | Classification and prediction of frontotemporal dementia based on plasma microRNAs[END_REF], none in the others [START_REF] Grasso | Plasma microRNA profiling distinguishes patients with frontotemporal dementia from healthy subjects[END_REF][START_REF] Piscopo | Circulating miR-127-3p as a Potential Biomarker for Differential Diagnosis in Frontotemporal Dementia[END_REF][START_REF] Sheinerman | Circulating brain-enriched microR-NAs as novel biomarkers for detection and differentiation of neurodegenerative diseases[END_REF]. Results are conflicting probably due to restricted choices for the analyzed miRNAs [START_REF] Piscopo | Circulating miR-127-3p as a Potential Biomarker for Differential Diagnosis in Frontotemporal Dementia[END_REF][START_REF] Sheinerman | Circulating brain-enriched microR-NAs as novel biomarkers for detection and differentiation of neurodegenerative diseases[END_REF] and heterogeneous cohorts, either with sporadic forms [START_REF] Grasso | Plasma microRNA profiling distinguishes patients with frontotemporal dementia from healthy subjects[END_REF][START_REF] Piscopo | Circulating miR-127-3p as a Potential Biomarker for Differential Diagnosis in Frontotemporal Dementia[END_REF] or a mixture of sporadic and familial forms with different mutations [START_REF] Magen | Classification and prediction of frontotemporal dementia based on plasma microRNAs[END_REF]. To the best of our knowledge, the present work is the first to compare the expression levels of plasma miRNAs between presymptomatic and symptomatic carriers focusing on C9orf72 mutation, in addition to providing a plasma miRNA signature that may contribute to the assessment of preclinical progression for C9orf72-associated FTD and ALS. Overall, our work suggests that miR-34a-5p, miR-345-5p, miR-200c-3p and miR-10a-3p are likely involved in neuronal degeneration and C9orf72-associated pathogenesis. Among the KEGG pathways identified in this study, some involved in neurodevelopment (Hippo signaling, FoxO signaling), inflammation (TGF-beta signaling), intracellular transduction (neurotrophin signaling), and apoptosis (TGF-beta, FoxO signaling) were relevant as previously shown to be involved in C9orf72-disease [START_REF] Atkinson | C9ORF72 expression and cellular localization over mouse development[END_REF][START_REF] Burberry | C9orf72 suppresses systemic and neural inflammation induced by gut bacteria[END_REF][START_REF] Farg | The DNA damage response (DDR) is induced by the C9orf72 repeat expansion in amyotrophic lateral sclerosis[END_REF]. Accordingly, these four miRNAs have been previously linked with a range of neurodevelopmental processes, neuropsychiatric and neurodegenerative conditions [START_REF] Berg | Circulating microRNAs as potential biomarkers for psychiatric and neurodegenerative disorders[END_REF][START_REF] Chua | miR-34a in Neurophysiology and Neuropathology[END_REF][START_REF] Cosín-Tomás | Plasma miR-34a-5p and miR-545-3p as Early Biomarkers of Alzheimer's Disease: Potential and Limitations[END_REF][START_REF] Fu | Regulatory roles of the miR-200 family in neurodegenerative diseases[END_REF]. For instance, miR-200c and miR-34a family members are implicated in synaptic function, neuronal maturation, differentiation, and survival [START_REF] Jauhari | Regulation of miR-34 Family in Neuronal Development[END_REF][START_REF] Jin | Interrogation of brain miRNA and mRNA expression profiles reveals a molecular regulatory network that is perturbed by mutant huntingtin[END_REF]. Aberrant expression of miR-34a and miR-345 are also associated with neuronal apoptosis [START_REF] Modi | Regulation of Neuronal Cell Cycle and Apoptosis by MicroRNA 34a[END_REF], whereas members of miR-10a family were found to be differentially expressed in the muscle tissue of ALS patients [START_REF] Kovanda | Differential expression of microRNAs and other small RNAs in muscle tissue of patients with ALS and healthy age-matched controls[END_REF].

How these four miRNAs are implicated in C9orf72-associated pathogenesis, and their relevance in brain pathology are important questions to go further. So far, only few studies addressing miRNA dysregulation in brain tissues of FTD/ALS patients have been performed, and are summarized in Appendix A Table A5. They specifically addressed GRN-associated (Chen-Plotkin et al., 2012;Kocerha et al., 2011), sporadic FTD (Gascon et al., 2014;Hébert et al., 2013), sporadic (Jawaid et al., 2019) or mixed genetic-sporadic ALS patients (Helferich et al., 2018). Notably, there was 4.5. Discussion 53 no miRNA dysregulation in common between the aforementioned studies, nor between any of those studies on the brain and ours on plasma. Those discrepancies may stem from the heterogeneity of the previous autoptic cohorts and the differences in the methods of miRNA expression analysis. Noteworthy, and differently from our investigation, none of the patient cohorts mentioned in Appendix A Table A5 were exclusively made up of C9orf72 carriers. Additionally, the observed differences between brain tissue and plasma miRNA profiles may be due to the tissuespecific expression of miRNA on the one hand, and to the time-dependent variations of detectable miRNAs all along the disease course on the other. Due to the disease process itself and other potential confounding factors, significant changes in miRNA expression are likely to occur between a relatively early phase of the disease, in which plasma miRNAs may be used as biomarkers, and the ultimate disease stage, at the moment of brain sampling. At this point, further miRNA profiling studies on C9orf72 brain tissue are needed to better understand whether tissue miR-NAs correlate with plasma expression profiles and their contribution to the disease pathogenesis.

Regardless, it is noteworthy that some studies pointed towards a direct relationship between these miRNAs and C9orf72 pathogenesis. C9orf72 stands as a putative target of miR-34a-5p, likely acting as a negative regulator of C9orf72 mRNA expression [START_REF] Lal | Capture of MicroRNA-Bound mRNAs Identifies the Tumor Suppressor miR-34a as a Regulator of Growth Factor Signaling[END_REF]. Additionally, miR-200c-3p and miR-345-5p are down-and up-regulated, respectively, in the extracellular vesicles secreted by induced astrocytes obtained from C9orf72 patients [START_REF] Varcianna | Micro-RNAs secreted through astrocyte-derived extracellular vesicles cause neuronal network degeneration in C9orf72 ALS[END_REF]. Even if not completely explained so far, these important results parallel our study showing a comparable upregulation of miR-34a-5p and miR-345-5p and downregulation of miR-200c-3p in carriers, and provide converging evidence for a link between our set of miRNAs and C9orf72-pathogenesis, which will need further investigations.

Previous studies have provided the proof-of-concept that specific sets of miR-NAs have the potential to serve as biomarkers of the preclinical/premanifest stages of other neurodegenerative diseases, such as ALS [START_REF] Freischmidt | Serum microRNAs in patients with genetic amyotrophic lateral sclerosis and pre-manifest mutation carriers[END_REF], Huntington [START_REF] Jin | Interrogation of brain miRNA and mRNA expression profiles reveals a molecular regulatory network that is perturbed by mutant huntingtin[END_REF], and Prion diseases [START_REF] Boese | Mi-croRNA abundance is altered in synaptoneurosomes during prion disease[END_REF]. Our study supports the usefulness of our four miRNAs as biomarkers of disease progression from the presymptomatic to the symptomatic phase of C9orf72 disease. Nevertheless, some of them may be dysregulated in a broader range of neurodegenerative conditions.

For instance, miR-345 and miR-200c-3p were also dysregulated during the presymptomatic stage of Prion [START_REF] Boese | Mi-croRNA abundance is altered in synaptoneurosomes during prion disease[END_REF]) and Huntington's diseases [START_REF] Jin | Interrogation of brain miRNA and mRNA expression profiles reveals a molecular regulatory network that is perturbed by mutant huntingtin[END_REF], respectively. This would not prevent, however, their use in longitudinal monitoring of specific genetic neurodegenerative disorders, possibly in combination with other biomarkers. Together, all these studies and ours suggest that dysregulation of such miRNAs is dynamically altered throughout neurodegenerative diseases progression, and can be detectable even long before clinical onset.

The current study has limitations. Firstly, the significant age difference between patients and the other clinical groups may have introduced a confounding factor, Chapter 4. Plasma microRNA signature in C9orf72-associated FTD and ALS which we considered by including age as a covariate. Secondly, the absence of validation in other tissues or of a replication cohort means that further studies in independent cohorts are required to confirm our results, even though our generalization analysis confirmed the identified miRNA signature. Finally, the limited number of patients does not allow any conclusions about the correlation of plasma miRNAs and different disease phenotypes. Future work will explore longitudinal analyses of plasma miRNAs to assess their use as biomarkers of FTD and ALS progression.

In summary, the current work revealed significant differences in miRNA expression levels in plasma when comparing healthy controls, presymptomatic and symptomatic C9orf72 mutation carriers. Specifically, we highlighted the potential of miR-34a-5p, miR-345-5p, miR-200c-3p and miR-10a-3p expression levels in plasma as biomarkers of preclinical progression for C9orf72-associated FTD and ALS. Our results encourage the use of plasma miRNAs, possibly in combination with other markers, to improve the design of clinical trials for these neurodegenerative disorders.

Chapter 5

Circulating microRNA signatures as potential biomarkers of genetic frontotemporal dementia and amyotrophic lateral sclerosis

This chapter has been submitted as an original research article to Molecular Psychiatry:

• Kmetzsch, V., Latouche, M., Saracino, D., Rinaldi, D., Camuzat, A., Gareau, T., the French research network on FTD/ALS, Le Ber, I., Colliot, O., Becker, E, "Circulating microRNA signatures as potential biomarkers in genetic frontotemporal dementia and amyotrophic lateral sclerosis". Submitted to Molecular Psychiatry.

Abstract

MicroRNAs are promising biomarkers of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS), but discrepant results between different studies have so far hampered their use in clinical trials. We aim to assess all previously identified circulating microRNA signatures as potential biomarkers of genetic FTD and/or ALS, using homogeneous, independent validation cohorts of C9orf72 and GRN mutation carriers. Between 2011 and 2021, 104 individuals carrying a C9orf72 or a GRN mutation, along with 31 controls, were recruited through the French research network on FTD/ALS. All subjects underwent blood sampling, from which circulating microRNAs were extracted. We measured differences in the expression levels of 65 microRNAs, selected from 15 published studies about FTD or ALS, between controls, C9orf72 presymptomatic subjects, and C9orf72 patients. We also assessed differences in the expression levels of 30 microRNAs, selected from five studies about FTD, between controls, GRN presymptomatic subjects, and GRN patients. More than half (35/65) of the selected microRNAs were differentially expressed in the Chapter 5. Validation of microRNA signatures in genetic FTD and ALS C9orf72 cohort, while only a small proportion (5/30) of microRNAs were differentially expressed in the GRN cohort. In multivariate analyses, only individuals in the C9orf72 cohort could be adequately classified (ROC AUC up to 0.98 for controls vs. presymptomatic subjects, 0.94 for controls vs. patients, and 0.77 for presymptomatic subjects vs. patients) with some of the signatures. Our results suggest that previously identified microRNAs using sporadic or mixed cohorts of FTD and ALS patients could potentially serve as biomarkers of C9orf72-associated disease, but not GRN-associated disease.

Introduction

Frontotemporal dementia (FTD) is a neurodegenerative disease characterized by brain atrophy in the frontal and temporal lobes, causing severe changes in personality and social behavior [START_REF] Neary | Frontotemporal dementia[END_REF]. The most prevalent genetic causes of FTD are GGGGCC repeat expansions in the C9orf72 gene and mutations in the GRN gene [START_REF] Dejesus Hernandez | Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS[END_REF][START_REF] Renton | A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD[END_REF]. FTD shares disease pathways with amyotrophic lateral sclerosis (ALS), a debilitating motor neuron disease that causes progressive motor deficit and muscle wasting [START_REF] Pasinelli | Molecular biology of amyotrophic lateral sclerosis: insights from genetics[END_REF]. The C9orf72 hexanucleotide repeat expansion has been identified as the most common genetic cause of both familial FTD and ALS, as well as of their sporadic counterparts [START_REF] Dejesus Hernandez | Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS[END_REF].

There are currently no disease-modifying treatments that can stop the course of FTD or ALS. New therapeutic trials depend on robust progression biomarkers to assess treatment outcomes. The study of FTD/ALS genetic forms is particularly important, since asymptomatic mutation carriers may provide insights about the early disease stages, before any irreversible neuronal damage [START_REF] Rohrer | Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis[END_REF].

Among the potential non-invasive biomarkers of neurodegenerative diseases, circulating microRNAs (miRNAs) constitute a promising approach [START_REF] Grasso | Circulating microRNAs in Disease Diagnostics and their Potential Biological Relevance[END_REF]. MicroRNAs are short noncoding RNAs that negatively regulate gene expression [START_REF] Huntzinger | Gene silencing by microRNAs: contributions of translational repression and mRNA decay[END_REF]. There is increasing evidence of a link between miRNA expression levels and the diagnosis of FTD [START_REF] Denk | Specific serum and CSF mi-croRNA profiles distinguish sporadic behavioural variant of frontotemporal dementia compared with Alzheimer patients and cognitively healthy controls[END_REF][START_REF] Grasso | Plasma microRNA profiling distinguishes patients with frontotemporal dementia from healthy subjects[END_REF][START_REF] Kmetzsch | Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis[END_REF][START_REF] Piscopo | Circulating miR-127-3p as a Potential Biomarker for Differential Diagnosis in Frontotemporal Dementia[END_REF][START_REF] Sheinerman | Circulating brain-enriched microR-NAs as novel biomarkers for detection and differentiation of neurodegenerative diseases[END_REF] and ALS [START_REF] De Felice | miR-338-3p is over-expressed in blood, CFS, serum and spinal cord from sporadic amyotrophic lateral sclerosis patients[END_REF][START_REF] Dobrowolny | A longitudinal study defined circulating microRNAs as reliable biomarkers for disease prognosis and progression in ALS human patients[END_REF][START_REF] Freischmidt | Serum microRNAs in sporadic amyotrophic lateral sclerosis[END_REF][START_REF] Freischmidt | Serum microRNAs in patients with genetic amyotrophic lateral sclerosis and pre-manifest mutation carriers[END_REF][START_REF] Kmetzsch | Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis[END_REF][START_REF] Magen | Circulating miR-181 is a prognostic biomarker for amyotrophic lateral sclerosis[END_REF][START_REF] Raheja | Correlating serum microRNAs and clinical parameters in Amyotrophic lateral sclerosis[END_REF][START_REF] Sheinerman | Circulating brain-enriched microR-NAs as novel biomarkers for detection and differentiation of neurodegenerative diseases[END_REF][START_REF] Soliman | Assessment of diagnostic potential of some circulating microRNAs in Amyotrophic Lateral Sclerosis Patients, an Egyptian study[END_REF][START_REF] Takahashi | Identification of plasma microRNAs as a biomarker of sporadic Amyotrophic Lateral Sclerosis[END_REF][START_REF] Tasca | Circulating microR-NAs as biomarkers of muscle differentiation and atrophy in ALS[END_REF][START_REF] Waller | Serum miR-NAs miR-206, 143-3p and 374b-5p as potential biomarkers for amyotrophic lateral sclerosis (ALS)[END_REF].

However, there are strong inconsistencies between the identified miRNA signatures in different studies. The examined cohorts are highly heterogeneous, most of them being sporadic or mixed cohorts of sporadic and genetic forms. Importantly, it is unclear which miRNAs are specific to a particular genetic mutation or might serve as biomarkers for several genetic forms. It is also uncertain whether miRNAs found in sporadic forms are differentially expressed in genetic forms. Furthermore, several of the published articles lacked an independent validation cohort, which also might have caused disparity between results. This absence of convergence amongst different studies so far hinders the use of miRNAs in clinical trials.

The present work aims at testing circulating miRNA signatures identified in the literature, using two independent homogeneous cohorts of patients and presymptomatic carriers: one focused on C9orf72 expansion carriers and another comprising GRN mutation carriers. For that purpose, we selected all published studies that identified specific miRNAs isolated from plasma or serum as potential biomarkers of FTD and/or ALS. With a preregistered study design, we investigated whether

(1) miRNAs revealed in cohorts of sporadic patients (or in mixed cohorts with sporadic and genetic forms) may be biomarkers in C9orf72 and/or GRN genetic forms,

(2) miRNAs identified in a C9orf72 cohort are validated in an independent C9orf72 cohort, and ( 3) miRNAs discovered in a C9orf72 cohort may be relevant in a GRN cohort.

We hypothesize that if a miRNA is a potential progression biomarker in a particular genetic form, it will be differentially expressed (adjusted p-value below 0.05) between controls and presymptomatic subjects, controls and patients, or presymptomatic subjects and patients in an independent cohort of subjects carrying that mutation. Moreover, we consider that a miRNA signature will constitute a promising biomarker if a logistic regression model (using these miRNAs as features) classifies subjects between clinical groups with an acceptable area under the ROC curve (above 0.70).

Material and Methods

This research was conducted according to the preregistration available in https:// osf.io/4pw8f.

Participants of the validation cohorts

Plasma preparation, miRNA sequencing and computation pipeline

Blood samples from all participants were collected on EDTA following standardized collection and handling procedures. The mean disease duration at sampling was 4.9 (SD 3.8) years in the C9orf72 patients' group and 3.2 (SD 1.4) years in the GRN patients' group. MiRNA extraction and sequencing were performed as previously reported [START_REF] Kmetzsch | Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis[END_REF], with the only differences being that plasma was slowly melted at 4°C before adding it to the QIAzol solution, and sequencing was performed in two batches.

Finally, the quantification of miRNAs was carried out according to recommendations by [START_REF] Potla | A bioinformatics approach to microRNAsequencing analysis[END_REF].

Selected studies

We aimed to find all papers that identified specific miRNAs extracted from human plasma or serum as potential biomarkers of FTD and/or ALS, excluding reviews and meta-analyses. We thus conducted the following search in PubMed 1 , on March This search yielded 19 results. Two papers [START_REF] Brennan | Panoramic Visualization of Circulating MicroRNAs Across Neurodegenerative Diseases in Humans[END_REF][START_REF] Grasso | Circulating microRNAs in Disease Diagnostics and their Potential Biological Relevance[END_REF] were excluded because they were review studies, one [START_REF] Freischmidt | A serum microRNA sequence reveals fragile X protein pathology in amyotrophic lateral sclerosis[END_REF] 1 https:// pubmed.ncbi.nlm.nih.gov/

Material and Methods

59

was discarded because it was focused on protein levels, and one [START_REF] Xu | Comparison of the extraction and determination of serum exosome and miRNA in serum and the detection of miR-27a-3p in serum exosome of ALS patients[END_REF] was excluded because it was focused on one microRNA from serum exosomes.

Our final selection therefore contained 15 articles. Three of these studies identified miRNA signatures exclusively associated with FTD [START_REF] Denk | Specific serum and CSF mi-croRNA profiles distinguish sporadic behavioural variant of frontotemporal dementia compared with Alzheimer patients and cognitively healthy controls[END_REF][START_REF] Grasso | Plasma microRNA profiling distinguishes patients with frontotemporal dementia from healthy subjects[END_REF][START_REF] Piscopo | Circulating miR-127-3p as a Potential Biomarker for Differential Diagnosis in Frontotemporal Dementia[END_REF], one revealed a single miRNA signature for both FTD and ALS [START_REF] Kmetzsch | Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis[END_REF], one detected separate signatures for FTD and ALS [START_REF] Sheinerman | Circulating brain-enriched microR-NAs as novel biomarkers for detection and differentiation of neurodegenerative diseases[END_REF], and ten of them were entirely focused on ALS [START_REF] De Felice | miR-338-3p is over-expressed in blood, CFS, serum and spinal cord from sporadic amyotrophic lateral sclerosis patients[END_REF][START_REF] Dobrowolny | A longitudinal study defined circulating microRNAs as reliable biomarkers for disease prognosis and progression in ALS human patients[END_REF][START_REF] Freischmidt | Serum microRNAs in sporadic amyotrophic lateral sclerosis[END_REF][START_REF] Freischmidt | Serum microRNAs in patients with genetic amyotrophic lateral sclerosis and pre-manifest mutation carriers[END_REF][START_REF] Magen | Circulating miR-181 is a prognostic biomarker for amyotrophic lateral sclerosis[END_REF][START_REF] Raheja | Correlating serum microRNAs and clinical parameters in Amyotrophic lateral sclerosis[END_REF][START_REF] Soliman | Assessment of diagnostic potential of some circulating microRNAs in Amyotrophic Lateral Sclerosis Patients, an Egyptian study[END_REF][START_REF] Takahashi | Identification of plasma microRNAs as a biomarker of sporadic Amyotrophic Lateral Sclerosis[END_REF][START_REF] Tasca | Circulating microR-NAs as biomarkers of muscle differentiation and atrophy in ALS[END_REF][START_REF] Waller | Serum miR-NAs miR-206, 143-3p and 374b-5p as potential biomarkers for amyotrophic lateral sclerosis (ALS)[END_REF]. The selected papers, their associated microRNAs, diseases, cohort types, numbers of patients, and methods of analyses are displayed in Table 5.2.

Since C9orf72 expansions can cause both FTD and ALS, and GRN mutations only cause FTD, there are 16 miRNA signatures (FTD and/or ALS) to be tested with our C9orf72 cohort and five miRNA signatures (FTD) to be tested with our GRN cohort.

Some miRNAs were identified by multiple studies, for instance miR-206 [START_REF] Grasso | Plasma microRNA profiling distinguishes patients with frontotemporal dementia from healthy subjects[END_REF][START_REF] Sheinerman | Circulating brain-enriched microR-NAs as novel biomarkers for detection and differentiation of neurodegenerative diseases[END_REF][START_REF] Soliman | Assessment of diagnostic potential of some circulating microRNAs in Amyotrophic Lateral Sclerosis Patients, an Egyptian study[END_REF][START_REF] Tasca | Circulating microR-NAs as biomarkers of muscle differentiation and atrophy in ALS[END_REF][START_REF] Waller | Serum miR-NAs miR-206, 143-3p and 374b-5p as potential biomarkers for amyotrophic lateral sclerosis (ALS)[END_REF], but most miRNAs were found by a single study. Considering all the selected articles, the set of miRNAs associated with either FTD or ALS is composed of 65 miRNAs, and the set of miRNAs associated only with FTD is composed of 30 miRNAs.

Most of the selected articles were based on previous versions of the miRBase.

Since we used miRBase version 22.1 in our computation pipeline, the following conversions were performed:

• miR-320a: miR-320a-5p plus miR-320a-3p

• miR-9: miR-9-5p plus miR-9-3p

• let-7e: let-7e-5p plus let-7e-3p

• miR-1: miR-1-5p plus miR-1-3p

• miR-133-a: miR-133-a-5p plus miR-133a-3p

• miR-27a: miR-27a-5p plus miR-27a-3p

• miR-7: miR-7-5p

• miR-451: miR-451a

• miR-129-3p: miR-129-1-3p

Differential expression

Differential expression analyses were performed using the R package EdgeR (Robinson et al., 2010). After microRNA extraction and sequencing, our dataset contained the expression levels of 2656 miRNAs (denoted by i) for each of the 135 subjects -29b-3p, miR-320c, miR-34a-5p, miR-29c-3p, miR-320a, miR-22-3p, miR-1, miR-133a-3p, miR-191-5p, miR-144-5p, miR-320b, miR-423-3p, miR-192-5p, miR-133b, miR-194-5p, miR-7-1-3p, miR-19a-3p, miR-425-5p, miR-145-5p, miR- (represented by j). First, we created two count matrices: one containing the miRNA counts from the C9orf72 patients, presymptomatic subjects and controls, and another containing the miRNA counts from the GRN patients, presymptomatic individuals, and controls. Second, for each count matrix, we fit a log-linear model to each miRNA, following a negative binomial distribution with mean µ ij and dispersion ϕ i :

log 2 µ ij = x T j β i (5.1)
where x j denotes the covariates describing sample j and β i denotes the coefficients to be fitted for miRNA i. In addition to the clinical group (control, presymptomatic or patient), we included batch, center, age and gender as covariates in the model.

A trimmed mean of M-values [START_REF] Robinson | A scaling normalization method for differential expression analysis of RNA-seq data[END_REF] was used to normalize raw counts. Finally, after the log-linear models were fitted, quasi-likelihood (QL)

F-tests were used to identify the differentially expressed miRNAs. All p-values were 2-tailed, and the level of statistical significance was set at 0.05, while p-values between 0.05 and 0.1 were considered as suggestive. The Benjamini-Hochberg [START_REF] Benjamini | Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing[END_REF] procedure was used to adjust p-values for multiple testing.

Concretely

Binary classification

To test if the miRNA signatures described in the literature could discriminate be- Each logistic regression model was trained with a nested 5-fold cross-validation strategy, as previously detailed [START_REF] Kmetzsch | Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis[END_REF]. We computed the area under the ROC curve for each model, as well as 90% empirical confidence intervals from 2000 bootstrap samples. A miRNA signature was considered a promising biomarker for a given comparison if the corresponding ROC AUC was above 0.70.

tween

Results

Differential expression in the C9orf72 cohort

The first analysis consisted of testing which miRNAs identified in our literature search were differentially expressed in the C9orf72 cohort. Of the 65 selected miR- miR-145-5p, miR-92a-3p, let-7g-5p, miR-199a-5p, miR-206, miR-30b-5p, miR-191-5p, miR-27a, miR-320b, miR-143-3p, miR-1246, miR-223-3p, miR-144-3p, miR-451, miR-194-5p, miR-144-5p, miR-29b-3p, miR-29c-3p, miR-192-5p, miR-19a-3p, miR-502-3p, miR-15a-5p, miR-374b-5p, miR-7-1-3p, miR-320c, miR-106b-5p, miR-146a-5p, miR-133b, let-7b-5p, miR-345-5p, and miR-22-3p. The meta-signature consisting of the miRNAs with adjusted p-values lower than 0.1 has nine more miRNAs: miR-151a-5p, miR-1234-3p, miR-26a-5p, miR-301a-3p, let-7e, miR-18b-5p, miR-106a-5p, miR-1915-3p, and miR-9.

Differential expression in the GRN cohort

The second analysis focused on identifying which of the 30 miRNAs linked with FTD in the literature were differentially expressed in the GRN cohort. In at least one comparison, five miRNAs were significantly differentially expressed (adjusted p-values lower than 0.05), whereas four miRNAs had a p-value between 0.05 and 0.1. Table 5.4 lists all differentially expressed miRNAs, including log-fold changes reflecting the degree of underexpression or overexpression and the calculated pvalues, while Appendix B Supplementary Table 2 summarizes the complete results of the differential expression experiments in the GRN cohort. The meta-signature of the GRN cohort containing all differentially expressed miRNAs with adjusted p-values lower than 0.05 is comprised of the following five miRNAs: miR-451, miR-15a-5p, miR-502-3p, miR-7, and miR-18b-5p. The metasignature consisting of the miRNAs with adjusted p-values smaller than 0.1 has four more miRNAs: miR-106a-5p, miR-92a-3p, miR-106b-5p, and let-7b-5p.

Results

63

Finally, Table 5.5 allows comparing the differentially expressed miRNAs between the C9orf72 and the GRN cohorts.

Binary classification in the C9orf72 cohort

The first set of logistic regression classifiers focused on the C9orf72 cohort. We presymptomatic subjects), [START_REF] Raheja | Correlating serum microRNAs and clinical parameters in Amyotrophic lateral sclerosis[END_REF] (0.94 for controls vs. patients), and the meta-signature with p-value < 0.1 (0.77 for presymptomatic subjects vs. patients). First, differential expression results (Table 5.3) showed that more than half (35/65) of the miRNAs linked with FTD and/or ALS in the literature were significantly differentially expressed in the C9orf72 cohort. Remarkably, only four of the 15 selected studies included C9orf72 mutation carriers [START_REF] Freischmidt | Serum microRNAs in patients with genetic amyotrophic lateral sclerosis and pre-manifest mutation carriers[END_REF][START_REF] Kmetzsch | Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis[END_REF][START_REF] Magen | Circulating miR-181 is a prognostic biomarker for amyotrophic lateral sclerosis[END_REF][START_REF] Raheja | Correlating serum microRNAs and clinical parameters in Amyotrophic lateral sclerosis[END_REF], three of which focused exclusively on ALS [START_REF] Freischmidt | Serum microRNAs in patients with genetic amyotrophic lateral sclerosis and pre-manifest mutation carriers[END_REF][START_REF] Magen | Circulating miR-181 is a prognostic biomarker for amyotrophic lateral sclerosis[END_REF][START_REF] Raheja | Correlating serum microRNAs and clinical parameters in Amyotrophic lateral sclerosis[END_REF]. Therefore, these outcomes reveal strong miRNA expression similarities between individuals with sporadic forms of FTD/ALS and C9orf72-associated disease. Classification results with the C9orf72 cohort (Figure 5.1) also corroborate these findings, since half of the examined miRNA signatures (8/16) yielded at least one pairwise comparison with acceptable performance (ROC AUC above 0.70), and all comparisons employing the meta-signatures had satisfactory performance.

Results

65

Next, we observed that a miRNA signature previously identified in a homogeneous C9orf72 cohort [START_REF] Kmetzsch | Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis[END_REF] and another one revealed in a mixed cohort of sporadic and familial ALS [START_REF] Raheja | Correlating serum microRNAs and clinical parameters in Amyotrophic lateral sclerosis[END_REF] displayed an outstanding result (ROC AUC above 0.90) when classifying controls vs. C9orf72 presymptomatic subjects and controls vs. C9orf72 patients (Figure 5.1). These two signatures have in common the presence of miR-34a-5p, which has the smallest adjusted p-value in the differential expression analyses regarding these comparisons (Table 5.3, respectively p-value = 2.42E-08 and p-value = 5.06E-06). In contrast, the performance of both of these signatures classifying C9orf72 presymptomatic individuals from patients was unsatisfactory. Indeed, neither of them contained miR-206, which is the most differentially expressed miRNA in this comparison (Table 5.3, p-value = 9.04E-05). The overexpression of miR-206 in ALS patients had already been evidenced [START_REF] Toivonen | MicroRNA-206: A Potential Circulating Biomarker Candidate for Amyotrophic Lateral Sclerosis[END_REF], and the results of the present work extend this association also to C9orf72associated disease. Nevertheless, even using the expression levels of miR-206, the classification of C9orf72 presymptomatic subjects vs. patients led to lower performances than comparisons involving the control group: the highest ROC AUC was 0.77, using the meta-signature with p-value < 0.1.

Finally, our results with the GRN cohort suggest that previously identified miR-

NAs have a weaker correlation with disease diagnosis and progression in this genetic form. Only a small proportion (5/30) of the miRNAs associated with FTD in previous studies was significantly differentially expressed in the GRN cohort (Table 5.4), and not a single miRNA was differentially expressed between controls and presymptomatic GRN carriers. Regarding the classification experiments, none of the studied miRNA signatures in the GRN cohort exhibited an acceptable performance (Figure 5.2), and the only ROC AUC slightly above 0.70 was obtained when classifying GRN presymptomatic carriers and patients using the largest meta-signature (miRNAs with p-value < 0.1). One should note that none of the previous studies included GRN participants. Thus, our results demonstrate that miRNAs associated with sporadic FTD or genetic FTD due to C9orf72 are not relevant for GRN-associated disease. Further studies are needed to determine if other miRNAs, not analyzed in the present paper, are useful in GRN-associated disease.

Validation studies using independent datasets, such as this one, are crucial to assess the utility of biomarker candidates, fostering research rigor and reproducibility.

Notably, we carefully defined our research questions and analysis plan before data analysis, and preregistered our study. Preregistration has the strong benefit of leaving no flexibility for changes in analytical decisions after observing the data, which has been highlighted as a major source of false discoveries and replication failure [START_REF] Nosek | The preregistration revolution[END_REF].

The main limitation of this work is the size of the studied cohorts, particularly the small group of C9orf72 presymptomatic carriers (17) in comparison with the other groups, due to the rarity of genetic FTD. Additionally, due to the low number of C9orf72 patients with different phenotypes (20 FTD, 6 FTD/ALS and 3 ALS), no conclusions can be drawn concerning the relationship between miRNAs and different disease manifestations. Future work will explore the combination of circulating mi-croRNAs with other biomarkers, such as gray matter volume [START_REF] Rohrer | Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis[END_REF], white matter integrity [START_REF] Bertrand | Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years[END_REF], and neurofilament light chain level [START_REF] Saracino | Plasma NfL levels and longitudinal change rates in C9orf72 and GRN-associated diseases: from tailored references to clinical applications[END_REF]. Multimodality will be crucial to accurately assess progression in GRN-associated FTD, and will likely improve the understanding of C9orf72associated disease.

In summary, the present work revealed that most miRNAs previously identified in sporadic or mixed FTD/ALS cohorts are potential biomarkers of C9orf72associated FTD/ALS, but not of GRN-associated FTD. Longitudinal studies are needed to confirm our findings, before circulating microRNAs can be used to evaluate C9orf72associated disease progression in clinical trials.

Chapter 6

Disease progression score estimation from multimodal imaging and microRNA data using supervised variational autoencoders

This chapter is under review as an original research article at the IEEE Journal of Biomedical and Health Informatics:

• Kmetzsch, V., Becker, E., Saracino, D., Rinaldi, D., Camuzat, A., Le Ber, I., Colliot, O., "Disease progression score estimation from multimodal imaging and microRNA data using supervised variational autoencoders". Under review at the IEEE Journal of Biomedical and Health Informatics.

Abstract

Frontotemporal dementia and amyotrophic lateral sclerosis are rare neurodegenerative diseases with no effective treatment. The development of biomarkers allowing an accurate assessment of disease progression is crucial for evaluating new therapies. Concretely, neuroimaging and transcriptomic (microRNA) data have been

shown useful in tracking their progression. However, no single biomarker can accurately measure progression in these complex diseases. Additionally, large samples are not available for such rare disorders. It is thus essential to develop methods that can model disease progression by combining multiple biomarkers from small samples. In this paper, we propose a new framework for computing a disease progression score (DPS) from cross-sectional multimodal data. Specifically, we introduce a supervised multimodal variational autoencoder that can infer a meaningful latent space, where latent representations are placed along a disease trajectory. A score is computed by orthogonal projections onto this path. We evaluate our framework with multiple synthetic datasets and with a real dataset containing 14 patients, 40

Chapter 6. Disease progression score estimation using a supervised VAE presymptomatic genetic mutation carriers and 37 controls from the PREV-DEMALS study. There is no ground truth for the DPS in real-world scenarios, therefore we use the area under the ROC curve (AUC) as a proxy metric. Results with the synthetic datasets support this choice, since the higher the AUC, the more accurate the predicted simulated DPS. Experiments with the real dataset demonstrate better performance in comparison with a state-of-the-art approach. The proposed framework thus leverages cross-sectional multimodal datasets with small sample sizes to objectively measure disease progression, with potential application in clinical trials.

Introduction

Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) are rare neurodegenerative disorders that have devastating personal and social consequences.

Progressive cognitive and behavioural changes, emotional instability, and language impairment are the main symptoms of FTD [START_REF] Rascovsky | Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia[END_REF]. ALS is a motor neuron disease characterized by gradual muscle wasting, ultimately leading to disability [START_REF] Pasinelli | Molecular biology of amyotrophic lateral sclerosis: insights from genetics[END_REF]. FTD and ALS may be sporadic (no previous family history) or genetically inherited. The most common genetic cause of FTD and ALS is a hexanucleotide repeat expansion in the C9orf72 gene [START_REF] Dejesus Hernandez | Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS[END_REF][START_REF] Renton | A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD[END_REF]. These fatal conditions can sometimes coexist in C9orf72-mutated individuals, and have no cure or standard treatment to date.

Carriers of the C9orf72 mutation that do not present clinical symptoms are considered presymptomatic, since they have a very high probability of manifesting FTD and/or ALS later in life. Clinical trials for potential therapies are likely to be most effective at this presymptomatic stage, before any irreversible brain damage has occurred. However, the evaluation of new treatments depends on an accurate measure of disease progression, which is not evident without observable symptoms. Therefore, it is crucial to identify biomarkers to assess disease progression in presymptomatic subjects. Indeed, previous work has shown the relevance of neuroimaging [START_REF] Bertrand | Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years[END_REF][START_REF] Rohrer | Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis[END_REF] and transcriptomic (microRNA) [START_REF] Kmetzsch | Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis[END_REF] biomarkers for a better understanding of C9orf72-associated disease in presymptomatic carriers. Nevertheless, when these modalities are analyzed separately, they provide only an incomplete picture of these complex neurodegenerative diseases. It is thus essential to develop methods that leverage the complementary information available from different modalities to accurately measure disease progression. As different biomarkers characterise distinct disease stages, various biomarkers can be combined to represent the entire disease course with a single measure, commonly referred in the literature as the disease progression score (DPS).

The idea of computing disease progression scores falls within the larger topic of modeling disease progression. In the past years, many approaches have been developed for data-driven modeling of disease progression, such as event-based models (EBM) [START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF][START_REF] Venkatraghavan | Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling[END_REF], different algorithms fitting 6.2. Introduction 73 logistic functions to biomarker trajectories [START_REF] Jedynak | A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort[END_REF][START_REF] Mehdipour Ghazi | Robust parametric modeling of Alzheimer's disease progression[END_REF], non-linear mixed-effects models [START_REF] Koval | AD Course Map charts Alzheimer's disease progression[END_REF]Schiratti et al., 2017), a vertex-wise model of brain diseases fitted with expectation-maximisation [START_REF] Marinescu | DIVE: A spatiotemporal progression model of brain pathology in Bibliography neurodegenerative disorders[END_REF], Gaussian processes (Lorenzi et al., 2019a), topological profiles reflecting brain connectivity [START_REF] Garbarino | Differences in topological progression profile among neurodegenerative diseases from imaging data[END_REF], Bayesian multi-task learning [START_REF] Aksman | Modeling longitudinal imaging biomarkers with parametric Bayesian multi-task learning[END_REF], and recurrent neural networks [START_REF] Mehdipour Ghazi | Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling[END_REF].

Most of these approaches require longitudinal data. For instance, the authors of [START_REF] Jedynak | A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort[END_REF] To the best of our knowledge, the only disease modeling approaches that infer a DPS from cross-sectional data are EBM [START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF][START_REF] Venkatraghavan | Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling[END_REF]. These models explore the temporal sequence in which biomarkers become abnormal in the course of a disease. They have been successfully applied to a variety of diseases including AD [START_REF] Archetti | Multi-study validation of data-driven disease progression models to characterize evolution of biomarkers in Alzheimer's disease[END_REF][START_REF] Firth | Sequences of cognitive decline in typical Alzheimer's disease and posterior cortical atrophy estimated using a novel event-based model of disease progression[END_REF][START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF][START_REF] Oxtoby | Data-driven models of dominantly-inherited Alzheimer's disease progression[END_REF][START_REF] Venkatraghavan | Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling[END_REF]Young et al., 2014), multiple sclerosis [START_REF] Dekker | The sequence of structural, functional and cognitive changes in multiple sclerosis[END_REF][START_REF] Eshaghi | Progression of regional grey matter atrophy in multiple sclerosis[END_REF]), Parkinson's disease [START_REF] Oxtoby | Sequence of clinical and neurodegeneration events in Parkinson's disease progression[END_REF]), Huntington's disease [START_REF] Wijeratne | A Multi-Study Model-Based Evaluation of the Sequence of Imaging and Clinical Biomarker Changes in Huntington's Disease[END_REF] as well as FTD [START_REF] Ende | A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia[END_REF][START_REF] Panman | Modelling the cascade of biomarker changes in GRN-related frontotemporal dementia[END_REF] and ALS [START_REF] Gabel | Evolution of white matter damage in amyotrophic lateral sclerosis[END_REF]. However, in these works, EBMs were applied to a relatively small number of features (typically 10-50) and it is unknown if they would perform well in higher dimensions.

Despite the recognized importance of estimating neurodegenerative diseases progression, research has tended to focus mostly on higher prevalence conditions. Existing solutions are thus inadequate to model rare diseases with high-dimensional cross-sectional data, for three main reasons. First, we observe that longitudinal data is needed for the vast majority of approaches. However, C9orf72-associated FTD and ALS are slowly progressive conditions in the presymptomatic phase, which hinders the collection of meaningful longitudinal data. Second, most published methods benefit from large samples, which are not available for very low prevalence disorders such as genetic FTD and ALS. Finally, it is unclear if event-based models, the only methods suitable for cross-sectional data, can be robustly applied to highdimensional microRNA expression data, which comprise hundreds of biomarkers.

In this paper, we thus present a novel framework to estimate disease progression scores for rare neurodegenerative disorders using only cross-sectional data. To that purpose, we introduce a new supervised multimodal variational autoencoder (VAE)

trained with neuroimaging and microRNA data. Our working hypothesis is that disease progression scores may be modelled as underlying latent traits. Concretely, we aim to learn a meaningful latent space, where the relative positions of latent representations indicate the distance travelled along the disease pathophysiological pathway.

VAEs are powerful generative models that project data into a low-dimensional regularized latent space (Kingma and Welling, 2014). These models have been previously used with multimodal data [START_REF] Antelmi | Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data[END_REF][START_REF] Cheng | Multimodal Disentangled Variational Autoencoder With Game Theoretic Interpretability for Glioma Grading[END_REF][START_REF] Xu | Explainable Dynamic Multimodal Variational Autoencoder for the Prediction of Patients with Suspected Central Precocious Puberty[END_REF], but not for the purpose of inferring a DPS. Usually VAEs are trained in an unsupervised manner. However, extensions have been proposed for semi-supervised [START_REF] Berkhahn | Augmenting Variational Autoencoders with Sparse Labels: A Unified Framework for Unsupervised, Semi-(un)supervised, and Supervised Learning[END_REF]Kingma et al., 2014;[START_REF] Paige | Learning Disentangled Representations with Semi-Supervised Deep Generative Models[END_REF] or supervised [START_REF] Ji | Multi-Modal Anomaly Detection for Unstructured and Uncertain Environments[END_REF] tasks. These studies demonstrate that providing supervision to the model imposes specific semantics on the latent space, resulting in more meaningful and robust representations. In our context, explicit labels (control, presymptomatic, patient) are already available for all subjects. We thus add supervision during training, leveraging this information to improve the separation of the groups in the latent space. Additionally, we propose to split high-dimensional (neuroimaging and microRNA data)

and low-dimensional (demographic information) modalities. Our model thus couples two neural networks with different inputs: (1) an encoder/decoder that learns a latent space from the high-dimensional features, and (2) a classifier having as input the latent variables concatenated with the low dimensional features, useful for the classification task. As no ground truth is available for the DPS in real-world scenarios, we evaluate our models with a proxy metric: the area under the ROC curve (AUC) for each pairwise classification between clinical groups, computed using only the inferred DPS.

A preliminary version of this work has been published at the SPIE Medical Imaging 2022 conference [START_REF] Kmetzsch | A multimodal variational autoencoder for estimating progression scores from imaging and microRNA data in rare neurodegenerative diseases[END_REF]. Compared to the conference version, the present paper introduces the following novelties: (1) a supervised instead of a standard unsupervised VAE approach, (2) data split between low-dimensional and highdimensional modalities, (3) disease trajectory computation in the latent space using principal curves instead of straight lines, ( 4) additional experiments with multiple synthetic datasets, ( 5) a comparison with event-based models, and ( 6) an ablation study. The manuscript is organized as follows. Section 6.3 explains our proposed framework, section 6.4 describes the analyzed datasets, section 6.5 details our experiments and corresponding outcomes, and finally section 6.6 examines the meaning of our results and highlights the broader implications of our study.

Methodology

We consider a dataset (X , Y ) = {(x 1 , y 1 ), ..., (x n , y n )}. The i-th subject is characterized by a feature vector x i ∈ R m and a label y i ∈ {0, 1, 2} denoting the clinical group (control, presymptomatic, patient). Our aim is to estimate a DPS, denoted as v i ∈ [1, 100] (the interval for the scores is arbitrary), where a greater score corresponds to a higher disease severity. To that purpose, we assume that the observations have corresponding latent variables z i ∈ R ℓ . We will thus aim to estimate a latent representation and the DPS will be computed from a trajectory in the latent space.

Our framework is composed of three main steps, as illustrated in Fig. 6.1. First, we propose a supervised multimodal variational autoencoder to estimate the latent space. We leverage the fact that participants belong to different groups to introduce some supervision in order to improve the VAE training. The model aims at simultaneously reconstructing the data and classifying the participants. We propose to split low-dimensional sociodemographic data (denoted X ld , used only for the classification) from high-dimensional multimodal neuroimaging and transcriptomic data (denoted X hd , used both for reconstruction and classification). Second, we build a curve representing disease trajectory in the latent space. Finally, data from new subjects, not included in the training set, are encoded in the latent space and projected onto this trajectory, in order to obtain their DPS.

In this section, we first explain the three main steps of our framework, then we describe implementation details.

Supervised multimodal VAE

A variational autoencoder (VAE) (Kingma and Welling, 2014) is a generative model that learns the training data distribution p(x) using a latent representation model:

p(x) = p(x|z)p(z)dz,
where z is a continuous latent variable living in a lower dimensional space and p(z)

is its prior distribution, commonly a Gaussian with zero mean and identity covariance matrix. The solution of the inference problem to describe the latent space is given by deriving the posterior p(z|x). However, there is no closed-form solution for complex real-world datasets. Therefore, VAEs introduce the idea of learning a variational approximation q ϕ (z|x) of the true posterior, in the form of a neural network referred to as the encoder. The encoder maps data x to a mean vector z mean and a log-variance vector z logvar , that parametrize a Gaussian distribution from which we obtain the latent representation z. VAEs are also equipped with a generative function p θ (x|z), parametrized by a neural network referred to as the decoder. The decoder transforms the latent representation z back to the original input space.

During training, the vanilla VAE aims at maximizing the variational lower bound of the marginal log-likelihood, known as the evidence lower bound (ELBO). This is equivalent to minimizing a loss function with two terms:

L = L r (x, x) + L KL (q ϕ (z|x), p(z)).
The first term is the reconstruction error between the input data x and the reconstructed data x, typically a mean squared error (MSE). The second term is the Kullback-Leibler divergence between the approximated posterior q ϕ (z|x) and the prior distribution p(z), acting as a regularization term.

We propose to insert a supervised branch in the vanilla VAE architecture in order to exploit the fact that our samples have different diagnostic labels, even though their DPS is unknown. Denoting y as the true class label and ŷ as the predicted class label, we define our training objective as:

L = α 1 • L r (x, x) + α 2 • L KL (q ϕ (z|x), p(z)) + α 3 • L c (y, ŷ),
where L r and L KL correspond to the ELBO in vanilla VAEs and L c is a cross-entropy term that penalizes the classification error. The hyperparameters α k control the relative weights between the different loss terms (∑ 3 k=1 α k = 1). Before training, we split the high-dimensional modalities (miRNA expression and neuroimaging) from the low-dimensional (demographic information). As it will be mentioned later in the datasets description, we consider one low-dimensional feature and m -1 high-dimensional features, although the same concepts can be applied to more low-dimensional features. So we use m -1 features to feed the encoder and one feature concatenated to the latent code to feed the classifier. Features are rescaled from 0 to 1. Our encoder consists of fully-connected layers of sizes (m -1) → 50 → 2, meaning our latent space is 2-dimensional. The decoder is implemented with fully-connected layers of sizes 2 → 50 → (m -1). The nonlinear activation function is the leaky rectified linear unit (ReLU) in all layers except the decoder's last layer which uses a sigmoid function to constrain the output between 0 and 1. The classifier network has one fully connected layer of 3 → 3 units, with a softmax function to normalize the output to probabilities over the predicted classes.

We use the mean squared error as the reconstruction loss L r and the cross-entropy as the classification loss L c .

Trajectory definition

Once the model is trained, the next step is to encode the training data in the latent space. We then compute the straight line passing through the centroids of the control and patient clusters. This straight line could be used in downstream analyses as a rudimentary disease trajectory in the latent space. Instead, we obtain an improved nonlinear trajectory by using this line as initialization for the principal curve algorithm [START_REF] Hastie | Principal Curves[END_REF]. A principal curve is a smooth one-dimensional curve passing through the middle of given data points. The algorithm detailed in [START_REF] Hastie | Principal Curves[END_REF]) finds a nonparametric curve by iteratively minimizing the orthogonal distances to the points until convergence.

DPS computation

Once the disease trajectory curve is computed in the latent space, we can encode the test data. The next step is to orthogonally project the latent codes onto the computed curve. The DPS v i ∈ [START_REF] Kocerha | Altered microRNA expression in frontotemporal lobar degeneration with TDP-43 pathology caused by progranulin mutations[END_REF]100] for each subject is the coordinate of their projection along this curve, 1 corresponding to the beginning and 100 to the end of the curve.

The pseudo-code from model training to DPS computation is shown in Algorithm 1.

Implementation details

The hyperparameters of the training objective were set as α 1 = 0.2, α 2 = 0.2, and α 3 = 0.6. The loss function was optimized using Adam [START_REF] Kingma | Adam: A Method for Stochastic Optimization[END_REF], with a learning rate of 10 -3 , batches of 32 observations and 250 epochs.

Algorithm 1 DPS computation from latent representation

Input: features X = {x i } n i=1 ∈ R m , labels Y = {y i } n i=1 ∈ {0,
(X j , Y j ) do X hd , X ld ← split_high_low_dimension(X j ) Z mean , Z logvar ← encoder(X hd ) Draw latent codes Z ∼ N (Z mean , e Z logvar ) Ŷy ← classifier(concatenate(X ld , Z mean )) Xhd ← decoder(Z ) L r ← mean_squared_error(X hd , Xhd ) L KL ← kl_divergence(N (Z mean , e Z logvar ), N (0, I)) L c ← cross_entropy(Y y , Ŷy ) L ← α 1 • L r + α 2 • L KL + α 3 • L c
Compute gradients, update network to minimize L end for end for /* second step: trajectory definition */ Z, __ ← encoder(X I te ) c control ← mean({Z j : y j == 0}) c patient ← mean({Z j : y j == 2}) pc ← principal_curve(c control , c patient , degree = 2) /* third step: DPS computation */ for i in I te do z pc ← projection of z i into pc v i ← coordinate of z pc ∈ [0, 100] end for return {v i } i=I te

We carried out the experiments on a computer equipped with a 2.4 GHz Intel Quad-Core Core i5 processor and 16 GB of RAM. Models were implemented in Python 3.8.5 using PyTorch 1.8.1 and Scikit-learn 0.23.2 [START_REF] Pedregosa | Scikit-learn: Machine Learning in Python[END_REF].

For the principal curves computation, we used the implementation provided in the Python package pcurvepy 0.0.101 , specifying 2 as the degree of the smoothing spline.

Datasets

Synthetic datasets

Since ground truth disease progression scores are not available in real-world scenarios, we created synthetic datasets to better evaluate the proposed framework. Multiple datasets were generated, with different noise levels and distinct proportions of features correlating with the DPS. Let Y ∈ {0, 1, 2} indicate the class labels (respectively control, presymptomatic and patient). We created n = 111 synthetic participants (a number close to that of our real dataset) with class labels denoted by y i (i = 1, ..., 111), y i=1,...,37 = 0 y i=38,...,74 = 1 y i=75,...,111 = 2.

Next, we modeled the disease progression scores as continuous random variables following uniform distributions. Let V ∈ [1, 100) represent the DPS values. We defined the conditional distribution of the DPS given the class labels as follows:

V|Y = 0 ∼ U[1, 34) V|Y = 1 ∼ U[34, 67) V|Y = 2 ∼ U[67, 100)
We then sampled the corresponding DPS v i from the conditional distributions defined above. The obtained disease progression scores are displayed in Fig. 6.2.

Once the synthetic ground truth DPS were created, we generated multiple datasets D ∈ R n×m containing n = 111 participants and m = 160 features. In order to simulate two modalities, features were initially sampled from two distributions: half from a negative binomial distribution (typical of miRNA expression data) and half from a normal distribution (representative of various real-world datasets). We denote the columns of D by C 1 , ..., C m . The format of the synthetic datasets is illustrated in Fig. 6.3.

Each created dataset had a distinct proportion of features correlating with the

Real dataset

Participants were recruited through the PREV-DEMALS (https://clinicaltrials.gov, ID NCT02590276) study, a French multicentric prospective cohort focused on C9orf72 expansion carriers. Written informed consents were obtained from all participants.

The study was approved by the ethics committee (Comité de Protection des Personnes CPP Ile-De-France VI, CPP 68-15 and ID RCB 2015-A00856-43). A detailed description of this cohort and its demographic profile can be found in [START_REF] Kmetzsch | Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis[END_REF].

We included 110 individuals in our analyses, divided into three groups, according to their clinical status:

• Patient group: 22 symptomatic (15 FTD, 4 FTD/ALS and 3 ALS) carriers of a pathogenic C9orf72 expansion;

• Presymptomatic group: 45 asymptomatic carriers;

• Control group: 43 asymptomatic non-carriers.

Datasets

81

The dataset comprised multimodal data including microRNA (miRNA) sequencing data and neuroimaging data. These two modalities are described below.

MicroRNA data

MicroRNAs are a class of small noncoding RNAs that negatively regulate gene expression [START_REF] Huntzinger | Gene silencing by microRNAs: contributions of translational repression and mRNA decay[END_REF]. MicroRNAs expression in blood plasma has been shown to correlate with the diagnosis and progression of many neurodegenerative diseases [START_REF] Grasso | Circulating miR-NAs as biomarkers for neurodegenerative disorders[END_REF], including FTD and ALS. All individuals included in this cohort underwent plasma sampling, from which miRNA sequencing was performed. Plasma collection and preparation, miRNA extraction and sequencing, quality control and the computational pipeline to obtain the miRNA counts are detailed in [START_REF] Kmetzsch | Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis[END_REF]. The initial miRNA dataset contained expression levels for all miRNAs mapped in the human genome (2576 miRNAs). We retained the 589 miRNAs with expression profiles above noise level (minimum total count of 1000 reads and at least 50 reads for one sample). A trimmed mean of M-values [START_REF] Robinson | A scaling normalization method for differential expression analysis of RNA-seq data[END_REF] implemented in the R package EdgeR (Robinson et al., 2010) was used to normalize the raw counts.

Neuroimaging data

Neuroimaging data consisted of gray matter volumes extracted from T1-weighted anatomical magnetic resonance imaging (MRI), including the estimated total intracranial volume (TIV), 68 cortical regions of interest (ROIs) using the Desikan atlas and 18 subcortical ROIs using the Aseg nomenclature, thus resulting in 87 neuroimaging features. The TIV was used to normalize the volume of each ROI,

NV ROI = TIV m × V ROI TIV ,
where V ROI is the original volume of the ROI, NV ROI is the corresponding normalized volume and TIV m is the average TIV computed across all subjects. The MRI acquisition parameters, quality check and processing pipeline are thoroughly described in [START_REF] Bertrand | Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years[END_REF].

Only 91 subjects (14 patients, 40 presymptomatic carriers and 37 controls) had MRI scans collected. Hence, we divided our dataset into two subsets: 19 subjects that only had miRNA data available, and 91 subjects with multimodal neuroimaging and miRNA data. The former subset was used as a discovery set for miRNA feature selection: we used these 19 individuals to perform differential expression analysis (as described in [START_REF] Kmetzsch | Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis[END_REF]). The 68 miRNAs with the lowest p-values

were selected for all downstream analyses.

Lastly, we also included age as demographic information for all subjects. So the total dimension of each feature vector was m = 87 + 68 + 1 = 156.

Experiments and results

Synthetic datasets

We applied our framework to 60 synthetic datasets (described in Section 6.4.1) with different noise levels and distinct number of features correlating with the ground truth DPS. Each synthetic dataset was divided into a training set of 90 subjects (30 per clinical group) and a test set of 21 individuals (7 per group). We trained one model per dataset, using the same hyperparameters as the experiments with the real dataset. After training each model, we computed the DPS for the subjects from the test set. We then calculated the Spearman correlations between the simulated ground-truth scores and the predicted scores. Finally, we evaluated the ROC AUC for each pairwise comparison between the three simulated clinical groups. The results of the Spearman correlation between the estimated DPS and the ground truth data, as well as the average ROC AUC scores for the three pairwise comparison between groups, are showed in Fig. 6.5. As expected, we can observe that the DPS is very well estimated for lower noise levels and higher proportion of relevant features, while the performances decrease when the noise level becomes very high and when only few features are correlated with the DPS. Importantly, we observe that the Spearman correlation of the DPS and the ROC AUC have similar behaviors, indicating that the ROC AUC of pairwise comparisons is a reasonable proxy to evaluate the DPS, as will be done with the real dataset.

Real dataset

Experiments with the real dataset (described in Section 6.4.2) were carried out with a cross-validation of 100 stratified randomized folds, using data from the 91 subjects that underwent both plasma sampling and MRI scans. Unlike for the synthetic dataset, there is no ground truth for the DPS in the real dataset. We thus applied a proxy metric to assess model performance: using only the inferred DPS, we did pairwise comparisons between the clinical groups and computed the corresponding areas under the ROC curves. Specifically, we present the following experiments: (1) evaluation of the proposed method, (2) comparison with Chapter 6. Disease progression score estimation using a supervised VAE a state-of-the-art method for modeling disease progression, the discriminative eventbased model, (3) ablation study, and (4) variation of hyperparameters.

Evaluation of the proposed method

First, we used the DPS computed in each fold to build ROC curves for the three pairwise comparisons between clinical groups. The average ROC curves are shown in Fig. 6.7. The ROC AUC for the classification of controls and presymptomatic subjects was 0.74 ± 0.13, for controls and patients was 0.98 ± 0.05 and to distinguish presymptomatic carriers and patients was 0.96 ± 0.07. These results reveal that it is harder to differentiate controls from presymptomatic individuals than it is to distinguish between patients and the other two groups. The histogram displayed in Fig. 6.8 illustrates the disease progression scores computed over all 100 test folds (18 subjects per test fold, corresponding to 1800 DPS). The distribution shapes highlight a clear separation between the patient group and the other groups. The distribution of the DPS for the presymptomatic group is more spread, which was expected as this group is the most heterogeneous. Some presymptomatic subjects are very far from onset and the neurodegenerative process has barely begun, they are thus closer to controls. Other presymptomatic subjects are closer to disease onset and thus their DPS is closer to that of patients.

Comparison with DEBM

Next, we compared our results to a discriminative event-based model (DEBM) (Venkatraghavan et al., 2019), a method that also infers a DPS from cross-sectional data.

For that experiment, the same cross-validation strategy of 100 stratified folds was applied. We built the DEBM models and computed the DPS using the Python package pyebm2 2.0.3. Table 6.1 displays the corresponding ROC AUC results for each pairwise comparison. We can observe that our model achieves a substantially better classification performance for all pairwise comparisons. Additionally, our approach used less computing time: our framework took 2 seconds per fold for training and DPS computation, while the DEBM algorithm took on average 180 seconds per fold. TABLE 6.1: Results on real data: comparison between our approach and a discriminative event-based model (DEBM) (Venkatraghavan et al., 2019). ROC AUC (mean ± standard deviation) over 100 stratified splits.

Comparison

Our model DEBM Control vs. Pre 0.74 ± 0.13 0.67 ± 0.14 Control vs. Patient 0.98 ± 0.05 0.76 ± 0.17 Pre vs. Patient 0.96 ± 0.07 0.65 ± 0.17

Ablation study

Afterwards, to investigate the impact of certain components of our framework, we conducted an ablation study. We changed some elements of the proposed approach to obtain three alternative models:

• Linear instead of curved trajectory: rather than computing the trajectory in the latent space using principal curves, we simply used a straight line.

• No supervised branch: we removed the classification component of the loss function, thus performing unsupervised training.

• Joint low-dimensional modality: we concatenated the low-dimensional modality (demographic information) with the high-dimensional modalities (neuroimaging and miRNA expression) in the encoder input, and used only the latent codes as input for the classifier.

For each alternative model, we conducted the same cross-validation strategy of 100 stratified folds, computing the DPS for the test sets and the corresponding areas under the ROC curves. The results, displayed in Table 6.2, show that the proposed model has a better and more stable performance in all comparisons, with the highest average ROC AUC and lowest standard deviation among the splits.

Variation of hyperparameters

Finally, we checked whether our results were robust to reasonable changes in the hyperparameters. Notably, we tested different numbers of hidden units in the fullyconnected layers, and different combinations of the relative weights between the Weights α k 0.2, 0.2, 0.6 0.1, 0.1, 0.8 0.1, 0.2, 0.7 0.3, 0.2, 0.5 Control vs. Pre 0.74 ± 0.13 0.72 ± 0.12 0.73 ± 0.12 0.72 ± 0.14 Control vs. Patient 0.98 ± 0.05 0.97 ± 0.08 0.97 ± 0.06 0.98 ± 0.05 Pre vs. Patient 0.96 ± 0.07 0.94 ± 0.10 0.95 ± 0.09 0.96 ± 0.07

Discussion

In this paper, we proposed a new approach for estimating disease progression scores from cross-sectional neuroimaging and transcriptomic data that is applicable in small samples, which are typically found in rare diseases. The approach was designed and evaluated on data from C9orf72-associated FTD and ALS, but is potentially applicable to other diseases. Results on synthetic data demonstrated the ability of the method to accurately estimate the DPS, and experiments on real data, in the absence of ground truth DPS, showed the separation of different diagnostic classes. The findings of this study supported the usefulness of supervised variational autoencoders to infer disease trajectories from cross-sectional multimodal data, indicating that a Chapter 6. Disease progression score estimation using a supervised VAE single disease progression score may be used to represent progression of neurodegenerative diseases. Remarkably, our results revealed that the DPS may be inferred using only cross-sectional data from a small sample of subjects.

Experiments with a cohort of C9orf72-mutation carriers demonstrate that subjects from the same clinical groups (patients, presymptomatic individuals and controls)

are clustered together in the latent space (Fig. 6.6), allowing the inference of a disease trajectory. After training the model, data from new individuals is encoded in the latent space and orthogonally projected onto this trajectory to compute the DPS.

Notably, using only the computed DPS, we are able to classify presymptomatic subjects and patients with an average ROC AUC of 0.96 over 100 stratified fold splits (Fig. 6.7). Of the three possible pairwise comparisons between clinical groups, this is the most relevant. It illustrates how much the DPS reflects the degree of disease progression in mutation carriers. Unsurprisingly, it is harder to differentiate between controls and presymptomatic individuals, as indicated by the average ROC AUC of 0.74 and displayed in Fig. 6.8. This stems from the fact that, during earlier disease stages, most biomarker levels are closer to normal ranges, so the presymptomatic class is more heterogeneous.

To the best our of knowledge, event-based models are the only published methods to compute disease progression scores from cross-sectional data, other approaches requiring longitudinal data. The comparison summarized in Table 6.1 reveals that our approach resulted in considerably higher ROC AUC than DEBM for all pairwise classifications. This suggests that the proposed approach is more suitable than event-based models for DPS computation with high-dimensional features, such as microRNA data. Indeed, published studies using event-based models explored a substantially lower number of features. For instance, in Alzheimer's disease, EBM experiments were carried our with 13 to 50 biomarkers [START_REF] Archetti | Multi-study validation of data-driven disease progression models to characterize evolution of biomarkers in Alzheimer's disease[END_REF][START_REF] Firth | Sequences of cognitive decline in typical Alzheimer's disease and posterior cortical atrophy estimated using a novel event-based model of disease progression[END_REF][START_REF] Fonteijn | An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease[END_REF][START_REF] Oxtoby | Data-driven models of dominantly-inherited Alzheimer's disease progression[END_REF][START_REF] Venkatraghavan | Disease progression timeline estimation for Alzheimer's disease using discriminative event based modeling[END_REF]Young et al., 2014). Studies focusing on FTD analyzed 21 [START_REF] Panman | Modelling the cascade of biomarker changes in GRN-related frontotemporal dementia[END_REF] or 7 [START_REF] Ende | A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia[END_REF] biomarkers, while multiple sclerosis was investigated with 25 [START_REF] Dekker | The sequence of structural, functional and cognitive changes in multiple sclerosis[END_REF] or 24 [START_REF] Eshaghi | Progression of regional grey matter atrophy in multiple sclerosis[END_REF] biomarkers. Other conditions such as Parkinson's disease [START_REF] Oxtoby | Sequence of clinical and neurodegeneration events in Parkinson's disease progression[END_REF], ALS [START_REF] Gabel | Evolution of white matter damage in amyotrophic lateral sclerosis[END_REF] and Huntington's disease [START_REF] Wijeratne | A Multi-Study Model-Based Evaluation of the Sequence of Imaging and Clinical Biomarker Changes in Huntington's Disease[END_REF] were modeled with respectively 42, 19 and 8 biomarkers. Nevertheless, the EBM model presents useful additional features, beyond the computation of DPS. In particular, it can provide a temporal ordering of when the different biomarkers become abnormal, which is useful for understanding disease progression. Moreover, a balance has to be found between the number of features and the number of subjects in each dataset. Indeed, we also had to perform feature selection to decrease the number of microRNAs in our study. It should be noted that this feature selection was unbiased, since it was performed using a completely separate set of participants that was not used in the rest of the study.

The proposed framework was able to achieve a good performance with 156 features and less than a hundred subjects, thus demonstrating its potential for dealing with 6.6. Discussion 91 higher dimensional datasets.

An ablation study evaluated the impact of different components of our approach (Table 6.2). We observed that each component positively impacted the framework's performance. First, it can be seen that a curved trajectory better fits the disease pathway in the latent space when compared to a straight line. The use of principal curves has been inspired from their application in a similar task: pseudotime inference for single-cell transcriptomics, as shown in [START_REF] Street | Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics[END_REF]. In that context, pseudotime represents an underlying temporal variable driving a smooth transition between cellular states, and principal curves are used to infer a trajectory in a lowdimensional space. Second, it is clear that the addition of supervision with a classifier branch improves the separation between clinical groups in the latent space.

Rather than discrete clusters, our experiments demonstrate that latent representations are placed along a continuous path. Specifically, supervision adds meaning to the relative positions between points in the latent space. Finally, results show the contribution of splitting high and low-dimensional features. When using the low-dimensional features concatenated with the latent codes as inputs to the classifier, the model's performance is enhanced. The same pattern is observed in [START_REF] Ji | Multi-Modal Anomaly Detection for Unstructured and Uncertain Environments[END_REF], although in a totally different context (failure detection in robotics). Concretely, a low-dimensional feature can directly contribute to the classifier, without the need for encoding.

Regarding the experiments with simulated datasets, it is crucial to highlight the relationship of the average ROC AUC with the Spearman correlation between ground truth and estimated DPS (Fig. 6.5). The simulation supports that the higher the ROC AUC, the more accurate the predicted DPS. Therefore, for real-world scenarios without ground truth DPS, our choice of the ROC AUC as proxy metric is corroborated. Furthermore, evidence was found that the models do not overfit the data, since it is clear that larger noise levels lead to poorer results, eventually equivalent to random chance. The effect of noise is further illustrated in Fig. 6.4. We observe that lower noise levels induce more evident clusters and more meaningful trajectories in the latent space. Consequently, the estimated DPS are closer to the ground truth. These simulations also confirm one intuition behind our model: the more features correlate with disease progression, the closer the estimated DPS are to the ground truth.

Our study has the following limitations. First, there is no ground truth for the progression scores in real datasets. Although the experiments with synthetic data showed that the ROC AUC is an adequate proxy metric, long-term follow-up of patients will be necessary to assess the accuracy of the computed DPS. For instance, we need follow-up data to confirm the hypothesis that a higher DPS implies an earlier disease onset for a presymptomatic subject. Another limitation was the lack of a replication cohort. This will be necessary to further support the clinical relevance of our findings. Future work will concentrate on the integration of more data sources, such as positron emission tomography (PET) scans and neurofilament light chain (NfL) levels in blood.

In conclusion, we proposed a new approach to measure disease progression from multimodal imaging and microRNA data in rare neurodegenerative disorders using only cross-sectional data. Even though we focused on C9orf72-associated FTD and ALS, our framework is generic. It has the potential to be useful for a variety of other diseases, enabling the evaluation of novel treatments even when only cross-sectional data from small cohorts are available.

Chapter 7

Conclusion and perspectives

The goals of this interdisciplinary thesis, combining biomedical and machine learning research, were ( 1) to assess circulating microRNAs as progression biomarkers of genetic frontotemporal dementia and amyotrophic lateral sclerosis, and ( 2) to propose a method to estimate disease progression using cross-sectional multimodal data from small samples. Specifically, clinical trials testing novel treatments for FTD and ALS need non-invasive and robust biomarkers to measure disease progression in subjects without or with mild clinical symptoms. We thus conducted three studies. First, we analyzed the expression levels of plasma miRNAs in a cohort of C9orf72 expansion carriers, to investigate whether circulating miRNAs are promising progression biomarkers of C9orf72-associated FTD and ALS. Second, we used independent homogeneous cohorts of C9orf72 and GRN mutation carriers, to perform a comprehensive evaluation of all previously identified miRNA signatures associated with FTD and/or ALS and to determine their usefulness in assessing the progression of genetic FTD and ALS. Third, we designed, implemented, and tested a new generic model to estimate a disease progression score (DPS) from cross-sectional multimodal datasets with small sample sizes, and demonstrated its usefulness with miRNA and neuroimaging datasets obtained from a cohort of C9orf72 expansion carriers.

The contributions of this thesis, as well as future directions for our work, are summarized below.

MicroRNAs are potential preclinical progression biomarkers of C9orf72-associated

FTD and ALS

Before this thesis, several studies had observed circulating miRNAs as potential biomarkers of FTD and ALS. However, the lack of concordance between results undermined the reliability in miRNAs for assessing disease progression in clinical trials. Previous investigations were performed with heterogeneous cohorts (patients with sporadic disease forms, or mixed cohorts with sporadic and genetic disease forms) and prior assumptions about the subset of miRNAs to be analyzed. Our study presented in Chapter 4 was the first to evaluate the expression levels of plasma miRNAs in a cohort focused on C9orf72 expansion carriers, without a priori assumptions. After performing large scale RNA-sequencing analyses in plasma samples from 110 individuals (22 patients, 45 presymptomatic subjects, and 43 controls),

Chapter 7. Conclusion and perspectives

we identified four miRNAs differentially expressed between groups: miR-34a-5p, miR-345-5p, miR-200c-3p and miR-10a-3p. In the absence of an independent validation cohort, a generalization analysis demonstrated a satisfactory prediction performance when classifying subjects between clinical groups. Additionally, target prediction and pathway analyses identified relevant pathways, previously mentioned in the literature as involved in C9orf72-associated disease. This first study therefore highlighted the potential of circulating miRNAs as progression biomarkers of C9orf72-associated FTD and ALS.

Remarkably, our comprehensive validation study, described in Chapter 5, reinforced the usefulness of circulating miRNAs as biomarkers of C9orf72-associated disease. In this work, we evaluated all previously identified circulating miRNA signatures associated with FTD or ALS (from 15 papers, including our study from Chapter A2). Indeed, miR-206 illustrates the idea from [START_REF] Bzdok | Inference and Prediction Diverge in Biomedicine[END_REF], that features relevant for prediction are often not identified as statistically significant in biomedical datasets.

As a result of both our discovery and our validation studies, we encourage the use of plasma miRNAs as non-invasive progression biomarkers of C9orf72-associated FTD and ALS. In particular, the broader meta-signature proposed in Chapter 5 might be useful for C9orf72-disease progression assessment in clinical trials.

MicroRNA signatures identified in sporadic or mixed cohorts of FTD and ALS patients are useful as biomarkers of C9orf72 disease, but not GRN disease

The extensive validation study presented in Chapter 5 demonstrated that more than half of the investigated miRNAs (35/65) were differentially expressed in at least one pairwise comparison in the C9orf72 cohort. Moreover, when analyzing the prediction performance of the previously published miRNA signatures, half of them (8/16) resulted in an average ROC AUC above 0.70 for at least one classification task.

Strikingly, the vast majority of the studied miRNA signatures had been identified in sporadic or mixed cohorts. Therefore, these findings indicated miRNA expression similarities between subjects with sporadic FTD or sporadic ALS and individuals carrying the C9orf72 expansion.

Chapter 7. Conclusion and perspectives

95

Nevertheless, these miRNA expression similarities were not observed between previous studies on FTD and our validation cohort of GRN mutation carriers. Indeed, only a small minority (5/30) of miRNAs previously linked to FTD were found to be differently expressed in the GRN cohort. In addition, none of the five miRNA signatures investigated in this cohort exhibited an adequate classification performance. Therefore, our data suggest that the studied miRNAs are not useful biomarkers of GRN-associated FTD. Furthermore, our findings corroborate the heterogeneity of complex disorders such as FTD and ALS, indicating that miRNA expression profiles are most likely mutation specific.

This work illustrates the importance of validation studies in independent cohorts, which could be more encouraged and valued by the research community, particularly by scientific journals. In addition, it is noteworthy that the preregistration of our validation study has shown to be highly beneficial for several reasons, as thoroughly explained in [START_REF] Nosek | The preregistration revolution[END_REF]. First, preregistration imposes a detailed study design beforehand, thus avoiding false discoveries due to a possibly too flexible statistical analysis. Second, describing the details of the study before conducting the analyses clearly separates confirmatory and exploratory aspects, improving the credibility of results. Last, the few additional steps required to preregister a study on a registry such as The Open Science Framework1 add very little burden, and actually save time afterwards during data analysis.

Disease progression scores can be estimated from cross-sectional neuroimaging and microRNA data from small samples

Several approaches have been proposed in the literature to model disease progression in a data-driven fashion, most of them depending on longitudinal data. Rare neurodegenerative disorders, such as FTD and ALS, could not be modeled with such approaches, for lack of longitudinal datasets with sufficient sample sizes. We thus presented (in Chapter 6) a novel framework to estimate disease progression using cross-sectional datasets from small samples. The main hypothesis of this work was that disease progression may be modeled as a latent trait: the disease pathophysiological pathway was represented by a trajectory in the latent space, and relative positions in this trajectory indicated disease progression scores. The main challenge in developing this framework was the absence of ground truth data for the DPS.

Hence, we proposed a proxy metric to evaluate our models: classification performance (ROC AUC) was computed using the DPS as the only feature. Experiments with synthetic data corroborated the choice for the proxy metric, since the ROC AUC was positively correlated with the accuracy in estimating the simulated DPS. Moreover, analyses with a real multimodal dataset of miRNA and neuroimaging data demonstrated a good classification performance using only the inferred DPS as a feature: average ROC AUC of 0.74 for controls vs. presymptomatic subjects, 0.98 for 96 Chapter 7. Conclusion and perspectives controls vs. patients, and 0.96 for presymptomatic subjects vs. patients. The only disease progression models supporting cross-sectional data found in the literature were event-based models. When compared to our results, a discriminative eventbased model yielded substantially lower performance (Table 6.1).

Although we used data from a cohort focused on C9orf72 mutation carriers, the framework is generic and could be applied to other cross-sectional datasets. We believe that the proposed approach might be useful to infer a disease progression score for other rare disorders, for which longitudinal datasets with appropriate sample sizes are not available, and previously published methods cannot be applied.

Perspectives

There are several future directions for our work.

First, a straightforward path for future research is to analyze larger cohorts of C9orf72 expansion carriers. It has been demonstrated that for predictive applications, such as biomarkers discovery and validation, larger samples lead to better generalization performance estimation [START_REF] Varoquaux | Cross-validation failure: Small sample sizes lead to large error bars[END_REF]. For instance, with a larger miRNA expression dataset from C9orf72 carriers, we could more precisely assess the generalization capabilities of the meta-signatures proposed in Chapter 5, or even identify other miRNA signatures with better prediction power. Moreover, training the model presented in Chapter 6 with larger multimodal datasets would improve DPS estimation on unseen data, an essential step towards deploying the model in a clinical setting. Furthermore, the investigation of a larger number of patients carrying the C9orf72 expansion could shed a light on the differences in miRNA expression observed between different phenotypes (FTD, FTD/ALS, and ALS), and possibly improve the stratification of participants in clinical trials.

Since FTD and ALS are rare conditions, it is unlikely that a single entity (university, research institution, or hospital) can bring together a cohort with hundreds of patients and presymptomatic individuals. It is therefore crucial to investigate alternative strategies to increase sample size. One solution could be to explore federated learning, a decentralized machine learning paradigm that allows different institutions to share their medical datasets while preserving data protection and patient privacy [START_REF] Rieke | The future of digital health with federated learning[END_REF]. Federated learning has recently received much attention, due to its potential benefits in machine learning for healthcare applications [START_REF] Li | Federated Learning: Challenges, Methods, and Future Directions[END_REF][START_REF] Prayitno | A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications[END_REF]. Further research could then be undertaken to deploy our proposed disease progression score model from Chapter 6 in a federated learning setting, with a considerably larger and more representative dataset, which would very likely lead to more robust results.

Additionally, as discussed in Chapter 5, the findings of our validation study showed that most miRNAs identified in the literature as potential FTD biomarkers are not relevant for measuring the progression of GRN-associated disease. However, since GRN mutation carriers were not included in any of the prior investigations, Chapter 7. Conclusion and perspectives 97 further work is needed to establish whether other miRNAs may be useful in GRNassociated FTD. An independent cohort of subjects carrying the GRN mutation is required, so that one cohort is used to identify miRNAs as biomarker candidates (such as was performed in Chapter 4 for the C9orf72 mutation), and another cohort is employed to validate these miRNAs.

Furthermore, adding new modalities to the DPS computation would most likely improve model performance, since different modalities bring complementary information about the disease process [START_REF] Carreiro | Integrative biomarker discovery in neurodegenerative diseases[END_REF]. Futures studies could include blood levels of neurofilaments [START_REF] Saracino | Plasma NfL levels and longitudinal change rates in C9orf72 and GRN-associated diseases: from tailored references to clinical applications[END_REF], features extracted from positron emission tomography (PET) scans [START_REF] Meeter | Imaging and fluid biomarkers in frontotemporal dementia[END_REF], diffusion tensor imaging (DTI) [START_REF] Bertrand | Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years[END_REF], or neurite orientation dispersion and density imaging (NODDI) [START_REF] Wen | Predict to Prevent Frontotemporal Lobar Degeneration and Amyotrophic Lateral Sclerosis (PREV-DEMALS) Study Group[END_REF]. A relevant question would be to investigate which modalities offer the best prediction performance. Multiple DPS models could be trained with different combinations of modalities, and a proxy metric (ROC AUC) comparison could indicate which modalities are most strongly correlated with disease progression.

Lastly, the validation of our proposed framework for DPS computation (Chapter 6) will require longitudinal follow-up over a long period of time, to determine whether presymptomatic individuals with higher scores will have earlier disease onsets. Even though this thesis focused on two rare neurodegenerative disorders, further experiments with datasets from more prevalent conditions, such as Alzheimer's disease, could be performed to evaluate the accuracy of the estimated DPS. For instance, we could take cross-sectional baseline data from the Alzheimer's Disease Neuroimaging Initiative (ADNI)2 dataset to estimate the DPS of individuals with mild cognitive impairment at baseline. Then, we could analyze longitudinal data to verify whether a higher DPS at baseline implies an earlier Alzheimer's disease onset. Additionally, training our model with baseline data from ADNI would allow a comparison with disease progression models that leverage longitudinal data (Section 2.5). Clearly, it is expected that an approach estimating disease progression from cross-sectional data will be less accurate than a method using longitudinal data [START_REF] Jack | Update on hypothetical model of Alzheimer's disease biomarkers[END_REF]. Nevertheless, this comparison would be important to determine how much less accurate is our DPS model, for different training sample sizes. The results of this research could further support the clinical relevance of disease progression models trained with cross-sectional data from small cohorts.

Concluding remarks

In summary, this interdisciplinary thesis has demonstrated that circulating miRNAs are useful as non-invasive biomarkers of genetic frontotemporal dementia and amyotrophic lateral sclerosis associated with the expansion in the C9orf72 gene. Moreover, we have conceived and implemented a methodology for estimating disease

The PREV-DEMALS cognitive evaluation included standardized neuropsychological tests to investigate all cognitive domains, and in particular frontal lobe functions. The scores were provided previously [START_REF] Bertrand | Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years[END_REF]. battery. Limb-kinetic and ideo-motor apraxias were evaluated using a French validated scale.

Method A2. Description of the four cases at the transitional stage

Four C9orf72 presymptomatic carriers have developed frontal cognitive and/or behavioral changes and/or subtle motor signs/symptoms during a 3-year follow-up period, without fitting diagnostic criteria for FTD or ALS, suggesting they were in the transitional 'prodromal' phase at the moment of or just after their baseline visit. They are described below.

The case 1 was a right-handed 42-year-old female with 13 years of schooling.

Neurological examination, behavioral scores (FBI: 0; AES 9/42), and cognitive scores were

Table A1. Clinical status and center proportion in each batch

AMOTL2 CAD FERMT1 C3orf80 LGR4 CEP83 CMTR2 SMAD4 CFL2 BRE NOH61(hsa ) RAB14 G3BP1 C21orf58 ANKIB1 C7orf49 SLC16A5 ACADVL SRM300(hsa) SLC35G2 TFG E2F1 PTCH1 MAGEB2 UNG ST6GAL1 RBM47 HDLBP SLC39A10 MAML1 HSPA1A GS3786(h sa) SH3GL1 RBM38 SLC35D1 RPS18 DDAH1 CKAP2 ZNF641 RAB3GAP2 ECK(hsa) M11S1(hs a) SPEG FNDC3B CDCA7 SLC10A7 CG1(hsa) TMEM200A RPS23 AGO3 NRGN KLF10 SEPHS1 RBBP4 ANKMY1 C21orf2 PON2 PDCD4 DBN1 CHRNB4 SNPH C2CD5 KLHDC3 GAB1 TGIF2 ZFAND3 ARHGEF5 MMP15 C22orf23 SLC25A1 ICMT ID1 ZNF35 XXbac- BCX40G17. 4-002(hsa) MGAT4B MASTL CABLES 2 DCBLD2 SFXN2 RLTPR GPR3 PPARG HIST1H2B B MED22 PCDC5RP( hsa) RAD51 APH1A RASSF7 CTNNB1 APBA2 TMEM245 RABEPK PVRL1 MMS19 PIP5K1C PPP6C MPP5 PLA2G12A MRPL10 ZBTB3 LDLR STRADA ZNF318 RAB25 NR4A2 NT5CP(hsa) DLGAP3 NOL11 ARL5B SLX4 RPL15 TUFT1 Nbla1054 5(hsa) UBL3 SLC35B3 RHNO1 MCPH1 PRIMA1 PDAP1 AMN1 COMMD1 H1F1(hsa ) CCDC14 CCDC85B BAF170(hsa ) PANX1 KCNAB2 SLC29A3 ATP2C2 TBC1D13 ABCB9 ATP6V1D TMEM181 KIAA0153( hsa) TERT PIGT TMEM25 PLA2G15 ATN1 MOCOS BMP6 ZBTB37 ZC3H12C FAIM ANKHD1- EIF4EBP3 MARCH6 MIER3 GARS DBT RHOA MLEC ECT2 MALSU1 CNEP1R1 SPTBN1 VAMP2 FAM32A BACH1 EIF1AX LARP1 PSMD14 KIAA0100 KIAA0930 MUM1 UBR4 MTHFD2 KIAA0319L ATP6V1G1 ZC4H2 HIC1 MED28 DONSON MAPK1 INO80D STK4 E4F1 GBF1 HDGF ZC3H13 EIF2S1 VPS28 MTMR3 TXNIP MMS22L GSE1 GSTO1 KLHDC10 RAB18 JADE2 TNPO2 FBXW11 GREM1 KIF1B NFIL3 CHD4 AGO1 GSTP1 GSK3A BTBD7 RPL27A SYS1 UBQLN4 GPSM1 SMARCD1 BTG2 PPM1L SLC39A6 CKS2 TET2 MARCKS ZNFX1 PERM1 SLCO3A1 FBN2 EIF4ENIF1 LMAN1 MPPED2 SYNGR1 PCGF2 BRD8 NHLRC3 CHMP4B TOP2A NRP1 PRDM2 CNBP SART3 MAGIX UBR5 FBXO4 TMEM145 LPAR1 IPMK ZNF35 SASH1 RAD9B MYO18A miR-200c-3p TERF2 COPS8 ORMDL3 QSER1 EDEM3 MCTP2 KBTBD11 FBXO22 LATS1 RTN4 MSMO1 LMO7 HNRNPU FAM46A GDAP2 EIF3J FN1 THNSL1 RPRD2 ZNF605 FZD6 HSDL1 PPP3CA ANP32B C6orf62 MYCN ALDH1A1 SLC9A2 CCNL2 SOAT1 PPP1R18 DPY19L4 CEP41 MAPRE1 UBN2 CAB39 HSPA13 MCM8 RDX SPRED2 UBA6 YEATS4 PTP4A1 C22orf39 ATP13A3 ANP32E GNS TOB1 VLDLR CHURC1 DNAJB9 SHOC2 SPTSSA PHOSPHO2 ADAM9 HNRNPD INPP5F UBE2I PTK2 PPP2R2A DNMT3A RAB22A NUPL1 CCNG1 VAT1L CTBP2 VEPH1 C9orf41 RNF24 SESN1 LRIF1 RNF168 DUSP4 DENND4B TMEM167B KRAS TTC8 KIDINS220 SIAH1 BAG6 AMOTL1 RAB4A FBXO30 GAN EPDR1 NLK LNPEP ANKRD46 RBM12B LATS2 C22orf46 TCF4 WNK1 HEG1 CEBPB GPR98 PPP1R9B ZNF92 FBXW11 NEK7 TMED7 GSE1 IPO7 MGAT3 QKI SDE2 UHMK1 PPP2R1B ZEB2 APC HOXB5 SEPT7 SEPSECS ASXL3 SCO1 LHFP GREB1 MLK4 CRK PBX1 SETD2 ISOC1 SON KIF11 BIRC3 ABL2 ZMAT3 FBXW7 B4GALT6 SLC35A4 ARHGAP18 LPL TNFRSF10B ATG12 KDR JARID2 DHX29 NBR1 SERINC1 TUBB4A DOCK5 SURF4 BDP1 USP33 CASP8AP2 UHRF1BP1L TRPS1 FSTL1 GART MARCH8 FAM220A SYNE2 RANBP9 CNOT6 SHC1 MGA FBXL16 CCDC127 MKLN1 CHD2 NAA16 RAD21 GSKIP SIRT1 GAL3ST1 SLC16A9 BRCA1 SMIM15 DZIP1 ARF6 KMT2C FBXL3 NLN CLCC1 PHF10 ZNF217 PLK2 C16orf72 USP9X CDK16 NTRK2 RUNX1 NET1 TCP1 ITGAV MYO9A FAM199X MBNL1 ADCY9 PKIA ABCC1 TMEM64 CPD STK17B WSB1 ST7 PRC1 HELLS UBFD1 CLCN3 KLHL20 MSN ZNF697 TP53INP1 TSC22D2 NFIA BTG1 COA4 PAK2 SLC22A25 ZCCHC2 TRIM59 ELMSAN1 PPM1F TBP PCDH9 SLMAP ATMIN SMARCAD1 NUDCD1 TMX4 SERPINI1 BTAF1 GPM6A CAPRIN2 TSC1 AKIRIN2 PTDSS1 NAP1L5 PSMB4 SNX1 CLASP1 CNEP1R1 BAP1 PIM2 ANKRD52 BCL2 LBR CCDC43 TMBIM4 ZC3H11A EDNRA MRPL15 RBM27 COX11 CDKN1B TAOK1 PMAIP1 GLI3 ADNP ALG13 PARD6B PEAK1 SPATA2 SRGAP1 RBAK CDH1 ARPC2 HSPA8

Supplementary Table 1. Complete output from differential expression analyses in the

  La démence frontotemporale (DFT) représente le deuxième type de démence le plus fréquent chez les adultes de moins de 65 ans. Il n'existe aucun traitement capable de guérir cette maladie. Dans ce contexte, il est essentiel d'identifier des biomarqueurs capables d'évaluer la progression de la maladie. Les personnes asymptomatiques porteuses d'une mutation génétique constituent la population idéale pour cette recherche.

Figure 2 .

 2 Figure 2.1 shows the clinical, pathological and genetic heterogeneity of FTD.

FIGURE 2 . 3 :

 23 FIGURE 2.3: Main steps of the RNA Sequencing methodology (reproduced from (Lowe et al., 2017), CC BY 4.0). The three colored segments represent respectively the steps happening in the living organism, carried out in the laboratory, and performed by computational tools. Pre-mRNA: precursor messenger RNA; ds-cDNA: doublestranded complementary DNA.

  Figure 2.4 displays axial slices from an FTD patient's brain, obtained with T1-weighted MRI, where focal brain atrophy is clearly observed.

FIGURE 2 . 4 :

 24 FIGURE 2.4: Axial views of T1-weighted MRI from an FTD patient (reproduced from[START_REF] Ramirez | Ontario Neurodegenerative Disease Research Initiative (ONDRI): Structural MRI Methods and Outcome Measures[END_REF], CC BY 4.0). The red arrows highlight the regions with significant brain atrophy.

FIGURE 2 . 5 :

 25 FIGURE 2.5: Model of the pathological cascade of AD biomarkers, for a single prototypical individual (reproduced from (Jack et al., 2010), with permission from the publisher). The horizontal axis represents progressive cognitive impairment, while the vertical axis indicates the level of abnormality of each biomarker. Aβ: amyloid beta; MCI: mild cognitive impairment.

FIGURE 2 . 6 :

 26 FIGURE 2.6: (a) Estimated biomarker dynamics of the ADNI population as a function of DPS, and conditional probability densities of the DPS given the clinical status of each individual. (b) Inflection point for each curve, along with 90% confidence intervals (reproduced from (Jedynak et al., 2012), with permission from the publisher). ADPS: Alzheimer's disease progression score; N: normal subjects; MCI: mild cognitive impairment; AD: Alzheimer's disease; RAVLT30: Rey auditory verbal learning test, 30 minute recall; HIPPO: sum of the two lateral hippocampal volumes; ABETA: Amyloid beta; TAU: tau protein; CDRSB: Clinical dementia rating sum of boxes score; MMSE: Mini-mental state examination score; ADAS: Alzheimer's disease assessment scale-cognitive subscale.

Figure 2

 2 Figure 2.8.

  Blood samples were stored in the Paris Brain Institute DNA and cell bank. Mi-croRNAs were extracted and sequenced in December 2019. The resulting dataset consisted of normalized log2 expression levels of 2576 miRNAs (corresponding to all miRNAs listed in mirBase 2 version 22) from 110 individuals divided into three groups: • Control group: 43 non-carriers of the C9orf72 expansion.Chapter 3. Datasets • Presymptomatic group: 45 C9orf72 expansion carriers without neurological symptoms.

  This chapter has been published as an original research article in the Journal of Neurology, Neurosurgery & Psychiatry (Kmetzsch et al., 2021): • Kmetzsch, V., Anquetil, V., Saracino, D., Rinaldi, D., Camuzat, A., Gareau, T., Jornea, L., Forlani, S., Couratier, P., Wallon, D., Pasquier, F., Robil, N., de la Grange, P., Moszer, I., Le Ber, I., Colliot, O., Becker, E., PREV-DEMALS study group, "Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis", Journal of Neurology, Neurosurgery & Psychiatry, 92(5):485-493 (2021). doi: 10.1136/jnnp-2020-324647 -hal-03046771

Figure 4 .

 4 Figure 4.1 displays boxplots with the expression levels, for each clinical group, of the four miRNAs identified as differentially expressed. There is a clear difference in miR-34a-5p expression levels between controls and C9orf72 expansion carriers (presymptomatic and symptomatic). Moreover, the other three identified miRNAs differentiate the mutation carriers at different stages of the pathology: miR-345-5p

Figure 4 . 2 )

 42 . The distributions of the bootstrapped ROC AUC scores are displayed in Appendix A Figure A3.

  validation scheme (Appendix A FigureA1), by using only the training data from the outer cross-validation loop to compute differentially expressed miRNAs. shows the distribution of miRNAs identified as differentially expressed after performing nested 5-fold cross-validation with 100 different fold splits. Notably, the most frequent miRNAs (highlighted in blue) correspond to the ones computed using the entire dataset: miR-34a-5p (500 occurrences) when comparing healthy controls and presymptomatic mutation carriers;miR-34a-5p, miR-345-5p, miR-200c-3p

FIGURE 4 . 1 :

 41 FIGURE 4.1: Boxplots depicting the normalised log2 expression levels of the four microRNAs identified as differentially expressed. Box boundaries represent the first and third quartiles and the median is indicated by the line dividing the interquartile range (IQR). The upper whiskers extend to the values that are within 1.5 × IQR over the third quartiles. The lower whiskers extend to the values that are within 1.5 × IQR under the first quartiles.

FIGURE 4 . 2 :

 42 FIGURE 4.2: ROC (receiver operating characteristic) curves for each pairwise classification (control vs presymptomatic, control vs patient and presymptomatic vs patient) obtained with logistic regression using as features the expression levels of the microRNAs signature(miR-34a-5p, miR-345-5p, miR-200c-3p and miR-10a-3p). Bootstrapped 90% CIs are reported in brackets. AUC: area under the ROC curve.

FIGURE 4 . 3 :

 43 FIGURE 4.3: Number of times each miRNA was found differentially expressed, when performing a repeated 5-fold nested crossvalidation for 100 times with different fold splits. In each step of the outer cross-validation loop, four of the five folds were used to identify differentially expressed miRNAs. Since one outer loop consists of five steps, and we performed 100 repetitions, 500 sets of miRNAs were computed for each pairwise comparison between groups, respectively: control vs presymptomatic, control vs patient and presymptomatic vs patient. MiRNAs from the signature computed with the entire data set are highlighted. miRNA: microRNA.

Chapter 4 .

 4 Plasma microRNA signature in C9orf72-associated FTD and ALS all above 0.50, indicating a stronger similarity with patients: 0.54, 0.75, 0.80 and 0.82.The distribution of probability scores for all presymptomatic subjects is displayed in Appendix A FigureA5.

  Title] OR microRNAs[Title] OR miR[Title] OR miRNA[Title]) AND (serum[Title] OR circulating[Title] OR plasma[Title]) AND (ALS[Title] OR FTD[Title] OR amyotrophic[Title] OR frontotemporal[Title] OR (neurodegenerative[Title] AND (frontotemporal[Title/Abstract] OR amyotrophic[Title/Abstract]))) NOT mice [Title/Abstract] NOT mouse[Title/Abstract] NOT extracellular vesicles[Title] NOT review[PT] NOT meta-analysis[PT] NOT (comment[PT])

  , we tested each of the 65 miRNAs associated with either FTD or ALS in the literature, to identify which were differentially expressed between (a) controls vs. C9orf72 presymptomatic subjects, (b) controls vs. C9orf72 patients, and (c) C9orf72 presymptomatic subjects vs. C9orf72 patients. Additionally, we tested the 30 miRNAs associated with only FTD in the literature, to highlight which were differentially expressed between (d) controls vs. GRN presymptomatic subjects, (e) controls vs. GRN patients, and (f) GRN presymptomatic subjects vs. GRN patients.

  clinical groups, we trained L2-regularized logistic regression classifiers, using Python 3.8.5 with scikit-learn[START_REF] Pedregosa | Scikit-learn: Machine Learning in Python[END_REF] 0.23.2. We first organized the miRNA expression data into six datasets, one for each relevant pairwise comparison: (a) controls vs. C9orf72 presymptomatic subjects, (b) controls vs. C9orf72 patients, (c) C9orf72 presymptomatic subjects vs. C9orf72 patients, (d) controls vs. GRN presymptomatic subjects, (e) controls vs. GRN patients, and (f) GRN presymptomatic subjects vs. GRN patients. A total of 18 classifiers were trained for each of the comparisons (a), (b) and (c): 16 classifiers used as features each of the miRNA signatures identified in the literature, and two were trained with meta-signatures containing the differentially expressed miRNAs identified in the univariate analyses (a), (b) and (c), respectively with adjusted p-values lower than 0.05 and 0.1. In addition, seven classifiers were built for each of the comparisons (d), (e) and (f): five of them used as features each of the miRNA signatures associated with FTD in the literature, and two were trained with meta-signatures containing the differentially expressed miRNAs identified in the univariate analyses (d), (e) and (f), respectively with adjusted p-values lower than 0.05 and 0.1.

  trained 18 classifiers for each pairwise comparison, using the 16 miRNA signatures identified in the literature and two meta-signatures obtained from the differential expression analyses. The ROC AUC results and the 90% confidence intervals obtained with 2000 bootstrap samples are displayed in Figure5.1. The miRNA signatures with the largest ROC AUC were from[START_REF] Kmetzsch | Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis[END_REF] (0.98 for controls vs.

FIGURE 5 . 1 :

 51 FIGURE 5.1: Area under the ROC curve results when classifying groups from the C9orf72 cohort. The solid circles indicate the areas under the ROC curves obtained for each pairwise comparison using 18 different miRNA signatures. The whiskers denote empirical 90% confidence intervals obtained with 2000 bootstrap samples

FIGURE 5 . 2 : 5 Discussion

 525 FIGURE 5.2: Area under the ROC curve results when classifying groups from the GRN cohort. The solid circles indicate the areas under the ROC curves obtained for each pairwise comparison using seven different miRNA signatures. The whiskers denote empirical 90% confidence intervals obtained with 2000 bootstrap samples

  assume that the longitudinal dynamic of each biomarker can be represented as a sigmoidal function of the DPS. They propose a joint optimization algorithm to compute the DPS, fit one sigmoid function per biomarker using alternating least squares, and apply their work to hundreds of patients with Alzheimer's disease (AD). Similarly, a more recent method[START_REF] Mehdipour Ghazi | Robust parametric modeling of Alzheimer's disease progression[END_REF], also applied to AD, uses M-estimation to map each subject's age to a DPS, jointly fitting generalized logistic functions to the longitudinal dynamics of biomarkers as functions of the DPS. Schiratti and colleagues(Schiratti et al., 2017) propose a general non-linear mixed-effects model for longitudinal data based on concepts from Riemannian geometry. The application of this framework to AD, called AD Course Map[START_REF] Koval | AD Course Map charts Alzheimer's disease progression[END_REF], allowed to map each subject to their corresponding disease stage. The authors of(Lorenzi et al., 2019a) propose a probabilistic approach based on Gaussian process regression from time-series of biomarker measurements. Yet another framework, named Data-driven Inference of Vertexwise Evolution (DIVE)(Marinescu et al., 2019) consists in identifying clusters of vertex-wise biomarker measurements in the brain, and estimating representative trajectories for these clusters. Finally,[START_REF] Mehdipour Ghazi | Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling[END_REF] uses recurrent neural networks to predict biomarker values without parametric assumptions about trajectories, with application to AD.

FIGURE 6 . 1 :

 61 FIGURE 6.1: Illustration of the proposed framework for disease progression scores (DPS) computation. 1) High-dimensional (neuroimaging and microRNAs expression data) and low-dimensional (demographic information) features are extracted; the former are fed to the encoder, the latter are concatenated with latent codes and fed to the classifier. 2) Once the model is trained, all training examples are encoded in the latent space and a principal curve is calculated to define the disease trajectory. 3) Test examples are encoded in the latent space and the latent representations are orthogonally projected onto the previously computed curve; the DPS correspond to their coordinates along the curve.

FIGURE 6 . 2 :

 62 FIGURE 6.2: Synthetic ground truth disease progression scores {v i } n i=1 ∈ [0, 100) for n = 111 subjects (37 subjects per group).

FIGURE 6 . 3 :

 63 FIGURE 6.3: Format of the synthetic datasets D ∈ R n×m containing m features from n individuals. Half of the features are initially sampled from a negative binomial distribution and half from a normal distribution.

Fig. 6 .

 6 Fig. 6.4 presents the computed trajectories and the DPS obtained when 50% of the features are correlated (25% positively and 25% negatively correlated) with the disease progression, for different noise levels. The correlation matrices illustrate the strength of the relationships between the simulated features, for all investigated noise levels.

  For each fold split, we trained a model using 73 training subjects, and then computed the DPS for the 18 individuals in the test set. Fig. 6.6 displays an example of the latent space trajectory computed with one representative training data split, the corresponding test set projected in the latent space, and the obtained disease progression scores.

FIGURE 6 . 4 :

 64 FIGURE 6.4: Results on synthetic data when 50% of the features are correlated with the disease progression score. The rows indicate different noise levels (zero-mean Gaussian noise with different standard deviations). Each column displays, respectively: (1) correlation matrices showing the strength of the relationships between the simulated features, (2) inferred trajectories and test sets projected in the latent space, and (3) estimated DPS vs. ground truth DPS.

FIGURE 6 . 5 :

 65 FIGURE 6.5: Results on synthetic data. Macro-average ROC AUC and Spearman correlation between ground truth and estimated DPS, for different noise levels (zero-mean Gaussian with 0.001, 0.2, 0.5, 0.8, 1, and 5 as standard deviation) and several proportions (0% to 100%) of features correlating with the disease progression score. Random chance is denoted by the dashed lines (ROC AUC = 0.5 and Spearman Rho = 0).

FIGURE 6 . 6 :

 66 FIGURE 6.6: Results on real data. (a) Training data projected in the latent space and the corresponding computed trajectory for one of the 100 fold splits. (b) Test data projected in the latent space, along with the previously computed trajectory. (c) Scores computed after the projection of the latent representation of the test data onto the trajectory.

FIGURE 6 . 7 :

 67 FIGURE 6.7: Results on real data. Average ROC (receiver operating characteristic) curves for each pairwise comparison between clinical groups, over 100 stratified splits. The shaded areas correspond to one standard deviation. The areas under the ROC curves (ROC AUC) are shown as mean ± standard deviation. Random chance is indicated by the dashed line.

FIGURE 6 .

 6 FIGURE 6.8: Results on real data. Histogram of the disease progression scores (DPS) inferred for 18 test subjects over 100 stratified splits. The distribution shapes are approximated with kernel density estimates.

4 )

 4 , using a homogeneous, independent validation cohort of C9orf72 expansion carriers (29 patients, 17 presymptomatic carriers, and 31 controls). Results indicated an outstanding prediction performance (average ROC AUC greater than 0.90) when using the miRNA signature identified in Chapter 4 to classify controls vs. C9orf72 patients and controls vs. C9orf72 presymptomatic subjects. The most challenging problem was to classify C9orf72 presymptomatic individuals vs. C9orf72 patients, task for which the levels of miR-206 were found to be crucial. Interestingly, although miR-206 was not significantly differentially expressed in our discovery study from Chapter 4, it was the second most frequent miRNA identified in this comparison in the generalization analysis (Figure 4.3), with an adjusted p-value close to significance level (p-value = 0.06, Appendix A Table

  figure copy and the cube analysis task from the Visual Object and Space Perception (VOSP)

  normal (MDRS 138/144; FAB 18/18; WCST 20/20; forward/backward digit spans: 7/4), except for isolated moderate decrease of the faux-pas test score (21/30). Ekman test score was normal (31/35). At follow-up three years later (45 years), cognitive scores exhibited attentional deficit (forward/backward digit spans: 5/3), perseverations and social cognition deficit (faux-pas test 21/30). The Ekman score was 28/35. Neurological examination revealed upper and lower limbs brisk reflexes with propagation of reflexes. The case 2 was a left-handed 47-year-old male with 15 years of education. When included in the study, neurological examination and behavior evaluation were normal. Cognitive scores (MMSE: 30; MDRS 134/144; WCST: 20/20) and behavioral scores (FBI: 4; AES score: 2) were normal except for mild impairment of social cognition (faux-pas test: 19/30). The frontal adapted version of CDR®+NACC-FTLD scored 0.5 at baseline. At clinical follow-up evaluation, three years later (50 years), he exhibited inappropriate familiarity and joviality, without any other behavioral changes. The AES score mildly increased (9/42) and the CDR®+NACC-FTLD reached a score of 1.0. Cognitive tests revealed attentional deficit (forward/backward digit spans: 5/3), slow processing of information and decline in several cognitive scores, among which MDRS (130/144) and faux-pas test (18/30). Motor evaluation revealed cramps and rare fasciculations. The case 3 was a right-handed 69-year-old male with 11 years of schooling. At inclusion, neurological and behavioral evaluations were normal (FBI: 6/42; AES: 13). MDRS (135/142) and WCST (18/20) were normal but executive dysfunction (FAB: 12/18) and social cognition deficit (faux-pas test: 18/30, Ekman 29/35) were present. Two years later, he presented loss of interests and apathy (AES 19, cut-off >13), irritability, familiarity and disinhibition (FBI: 12), and imitation behavior. Cognitive tests exhibited attentional (forward/backward digit spans: 3/3) and planning difficulties. The WCST was scored 9/20. Scores of MDRS, FAB, and faux-pas test were stable. Ekman score was 26/35. Motor examination was normal. The case 4 was a right-handed female of high education level (17 years). She was included in the study at age 64. Neurological examination was normal at inclusion, as well as behavior. The FBI scored 0 and the AES scored 8. Cognitive scores were within normal ranges or just above the lower limits according to her age. MDRS scored 138/144, MMSE 26/30 (2 errors in attention subtest) FAB 17/18, WCST 18/20, and TMT was completely normal (0 errors). Reevaluation at age 67, three years after her inclusion in the study, evidenced decline in several cognitive scores, with the occurrence of executive dysfunction, deficit in mental flexibility and perseverations. WCST scored 9/20, MMSE 24/30 and FAB 15/18. She made 10 errors at the TMT. She presented global slowness, scoring 12 at the AES apathy scale. Her relative did not mention any other behavioral disorders (FBI score: 0). At motor evaluation, she exhibited decreased Achilles tendon reflexes, cramps and rare fasciculations never noticed before.

Figure A1 .

 A1 Figure A1. Stratified nested cross-validation

Figure A2 .

 A2 Figure A2. Expression heatmap of miRNA signature

Figure A3 .

 A3 Figure A3. Bootstrapped ROC AUC scores

Figure A4 .

 A4 Figure A4. ROC AUC scores with 100 different fold splits

Figure A5 .

 A5 Figure A5. Presymptomatic subjects probability scores

Figure A6 .

 A6 Figure A6. Heatmap of the level of enrichment in KEGG pathways

  C9orf72 cohort, for each pairwise comparison between the clinical groups. The columns show the 30 studied miRNAs, the logfold change when comparing the clinical groups, the unadjusted P values, and finally the adjusted P values after Benjamini-Hochberg. For each pairwise comparison, a positive log-fold change means that the miRNA is overexpressed in the first group. Controls (n=31), C9orf72 presymptomatic subjects (n=17), and C9orf72 patients (n=29

  end for D ← D + N (µ=0, σ=s, size=(n, m)) L = L ∪ {D} end for end for return L

TABLE 4 .

 4 1: Demographic and clinical characteristics of the studied population. Values are expressed as mean ± standard deviation, or as number (%). Demographic characteristics were compared between groups using the χ 2 test for gender and Kruskal-Wallis with Dunn's test for numerical variables. Statistically significant p-values are in bold. ALS-FRS: ALS Functional Rating Scale; MMSE: Mini-Mental State Examination; MDRS: Mattis Dementia Rating Scale; FAB: Frontal Assessment Battery; FBI: Frontal Behavioral Inventory; AES: Apathy Evaluation Scale.

		Control	Presymptomatic	Patient
		(n=43)	(n=45)	(n=22)
					χ 2
					p-value
	Female gender	23 (53.5%)	28 (62.2%)	10 (45.4%)	.408
					Kruskal-Wallis p-value	Comparison	Dunn's test p-value
						Control vs. Presymptomatic	.118
	Age at inclusion (years) 46.4 ± 13.5	41.8 ± 11.8	62.7 ± 10.5	<.001	Control vs. Patient	<.001
						Presymptomatic vs. Patient	<.001
						Control vs. Presymptomatic	.827
	ALS-FRS	39.5 ± 1.3	39.5 ± 1.9	33.4 ± 7.7	<.001	Control vs. Patient	<.001
						Presymptomatic vs. Patient	<.001
						Control vs. Presymptomatic	.183
	MMSE	29 ± 1.2	28.5 ± 1.4	17.8 ± 8.4	<.001	Control vs. Patient	<.001
						Presymptomatic vs. Patient	<.001
						Control vs. Presymptomatic	.431
	MDRS	142.1 ± 1.8	141.2 ± 3.0	97.3 ± 36.7	<.001	Control vs. Patient	<.001
						Presymptomatic vs. Patient	<.001
						Control vs. Presymptomatic	.583
	FAB	17 ± 1.2	17.2 ± 0.9	9.7 ± 5.3	<.001	Control vs. Patient	<.001
						Presymptomatic vs. Patient	<.001
						Control vs. Presymptomatic	.694
	Ekman faces test	30.1 ± 2.6	30.1 ± 2.3	18 ± 9.1	.001	Control vs. Patient	<.001
						Presymptomatic vs. Patient	.001
						Control vs. Presymptomatic	.387
	FBI	0.9 ± 1.8	1.5 ± 2.7	28.5 ± 15.2	<.001	Control vs. Patient	<.001
						Presymptomatic vs. Patient	<.001
						Control vs. Presymptomatic	.095
	AES	4.8 ± 3.9	6.5 ± 3.6	23.5 ± 13.1	<.001	Control vs. Patient	<.001
						Presymptomatic vs. Patient	.004

TABLE 4 .

 4 2: Differentially expressed miRNAs identified by EdgeR, after correction for multiple comparisons, for each pairwise comparison between clinical status: Control (n=43), Presymptomatic (n=45), Patient (n=22).

		Control vs. Presymptomatic
	miR-34a-5p	-1.433	5.251e-16	3.093e-13
		Control vs. Patient
	miR-34a-5p	-1.239	1.650e-8	9.720e-6
	miR-345-5p	-0.540	1.131e-5	3.330e-3
	miR-200c-3p	0.333	3.109e-5	6.104e-3
	miR-10a-3p	0.697	7.141e-5	1.051e-2
		Presymptomatic vs. Patient
	miR-345-5p	-0.528	3.610e-5	2.126e-2

miRNA log-fold change p-value adjusted p-value showed increased expression in patients, while miR-200c-3p and miR-10a-3p exhibited decreased expression. An expression heatmap of the miRNA signature is displayed in Appendix A Figure A2.

Table 4 .

 4 Hochberg correction). Complete outputs concerning the list of the putative target genes and their related pathways are given in Appendix A TableA3 and A4.

	3. The in silico miRNA target

3 reports the 13 pathways that were identified by both methods and have significant adjusted p-values in at least one of them.

TABLE 4 .

 4

		Proteoglycans in cancer	7.941e-4	4.259e-8
		MicroRNAs in cancer	1.386e-3	3.356e-8
	Cancer	Glioma	6.554e-2	1.423e-2
		Renal cell carcinoma	1.098e-2	9.254e-2
		Small cell lung cancer	3.220e-1	3.341e-2
		Hippo signaling pathway	4.556e-2	5.622e-4
		TGF-beta signaling pathway	5.008e-2	9.288e-4
	Cell signaling/apoptosis	Thyroid hormone signaling pathway	2.132e-3	1.502e-2
		FoxO signaling pathway	2.368e-1	1.449e-2
		Neurotrophin signaling pathway	9.801e-3	3.113e-1
	Intermediary metabolism	Lysine degradation Glycosphingolipid biosynthesis -lacto and neolacto series	1.606e-2 3.885e-10	7.882e-4 4.423e-2
	Meiosis	Oocyte meiosis	2.487e-1	2.446e-3

3: Results from pathway analysis using the four differentially expressed miRNAs as input. Only significant pathways for at least one approach are shown. Statistically significant p-values are in bold.

Category KEGG pathway p-value microT-CDS p-value TarBase

Table 4

 4

	.4 dis-

or plasma) of FTD and/or ALS patients.

TABLE 4 .

 4 4: Comparison of studies investigating miRNAs from blood samples (serum or plasma) of FTD and/or ALS patients. FTD: Frontotemporal Dementia; ALS: Amyotrophic Lateral Sclerosis; qRT-PCR: Quantitative Real-Time Polymerase Chain Reaction; RNA-seq: RNA Sequencing; * in serum; † in plasma; ‡ SOD1, FUS, C9orf72, PFN1; § C9orf72, MAPT, GRN, TBK1

		Freischmidt et al., 2014 *	Sheinerman et al., 2017 †	Piscopo et al., 2018 †	Grasso et al., 2019 †	Magen et al., 2020 †	This study †
	Disease	ALS	FTD, ALS	FTD	FTD	FTD, ALS	FTD, ALS
	Cohort	Separate sporadic/genetic ‡	Not mentioned	Sporadic	Sporadic	Mixed sporadic/genetic §	C9orf72
	Patients, n= Discovery/replication	9/13 genetic 14 sporadic	50 FTD 50 ALS	54	10/48	52/117 FTD 115 ALS	22
	Presymptomatic carriers, n=	18	-	-	-	-	45
			37 selected	9 selected	752 selected	Large scale	Large scale
	Methods of analysis	Microarrays	miRNAs	miRNAs	miRNAs	sequencing	sequencing
			(qRT-PCR)	(qRT-PCR)	(qRT-PCR)	(RNA-seq)	(RNA-seq)
		miR-4745-5p	miR-9/let-7e
	Major deregulated miRNAs	miR-3665 miR-1915-3p miR-4530 (validated from panel of 30	miR-7/miR-451 miR335-5p/let-5e (FTD) miR-206/miR-338-3p miR-9/miR-129-3p miR-335-5p/miR-338-3p	miR-127-3p	miR-663a miR-502-3p miR-206	Panels of 20, 147, 121 miRNAs for each cohort	miR-34a-5p miR-345-5p miR-200c-3p miR-10a-3p
		miRNAs)	(ALS)

TABLE 5 .

 5 1: Demographic characteristics of the studied cohorts.

		C9orf72 patients	C9orf72 presymptomatic carriers	GRN patients	GRN carriers presymptomatic	Controls
	No.	29	17	28	30	31
	Female, No. (%)	14 (48.3)	10 (58.8)	10 (35.7)	17 (56.7)	18 (58.1)
	Age at inclusion, mean (SD)	66.2 (8.8)	51.7 (12.1)	62.9 (11.2)	42.5 (11)	47.1 (14.6)
	Standardized interviews with family members, full neurological examinations,
	quantitative motor testing, and extensive neuropsychological tests measuring all
	cognitive domains were used to assess each participant's cognitive and clinical con-
	ditions. All subjects underwent blood tests, and collected samples were stored in the
	Paris Brain Institute (ICM) DNA and cell bank.

TABLE 5 .

 5 2: Selected studies investigating circulating microRNA expression (from serum or plasma) of patients with FTD or ALS.

				Patients,	Presymto-
	Article	Disease	Cohort	No.	matic carriers,	Method of analysis	Dysregulated miRNAs
				(discovery/replication)	No.
	(Grasso et al., 2019)	FTD	Sporadic	10/48 split of same cohort	-	qRT-PCR of 752 miRNAs	miR-663a, miR-502-3p, miR-206
						qRT-PCR of
	(Piscopo et al., 2018)	FTD	Sporadic	54	-	9 miRNAs linked with	miR-127-3p
						apoptosis
							let-7b-5p, let-7g-5p,
							miR-106a-5p, miR-106b-5p,
						qRT-PCR of	miR-18b-5p, miR-223-3p,
	(Denk et al., 2018)	FTD	Sporadic	48	-	96 miRNAs identified in preliminary	miR-26a-5p, miR-26b-5p, miR-301a-3p, miR-30b-5p, miR-146a-5p, miR-15a-5p,
						study	miR-22-3p, miR-320a,
							miR-320b, miR-92a-3p,
							miR-1246
						RNA-	miR-34a-5p,
	(Kmetzsch et al., 2021)	FTD, ALS	Genetic (C9orf72)	22	45	sequencing of 2576	miR-345-5p, miR-200c-3p,
						miRNAs	miR-10a-3p
							miR-9/let-7e,
							miR-7/miR-451,
				For each		qRT-PCR of	miR-335-5p/let-7e
	(Sheinerman et al., 2017)	FTD, ALS Unspecified	disease, 25/25 split of	-	37 brain-enriched	(FTD) and miR-206/miR-338-3p,
				same cohort		miRNAs	miR-9/miR-129-3p,
							miR-335-5p/miR-338-3p
							(ALS)
						RNA-
	(Magen et al., 2021)	ALS	Mixed sporadic and genetic (C9orf72)	126/122 split of same cohort	-	sequencing longitudinal of 125 miRNAs identified in	miR-181a-5p, miR-181b-5p
						study
	(Soliman et al., 2021)	ALS	Mixed sporadic and genetic (unspecified mutation)	30	-	qRT-PCR of 7 miRNAs involved in ALS	miR-206, miR-143-3p, miR-142-3p
	(Dobrowolny et al., 2021)	ALS	Mixed sporadic and genetic (unspecified mutation)	13/23	-	RNA-sequencing followed by qRT-PCR	miR-151a-5p, miR-199a-5p, miR-423-3p
							miR
	(Raheja et al., 2018)	ALS	Mixed sporadic and genetic (C9orf72, SOD1)	23	-	qRT-PCR of 191 miRNAs identified on prior study

TABLE 5 .

 5 3: Differentially expressed miRNAs in the C9orf72 cohort, for each pairwise comparison between the clinical groups. A positive log-fold change means that the miRNA is overexpressed in the first group. Controls (n=31), C9orf72 presymptomatic subjects (n=17), and C9orf72 patients (n=29). All miRNAs with adjusted p-values lower than 0.1 are displayed, and adjusted p-values lower than 0.05 are shown in bold.

	miRNA	log-fold

change p-value adjusted p-value Controls vs. C9orf72 presymptomatic subjects

	miR-34a-5p	-1.58	3.72E-10	2.42E-08
	miR-338-3p	-0.79	3.48E-04	9.53E-03
	miR-142-3p	-0.82	4.90E-04	9.53E-03
	miR-320a	0.74	5.87E-04	9.53E-03
	miR-145-5p	-0.94	2.29E-03	2.53E-02
	miR-92a-3p	0.75	2.63E-03	2.53E-02
	let-7g-5p	-0.46	2.73E-03	2.53E-02
	miR-199a-5p	-1.13	3.62E-03	2.62E-02
	miR-206	2.04	3.62E-03	2.62E-02
	miR-30b-5p	-1.17	4.45E-03	2.89E-02
	miR-191-5p	-0.44	5.34E-03	3.00E-02
	miR-27a	-0.89	5.53E-03	3.00E-02
	miR-320b	0.76	7.88E-03	3.94E-02
	miR-143-3p	-0.67	9.46E-03	4.22E-02
	miR-1246	1.10	9.73E-03	4.22E-02
	miR-223-3p	-0.70	1.08E-02	4.38E-02
	miR-144-3p	0.87	1.17E-02	4.46E-02
	let-7b-5p	0.39	1.52E-02	5.50E-02
	miR-151a-5p	-0.50	1.83E-02	6.25E-02
	miR-1234-3p	1.40	2.01E-02	6.52E-02
	miR-26a-5p	-0.49	2.11E-02	6.52E-02
	miR-374b-5p	-0.80	2.45E-02	7.01E-02
	miR-146a-5p	-0.68	2.48E-02	7.01E-02
	miR-320c	0.58	3.60E-02	9.37E-02
	miR-301a-3p	-0.46	3.60E-02	9.37E-02
		Controls vs. C9orf72 patients
	miR-34a-5p	-1.49	7.78E-08	5.06E-06
	miR-451	2.20	2.87E-05	6.80E-04
	miR-194-5p	1.55	3.21E-05	6.80E-04
	miR-144-5p	2.68	4.19E-05	6.80E-04
	miR-29b-3p	1.01	2.86E-04	3.72E-03
	miR-29c-3p	0.80	6.61E-04	7.16E-03
	miR-192-5p	0.99	9.39E-04	8.72E-03
	miR-19a-3p	1.25	1.32E-03	1.08E-02
	miR-502-3p	0.80	4.84E-03	3.50E-02
	miR-15a-5p	0.62	6.70E-03	4.36E-02
	miR-206	-1.81	7.92E-03	4.68E-02
	let-7e	-0.74	9.91E-03	5.37E-02
	miR-133b	1.11	1.45E-02	7.26E-02
	miR-18b-5p	0.62	1.59E-02	7.37E-02
	miR-106a-5p	0.69	1.88E-02	8.17E-02
	miR-1915-3p	1.44	2.04E-02	8.29E-02
	C9orf72 presymptomatic subjects vs. C9orf72 patients
	miR-206	-3.85	2.35E-06	9.04E-05
	miR-29b-3p	1.42	2.78E-06	9.04E-05
	miR-30b-5p	1.90	3.15E-05	6.83E-04
	miR-199a-5p	1.70	6.58E-05	8.93E-04
	miR-27a	1.41	6.87E-05	8.93E-04
	miR-29c-3p	0.92	1.86E-04	2.01E-03
	miR-320a	-0.85	2.17E-04	2.02E-03
	miR-374b-5p	1.42	2.87E-04	2.33E-03
	miR-7-1-3p	1.08	4.35E-04	3.14E-03
	miR-19a-3p	1.37	6.33E-04	4.11E-03
	miR-338-3p	0.76	1.26E-03	7.43E-03
	miR-145-5p	1.04	1.41E-03	7.61E-03
	miR-142-3p	0.79	1.57E-03	7.83E-03
	miR-320b	-0.94	1.86E-03	8.61E-03
	miR-320c	-0.92	2.09E-03	9.06E-03
	miR-106b-5p	0.72	2.97E-03	1.21E-02
	miR-146a-5p	0.96	3.36E-03	1.28E-02
	miR-133b	1.31	6.20E-03	2.23E-02
	miR-223-3p	0.82	6.53E-03	2.23E-02
	let-7b-5p	-0.46	7.16E-03	2.33E-02
	miR-345-5p	0.61	7.69E-03	2.38E-02
	miR-194-5p	1.01	8.56E-03	2.53E-02
	miR-143-3p	0.68	1.13E-02	3.19E-02
	miR-22-3p	0.36	1.77E-02	4.80E-02
	miR-301a-3p	0.54	2.24E-02	5.83E-02
	miR-144-5p	1.50	2.38E-02	5.91E-02
	miR-9	0.46	2.45E-02	5.91E-02
	miR-451	1.18	2.76E-02	6.40E-02

TABLE 5 .

 5 4: Differentially expressed miRNAs in the GRN cohort, for each pairwise comparison between the clinical groups. A positive log-fold change means that the miRNA is overexpressed in the first group. Controls (n=31), GRN presymptomatic subjects (n=30), GRN patients (n=28). All miRNAs with adjusted p-values lower than 0.1 are displayed, and adjusted p-values lower than 0.05 are shown in bold.

	miRNA	log-fold

change p-value adjusted p-value Controls vs. GRN presymptomatic subjects

	(No miRNA was differentially expressed between
	controls and GRN presymptomatic subjects)
		Controls vs. GRN patients
	miR-451	2.23	2.65E-06	7.96E-05
	miR-15a-5p	0.77	3.03E-04	4.54E-03
	miR-502-3p	0.82	1.73E-03	1.73E-02
	miR-7	0.56	4.56E-03	3.42E-02
	miR-18b-5p	0.64	7.39E-03	4.44E-02
	miR-106a-5p	0.68	1.14E-02	5.72E-02
	miR-92a-3p	0.51	1.57E-02	6.72E-02
	miR-106b-5p	0.47	2.67E-02	9.44E-02
	let-7b-5p	0.33	2.83E-02	9.44E-02
	GRN presymptomatic subjects vs. GRN patients
	miR-451	1.45	3.35E-03	6.92E-02
	miR-7	0.61	4.61E-03	6.92E-02

TABLE 5 .

 5 5: Differentially expressed miRNAs for at least one pairwise comparison between clinical groups, considering both cohorts. The * indicates in which comparisons each miRNA was significantly differentially expressed (adjusted p-values below 0.05), while the (+) denotes adjusted p-values between 0.05 and 0.1.

		Controls vs.
		C9orf72
	miRNA	presympto-

matic subjects Controls vs. C9orf72 patients C9orf72 pre- symptomatic subjects vs. C9orf72 patients Controls vs. GRN presympto- matic subjects Controls vs. GRN patients GRN pre- symptomatic subjects vs.

						GRN patients
	miR-34a-5p	*	*
	miR-338-3p	*		*
	miR-142-3p	*		*
	miR-320a	*		*
	miR-145-5p	*		*
	miR-92a-3p	*			(+)
	let-7g-5p	*
	miR-199a-5p	*		*
	miR-206	*	*	*
	miR-30b-5p	*		*
	miR-191-5p	*
	miR-27a	*		*
	miR-320b	*		*
	miR-143-3p	*		*
	miR-1246	*
	miR-223-3p	*		*
	miR-144-3p	*
	miR-451		*	(+)	*	(+)
	miR-194-5p		*	*
	miR-144-5p		*	(+)
	miR-29b-3p		*	*
	miR-29c-3p		*	*
	miR-192-5p		*
	miR-19a-3p		*	*
	miR-502-3p		*		*
	miR-15a-5p		*		*
	miR-374b-5p	(+)		*
	miR-7-1-3p			*
	miR-320c	(+)		*
	miR-106b-5p			*	(+)
	miR-146a-5p	(+)		*
	miR-133b		(+)	*
	let-7b-5p	(+)		*	(+)
	miR-345-5p			*
	miR-22-3p			*
	miR-7				*	(+)
	miR-18b-5p		(+)		*
	miR-151a-5p	(+)
	miR-1234-3p	(+)
	miR-26a-5p	(+)
	miR-301a-3p	(+)		(+)
	let-7e		(+)
	miR-106a-5p		(+)		(+)
	miR-1915-3p		(+)
	miR-9			(+)

  1, 2}, training set indices I tr and test set indices I te for one data split into training and test set. Output: DPS {v i } i=I te of the subjects in the test set. /* first step: supervised VAE training */ for epoch in [1,250] do Sample batches (X j , Y j ) from (X I tr , Y I tr ) for each batch

TABLE 6 .

 6 2: Results on real data: ablation study. ROC AUC results (mean ± standard deviation) for the proposed model and three alternative models from the ablation study, respectively using a linear instead of a curved trajectory, removing the classification branch, and concatenating the low-dimensional modality with the high-dimensional ones. These results are summarized in Table6.3 and Table6.4. The slightly different but overall similar results demonstrate that our hyperparameter choice is not overfitting the data.

	Comparison	Proposed model Linear trajectory No supervision Joint low-dim.
	Control vs. Pre Control vs. Patient Pre vs. Patient	0.74 ± 0.13 0.98 ± 0.05 0.96 ± 0.07	0.62 ± 0.15 0.93 ± 0.12 0.93 ± 0.11	0.67 ± 0.15 0.96 ± 0.06 0.94 ± 0.10	0.72 ± 0.15 0.95 ± 0.17 0.91 ± 0.18
	loss terms.

TABLE 6 .

 6 3: Results on real data. ROC AUC results (mean ± standard deviation) over 100 stratified splits when changing the number of units of the hidden layers. Original results, with 50 units, are shown in bold. Pre 0.74 ± 0.13 0.73 ± 0.13 0.71 ± 0.12 0.71 ± 0.13 Control vs. Patient 0.98 ± 0.05 0.98 ± 0.04 0.97 ± 0.05 0.98 ± 0.05 Pre vs. Patient 0.96 ± 0.07 0.96 ± 0.06 0.96 ± 0.06 0.96 ± 0.06

	Hidden units	50	100	80	25
	Control vs.

TABLE 6 .

 6

4: Results on real data. ROC AUC results (mean ± standard deviation) over 100 stratified splits when changing the weights of the loss function terms. Original results, with α 1 =0.2, α 2 =0.2, α 3 =0.6, are shown in bold.

Table A1 . Number of subjects analyzed in each RNA-seq batch, by clinical status (above) and by center (below).

 A1

			Clinical status
	Batch	Control	Presymptomatic	Patient	Total
	Batch 1	25 (39.7%)	27 (42.9%)	11 (17.4%)	63 (100%)
	Batch 2	9 (37.5%)	10 (41.7%)	5 (20.8%)	24 (100%)
	Batch 3	9 (39.1%)	8 (34.8%)	6 (26.1%)	23 (100%)
			Center
	Batch	Paris	Rouen	Limoges	Lille	Total
	Batch 1	51 (81.0%)	5 (7.9%)	5 (7.9%)	2 (3.2%)	63 (100%)
	Batch 2	18 (75%)	2 (8.3%)	3 (12.5%)	1 (4.2%)	24 (100%)
	Batch 3	15 (65.2%)	3 (13.0%)	4 (17.4%)	1 (4.4%)	23 (100%)

Table A2 . Complete output from EdgeR Table A2. Complete output from EdgeR, for each pairwise comparison between clinical groups (control vs. presymptomatic, control vs. patient, presymptomatic vs. patient). The columns show the 589 analyzed miRNAs (above noise level), the log-fold change when comparing the clinical groups, the average log-counts per million, the unadjusted p-values and finally the adjusted p-values after Benjamini-Hochberg.

 A2

	miR-148b-5p miR-27b-5p miR-758-3p miR-6500-3p miR-5010-5p miR-143-3p miR-183-5p miR-1273d miR-31-5p miR-342-3p miR-551b-3p miR-493-5p miR-539-3p miR-22-3p miR-141-3p	-0.113 0.133 -0.186 -0.300 0.102 0.024 -0.553 -1.478 -0.387 -0.109 0.079 0.027 0.815 -0.045 0.044	2.775 1.148 1.734 -0.223 0.210 9.208 5.546 4.498 2.641 10.283 1.815 5.498 1.473 8.495 3.127	0.083 0.191 0.335 0.479 0.678 0.887 0.049 0.155 0.305 0.493 0.674 0.905 0.023 0.559 0.802	0.605 0.638 0.726 0.768 0.860 0.931 0.443 0.556 0.695 0.816 0.875 0.968 0.240 0.792 0.933
	miR-31-5p miR-25-5p miR-181a-5p miR-15b-3p miR-203a miR-6859-3p miR-664a-3p miR-16-2-3p miR-483-5p miR-210-3p miR-548i miR-378g miR-7976 miR-3194-3p miR-708-5p	-0.473 0.181 0.102 -0.092 0.087 -0.050 0.222 -0.272 -0.617 -0.083 -0.250 0.033 -0.387 0.272 0.161	2.641 2.573 10.817 5.648 -0.173 0.719 4.261 3.631 1.834 2.393 3.203 0.027 1.801 0.002 2.615	0.085 0.192 0.335 0.480 0.682 0.890 0.051 0.156 0.315 0.495 0.675 0.905 0.023 0.560 0.807	0.605 0.638 0.726 0.768 0.860 0.932 0.443 0.556 0.712 0.816 0.875 0.968 0.240 0.792 0.937
	miR-424-3p miR-664b-3p miR-4477b miR-328-3p miR-6511b-5p miR-3065-3p miR-339-3p miR-340-5p miR-144-5p miR-548a-3p miR-3133 miR-5187-5p miR-1289 miR-4446-3p miR-19b-3p	0.218 0.132 -0.222 0.114 0.183 -0.018 -0.196 0.244 -0.293 0.130 -0.280 0.024 -1.074 0.125 0.060	0.968 2.162 0.399 5.945 1.158 0.404 5.037 6.743 7.018 5.676 -0.564 2.108 -0.148 3.069 10.256	0.086 0.192 0.335 0.481 0.683 0.892 0.051 0.158 0.316 0.496 0.688 0.910 0.023 0.563 0.809	0.605 0.638 0.726 0.768 0.860 0.932 0.443 0.556 0.712 0.816 0.880 0.969 0.240 0.795 0.938
	miR-6511a-3p miR-3194-3p miR-339-5p miR-106b-5p miR-181b-3p miR-125a-3p miR-1306-3p miR-598-3p miR-130b-5p miR-574-3p miR-199b-5p miR-154-3p miR-125a-5p let-7f-1-3p miR-376c-5p miR-378g let-7a-5p miR-145-5p miR-1273d miR-29b-1-5p miR-5193 miR-134-5p miR-374a-5p miR-1277-3p miR-501-3p miR-548an miR-28-3p miR-1301-3p miR-548d-3p miR-2115-3p miR-1972 miR-425-5p miR-148a-5p miR-331-3p miR-16-5p miR-181a-3p miR-654-3p miR-548n miR-548ap-3p miR-378a-5p miR-1304-5p miR-766-3p miR-494-3p miR-3180 miR-3180-3p miR-4732-5p miR-3187-3p miR-4435 miR-1273h-5p miR-483-3p miR-1271-5p miR-5096 miR-412-5p miR-1271-3p miR-548b-5p miR-132-5p miR-543 miR-3074-5p miR-576-3p miR-3140-3p miR-618 miR-363-3p miR-5584-5p miR-130a-3p miR-192-5p miR-6513-3p miR-203a miR-642a-3p miR-139-5p miR-148b-3p miR-382-3p let-7f-1-3p miR-185-3p let-7i-3p miR-431-3p miR-375 miR-3180-5p miR-545-5p miR-1273h-3p miR-548ab miR-5010-5p miR-320a miR-1285-3p miR-5096 miR-378a-3p miR-1246 miR-320b miR-5585-3p miR-548a-5p miR-6516-5p miR-3194-3p miR-2355-5p miR-381-3p miR-548h-3p miR-548z miR-1268b miR-487a-5p miR-22-5p miR-191-3p miR-218-5p miR-548aj-5p miR-548g-5p miR-548x-5p miR-543 miR-181c-3p miR-148b-5p miR-1179 miR-4662a-5p miR-181a-3p let-7i-5p miR-5189-3p miR-6859-3p miR-642a-3p miR-142-5p miR-18b-5p miR-584-3p miR-628-5p miR-136-5p miR-6772-3p miR-877-3p miR-100-5p let-7c-5p miR-877-5p miR-1249 miR-200c-3p miR-143-3p let-7a-3p miR-493-3p miR-6873-3p miR-28-3p miR-31-5p miR-15b-5p miR-92a-3p miR-4659b-3p miR-548aq-5p miR-369-5p miR-223-5p miR-30a-3p miR-5189-3p miR-548al miR-203a miR-548l miR-548ai miR-301a-5p miR-146a-5p miR-1299 miR-570-5p	0.198 -0.580 -0.139 -0.271 -0.397 0.248 0.210 -0.256 0.167 0.151 -0.278 -0.327 0.199 -0.225 -0.379 0.251 0.131 0.172 -1.451 0.154 0.104 -0.187 -0.135 -0.146 0.147 -0.264 0.059 0.130 -0.277 0.243 -0.999 0.050 0.075 -0.064 -0.049 -0.090 -0.114 -0.071 -0.159 0.083 -0.282 -0.039 -0.073 -0.174 -0.174 0.115 0.060 0.064 0.056 0.132 -0.013 -0.072 -0.039 -0.063 -0.016 0.010 -0.015 0.018 0.008 0.011 -0.905 -0.320 0.373 0.257 -0.286 0.167 -0.524 -0.565 0.384 0.223 0.367 -0.336 -0.222 -0.254 -0.343 -0.564 -0.853 0.338 -0.261 0.855 -0.365 -0.232 -0.156 -0.816 -0.114 -0.379 -0.272 -1.718 0.308 0.140 -0.309 -0.150 -0.152 -0.265 -0.265 0.419 0.159 0.089 -0.065 -0.374 -0.202 -0.202 -0.202 0.081 -0.053 -0.035 -0.051 0.072 0.070 0.033 0.046 -0.055 -0.683 0.109 0.043 -0.019 0.286 0.160 0.055 0.039 -0.778 -0.079 0.024 0.032 0.183 0.144 -0.058 -0.023 -0.586 -0.054 0.085 0.013 -0.495 0.080 0.187 0.018 -0.246 0.058 -0.093 0.066 -0.610 -0.098 0.194 -0.015 0.232 -0.475 0.194	4.865 0.002 11.621 4.658 0.083 0.692 2.215 4.565 5.414 5.403 4.252 0.419 9.141 0.491 1.518 0.027 17.835 6.318 4.498 0.138 1.173 7.091 7.594 1.948 2.746 1.688 10.686 5.720 1.169 0.295 5.510 10.913 0.933 4.398 17.408 4.944 7.125 4.212 10.374 2.841 0.167 5.380 5.857 0.868 0.868 3.999 0.369 1.570 11.451 1.205 1.919 11.722 2.206 -0.768 7.793 0.644 3.379 1.854 5.501 0.699 -0.303 6.300 0.672 7.658 7.507 1.835 -0.173 0.484 6.068 9.436 3.232 0.491 4.607 2.370 0.193 2.730 -0.295 1.482 7.277 4.211 0.210 11.779 8.508 11.722 9.082 5.861 8.897 6.719 1.849 1.586 0.002 0.457 5.537 8.937 8.937 0.512 0.384 3.999 3.805 1.231 7.504 7.504 7.504 3.379 3.538 2.775 1.832 1.907 4.944 13.259 0.435 0.719 0.484 10.854 2.410 0.426 4.677 2.770 0.690 2.150 2.346 9.528 2.378 0.351 5.273 9.208 4.497 5.508 5.014 10.686 2.641 9.487 13.677 0.948 2.543 5.217 7.682 3.079 0.435 -0.392 -0.173 1.877 0.527 1.602 12.934 4.630 0.527	0.086 0.088 0.092 0.092 0.093 0.095 0.096 0.097 0.097 0.098 0.193 0.193 0.193 0.194 0.196 0.198 0.198 0.199 0.201 0.204 0.338 0.340 0.342 0.345 0.345 0.347 0.352 0.353 0.354 0.355 0.486 0.486 0.486 0.490 0.491 0.491 0.495 0.498 0.501 0.501 0.683 0.684 0.686 0.692 0.692 0.701 0.705 0.705 0.709 0.709 0.893 0.901 0.910 0.914 0.915 0.917 0.919 0.920 0.922 0.923 0.051 0.052 0.053 0.054 0.055 0.055 0.056 0.057 0.058 0.060 0.159 0.159 0.159 0.160 0.161 0.161 0.161 0.165 0.166 0.168 0.318 0.320 0.322 0.324 0.324 0.326 0.327 0.329 0.334 0.335 0.498 0.501 0.502 0.505 0.505 0.512 0.513 0.514 0.522 0.523 0.689 0.689 0.689 0.689 0.689 0.690 0.693 0.693 0.693 0.701 0.911 0.912 0.024 0.567 0.812 0.913 0.024 0.568 0.816 0.916 0.025 0.568 0.817 0.919 0.025 0.571 0.823 0.919 0.025 0.571 0.826 0.928 0.025 0.573 0.827 0.932 0.025 0.575 0.831 0.936 0.026 0.575 0.832 0.940 0.026 0.577 0.832	0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.638 0.638 0.638 0.638 0.640 0.640 0.640 0.640 0.643 0.647 0.730 0.732 0.732 0.734 0.734 0.734 0.737 0.737 0.737 0.737 0.770 0.770 0.770 0.771 0.771 0.771 0.775 0.777 0.777 0.777 0.860 0.860 0.861 0.865 0.865 0.875 0.875 0.875 0.875 0.875 0.932 0.939 0.947 0.948 0.948 0.948 0.948 0.948 0.948 0.948 0.443 0.443 0.443 0.444 0.444 0.444 0.445 0.448 0.452 0.455 0.556 0.556 0.556 0.556 0.556 0.556 0.556 0.564 0.564 0.566 0.714 0.717 0.718 0.718 0.718 0.718 0.718 0.720 0.728 0.728 0.817 0.819 0.819 0.819 0.819 0.827 0.827 0.827 0.837 0.837 0.880 0.880 0.880 0.880 0.880 0.880 0.880 0.880 0.880 0.886 0.969 0.240 0.797 0.940 0.969 0.240 0.797 0.941 0.969 0.240 0.797 0.941 0.970 0.240 0.797 0.946 0.970 0.240 0.797 0.947 0.970 0.240 0.798 0.947 0.978 0.240 0.798 0.947 0.980 0.240 0.798 0.947 0.983 0.983 0.240 0.798 0.947
	miR-548ah-3p miR-146b-5p miR-1 miR-379-3p miR-1285-3p miR-3180-5p miR-340-3p miR-30c-1-3p miR-1468-5p miR-889-3p miR-122-5p miR-5010-3p miR-28-5p miR-584-5p miR-450b-5p	-0.782 -0.142 -0.241 -0.124 -0.041 0.037 0.180 0.129 0.124 0.169 -0.203 -0.008 0.196 -0.085 -0.053	2.757 9.310 8.318 1.468 8.508 -0.295 3.655 2.582 0.552 3.340 11.047 1.408 6.656 9.252 2.048	0.098 0.205 0.356 0.503 0.710 0.924 0.060 0.169 0.340 0.525 0.703 0.942 0.026 0.579 0.834	0.605 0.647 0.737 0.777 0.875 0.948 0.455 0.566 0.733 0.837 0.886 0.983 0.240 0.798 0.947
	miR-1249 miR-103a-2-5p miR-423-3p miR-548av-3p miR-144-5p miR-323a-3p miR-96-5p miR-92b-5p miR-320c miR-1301-3p miR-548ar-5p miR-6734-5p let-7f-5p miR-23a-3p miR-487b-3p	0.346 -0.266 0.124 -0.207 -0.078 -0.014 -0.563 -0.536 -0.261 -0.122 -0.201 -0.016 0.224 -0.051 0.048	0.351 -0.340 8.841 3.903 7.018 4.078 3.856 -0.009 8.553 5.720 7.739 0.555 16.225 11.962 5.402	0.099 0.209 0.356 0.504 0.710 0.927 0.062 0.169 0.340 0.528 0.706 0.946 0.028 0.581 0.835	0.605 0.647 0.737 0.777 0.875 0.949 0.455 0.566 0.733 0.837 0.886 0.983 0.259 0.798 0.947
	miR-873-5p miR-376b-5p miR-450b-5p miR-550a-5p miR-1255a miR-1289 miR-10b-5p miR-1255a miR-3143 miR-619-5p miR-101-3p miR-2277-5p miR-7706 miR-493-3p miR-548ap-3p	-0.418 -0.370 -0.153 0.087 0.075 0.028 -0.515 0.392 -0.152 0.709 -0.058 0.035 -0.588 0.126 -0.072	1.352 1.487 2.048 1.899 4.833 -0.148 4.540 4.833 -0.186 11.202 11.459 0.413 -0.037 5.508 10.374	0.099 0.210 0.357 0.506 0.714 0.932 0.062 0.171 0.344 0.529 0.706 0.947 0.030 0.581 0.837	0.605 0.647 0.737 0.777 0.878 0.953 0.455 0.570 0.740 0.837 0.886 0.983 0.264 0.798 0.947
	miR-496 miR-140-5p miR-1306-5p miR-378c miR-4785 miR-7976 miR-514a-3p miR-423-5p miR-378c miR-377-5p miR-145-5p miR-655-3p miR-3120-3p let-7a-5p miR-1	-0.337 -0.106 0.240 -0.064 -0.038 -0.008 0.836 -0.444 -0.129 -0.152 -0.073 0.025 0.288 -0.082 0.078	1.970 6.331 2.765 5.108 0.526 1.801 0.606 10.597 5.108 -0.095 6.318 -0.112 2.583 17.835 8.318	0.100 0.210 0.361 0.508 0.716 0.940 0.065 0.175 0.346 0.531 0.709 0.948 0.030 0.585 0.838	0.605 0.647 0.743 0.777 0.878 0.959 0.456 0.574 0.740 0.837 0.886 0.983 0.264 0.802 0.947
	miR-590-5p miR-411-5p miR-629-5p miR-6803-3p miR-589-3p miR-877-5p miR-374a-5p miR-576-5p miR-18b-5p miR-140-5p miR-487b-3p miR-3074-5p miR-378i miR-1273c miR-1229-3p	-0.316 -0.225 0.097 0.151 0.028 -0.005 0.353 -0.183 -0.162 -0.072 -0.084 -0.017 -0.285 -0.103 0.048	0.651 5.491 6.404 -0.112 1.137 2.378 7.594 6.414 2.410 6.331 5.402 1.854 4.394 1.024 0.765	0.100 0.211 0.363 0.509 0.718 0.941 0.066 0.175 0.348 0.531 0.710 0.949 0.030 0.588 0.840	0.605 0.647 0.743 0.777 0.878 0.959 0.456 0.574 0.741 0.837 0.886 0.983 0.264 0.802 0.948
	miRNA miR-106b-3p miR-3620-5p miR-379-5p miR-665 miR-181b-5p miR-107 miR-550a-3-5p miR-126-3p miR-191-5p miR-3161 miR-377-3p miR-361-5p miR-589-5p miR-1260b miR-6770-3p miR-18b-5p let-7f-2-3p miR-7706 miR-196a-5p miR-196b-5p miR-7851-3p miR-196a-5p miR-1307-5p miR-100-5p miR-99b-3p miR-23b-5p miR-548az-5p miR-484 miR-5193 miR-3064-5p miR-34a-5p miR-218-5p miR-1250-5p miR-625-3p miR-548au-5p miR-548am-c-o-5p miR-30e-5p miR-1307-5p miR-200c-3p miR-5010-3p miR-17-3p miR-7110-3p let-7e-5p miR-223-5p miR-337-5p miR-889-3p miR-1268a miR-377-3p miR-22-3p miR-205-5p miR-324-5p miR-3688-3p miR-548j-5p miR-6513-3p miR-10b-5p miR-941 miR-374a-3p miR-323b-3p miR-542-3p miR-3161 miR-664a-5p miR-885-5p miR-92a-3p miR-6859-5p miR-1270 miR-378a-3p miR-539-3p miR-20a-3p miR-320a miR-126-5p miR-148b-3p miR-548k miR-493-3p miR-548av-5p miR-4677-3p miR-99b-5p miR-3615 miR-27b-3p miR-424-5p miR-199a-3p let-7i-5p miR-483-5p miR-4659b-3p miR-378d miR-584-3p miR-145-3p miR-182-5p miR-671-5p miR-548ax miR-1233-3p miR-130b-3p miR-431-3p miR-25-3p miR-299-5p miR-223-3p miR-486-3p miR-204-5p miR-181a-2-3p miR-93-5p miR-1273h-3p miR-1299 miR-548am-3p miR-143-5p let-7f-5p miR-514a-3p miR-491-5p miR-29c-5p miR-7-1-3p miR-140-3p miR-500a-5p miR-200b-3p miR-26a-1-3p miR-1296-5p miR-551a miR-548c-3p miRNA miR-1233-5p miR-26a-5p miR-1180-3p miR-627-5p miR-486-5p miR-98-5p miR-3679-5p miR-4685-3p miR-629-5p miR-365a-3p miR-3180 miR-3180-3p miR-423-3p miR-766-3p miR-378d miR-7-5p miR-138-5p miR-548am-3p miR-106a-5p miR-548ay-5p miR-186-5p miR-432-5p miR-125b-5p miR-6852-5p miR-143-5p let-7f-2-3p miR-504-5p miR-1185-2-3p miR-1271-3p miR-671-3p miR-548o-3p miR-548x-3p miR-299-3p miR-625-5p miR-6500-3p miR-134-5p miR-29c-5p miR-3187-3p miR-485-5p miR-6859-5p miR-548ah-3p miR-19b-1-5p miR-548p miR-548ai miR-570-5p miR-141-3p miR-222-3p miR-548aq-5p let-7a-5p miR-1185-1-3p miR-28-3p miR-744-5p miR-382-3p miR-29c-5p miR-186-5p miR-219a-5p miR-1277-3p miR-3133 miR-582-5p miR-3065-5p miR-32-3p miR-505-5p miR-3065-5p miR-548l miR-1277-5p miR-485-3p miR-200a-3p miR-548at-5p miR-376b-3p miR-7849-3p miR-652-3p miR-30d-3p miR-133a-3p miR-5585-3p miR-181c-5p miR-337-3p miR-4677-3p miR-7851-3p miR-548ar-5p miR-30a-3p miR-9-3p miR-548ae miR-6741-3p miR-6511a-5p miR-545-5p miR-7-1-3p miR-628-3p miR-365b-3p miR-378i miR-1255b-5p miR-215-5p miR-323a-3p miR-1273h-5p miR-139-5p miR-548j-5p miR-548d-5p miR-370-3p miR-582-3p miR-125a-3p miR-18a-5p miR-301a-3p miR-433-3p miR-92b-5p miR-4662a-5p miR-4454 miR-22-3p miR-30d-5p miR-1294 miR-548aq-3p miR-200a-3p miR-652-3p miR-369-3p miR-429 miR-576-3p	log-fold change 0.155 0.702 -0.153 -0.139 0.035 -0.004 -0.320 0.109 0.079 0.517 0.093 -0.004 -0.253 -0.086 -0.062 -0.205 -0.256 0.159 -0.149 0.023 -0.013 -0.585 -0.161 -0.311 0.130 -0.071 0.025 -0.502 0.087 0.024 -1.433 1.475 -0.310 0.495 -0.275 -0.283 -0.153 -0.241 0.150 0.206 -0.238 0.366 0.212 0.119 -0.311 -0.322 -1.055 -0.306 -0.084 0.390 -0.103 -0.209 0.139 0.097 0.226 0.156 -0.192 -0.201 -0.221 0.757 0.142 0.370 0.174 0.193 0.226 0.098 -0.309 -0.083 0.145 -0.103 -0.077 -0.082 -0.149 -0.082 -0.107 0.118 0.175 -0.088 -0.141 -0.086 0.041 0.252 -0.063 -0.241 -0.084 0.089 0.085 0.084 -0.065 -0.379 -0.041 -0.111 0.046 -0.058 0.025 0.068 -0.062 -0.025 0.017 0.044 -0.190 -0.232 0.055 0.022 -0.097 0.027 -0.004 -0.006 -0.002 -0.005 -0.003 0.002 -0.002 -0.003 0.000 log-fold change -1.587 0.158 -0.699 -0.189 -0.615 0.169 -0.350 -0.526 -0.272 -0.506 -0.899 -0.899 -0.252 -0.179 -0.691 0.181 -0.323 1.170 0.226 -0.201 -0.110 -0.256 0.136 -0.223 0.225 -0.257 0.175 0.249 0.667 -0.187 -0.280 0.760 0.190 0.114 0.374 0.162 -0.053 -0.138 -0.194 -0.135 0.227 0.046 0.203 0.297 0.297 -0.060 -0.036 0.291 0.049 0.082 0.005 -0.010 0.578 -0.049 -0.024 0.013 0.473 -0.394 0.068 -0.008 0.424 -0.109 -0.030 -0.008 0.463 0.180 0.023 0.013 0.671 0.085 0.018 0.005 -0.671 -0.858 0.031 0.009 0.374 -0.161 0.098 -0.003 0.380 0.791 0.046 -0.016 0.537 0.084 -0.019 -0.506 -0.158 -0.217 0.120 -0.069 -0.004 0.454 0.063 0.025 -0.548 -0.385 -0.189 0.060 0.039 -0.005 -0.857 0.091 0.037 -0.129 -0.090 -0.198 0.109 0.039 -0.002 0.685 -0.075 0.017	log-counts per 6.995 3.024 7.187 0.677 8.044 10.028 2.398 14.192 12.565 -0.951 2.438 8.171 3.684 3.783 0.142 2.410 0.250 -0.037 2.899 5.658 3.495 2.899 5.708 2.346 2.773 1.691 5.839 7.854 1.173 0.924 million Control vs. Presymptomatic 4.676 1.231 0.691 8.394 14.473 14.295 10.655 5.708 5.273 1.408 3.215 -0.067 9.484 7.682 3.963 3.340 3.542 2.438 8.495 2.270 5.454 1.689 9.735 1.835 4.540 9.475 2.987 4.932 2.341 -0.951 5.295 0.498 13.677 3.735 3.003 9.082 1.473 1.922 11.779 11.057 9.436 1.443 5.508 1.444 0.746 7.865 5.538 10.051 3.748 15.594 13.259 1.834 0.948 0.023 0.426 1.763 7.556 4.210 5.062 -0.544 5.063 0.193 10.901 2.468 14.649 5.430 1.457 5.645 11.309 7.277 4.630 2.613 1.979 16.225 0.606 3.945 4.579 2.452 9.249 3.026 2.188 2.985 2.226 -0.321 2.905 log-counts per -0.221 15.391 2.024 0.873 14.249 8.594 2.384 0.669 6.404 4.151 0.868 0.868 8.841 5.380 0.023 10.227 0.000 2.613 1.761 13.403 8.853 7.959 8.080 2.746 1.979 0.250 0.213 0.123 -0.768 3.284 2.775 2.828 0.096 6.770 -0.223 7.091 4.579 0.369 3.492 3.735 2.757 0.495 1.823 0.527 0.527 3.127 8.603 2.543 17.835 3.358 10.686 7.986 3.232 4.579 8.853 1.164 1.948 -0.564 0.885 2.573 0.143 3.558 2.573 1.877 12.138 5.831 2.514 4.401 3.330 0.247 7.109 1.671 7.345 6.719 3.046 2.407 0.746 3.495 7.739 3.079 3.953 3.953 0.231 1.038 1.482 2.452 5.661 4.151 4.394 7.593 2.194 4.078 11.451 6.068 9.735 12.885 million 5.773 1.716 0.692 7.212 5.377 1.880 -0.009 1.907 9.159 Control vs. Patient 8.495 11.785 1.946 9.450 2.514 7.109 4.919 0.270 5.501	p-value 0.101 0.212 0.364 0.510 0.719 0.947 0.068 0.176 0.351 0.532 0.712 0.950 0.031 0.589 0.844 0.101 0.213 0.366 0.511 0.720 0.953 0.068 0.177 0.351 0.533 0.713 0.951 0.031 0.589 0.846 5.251e-16 0.000 0.001 0.002 0.005 0.005 0.006 0.006 0.007 0.008 0.010 0.102 0.102 0.103 0.104 0.104 0.104 0.105 0.106 0.111 0.115 0.116 0.118 0.119 0.214 0.214 0.217 0.219 0.219 0.221 0.222 0.227 0.227 0.228 0.228 0.229 0.229 0.366 0.371 0.372 0.374 0.377 0.378 0.378 0.380 0.380 0.381 0.381 0.382 0.383 0.511 0.512 0.521 0.524 0.524 0.525 0.525 0.526 0.530 0.533 0.534 0.535 0.536 0.724 0.727 0.729 0.732 0.733 0.735 0.740 0.741 0.742 0.747 0.751 0.755 0.756 0.955 0.959 0.971 0.975 0.976 0.978 0.978 0.988 1.000 p-value 0.069 0.069 0.070 0.070 0.071 0.071 0.071 0.072 0.073 0.075 0.180 0.180 0.181 0.183 0.184 0.190 0.191 0.194 0.196 0.197 0.351 0.352 0.356 0.357 0.357 0.358 0.361 0.361 0.367 0.368 0.537 0.537 0.539 0.541 0.543 0.543 0.549 0.550 0.551 0.554 0.715 0.716 0.717 0.718 0.718 0.720 0.726 0.731 0.733 0.740 0.954 0.956 0.032 0.594 0.848 0.957 0.032 0.596 0.853 0.958 0.033 0.597 0.854 0.959 0.033 0.601 0.855 0.959 0.034 0.609 0.857 0.974 0.034 0.618 0.866 0.975 0.036 0.627 0.868 0.976 0.037 0.627 0.875 0.980 0.037 0.627 0.877 0.075 0.200 0.368 0.557 0.740 0.983 0.038 0.628 0.878 0.075 0.201 0.369 0.559 0.740 0.984 0.039 0.632 0.881 0.075 0.203 0.378 0.560 0.746 0.985 0.039 0.632 0.882	0.605 0.647 0.743 0.777 0.878 0.963 0.456 0.574 0.741 0.837 0.886 0.983 0.264 0.802 0.950 adjusted p-value 0.605 0.647 0.743 0.777 0.878 0.968 0.456 0.576 0.741 0.837 0.886 0.983 0.264 0.802 0.951 3.093e-13 0.081 0.209 0.228 0.458 0.458 0.458 0.458 0.458 0.458 0.511 0.605 0.605 0.605 0.605 0.605 0.605 0.606 0.607 0.629 0.638 0.638 0.638 0.638 0.647 0.647 0.647 0.647 0.647 0.647 0.647 0.647 0.647 0.647 0.647 0.647 0.647 0.743 0.743 0.743 0.743 0.743 0.743 0.743 0.743 0.743 0.743 0.743 0.743 0.743 0.777 0.777 0.787 0.787 0.787 0.787 0.787 0.787 0.791 0.792 0.792 0.792 0.792 0.882 0.882 0.884 0.884 0.884 0.885 0.889 0.889 0.889 0.893 0.895 0.897 0.897 0.968 0.971 0.981 0.981 0.981 0.981 0.981 0.989 1.000 0.456 0.456 0.456 0.456 0.456 0.456 0.456 0.456 0.456 0.456 0.578 0.578 0.578 0.582 0.582 0.598 0.600 0.604 0.606 0.607 0.741 0.741 0.742 0.742 0.742 0.742 0.743 0.743 0.749 0.749 0.840 0.840 0.840 0.840 0.840 0.840 0.845 0.845 0.845 0.847 0.886 0.886 0.886 0.886 0.886 0.888 0.893 0.897 0.898 0.900 0.983 0.269 0.806 0.951 0.983 0.269 0.806 0.954 0.983 0.270 0.806 0.954 0.983 0.270 0.810 0.954 0.983 0.270 0.819 0.954 0.983 0.270 0.830 0.962 0.994 0.271 0.835 0.962 0.994 0.271 0.835 0.962 0.994 0.994 0.271 0.835 0.962 adjusted p-value 0.456 0.614 0.749 0.848 0.900 0.994 0.271 0.835 0.962 0.456 0.614 0.749 0.848 0.900 0.271 0.837 0.962 0.994 0.456 0.614 0.760 0.848 0.904 0.994 0.271 0.837 0.962
	miR-361-5p miR-5583-3p miR-376c-3p miR-199b-3p miR-548ae miR-504-5p miR-34a-5p miR-154-3p miR-25-5p miR-376a-3p miR-548q miR-409-3p miR-1273c miR-92b-3p let-7g-3p miR-433-3p	0.124 0.225 -0.268 -0.086 -0.626 0.042 -1.239 0.617 -0.254 0.259 0.122 0.074 0.003 -0.530 0.112 0.038	8.171 0.528 5.292 15.594 3.953 0.213 4.676 0.419 2.573 6.560 4.336 8.698 1.024 3.086 0.220 1.880	0.014 0.119 0.229 0.383 0.538 0.757 1.650E-08 0.081 0.205 0.378 0.561 0.747 0.986 0.040 0.638 0.885	0.605 0.638 0.647 0.743 0.792 0.897 9.720E-06 0.478 0.614 0.760 0.848 0.904 0.994 0.271 0.842 0.962
	miR-199a-5p miR-660-5p miR-1268b let-7a-3p miR-6511b-3p miR-1287-5p miR-345-5p miR-4732-3p miR-3928-3p miR-548f-5p miR-185-5p miR-27b-5p miR-148a-3p miR-3928-3p miR-338-3p miR-148a-3p	-0.260 -0.149 -0.555 -0.155 0.076 0.025 -0.540 -0.787 -0.227 -0.617 0.044 0.044 -0.002 -0.393 -0.082 0.020	8.817 5.628 0.512 4.497 2.078 3.050 4.607 3.928 0.931 0.762 9.811 1.148 10.002 0.931 5.028 10.002	0.016 0.121 0.230 0.384 0.539 0.758 1.131E-05 0.081 0.206 0.378 0.562 0.748 0.986 0.040 0.641 0.889	0.605 0.638 0.647 0.743 0.792 0.897 0.003 0.478 0.614 0.760 0.848 0.904 0.994 0.271 0.844 0.962
	miR-125b-5p miR-23b-5p miR-219a-5p miR-766-5p miR-335-3p miR-584-5p miR-185-3p miR-200c-3p miR-3120-3p miR-221-5p miR-320d miR-4477b miR-181c-5p miR-6511b-5p let-7b-5p miR-19b-1-5p miR-574-3p miR-548h-5p miR-29b-3p miR-10a-5p miR-23b-3p miR-32-5p miR-10a-3p let-7g-3p miR-509-3p miR-942-5p miR-197-3p miR-142-3p miR-21-3p miR-301a-3p miR-4742-3p miR-5584-5p miR-625-5p miR-375 miR-330-5p miR-100-5p miR-19a-3p miR-101-3p miR-582-5p miR-27a-3p miR-548y miR-548aq-3p miR-153-3p miR-377-5p miR-191-5p miR-548t-5p miR-29a-3p let-7c-5p miR-548ay-5p miR-215-5p miR-874-3p miR-548d-5p miR-29c-3p miR-136-5p miR-19b-3p miR-487a-3p miR-548ag miR-548p miR-326 miR-369-3p miR-551b-3p miR-30d-3p miR-135a-5p miR-33a-5p miR-181d-5p miR-154-5p let-7d-3p miR-376b-3p miR-548u miR-142-5p miR-6873-3p miR-18a-5p miR-133b miR-1260b let-7i-3p miR-548j-3p miR-138-5p miR-222-3p miR-2277-5p miR-340-5p miR-616-3p miR-339-3p miR-95-3p miR-1304-3p miR-136-3p miR-144-3p miR-548al miR-4286 miR-21-5p miR-369-5p miR-378i miR-651-5p miR-1285-5p miR-338-3p miR-548q miR-3198 miR-130a-3p miR-624-5p miR-7641 miR-30b-5p miR-664a-3p miR-362-5p miR-30a-5p miR-99a-5p miR-376a-5p miR-301a-5p miR-655-3p miR-582-3p miR-301b miR-320c miR-381-3p miR-548f-3p miR-320d miR-3913-5p miR-15a-5p miR-193a-5p miR-497-5p miR-548ap-5p miR-221-3p miR-9-3p miR-664b-5p miR-122-5p miR-556-3p miR-93-3p miR-299-3p miR-3138 miR-487a-5p let-7b-5p miR-374b-3p miR-320b miR-3605-3p miR-3928-3p miR-23c miR-133a-3p miR-6852-5p miR-18a-3p miR-9-5p miR-548e-5p miR-3613-5p miR-92b-5p miR-132-3p miR-505-5p miR-760 miR-4507 miR-3173-5p miR-548aa miR-548t-3p miR-4772-3p miR-382-5p miR-5095 miR-545-3p miR-361-3p miR-576-5p miR-186-3p miR-155-5p miR-590-3p miR-382-3p miR-744-5p miR-5187-5p miR-376a-3p miR-20a-5p miR-32-3p miR-545-5p miR-495-5p miR-92b-3p miR-409-5p miR-27a-5p miR-671-3p miR-2355-5p miR-619-5p miR-641 miR-98-3p miR-23a-5p miR-221-5p miR-589-5p miR-6842-3p miR-628-5p let-7d-5p miR-30e-3p miR-454-5p miR-548e-3p miR-99b-3p miR-29a-5p miR-627-5p miR-544a miR-186-5p miR-365a-3p miR-365b-3p miR-548a-3p miR-618 miR-1185-2-3p miR-548h-3p miR-548z let-7g-5p miR-6734-5p miR-23a-3p miR-103a-3p miR-550a-3-5p miR-548ak miR-548a-5p miR-628-3p miR-141-3p miR-429 miR-30a-3p miR-192-5p miR-3143 miR-1273c miR-296-5p miR-1277-5p miR-16-2-3p miR-1303 miR-4446-3p miR-127-5p miR-210-3p miR-574-5p miR-450a-5p miR-3200-3p miR-548f-5p miR-1307-3p miR-548az-5p miR-3120-3p miR-500a-3p miR-1294 miR-1226-3p miR-1185-1-3p miR-6513-5p miR-487b-3p miR-340-3p miR-7849-3p miR-191-3p miR-4659a-3p miR-5585-3p miR-548ar-5p miR-106a-5p miR-548l miR-3617-5p miR-206 miR-26b-5p miR-4433b-3p miR-744-3p miR-548ab miR-652-5p let-7b-3p miR-548x-3p miR-539-5p miR-185-5p miR-183-5p miR-548g-3p miR-409-3p miR-335-5p miR-1229-3p miR-1255b-5p miR-30b-3p miR-1908-5p miR-502-3p miR-548aj-5p miR-548g-5p miR-548x-5p miR-1185-5p miR-6772-3p miR-20b-5p miR-548as-5p miR-224-5p miR-1290 miR-642a-3p miR-1179 miR-30d-5p miR-28-5p miR-324-3p miR-139-3p miR-150-3p miR-181c-3p miR-423-5p miR-452-5p miR-22-5p miR-4433b-5p miR-485-3p miR-2110 miR-425-3p miR-126-3p miR-190a-5p miR-6741-3p miR-431-5p miR-769-3p miR-30c-1-3p miR-548w miR-3158-3p miR-542-5p miR-503-5p miR-2355-3p miR-195-5p miR-495-3p miR-337-3p miR-29b-2-5p miR-329-3p miR-17-5p miR-363-3p miR-4448 miR-5189-3p miR-451a miR-342-5p miR-139-5p miR-548at-5p miR-374b-5p miR-4301 miR-30c-5p miR-4732-3p miR-493-5p miR-548o-3p miR-1273g-3p miR-190b miR-421 miR-96-5p miR-194-5p miR-485-5p miR-1302 miR-98-5p miR-5009-5p miR-532-5p miR-26b-3p miR-3064-5p miR-1292-5p miR-1468-5p miR-1180-3p miR-152-3p miR-150-5p miR-652-3p miR-330-3p miR-432-5p miR-4645-3p miR-15b-5p miR-26a-5p miR-6770-3p miR-486-5p miR-6516-5p miR-342-3p miR-454-3p miR-1233-5p miR-146a-5p miR-484 miR-877-3p miR-3179 miR-487b-5p miR-370-3p miR-1246 miR-3133 miR-148a-3p miR-26a-2-3p miR-433-3p miR-3679-5p miR-500b-5p miR-505-3p miR-7-5p miR-24-3p miR-3960 miR-127-3p miR-181c-5p miR-128-3p miR-550a-3p miR-151a-5p miR-378f miR-3065-5p miR-146b-3p miR-151b miR-200a-3p miR-151a-3p miR-548i miR-4454 miR-19b-1-5p miR-4685-3p miR-769-5p miR-6511a-5p miR-548ai miR-570-5p miR-708-5p miR-329-5p miR-146a-3p miR-548aq-5p miR-4326 miR-345-5p miR-4662a-5p miR-532-3p miR-10a-3p miR-151a-5p miR-26a-1-3p let-7b-5p miR-548y miR-1302 miR-496 miR-409-5p miR-296-5p miR-221-5p miR-29a-3p miR-151b miR-664b-3p miR-5193 miR-548e-3p miR-99a-5p miR-6511b-3p miR-6770-3p miR-514a-3p miR-544a miR-4433b-5p miR-551a miR-133a-3p miR-206 miR-133b miR-542-5p miR-296-5p miR-660-5p miR-18a-3p miR-223-3p miR-205-5p miR-361-3p let-7b-3p miR-30a-5p miR-93-3p let-7f-5p miR-532-3p miR-144-3p miR-425-3p miR-550a-3p miR-10a-5p miR-17-5p miR-451a miR-7976 miR-196b-5p miR-135a-5p miR-106b-3p miR-148a-5p miR-25-3p miR-1289 miR-324-3p miR-5583-3p miR-30b-3p miR-23b-3p miR-326 miR-3158-3p miR-146a-5p miR-532-5p miR-1303 miR-378a-5p miR-16-5p miR-155-5p miR-3615 miR-26b-5p miR-140-3p miR-331-3p miR-484 miR-4785 miR-374a-3p miR-224-5p miR-502-3p miR-26b-3p miR-769-5p miR-412-5p miR-4286 miR-6513-5p miR-339-5p miR-30e-5p miR-28-5p miR-186-3p miR-146b-3p miR-500b-5p miR-941 miR-500a-5p miR-29b-2-5p miR-590-3p miR-7706 miR-150-5p miR-454-3p miR-1277-5p miR-665 miR-548u miR-93-5p miR-628-5p miR-550a-5p miR-548j-5p miR-548ap-5p miR-204-5p miR-1296-5p miR-4677-3p miR-652-5p miR-199b-5p miR-103a-3p miR-425-5p miR-486-3p miR-500a-3p miR-4645-3p miR-539-5p miR-589-5p miR-769-3p miR-495-3p miR-1277-3p miR-548am-c-o-5p miR-92a-3p miR-194-5p miR-21-3p miR-548au-5p miR-338-3p miR-766-5p miR-335-5p miR-152-3p miR-199a-3p miR-199b-3p miR-616-3p miR-26a-2-3p miR-874-3p miR-539-3p miR-150-3p miR-32-3p miR-181b-5p miR-424-5p miR-491-5p miR-1292-5p miR-27a-5p miR-590-5p miR-92b-3p miR-3200-3p miR-501-3p miR-3688-3p miR-223-5p miR-2115-3p miR-4732-5p miR-1908-5p miR-7641 miR-582-5p miR-641 miR-5009-5p miR-190b miR-3617-5p miR-9-3p miR-6511a-3p miR-4446-3p miR-1233-3p miR-29a-3p miR-1250-5p let-7e-5p miR-548d-5p miR-487a-3p miR-193a-5p miR-548d-3p miR-130b-5p miR-497-5p miR-1972 miR-6873-3p miR-4448 miR-624-5p miR-374b-3p miR-20b-5p miR-495-5p miR-301b miR-24-3p miR-671-5p miR-548c-3p miR-429 miR-2110 miR-7849-3p miR-19b-3p miR-330-3p miR-376a-5p let-7g-3p miR-3613-5p miR-545-3p let-7g-5p miR-15a-5p miR-181d-5p miR-1307-3p miR-126-5p miR-574-3p miR-221-3p miR-362-5p miR-130b-3p miR-1271-5p miR-548e-5p miR-3065-3p miR-29a-5p miR-95-3p miR-30e-3p miR-760 miR-450b-5p let-7a-3p miR-376b-3p miR-431-5p miR-136-5p miR-151a-3p miR-29b-3p miR-182-5p miR-15b-3p miR-548ax miR-20a-3p miR-548as-5p miR-1290 miR-452-5p miR-1299 miR-374b-5p miR-4507 miR-30c-5p miR-376c-3p miR-454-5p miR-199a-5p miR-1304-5p miR-6772-3p miR-4772-3p miR-483-3p miR-20a-5p miR-369-3p miR-411-5p miR-23a-5p miR-873-5p miR-4301 let-7c-5p miR-29c-3p miR-329-3p miR-27a-3p miR-1229-3p let-7d-3p miR-3138 miR-181a-5p miR-17-3p miR-548g-3p miR-153-3p miR-1306-5p miR-450a-5p miR-1273g-3p miR-548aa miR-548t-3p miR-548f-3p miR-503-5p miR-548ap-3p miR-142-5p miR-1260b miR-181a-2-3p miR-1268a miR-4326 miR-154-5p miR-143-3p miR-9-5p miR-3605-3p miR-5095 miR-328-3p miR-7851-3p miR-329-5p miR-6741-3p miR-125a-5p miR-548b-5p miR-99b-5p miR-324-5p miR-3198 miR-424-3p miR-197-3p miR-299-5p miR-598-3p miR-1285-5p miR-548j-3p miR-494-3p miR-548k miR-3179 miR-1270 miR-379-3p miR-548t-5p miR-379-5p miR-195-5p miR-3620-5p miR-33a-5p miR-142-3p miR-21-5p miR-3064-5p miR-146a-3p miR-4435 miR-548n miR-548av-5p miR-181b-3p miR-487b-5p miR-330-5p miR-32-5p miR-2355-3p miR-107 miR-7-1-3p miR-744-3p miR-190a-5p miR-3173-5p miR-342-5p miR-6803-3p miR-1 miR-3960 miR-628-3p miR-146b-5p miR-106b-5p miR-6842-3p miR-98-3p miR-625-3p miR-132-3p miR-103a-2-5p miR-4742-3p miR-4433b-3p miR-885-5p miR-1185-5p miR-421 let-7d-5p miR-548w miR-27b-3p miR-25-5p miR-543 miR-6511a-5p miR-576-5p miR-431-5p miR-3074-5p miR-1304-3p miRNA miR-7641 miR-378c miR-6511b-3p miR-1287-5p miR-301b miR-493-5p miR-497-5p miR-337-5p miR-200b-3p miR-29b-1-5p miR-505-5p miR-548ag miR-544a miR-382-5p miR-548h-5p miR-3140-3p miR-556-3p miR-4454 miR-574-5p miR-4659a-3p miR-4433b-5p miR-942-5p miR-509-3p miR-378f miR-376b-5p miR-376c-5p miR-19a-3p miR-128-3p miR-651-5p miR-576-3p miR-139-3p miR-127-3p miR-589-3p miR-3913-5p miR-542-3p miR-7110-3p miR-1226-3p miR-664a-5p miR-1306-3p miR-323b-3p miR-877-5p miR-548an miR-335-3p miR-23c miR-127-5p miR-548ak miR-708-5p miR-132-5p miR-136-3p miR-758-3p miR-664b-5p miR-485-3p miR-584-5p miR-30b-5p miR-505-3p miR-548av-3p miR-654-3p miR-4659b-3p miR-23a-3p miR-145-3p miR-548ae miR-196b-5p miR-5584-5p miR-548aa miR-345-5p miR-500a-3p let-7f-1-3p miR-548t-3p miR-205-5p miR-451a miR-329-5p let-7i-3p miR-206 miR-153-3p miR-146a-3p miR-450a-5p miR-18a-3p miR-652-5p let-7d-5p miR-619-5p miR-93-3p miR-425-3p miR-548q miR-874-3p miR-106b-3p miR-193a-5p miR-191-5p miR-342-5p miR-548am-c-o-5p miR-412-5p miR-107 miR-548aj-5p miR-548au-5p miR-374b-3p miR-4301 miR-548g-5p miR-551a miR-539-5p miR-873-5p miR-548x-5p miR-218-5p miR-199a-3p miR-1268a miR-624-5p miR-542-5p miR-199b-3p miR-337-3p miR-2355-3p let-7b-3p miR-550a-5p miR-329-3p miR-4433b-3p miR-151a-5p miR-142-3p miR-1273g-3p miR-2355-5p miR-151b miR-140-3p miR-548w miR-210-3p miR-223-3p miR-425-5p miR-3620-5p miR-99b-3p miR-374a-3p miR-32-5p miR-127-3p miR-3688-3p miR-130a-3p miR-501-3p miR-548at-5p let-7i-5p miR-375 miR-125a-3p miR-452-5p miR-4507 miR-1303 miR-362-5p miR-381-3p miR-744-3p miR-148a-5p miR-4732-3p miR-378f miR-548ay-5p miR-548u miR-197-3p miR-128-3p miR-99b-5p miR-10b-5p miR-23b-5p miR-125a-5p miR-15a-5p miR-17-5p miR-335-5p miR-1226-3p miR-132-5p miR-618 miR-1180-3p miR-584-3p miR-1287-5p miR-25-3p miR-26a-5p miR-548av-3p miR-221-3p miR-3615 miR-641 miR-3179 miR-376a-5p miR-625-3p miR-423-3p miR-548y miR-548e-5p miR-154-3p miR-548aq-3p miR-548f-5p miR-95-3p miR-26b-5p miR-5010-3p miR-98-3p miR-21-3p miR-378a-5p miR-486-5p miR-3143 miR-589-3p miR-550a-3p let-7d-3p miR-3140-3p miR-4772-3p miR-590-5p miR-495-3p miR-3960 miR-30e-5p miR-941 miR-3679-5p miR-487b-5p miR-138-5p miR-616-3p miR-769-3p miR-495-5p let-7e-5p miR-331-3p miR-3613-5p miR-145-3p miR-548h-3p miR-10a-3p miR-320c miR-15b-3p miR-548z miR-365a-3p miR-301a-3p miR-548i miR-1273d miR-365b-3p miR-4685-3p miR-485-5p miR-181c-3p miR-502-3p miR-532-5p miR-4659a-3p miR-130b-5p miR-340-5p miR-2115-3p miR-140-5p miR-421 miR-487a-3p miR-320b miR-15b-5p miR-6859-3p miR-374a-5p miR-769-5p miR-582-3p miR-6511a-3p miR-224-5p miR-148b-3p miR-550a-3-5p miR-532-3p miR-590-3p miR-340-3p miR-183-5p miR-629-5p miR-324-3p miR-200b-3p miR-361-3p miR-6511b-5p miR-548ab miR-323a-3p miR-33a-5p miR-877-3p miR-18a-5p miR-181d-5p miR-545-3p miR-505-3p miR-548h-5p miR-1273h-5p miR-150-3p miR-3161 let-7f-2-3p	0.245 0.327 -0.278 0.111 0.107 0.065 0.034 0.333 0.215 0.114 -0.202 0.180 0.056 0.010 -0.395 0.061 -0.018 -0.669 -0.423 0.179 0.140 -0.280 0.269 -0.359 0.470 0.140 0.207 -0.218 -0.316 -0.186 0.235 0.286 0.278 0.567 -0.111 0.466 -0.286 -0.227 -0.498 -0.306 -1.266 -0.265 -0.360 -0.350 0.116 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0.195 0.248 0.169 0.198 0.198 -0.313 0.331 -0.175 0.506 -0.405 0.273 -0.199 -0.337 -0.180 -0.399 0.329 0.371 -0.346 -0.439 -0.323 -0.227 -0.127 -0.478 -0.560 -0.442 -0.356 -0.430 0.128 -0.330 -0.101 0.260 0.209 0.196 0.245 1.024 -0.360 -0.155 0.217 -0.185 0.382 -0.485 0.454 0.165 -0.196 2.620 -0.289 -0.265 -0.302 0.198 -0.151 -0.277 0.174 -0.078 -0.210 -0.556 -0.167 -0.296 0.175 -0.263 0.090 -0.350 -0.248 -0.275 0.217 0.080 -0.157 0.136 -0.265 0.166 0.133 0.102 -0.142 0.095 -0.140 -0.272 -0.179 0.160 -0.236 0.149 -0.272 -0.205 -0.213 0.260 -0.187 -0.235 0.064 -0.212 -0.158 -0.148 0.114 0.103 0.295 -0.334 -0.198 -0.665 0.150 1.007 0.117 0.233 -0.061 -0.111 0.685 0.171 -0.276 -0.400 0.089 0.240 0.183 -0.189 -0.269 -0.455 0.099 -0.183 -0.157 -0.152 0.163 -0.066 -0.170 -0.106 -0.089 0.711 0.182 -0.256 -0.216 -1.238 -0.229 -0.229 0.207 -0.112 -0.231 -0.126 0.105 0.073 -0.657 -0.270 -0.214 0.167 0.117 -0.128 1.128 -0.123 -0.174 -0.149 0.153 0.119 0.111 0.103 -0.048 -0.168 -0.097 -0.073 0.123 -0.113 0.259 0.149 0.130 0.068 0.206 -0.143 0.129 -0.150 0.116 0.073 0.390 0.097 0.070 0.080 0.060 0.565 -0.119 0.073 0.064 -0.159 -0.121 -0.036 0.084 -0.050 -0.041 0.078 -0.132 0.077 -0.137 0.074 -0.148 -0.163 -0.367 0.053 0.070 0.097 -0.061 -0.108 -0.098 -0.048 0.126 0.068 -0.137 -0.203 0.090 0.021 0.036 0.003 0.000 -0.435 0.096 -0.091 -0.290 -0.101 -0.035 -0.211 log-fold change -0.638 -0.065 -0.023 0.033 0.333 0.103 -0.022 -0.074 -0.036 -0.050 0.056 -0.063 -0.137 0.070 0.110 -0.044 0.056 0.065 -0.198 0.047 0.064 -0.022 -0.079 -0.037 0.094 0.091 0.044 0.019 0.044 0.024 -0.064 -0.047 0.024 0.029 -0.052 0.063 0.043 -0.033 0.035 -0.044 0.019 0.077 -0.030 0.021 0.040 0.119 0.090 0.019 0.052 0.036 -0.031 0.047 -0.020 0.040 -0.010 -0.055 0.029 0.017 0.010 0.024 0.165 0.190 0.087 0.043 -0.528 -0.248 -0.111 0.043 -1.421 -0.491 -0.118 -0.024 -2.218 0.543 0.456 -0.040 -0.586 0.405 -0.054 -0.136 -0.426 -0.182 -0.092 -0.015 -0.477 -0.874 -0.037 0.021 0.497 1.044 -0.037 0.066 0.480 0.372 -0.249 0.066 -1.098 0.706 0.149 0.066 -1.849 0.283 0.398 -0.031 -0.709 0.283 0.121 -0.012 -0.484 -0.401 -0.081 0.037 0.394 0.288 -0.663 0.025 0.389 -0.166 0.143 -0.013 -0.300 -0.210 -0.312 -0.021 0.627 0.365 -0.079 -0.018 0.399 -0.470 0.101 -0.008 -1.131 -0.437 -0.092 -0.108 -1.405 -0.275 0.081 0.023 -0.428 -0.921 -0.058 0.012 1.339 -0.280 0.031 -0.014 -0.741 -0.398 -0.079 -0.011 0.416 0.338 -0.076 0.009 -1.220 -0.775 0.064 0.008 -0.293 0.175 0.152 0.006 -0.822 0.209 0.136 -0.019 -0.593 -0.376 0.353 -0.015 0.944 0.374 -0.235 0.011 0.296 -0.214 0.084 0.009 -0.478 -0.676 -0.055 -0.004 -0.674 -0.182 -0.055 0.013 0.686 0.483 -0.260 0.003 -0.475 -0.380 -0.078 -0.007 -0.532 -0.392 -0.079 0.005 0.332 -0.498 -0.065 0.009 0.428 -0.521 -0.056 0.009 -0.696 0.225 -0.182 -0.027 -0.696 -0.560 -0.093 0.002 -0.330 -0.246 -0.054 -0.002 0.441 -0.721 0.034 -0.001 0.824 -0.524 0.043 -0.005 0.488 -0.123 -0.088 -0.001 -0.706 0.301 -0.436 -0.462 0.630 0.235 -0.695 -0.370 -0.176 -0.033 -0.176 -0.173 1.175 -0.055 0.358 0.105 0.197 -0.035 0.383 -0.022 0.779 -0.060 -0.511 -0.240 -0.001	8.080 1.691 1.164 2.287 5.333 9.252 4.607 5.273 2.583 5.310 7.345 0.399 3.046 1.158 12.514 0.495 5.403 6.935 9.739 7.108 9.599 5.289 1.500 0.220 2.288 4.976 6.745 13.185 3.394 5.377 1.378 0.672 6.770 2.730 3.616 2.346 5.810 11.459 0.885 9.739 -0.318 9.450 0.310 -0.095 12.565 1.936 9.472 9.528 13.403 2.194 1.960 12.885 8.568 2.770 10.256 0.530 6.333 1.823 6.590 4.919 1.815 1.671 1.965 1.653 3.947 3.881 6.419 3.330 1.210 10.854 5.014 7.212 -0.138 3.783 2.370 0.393 0.000 8.603 0.413 6.743 0.543 5.037 2.108 3.254 3.534 5.293 -0.392 10.665 12.294 5.217 4.394 1.314 8.438 5.028 4.336 0.544 7.658 0.116 5.298 7.651 4.261 2.457 6.368 4.162 0.104 1.602 -0.112 1.716 1.088 8.553 5.537 5.197 7.345 2.448 9.211 3.243 1.523 9.773 11.513 3.953 1.474 11.047 1.254 4.195 0.096 2.724 0.384 12.514 1.235 8.897 1.394 0.931 0.292 7.345 2.746 2.363 5.867 1.838 9.506 -0.009 3.504 3.558 1.310 1.601 1.011 10.786 10.786 1.699 8.383 8.523 1.909 6.970 6.414 0.571 8.603 6.770 3.232 7.986 2.108 6.560 9.087 0.143 1.482 -0.270 3.086 3.705 2.220 3.284 0.457 11.202 1.385 0.779 1.003 5.310 3.684 1.371 4.677 10.427 7.059 3.780 2.682 2.773 0.831 0.873 0.669 8.853 4.151 4.151 5.676 -0.303 0.123 8.937 8.937 10.980 0.555 11.962 15.095 2.398 2.923 1.849 5.661 3.127 0.270 3.079 7.507 -0.186 1.024 1.358 12.138 3.631 4.458 3.069 1.654 2.393 14.568 0.853 0.193 0.762 7.329 5.839 2.583 4.766 1.946 1.073 3.358 0.012 5.402 3.655 0.247 3.805 0.124 6.719 7.739 1.761 1.877 0.442 1.104 12.787 2.351 -0.457 4.211 1.446 1.579 2.828 0.162 9.811 5.546 1.192 8.698 7.461 0.765 7.593 0.223 2.300 3.800 7.504 7.504 7.504 2.420 0.690 5.620 3.448 7.850 4.532 0.484 1.832 11.785 6.656 4.372 4.524 1.296 3.538 10.597 2.671 3.999 7.929 5.831 3.338 6.606 14.192 5.571 0.231 7.576 0.674 2.582 5.896 1.499 1.306 3.907 3.959 3.376 3.399 2.407 1.605 5.645 8.427 6.300 4.223 0.435 11.183 3.944 6.068 4.401 6.920 2.532 11.296 3.928 5.498 2.775 11.153 3.267 5.199 3.856 7.337 3.492 2.800 8.594 0.291 5.499 3.623 0.924 0.211 0.552 2.024 7.210 10.272 7.109 3.864 7.959 0.811 9.487 15.391 0.142 14.249 1.586 10.283 8.563 -0.221 12.934 7.854 2.150 -0.272 0.849 5.773 5.861 -0.564 10.002 -0.153 1.880 2.384 2.709 4.359 10.227 12.655 3.467 7.007 3.046 10.346 3.973 11.573 1.820 2.573 3.582 11.596 2.514 12.424 3.203 9.159 0.495 0.669 4.654 1.038 0.527 0.527 2.615 2.442 2.291 2.543 1.874 4.607 1.907 2.783 1.500 11.573 2.985 12.514 -0.318 2.800 1.970 3.705 1.358 5.310 9.472 11.596 2.162 1.173 2.682 4.162 2.078 0.142 0.606 0.669 7.929 -0.321 7.345 1.104 -0.138 1.306 1.358 5.628 2.363 14.649 2.270 6.970 1.579 6.368 4.195 16.225 2.783 5.293 6.606 3.973 7.108 8.427 11.183 1.801 5.658 1.965 6.995 0.933 10.901 -0.148 4.372 0.528 0.223 9.599 6.590 1.499 12.934 5.499 4.458 2.841 17.408 8.603 5.538 12.787 9.249 4.398 7.854 0.526 2.987 7.850 3.800 3.623 4.654 2.206 10.665 0.012 11.621 10.655 6.656 0.571 3.582 2.709 9.475 3.026 1.605 6.770 -0.037 10.272 8.563 12.138 0.677 1.210 11.309 4.677 1.899 9.735 9.773 1.457 2.226 0.746 1.446 4.252 15.095 10.913 5.430 4.766 0.811 0.162 3.684 0.674 3.399 1.948 14.295 13.677 7.337 3.394 14.473 5.028 2.287 7.461 7.210 15.594 15.594 0.543 -0.153 1.960 1.473 1.296 0.143 8.044 3.748 3.945 0.211 2.220 0.651 3.086 0.193 2.746 1.689 7.682 0.295 3.999 2.300 5.298 0.885 1.385 0.291 3.267 0.442 3.953 4.865 3.069 -0.544 9.472 0.691 9.484 12.885 0.530 3.243 1.169 5.414 1.523 5.510 5.014 4.223 0.116 1.235 5.620 -0.270 1.088 12.655 4.210 2.905 0.270 3.338 0.247 10.256 3.864 0.104 0.220 9.506 1.909 10.980 9.211 3.947 7.329 11.057 5.403 11.513 2.457 5.063 1.919 1.838 0.404 0.831 2.108 7.059 1.310 2.048 4.497 3.330 7.576 2.770 12.424 9.739 7.556 5.648 5.062 1.922 3.448 4.532 2.671 4.630 6.920 1.601 11.296 5.292 3.780 8.817 0.167 0.690 1.699 1.205 9.087 4.919 5.491 1.003 1.352 2.532 9.528 8.568 5.645 9.739 0.765 6.419 2.724 10.817 3.215 1.192 0.310 2.765 0.853 11.153 10.786 10.786 5.197 3.907 10.374 10.854 3.783 5.645 3.542 1.874 3.881 9.208 5.867 1.394 8.523 5.945 3.495 2.442 0.231 9.141 7.793 7.865 5.454 0.544 0.968 6.745 2.468 4.565 8.438 0.393 5.857 1.443 -0.272 3.003 1.468 1.936 7.187 3.376 3.024 1.653 13.185 12.294 0.924 2.291 1.570 4.212 1.444 0.083 0.849 3.616 5.289 3.959 10.028 2.452 -0.457 5.571 1.011 3.944 -0.112 8.318 3.467 5.661 9.310 4.658 1.371 0.779 8.394 3.504 -0.340 1.378 2.351 0.498 2.420 5.199 10.427 5.896 10.051 2.573 3.379 1.038 6.414 7.576 1.854 3.254 log-counts per 5.298 5.108 2.078 3.050 million 1.088 5.498 1.523 3.963 2.188 0.138 3.558 6.333 0.669 8.383 6.935 0.699 1.254 9.159 14.568 0.124 7.929 4.976 2.288 1.820 1.487 1.518 5.810 10.346 1.314 5.501 4.524 7.007 1.137 2.448 2.341 -0.067 1.073 5.295 2.215 4.932 2.378 1.688 5.333 0.292 1.654 2.923 2.615 0.644 3.534 1.734 1.474 5.831 9.252 7.651 4.359 3.903 7.125 0.948 11.962 1.763 3.953 5.658 0.672 10.786 Presymptomatic vs. Patient 4.607 4.766 0.491 10.786 2.270 11.183 2.442 2.370 1.104 0.310 2.291 0.853 2.363 1.446 10.427 11.202 4.195 6.606 4.336 1.960 6.995 3.243 12.565 3.944 14.295 2.206 10.028 7.504 14.473 1.235 2.532 7.504 -0.321 0.162 1.352 7.504 1.231 15.594 3.542 0.116 1.306 15.594 2.407 3.959 1.579 1.899 5.645 2.351 11.573 13.185 11.153 0.457 11.596 9.249 5.896 2.393 14.649 10.913 3.024 2.773 2.987 5.289 7.007 1.689 7.658 2.746 4.401 13.259 2.730 0.692 2.671 1.601 4.458 2.457 5.537 -0.457 0.933 3.928 1.820 13.403 1.210 6.745 10.346 7.865 4.540 1.691 9.141 9.211 8.427 7.461 1.073 0.644 -0.303 2.024 0.426 3.050 10.901 15.391 3.903 11.513 5.538 1.385 -0.272 0.104 8.394 8.841 -0.318 1.838 0.419 9.450 0.762 2.108 12.787 1.408 0.779 3.394 2.841 14.249 -0.186 1.137 3.973 6.419 0.699 1.699 0.651 3.399 3.467 10.655 9.475 2.384 0.849 0.000 0.543 0.674 -0.270 9.484 4.398 9.506 1.763 8.937 1.500 8.553 5.648 8.937 4.151 5.377 3.203 4.498 4.151 0.669 3.492 3.538 3.800 5.499 0.124 5.414 6.743 0.295 6.331 5.199 0.530 8.897 9.487 0.719 7.594 4.654 1.716 4.865 7.850 9.436 2.398 2.783 6.770 3.655 5.546 6.404 4.372 2.188 6.970 1.158 4.211 4.078 1.653 2.150 7.212 3.947 1.909 4.359 6.935 11.451 1.296 -0.951 0.250	0.017 0.018 0.123 0.230 0.385 0.541 0.762 3.109E-05 0.081 0.209 0.381 0.564 0.749 0.987 0.040 0.645 0.890 0.019 0.022 0.026 0.028 0.029 0.030 0.031 0.032 0.033 0.033 0.033 0.034 0.034 0.035 0.037 0.038 0.038 0.041 0.041 0.043 0.047 0.047 0.048 0.055 0.056 0.056 0.057 0.057 0.057 0.059 0.060 0.060 0.060 0.061 0.061 0.061 0.061 0.063 0.063 0.063 0.064 0.064 0.065 0.065 0.065 0.066 0.068 0.068 0.070 0.073 0.074 0.074 0.074 0.075 0.076 0.079 0.080 0.081 0.081 0.082 0.123 0.127 0.128 0.129 0.129 0.130 0.133 0.133 0.135 0.140 0.141 0.141 0.142 0.145 0.145 0.147 0.150 0.150 0.151 0.154 0.156 0.157 0.159 0.160 0.161 0.161 0.162 0.162 0.162 0.163 0.164 0.164 0.164 0.165 0.165 0.166 0.169 0.169 0.169 0.171 0.172 0.174 0.175 0.176 0.177 0.178 0.178 0.178 0.179 0.181 0.182 0.183 0.184 0.184 0.186 0.187 0.187 0.188 0.189 0.190 0.190 0.230 0.232 0.233 0.234 0.234 0.235 0.245 0.245 0.245 0.247 0.247 0.251 0.251 0.253 0.255 0.257 0.262 0.262 0.262 0.263 0.263 0.265 0.266 0.273 0.274 0.279 0.280 0.280 0.280 0.282 0.283 0.284 0.284 0.285 0.286 0.288 0.288 0.288 0.296 0.299 0.299 0.302 0.303 0.308 0.312 0.313 0.315 0.317 0.318 0.318 0.318 0.320 0.320 0.321 0.321 0.326 0.326 0.328 0.330 0.332 0.334 0.390 0.391 0.392 0.393 0.394 0.394 0.395 0.397 0.398 0.402 0.402 0.403 0.408 0.411 0.414 0.415 0.415 0.417 0.419 0.420 0.423 0.424 0.425 0.426 0.426 0.427 0.428 0.428 0.434 0.434 0.439 0.442 0.443 0.444 0.445 0.446 0.448 0.449 0.452 0.452 0.455 0.455 0.455 0.456 0.457 0.458 0.459 0.461 0.462 0.462 0.462 0.466 0.466 0.469 0.470 0.470 0.472 0.472 0.472 0.477 0.478 0.547 0.551 0.551 0.554 0.556 0.558 0.559 0.560 0.561 0.563 0.567 0.567 0.572 0.574 0.574 0.575 0.585 0.589 0.591 0.591 0.592 0.592 0.593 0.594 0.597 0.601 0.609 0.612 0.614 0.615 0.616 0.616 0.620 0.621 0.625 0.629 0.631 0.632 0.632 0.635 0.640 0.640 0.641 0.647 0.650 0.652 0.654 0.657 0.658 0.661 0.661 0.663 0.663 0.666 0.674 0.674 0.675 0.676 0.676 0.677 0.678 0.764 0.767 0.767 0.768 0.769 0.773 0.773 0.774 0.775 0.781 0.785 0.787 0.787 0.788 0.790 0.793 0.794 0.795 0.800 0.801 0.809 0.809 0.812 0.815 0.815 0.817 0.818 0.824 0.827 0.829 0.833 0.834 0.834 0.834 0.838 0.842 0.846 0.849 0.849 0.850 0.850 0.851 0.853 0.855 0.863 0.866 0.866 0.868 0.868 0.868 0.868 0.870 0.870 0.870 0.874 0.877 0.877 0.881 0.884 0.884 0.887 7.141E-05 0.001 0.084 0.213 0.382 0.567 0.756 0.990 0.040 0.645 0.894 0.001 0.084 0.214 0.383 0.568 0.757 0.992 0.040 0.646 0.895 0.001 0.002 0.002 0.002 0.003 0.006 0.006 0.007 0.007 0.008 0.008 0.008 0.009 0.009 0.011 0.011 0.012 0.012 0.013 0.013 0.014 0.016 0.016 0.017 0.017 0.018 0.021 0.022 0.023 0.024 0.024 0.026 0.028 0.028 0.028 0.029 0.029 0.029 0.030 0.032 0.032 0.033 0.034 0.036 0.036 0.037 0.038 0.040 0.040 0.042 0.043 0.044 0.045 0.045 0.047 0.047 0.048 0.049 0.086 0.087 0.089 0.090 0.093 0.093 0.096 0.096 0.097 0.097 0.098 0.098 0.101 0.101 0.102 0.103 0.103 0.103 0.105 0.107 0.109 0.109 0.111 0.113 0.114 0.115 0.117 0.117 0.119 0.119 0.119 0.119 0.121 0.122 0.122 0.124 0.126 0.127 0.128 0.131 0.134 0.134 0.136 0.136 0.136 0.136 0.136 0.137 0.141 0.141 0.142 0.143 0.147 0.147 0.148 0.149 0.151 0.153 0.154 0.214 0.214 0.215 0.216 0.217 0.218 0.219 0.219 0.220 0.221 0.221 0.222 0.222 0.222 0.223 0.227 0.228 0.229 0.229 0.237 0.243 0.243 0.246 0.248 0.249 0.249 0.249 0.250 0.254 0.254 0.260 0.261 0.264 0.264 0.266 0.267 0.267 0.268 0.270 0.272 0.274 0.278 0.280 0.281 0.283 0.283 0.285 0.286 0.286 0.288 0.288 0.288 0.290 0.291 0.297 0.300 0.300 0.303 0.305 0.384 0.385 0.387 0.392 0.394 0.397 0.398 0.398 0.401 0.407 0.408 0.408 0.411 0.418 0.420 0.421 0.430 0.434 0.436 0.439 0.440 0.442 0.444 0.445 0.445 0.448 0.448 0.449 0.449 0.449 0.450 0.451 0.455 0.461 0.463 0.466 0.466 0.467 0.471 0.473 0.473 0.473 0.474 0.479 0.480 0.481 0.481 0.481 0.481 0.481 0.482 0.482 0.485 0.486 0.487 0.489 0.490 0.490 0.493 0.572 0.573 0.575 0.577 0.581 0.582 0.583 0.584 0.585 0.588 0.589 0.589 0.590 0.590 0.590 0.591 0.592 0.595 0.595 0.596 0.597 0.597 0.602 0.602 0.603 0.604 0.605 0.608 0.608 0.610 0.613 0.614 0.615 0.617 0.620 0.621 0.628 0.631 0.632 0.633 0.634 0.638 0.642 0.644 0.647 0.647 0.647 0.648 0.649 0.650 0.652 0.658 0.659 0.660 0.660 0.661 0.662 0.664 0.670 0.762 0.764 0.764 0.994 0.998 0.041 0.648 0.895 0.042 0.650 0.897 0.767 p-value 0.042 0.655 0.898 0.770 0.043 0.660 0.900 0.772 0.772 0.773 0.774 0.775 0.776 0.778 0.780 0.780 0.781 0.783 0.786 0.795 0.797 0.807 0.808 0.811 0.812 0.816 0.817 0.818 0.819 0.821 0.822 0.824 0.827 0.834 0.835 0.839 0.840 0.840 0.842 0.846 0.846 0.847 0.858 0.859 0.867 0.881 0.882 0.883 0.886 0.887 0.888 0.889 0.889 0.892 0.893 0.893 0.900 0.901 0.904 0.905 0.905 0.043 0.662 0.900 3.610E-05 0.043 0.663 0.900 0.000 0.044 0.663 0.901 0.000 0.044 0.669 0.901 0.001 0.044 0.672 0.902 0.001 0.045 0.677 0.903 0.001 0.045 0.681 0.903 0.001 0.045 0.682 0.907 0.001 0.046 0.685 0.907 0.002 0.046 0.692 0.907 0.002 0.047 0.698 0.908 0.002 0.047 0.706 0.913 0.003 0.048 0.711 0.914 0.004 0.048 0.712 0.916 0.004 0.050 0.716 0.918 0.005 0.050 0.717 0.922 0.005 0.051 0.717 0.926 0.005 0.051 0.719 0.930 0.006 0.052 0.721 0.938 0.007 0.053 0.725 0.938 0.009 0.053 0.726 0.942 0.009 0.054 0.727 0.943 0.009 0.054 0.727 0.944 0.009 0.055 0.735 0.945 0.010 0.056 0.735 0.950 0.010 0.057 0.736 0.950 0.010 0.058 0.739 0.955 0.010 0.059 0.742 0.958 0.011 0.059 0.742 0.963 0.012 0.060 0.744 0.967 0.012 0.060 0.745 0.971 0.012 0.061 0.746 0.971 0.013 0.064 0.752 0.974 0.015 0.069 0.754 0.980 0.015 0.070 0.755 0.980 0.015 0.070 0.755 0.983 0.016 0.070 0.765 0.983 0.016 0.070 0.776 0.983 0.016 0.072 0.776 0.987 0.016 0.072 0.780 0.987 0.016 0.072 0.782 0.991 0.017 0.073 0.783 0.992 0.017 0.073 0.784 0.993 0.017 0.018 0.018 0.019 0.019 0.021 0.022 0.023 0.077 0.799 0.076 0.798 0.076 0.794 0.075 0.793 0.075 0.792 0.074 0.789 0.074 0.788 0.073 0.786 0.998	0.605 0.638 0.647 0.743 0.793 0.898 0.006 0.478 0.614 0.761 0.848 0.904 0.994 0.271 0.846 0.962 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.605 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.638 0.647 0.647 0.647 0.647 0.647 0.647 0.665 0.665 0.665 0.665 0.665 0.670 0.670 0.672 0.675 0.676 0.677 0.677 0.677 0.677 0.677 0.677 0.677 0.690 0.690 0.690 0.690 0.690 0.690 0.690 0.690 0.690 0.690 0.690 0.690 0.690 0.690 0.690 0.707 0.708 0.708 0.711 0.711 0.719 0.719 0.719 0.719 0.719 0.719 0.719 0.719 0.719 0.719 0.719 0.719 0.725 0.725 0.726 0.726 0.726 0.726 0.746 0.746 0.746 0.746 0.746 0.746 0.746 0.746 0.746 0.749 0.749 0.749 0.756 0.758 0.758 0.758 0.758 0.758 0.758 0.758 0.758 0.758 0.758 0.758 0.758 0.758 0.758 0.758 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.763 0.768 0.768 0.799 0.801 0.801 0.804 0.804 0.804 0.804 0.804 0.804 0.806 0.807 0.807 0.811 0.811 0.811 0.811 0.821 0.821 0.821 0.821 0.821 0.821 0.821 0.821 0.824 0.827 0.836 0.836 0.836 0.836 0.836 0.836 0.839 0.839 0.843 0.844 0.844 0.844 0.844 0.846 0.848 0.848 0.848 0.854 0.856 0.857 0.858 0.858 0.858 0.858 0.858 0.858 0.858 0.860 0.860 0.860 0.860 0.860 0.860 0.860 0.860 0.898 0.898 0.898 0.898 0.898 0.898 0.898 0.898 0.898 0.904 0.905 0.905 0.905 0.905 0.905 0.906 0.906 0.906 0.909 0.909 0.915 0.915 0.916 0.916 0.916 0.916 0.916 0.920 0.922 0.922 0.922 0.922 0.922 0.922 0.925 0.927 0.927 0.927 0.927 0.927 0.927 0.927 0.927 0.927 0.927 0.927 0.927 0.927 0.927 0.927 0.927 0.927 0.927 0.927 0.929 0.929 0.929 0.931 0.931 0.931 0.931 0.011 0.485 0.614 0.761 0.848 0.910 0.996 0.271 0.846 0.962 0.096 0.485 0.614 0.761 0.848 0.910 0.996 0.271 0.846 0.962 0.096 0.096 0.119 0.119 0.123 0.161 0.262 0.262 0.262 0.262 0.262 0.262 0.262 0.262 0.262 0.296 0.296 0.296 0.300 0.300 0.300 0.314 0.326 0.326 0.326 0.326 0.340 0.375 0.377 0.385 0.387 0.387 0.391 0.391 0.391 0.391 0.391 0.391 0.391 0.391 0.403 0.403 0.403 0.403 0.418 0.418 0.418 0.418 0.432 0.432 0.440 0.440 0.443 0.443 0.443 0.443 0.443 0.443 0.443 0.492 0.492 0.498 0.498 0.506 0.506 0.506 0.506 0.506 0.506 0.506 0.506 0.508 0.508 0.508 0.508 0.508 0.508 0.513 0.516 0.518 0.518 0.523 0.524 0.524 0.524 0.524 0.524 0.524 0.524 0.524 0.524 0.524 0.524 0.524 0.530 0.534 0.535 0.535 0.538 0.538 0.538 0.538 0.538 0.538 0.538 0.538 0.538 0.546 0.546 0.548 0.549 0.554 0.554 0.554 0.554 0.556 0.556 0.556 0.614 0.614 0.614 0.614 0.614 0.614 0.614 0.614 0.614 0.614 0.614 0.614 0.614 0.614 0.614 0.619 0.619 0.619 0.619 0.638 0.648 0.648 0.648 0.648 0.648 0.648 0.648 0.648 0.655 0.655 0.665 0.665 0.665 0.665 0.665 0.665 0.665 0.665 0.668 0.670 0.672 0.676 0.676 0.676 0.676 0.676 0.676 0.676 0.676 0.676 0.676 0.676 0.678 0.679 0.688 0.691 0.691 0.695 0.695 0.761 0.762 0.762 0.770 0.771 0.771 0.771 0.771 0.774 0.780 0.780 0.780 0.784 0.795 0.795 0.795 0.810 0.810 0.810 0.810 0.810 0.810 0.810 0.810 0.810 0.810 0.810 0.810 0.810 0.810 0.810 0.810 0.815 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.816 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.848 0.849 0.849 0.850 0.850 0.850 0.850 0.851 0.853 0.853 0.860 0.860 0.860 0.860 0.860 0.863 0.864 0.864 0.864 0.864 0.864 0.864 0.864 0.864 0.865 0.867 0.867 0.867 0.867 0.867 0.867 0.867 0.874 0.911 0.911 0.996 0.271 0.846 0.962 0.998 0.271 0.847 0.962 0.911 adjusted p-value 0.271 0.852 0.962 0.911 0.271 0.855 0.962 0.911 0.911 0.911 0.911 0.911 0.911 0.911 0.911 0.911 0.911 0.911 0.912 0.913 0.921 0.923 0.932 0.932 0.933 0.933 0.933 0.933 0.933 0.933 0.933 0.933 0.934 0.935 0.940 0.940 0.941 0.941 0.941 0.941 0.941 0.941 0.941 0.951 0.951 0.958 0.967 0.967 0.967 0.967 0.967 0.967 0.967 0.967 0.967 0.967 0.967 0.968 0.968 0.968 0.968 0.968 0.271 0.855 0.962 0.271 0.855 0.962 0.021 0.271 0.855 0.962 0.065 0.271 0.860 0.962 0.065 0.271 0.862 0.962 0.083 0.271 0.867 0.962 0.083 0.271 0.870 0.962 0.083 0.271 0.870 0.962 0.083 0.271 0.872 0.962 0.083 0.271 0.878 0.962 0.097 0.271 0.884 0.962 0.097 0.271 0.892 0.965 0.097 0.274 0.896 0.965 0.137 0.274 0.896 0.965 0.150 0.278 0.897 0.965 0.150 0.278 0.897 0.968 0.190 0.278 0.897 0.971 0.190 0.278 0.897 0.973 0.190 0.279 0.897 0.978 0.195 0.283 0.898 0.978 0.224 0.283 0.898 0.979 0.224 0.284 0.898 0.979 0.224 0.284 0.898 0.979 0.224 0.284 0.903 0.979 0.224 0.287 0.903 0.980 0.224 0.291 0.903 0.980 0.224 0.291 0.904 0.983 0.224 0.293 0.904 0.985 0.224 0.293 0.904 0.988 0.224 0.293 0.904 0.991 0.226 0.293 0.904 0.992 0.226 0.293 0.904 0.992 0.226 0.307 0.907 0.993 0.232 0.318 0.907 0.993 0.232 0.318 0.907 0.993 0.232 0.318 0.907 0.993 0.232 0.318 0.918 0.993 0.232 0.318 0.927 0.993 0.232 0.318 0.927 0.994 0.232 0.318 0.929 0.994 0.232 0.318 0.929 0.995 0.232 0.318 0.929 0.995 0.232 0.318 0.929 0.995 0.232 0.240 0.319 0.932 0.240 0.318 0.932 0.240 0.318 0.930 0.237 0.318 0.930 0.237 0.318 0.930 0.232 0.318 0.929 0.232 0.318 0.929 0.232 0.318 0.929 0.998

Table A3 . List of the putative target genes

 A3

Table A3 . List of the putative target genes for each miRNA from our signature (miR-34a-5p, miR-345-5p, miR-200c-3p, miR-10a-3p), as indicated by DIANA-miRPath v.3 using TarBase (experimentally supported approach).

 A3

	OAZ3 TMED8 TUBGCP3 FKBP3	CNOT4 NXPH4 TRIM37 CFL1	AREG GPSM1 PDXK CD151	MDH2 DNF15S2(h sa) TSH2B(hsa) ZNF575	H4/N(hsa) C3orf33 PABPC1L PAQR7	MYL9 C6orf106 KE04(hsa) ABHD14A	RP5-824I19__A.1-002(hsa) C3orf58 FBXW4 NFE2L1	hCG_33495 (hsa) RNF182 NANP NFIA	RFC3 MBD5 FKBP1B KPNB1
	FAM155B RNF11 SMAP2 HSD17B6 PSD4 LACS(hsa) SPARC	STK4 ARTN LZIC STXBP6 COL12A1 UCK2 IQGAP3	C7orf50 GOT1 ADO WNK2 UBA52 FNBP1L LRP11	BCAS4 RSRP1 ZNF107 ACTA2 ADIPOR2 EPC1 PAST(hsa)	PGM2L1 CCAR1 RIF1 SF3B3 AK6 CDKN1C JUN	CENPO CTBS CDC42EP4 FNBP3(hsa) PDE12 MOGS TTK	ARSG DKFZp686L08115(hsa ) MDM4 LARS NEDD4 SLC2A4RG NDUFB2	UBR7 TPP1 AP3S2 EIF4G3 KLRD1 TMEM30A NP95(hsa)	PRO0992(hsa) HSPB11 NUCB1 RBAF600(hsa ) TMPPE CAPN5 MYRF
	BRAF DHRS7B PHF7	VTA1 CDC47(h sa) FKSG27( hsa)	LACS2(hs a) CDCA4 ARSB	USP54 RHPN1 FAM60A	WDHD1 TYMS MCM3AP	MTPN LYSMD2 AAMP	PPP1R12C RTCA CNPPD1	ACSM3 ASTN2 SRSF11	SPT5(hsa) PATL1 BRWD1
	FHL2 MPC1 CRTC3 UGCG	DNASE1 L1 APLNR ERO1L ARF3	FAM69A PRKRIR C19orf60 SLC1A1	SLC35A4 PAX8 RAB17 CAPRIN1	CENPK TMED1 ICA1 KIAA1521( hsa)	STX17 XRCC3 ZMYM2 ERCC1	PM227(hsa) KIAA0661(hsa) SEMA4B FOXJ3	ZHX2 BAG6 CDKN1A HIST2H2A C	FHIT ZCCHC17 MRNP41(hsa) CBX5
	HGSNAT MEN1 LPPR2 POU2F1 SCYL3 SAR1A	RP11-835E18.2 DCTN5 CC2D1A MAP2K2 PLC1(hsa ) POLR3F	STK11 FRK NRSN2 PAPOLG REM2 HIST2H4 B	SNAPC2 RAP6(hsa) MTRF1L S100A11 H2AFD(hsa ) SLC3A2	MAL2 ANAPC4 ZDHHC2 DDX24 KCTD21 RPS12	SOCS5 APLP2 SAMD14 TFRC C19orf48 DIS3L2	CALD1 OGFRL1 ZNF414 ADNP MYO7B SFI1	ZO2(hsa) NAGPA ABHD12 RLF SGPP2 COBLL1	TMUB1 KLF4 RPL27A AIP LRRC40 STARD4
	SFT2D1 RNF44 GUCD1	c-met(hsa) MSJ1(hsa ) TS(hsa)	PPP3R1 MRPL2 SPT5H(hs a)	WDR62 CASKIN2 IER5	CSRP1 AGO1 FAM3A	CABLES1 DNAJA3 SUOX	LCLAT1 SSR3 MKI67	FUT10 BCLAF1 ATP2B4	RANBP9 FACL4(hsa) TTPAL
	ARHGAP1 BMI1 UBASH3B CTDSP1 STAT3 FASN CDH26 MOV10 DSCR4 TAGLN DHDH HYPJ(hsa) TMSB10 KDRF(hsa) RGP1 TGDS CDC6 HIST1H1A GPR63 SOX4 TRIM68 ACTB HID1 PDE4B MAN2A2 NA CTB-1144G6.4-H3FC(hsa) E2EPF(hsa) CDC46(hsa) ALR(hsa) 006(hsa) BLZF1 CDIP1 HPCAL1 NUFIP1 DCLRE1A FBXL3 PDGFRA PTPRK GOLPH3L VPS4A EAF1 ACAS2L(hs a) AFAR3(hsa ) TSPAN1 FBXO11 EFCAB14 HS1BP3 NOTUM ZDHHC18 TTC7B SNAP23 PYCR1 ARGLU1 PKIA DIP2A RTN4 SEMA3G C17orf85 DUSP4 PHLPP2 MTO1 PRO2286(h sa) PRKCQ PHKA2 SSX1 CHST12 FERMT2 ARPC5 PRLR ESYT1 ERLIN1 MPHOSPH 8 OAS3 ENPP3 EPB41L4A NEK3 FOXJ2 BSCL2 DTL TOX4 C15orf13(hs a) SDCCAG16 (hsa) RNF144A POMZP3 COPS7A DDAH2 LIG1 FARP1 CSNK2A2 PPP1CA E2F7 TRIB3 DHRS4-AS1 H3FI(hsa) HLN(hsa) ZSCAN5A DDAH(hsa) NBL1 CACNA1A SEC16A MSAP(hsa) PPP1R37 GMIP ELL2 TNK1 MIER1 EEF2K TMSA(hsa) SLC44A2 SLC2A14 FBXO5 SYNGR4 PFN1 SCLT1 KRT32 RNF114 C12orf73 RBM14 NSL1 SLC25A44 NA HSPA13 FCHO1 KLHL28 NDUFC1 CNOT6 ARL6IP5 HIPK2 TMEM63B DTYMK DPP3 TBCD SNRPD3 C18orf21 HIST1H3B SIN3B FAM195A A2LP(hsa) CASP8AP2 H3FM(hsa) ATG7 H4FN(hsa) TXNL1 .4-014(hsa) TRIM11 BPG296P20 XXbac-RHEBL1 TCF3 CWF19L1 NETO2 TROAP MYBL1 hsa) STAG3L3 ARG2 KIAA0765( AFAP1L2 KIAA0164( hsa) SCAMP2 PHF19 MLK4 UNRIP(hsa) SLC48A1 RAB3GAP1 NFIX H3FJ(hsa) ACKR3 KEL BAF155(hsa ) RNF183 TOB2 ZEB2 CDKN2C CRLF1 CDH2	TMEM33 POLI SLC25A4 6 SFMBT2 EMILIN3 RASA2 PPP2R3A TCF4 NUP153 RNF123 MIER2 NDUFAF 6 LRSAM1 ZNF76 SLC37A3 REEP6 ZNF775 MAP7D3 TLR1 MAP3K3 ZNF114 RBM45 ZNFX1 ARMC8 ARRDC4 MTERFD POLDIP2 SRL300(h sa) ELANH2( hsa) 2 VPS37D H2AFM(h UFC1 AP1G2 SIRT1 HOXB8 REXO1 TSPAN18 SLC35G1 MED14 sa) D3S48E(h sa) ETV6 PIGX FEN1 FUT3 THBS1 SOCS2 STOM TBC1D23 ADCY7 BCAT2 SNP70(hs a) COA1 XTP3TP A(hsa) RUNX1 MICAL1 LACS4(h sa) MMGT1 LIMD2 FGFR3 MORC2 FYCO1 CDC8(hsa ) FACL1(h sa) IPO9 GTF2H1 EIF4A3 INHBB ZNF521 BID NME4 H2AFC(h sa) STX5 CLIC4 GNAS EIF3H H2AFX LTN1 BTRC NPRL3 PCLO HIST3H2 A C1orf74 IP6K2 KLRC3 USP6NL FAM86C PRR3 CYR61 C1QTNF 6 MTMR6 1 ARF5 C19orf66 LPHN2 PTPRN2 CHD1 FAM195 B KIAA078 4(hsa) PTRF SPATA33 HIST1H2 AE SIRT6 LYRM5 CORO6 GTF3C4 GOSR1 TMPK(hs a) ANXA3 SLC25A1 5 REPS1 C16orf58 KCNE3 SCAF4 IP6K1 CRKL HIST1H1 C ATXN2L AK4 NAT8L C6orf136 HP1BP3 HN1 GPRC5C WHSC1L 1 ARHGAP 33 UVSSA SH3BP5 SSH1 CHST11 PITPNC1 sa) ALDH9(h NT5C2 3 GUCY1A 3D SPC25 OTX2 ANKRD1 HNRNPD RED(hsa) FAM111 B SRP54 RSP1(hsa ) CRK TAF9B RMI1 SLC25A3 PPME1 SEL1L3 RGS4 FBP11(hs a) TMOD2 HIST3H3 CLEC2D LEKR1 PTPN23 CCNA2 THAP9 CCNL2 NTE(hsa) ZNF10 BRPF3 ATE1 FAIM3 OBFC1 ACER3 CIB2 TMEM39 A HIST2H4 (hsa) MKL1 HDAC7 WARS ZMYND1 1 RCAN1 SIX3 LAMA5 HIST1H2 AM	HUWE1 MAP4 RDX MST1R PTPRM IFT122 HIST1H4 C FES ATP7A M7V1(hsa ) GYLTL1B MAP2K3 ETFDH FRS2 BIN1 TRIM7 HERC6 TOB1 GINS3 EXOSC10 HSD17B1 0 ZFP36 MMS19L( hsa) PHB CD40 LMNTD2 INPP5F KDELR3 SLC45A3 KLHL17 AP5Z1 ADAT2 PLIC2(hsa ) MYH9 HBP1 SYNJ2BP TCIRG1 FAM20A TPBG SPTB2(hs a) CAPZA2 CRYA1(h sa) XB130(hs a) ZNF764 RFX1 NBN CD3D IMMT DPYSL4 KRT15 WAC LIMCH1 BIRC5 TIAM2 TUBB GOLGA8 B KLF12 UTS2 MRPS12 MIEN1 IQGAP1 UBTD1 RANBP10 SLC31A1 CIT KIAA043 0 FBXO4 BCAM NRBP2 B3GALT4 RAD54B DMBT1 GAN C7orf43 SNX9 STXBP5 KIF2A PSME1 TRIP11 TRIM62 MACF1 ARHGAP 32 ZNF664 PTPN21 CCNF TPCN2 ASXL1 WNK1 ARFGAP3 TAF9 H2AFG(hs a) TIAF1 LOXL2 SPDEF MNT RUFY3 GMNN H4/G(hsa) SNTB2 PEX16 NRTN GSE1 MAPK8IP 1 TMTC3 ATPAF1 WIPF1 EME1 GTF3C1 BTBD19 GDF15 CLAPM1( hsa) PLEKHH3 TBC1D17 LTBP2 H2BFQ(hs a) CDCA8 MCM4 AC013268 .5.1 RBM23 SLC26A1 1 KNS1(hsa ) SETD3 DNAJC10 ORMDL3 RAF1 CPSF6 SLC17A5 TRPS1 CAT CANPL1( hsa) BCL2 NTN1 H4/A(hsa) LIN9 SRCAP ZNF706 IRGQ hsa) ANAPC8( KIAA043 2(hsa) ZNF507 ZSCAN22 ARID1B ERLIN2 NT5E D SEMA4F HIST2H3 CIDEC OPA3 TSGA10 NBR1 RPL37 82(hsa) CGI-DRAM1 CRTC1 ZNF501 C16orf46 EXD2 NT5(hsa) BCL11A VPS29 PBX1 1 SLC25A5 FTSJ1 ATG4D GPATCH 2 PMF1 FBRS NUP98 PIGM PHACTR4 CDC5L ECH1 VWCE CDA03(hs a)	HNRNPU TAB2 TGFB1 MTMR10 ARAF SERINC1 LIPT1 PP6R3(hsa) CDABP013 1(hsa) LYPLAL1 PDS5B EMS1(hsa) ANO9 FXN TCF19 SAPL(hsa) ZNF789 DNAJC9 DBF4B CAPG DDX10 NDFIP1 UMK(hsa) MTFR1L SPIN4 APOO MARK3 FGFBP1 KIAA1109 PPP1R18 COQ9 RPIA RSPH3 ZBTB20 CCDC64 CASP2 ALKBH5 MYSM1 DCTN2 C17orf99 PNPLA8 GDIL(hsa) BLOC1S3 ZNF337 ABI2 TMEM256-PLSCR3 NFX1 SHISA4 H4FI(hsa) CREB3 SHMT2 EPAS1 DPM2 LACS1(hsa) KRTAP2-3 GLI4 UBE2I MAGED1 SLC16A4 GBF1 COX16 ZNF189 NAP1L5 IFNAR2 LBR SP2 CKAP5 CREM ZC3H3 ADAM19 VEPH1 GXYLT1 CORO1C hCG_41078 (hsa) CRAT C6orf141 WIZ RDH5 MAP3K11 TBC1D30 GPR183 MCM7 NREP C12orf10 KIF12 SNX15 HIP1R FBL KCNE2 PREB EZH2 CDK6 HRG SORBS1 TCP1 AP1M1 SLC35A2 NCL POC1A FKBP1A CHD4 CXCL2 HIST1H2B D NAPG SH3BGRL3 NAB1 RPLP1 USP30 RHEB TFAP2A ACAA2 DCAF10 SPTAN1 VPS37B SLC25A29 SP1507(hsa) FOXN2 FYN NQO1 UREB1(hsa KIAA1307( HACE1 PUS10 IGF2BP1 TRAM2 PDS5A MORF4L2 MAPK13 OSGEPL1 PIP5K1B ZNF746 PDIA6 ITGB5 FAM204A SNIP1 PPM1M PACS2 KBTBD6 hsa) ASF1B CENPL VASN ) MSN BMP8B PLAGL1 TMEM104 ERP44 HES1 FOXRED2 KIAA0101 MTMR12 ZNF304 EFNA5 ZDHHC19 PPIA ABHD2 TYRO3 ZNF707 SSX3 A HIST1H2B MBD1 SIRPA DST DENND1A TFPI SMARCD1 ARHGDIB KIAA1612( FAM222B hsa) ERGIC2 NOGO(hsa) C11orf57 IKZF1 C4orf46 MT1F	miR-34a-5p KIAA1897( hsa) MMP9 MOCS2 MRPS2 EBF3 PFDN1 HCST FAM20B GPR19 NCAPG GSN VIM DMD DSN1 NECAP1 CD86 TRERF1 NDUFV3 CRTAP UBE2Q1 SLN WASF2 PPP1R15B C1orf213 RAD2(hsa) PTGER2 ZFPL1 LZTFL1 MED16 ABCD1 GDA POLR1B CRTC2 BMP2K CSE1L ABHD4 FBXL6 PIG4(hsa) HOTAIR CARF(hsa) RBMS2 TRIM33 CNIH1 RNF34 GGA2 GDE1 PDLIM2 CREBZF WDR33 TGOLN2 NCEH1 AF131216.5 KANSL1 CNRIP1 RP11-664D7.4 TBC1D31 HIST2H2A IL1RN A3 TPM4 BCL7B BRCA1 DIAPH1 KIAA0010( hsa) CXCL11 NAV2 NFKBIA IL9R SLC39A9 CDK3 CMTM4 RNF145 MAPKAPK3 CLAPB1(hs a) FBXO10 AMZ2 CPT2 C19orf54 GTF3A AGBL5 CHST6 THEM4 MLST8 POP7 TMED10 USP2 STIL DLG4 MYO5A TMEM173 WDR45B PKP4 NENF DSCR3 MAPT BABAM1 MUC1 GOLGA7 PTP4A2 KBTBD4 FOXA1 TSKU CBLC CYB5M(hsa ) MYCBP FAM209B DAG1 GBX1 PEREC1(hsa) NCAPD2 RNGTT GPC3 FGD3 LAD1 COG5 LYPD8 PPP1R14C FAF2 CHERP CERS2 WDR73 ANP32A DLL1 MAP3K14 HIST1H3C BBIP1 BEGAIN FAM208B ASNA1 SMG9 AK3 TOP2A SF3A2 RFXAP RNF19A CCND1 HIST1H4H SYNGR2 SNCA H3FA(hsa) CENPB GGA3 TK1 LPCAT4 ZDHHC4 SLC9A1 HRIHFB2157( hsa) H2BFR(hsa) FAHD1 IMPAD1 SMKR1 MCM2 RNASEK SP4 KPNA1 CDH1 RCN(hsa) HIST2H2A A4 H3F2(hsa) MAN2B1 INTS9 RP11-452K12.5-ORMDL2 PARP6 CORO1B CLIP1 NME7 GPC1 ZNF263 JAK2 ARL6IP6 012(hsa) EHBP1 MECP2 RAB11FIP2 SLC25A39 LHPP PIM1 NOTCH1 EIF4G1 GRPEL2 PAFAH1B3 SMG6 MAPRE3 SYNC ZFP36L1 POU6F1 ARV1 NUP214 H3FB(hsa) CENPA TPA1(hsa) PITPNM3 IL17RB GPR143 MIA3 ILF3 MSANTD2 ZNF133 SAP130 TGFB1I1 SRPR C9orf72 ZNF692 CHKB KIAA1462 SELM H3FK(hsa) TNFAIP2 TTLL1 RP4-697E16.3-004(hsa) RBBP5 HIST1H3I PABPC1 PFN2 TBC1D25 ANK3 CORIN CROT PDGFRL PSD3 ARID1A LARP1 UBA1 C15orf65 PANK2 FH STRAP HRIHFB20 RSRC1 CCDC82 MLLT1 TMEM86B STK17B SLC7A8 HNRNPUL ITFG2 F8 RNASEH1 SMARCA1 TRIM59 MYPOP GNPI(hsa) KXD1 MB DUT H3.3A(hsa) ZBED3 FOXL2 PARD3B RER(hsa) IRF3 B4GALT1 RBM26 NAV1 C10orf69(hsa) DGKZ HNRNPM DMWD DOK7 CMIP NYMEL3(hsa) ARL15 PLIN3 TH1(hsa) SYT1 TSC2 VEGFC DPPA4 LGALS3 SLC16A14 JMJD1C LYPD6 BEND4 AFAR(hsa) SUPT5H NCOR2 SCNM1 ENO1 XYLT2 ZNF697 AP2A2 C5orf55 hCG_2023614 (hsa) C1RL ACSF2 ZNF395 HIST1H2BG NCAM2 CCDC116 PPP1R12A HCN3 PDLIM1 RBL1 2 FAM126A PIKFYVE CACNB1 DNAL1 HIRA RAP1GAP DNAJC15 UBL7 ME1 POLR2M FCER1G PRDX4 PRDX1 MYH14 VCP BTG2 LY6E SLC30A8 PRKACA SFXN1 ZNF496 TMEFF1 KIT POLR2A DENND6A 91(hsa) FAM49B PITPNM2 COL1A1 SMC4 DNM1 FAM89B CITED1 RNF106(hsa ) CALML4 RSU1 NIPA2 ZC4H2 PRRT1 LIME1 ARNTL2 CAPN12 DAMA-236L13.16-TSR2 MR1 004(hsa) FMNL2 CEMIP WBSCR27 CDYL FECH ZC3H12C GANAB H4/E(hsa) PLCG1 MED31 SRK(hsa) MCMBP	SLC36A1 RDRC(hsa) NF1 DKFZp434G1035(hsa) TSPAN14 PI2(hsa) JPH1 CSNK1G1 ADI1 POC5 GOT2 AMHR2 A2RP(hsa) PRSS1 STAR KIF11 TMEM219 NAA11 SWS1(hsa) FNIP1 TAF1A IGBP1 SLC39A6 IGF1 RRP1B ELMOD1 ERCC4 SYNJ2 LMAN1 CITED2 NLGN2 SERPINB1 BTF3L4 KAT2B PAXBP1 FZD1 FUT11 KLC2 LRRC8E KDELR1 NUDT22 KDSR STK25 SMIM13 APEX2 PROSC ACD PRKAR1A NPIPB4 MYT1 FAM45A PRKCB PEF1 KLC1 VMA21 P1725(hsa) SURF4 N4BP4(hsa) PSMD9 CDM(hsa) MED28 UBAP2 CDC73 KIAA0109(hsa) MRPL24 MYADM RBPMS PROSER1 EMR2 PPA2 MPI WIPF3 ZNF398 UNQ2441/PRO5003/P RO9924(hsa) SAP30L ANX4(hsa) TMEM106C PQLC3 SPRY1 PIG28(hsa) SKA1 YAF2 TNRC18 CSNK1E SLC39A13 RAD1 RNASEH2C DTNA HPRT(hsa) RP11-452K12.5-010(hsa) PPP6R2 WBSCR20(hsa) KIAA1914(hsa) PUS7 SLCO4A1 ABCC3 CDC23 DESI1 SA2(hsa) PTK6 HIBADH GTF2IRD2 TRPC4AP CYB561A3 MARK1 EP300 POM121 ST7 SHARPIN HNGS1(hsa) PYGO2 PLXNB1 SLC12A9 IGF2BP3 NFRKB ELAVL1 ZBTB21 CDC20 AP2A1 CCDC50 JADE2 RPS6KA3 C11orf54 KPNA3 SFR1 KIAA1846(hsa) RAB40C EMP2 DUSP3 RAI14 JAG1 FOXF1 PLD2 LAMC1 CCDC88A KLHDC8B MELK GGT1 SNCG PIGB NSMCE4A ILF2 PHKA1 H3FD(hsa) NMT1 FAM178B VSNL1 PSMC3IP TESK1 DUSP7 REPS2 G3(hsa) B4GALT3 STK16 KIAA1491(hsa) ARID5B FYB YBX1 EEF1G C10orf88 CDC21(hsa) ARHGAP29 YY1 MTERFD3 KIAA1228(hsa) DAMA-236L13.16-003(hsa) CXorf3(hsa) MIDN SERF2 SUN1 HSD11B2 ECD PEAR1	KIAA0899( hsa) SLC30A3 SEC22C SWT1 OGFOD1 IGSF3 XPO4 MAP2K1 PCIF1 NOL10 STMN1 ANKHD1-EIF4EBP3 LRRC46 SGPP1 HDAC8 FAM111A ZNRF2 WBSCR20 A(hsa) UHMK1 DCAF15 MAMLD1 SLC29A4 RHOF TEAD2 GNG4 ATP1A1 ZNF311 TYW5 PDZK1IP1 KIAA0004( hsa) H4/K(hsa) ZNF585A RALB BHLHB9 TNFAIP1 QDPR POFUT1 DISP1 ZWILCH SNF8 ST20 KIAA0922 DHX9 NAPEPLD ATG9A WTAP RPMS13(hs a) HSD17B12 NUCKS(hsa ) CPLX1 ANKRD28 PIG30(hsa) LINC00337 IRAK2 EIF2S2 CNOT6L TACSTD2 AS3MT SNX5 PCYOX1L TCEA2 DERL1 SYNE2 ALG13 SLC25A19 PRKCH ATP8B2 HHLA3 LMNA C3orf38 DADB-EHD1 KNSL1(hsa) NDC1 333F21.4-ZNF354A 002(hsa) FLOT2 SLC7A2 CANT1 C13orf10(hs a) MON1B SLC2A12 AUH CLSPN EIF4EBP2 LSM12 CLSTN1 CCDC74A TULP4 JRK INA SSBP3 USP3 CHKA RSBN1 FN1 ACSL1 CHTF8 MLXIP HIGD2A ARHGEF9 AMACR IFFO1 TNFRSF12 A R3HDM4 C20orf27 ZNF524 IFI35 KCNK6 PICH(hsa) ITPR3 KIAA0731( hsa) MPP2 SERF1A FAM198A ZNF573 H4FG(hsa) ZER1 CTTNBP2N APOL2 hCG_17321 C21orf59 AZIN2 (hsa) C2orf27A CRIPT C14orf182 TNC WDR4 H4FC(hsa) GSTT2B PRC1 GRAP2 MBNL1 L SEMA4D IRX3 EFTUD1 AKTIP SAMD5 C1orf109 ALDH9A1 AKR7A2 PKN2 KHDC1 EMILIN2 RAD54L2 NA PXT1 RAB22A ARF6 EPS15L1 RRP12 HMCES VWA1 SNAI3 SLC2A1 ANXA11 SPTA2(hsa) ULBP2 FBXO18 TRIM21 GATAD2B SGTA TP53INP2 SEPT2 DOCK9 UBN2 H4F2(hsa) AAK1 RRM2 TNRC6A VGLL4 IPO8 BCAN TRAPPC1	PCYOX1 FAM129A NLRP11 DHCR7 SYNGR1 DIAPH2 ENTPD4 RAD9A TRIM32 DNMBP PODXL SCML2 RAD17 IL6 CMPK(hsa) HIST1H2AB VPS72 MTCL1 HMGCL SERPINE1 HMMR DMKN LMF1 MXRA7 PPFIA1 RNF141 ZNF33A ALG3 TBL1XR1 OK/SW-cl.29(hsa) TRIB1 FANCB RTN4IP1 HIST1H3J NKIRAS2 VPS35 SMOX TAF3 NAA60 CTSH hCG_2005629 SCARB2 FAM73B DYNC1I2 TMEM79 ATP6V0A2 WDR83OS ATRN (hsa) H1F4(hsa) GAS1 KIF4(hsa) FABP3 ZNRF3 CRISPLD2 HIST2H3A MAPK1 ADTB2(hsa) CLTB AGTRAP PLAU DAN26(hsa) TAX1BP3 TRIM13 H4FM(hsa) ASB9 TMUB2 ASXL2 IMP3 SMYD5 ZNF623 AMER1 CIRBP RP11-511I2.1-GPS1 003(hsa) FNDC3A SDCBP A2D(hsa) CDK16 H3FT(hsa) SECISBP2L SLAIN2 MTFR2 LYPLA1 KIAA1279 ESAM PLOD1 LPCAT3 CYBRD1 MYBL2 KLHL18 PAX9 ANO7 KANK3 LRRC41 MGAT4A TGFBR2 RUFY1 SLC1A5 CYB5R3 SCN1B CCNG2 HELLS COLGALT1 TMEM143 PCBD1 KIAA0224(hs DADB-a) 70P7.10-SUCLG1 RNF168 TLN1 PRO1777(hsa) C1QL1 KNSTRN ERCC6L DKFZp686N2 TSPYL2 HBP(hsa) FGF7 H3.3B(hsa) TLDC1 GPR161 176(hsa) MPP3 H2BFH(hsa) RNASEH2B HSPC225(hsa) PCBP4 GCFC2 ATF7 NFATC1 VAT1 NEIL3 L3MBTL3 C14orf159 UGT8 HSPC075(hsa) TMPRSS4 (hsa) DNAJC2 hCG_1685949 002(hsa) C22orf29 70P7.10-TRIT1 RNF26 DADB-SLC4A11 UACA ZSCAN9 222(hsa) DKFZp586K2 RRAGC A2LG(hsa) SIX2 ZBTB25 DYRK1B USP22 KCNQ1 SPIRE2 ANKRD10 ITSN1 PDK2 HIST1H2AC C11orf80 PIDD1 TNPO3 RAD51AP1 ROR2 003(hsa) SNX17 ABTB2 ARHGEF26 SLC39A1 AKNA PTCD3 GTSE1 BTG1 SCAMP4 GMEB1
	HMG20B	GATA3	DAGLB	ALDH6A1	HPS1	KCTD7	FAM124A	TMEM201	STK40

Table A4 . Complete output from DIANA-miRPath v.3 Table A4. Complete output from DIANA-miRPath v.3 when using the miRNA signature (miR-34a-5p, miR- 345-5p, miR-200c-3p, miR-10a-3p) as input, from both approaches: MicroT-CDS (in silico miRNA target prediction algorithm) and TarBase (experimentally supported approach). The columns show the influenced KEGG pathways, p-values after Benjamini-Hochberg correction, number of targeted genes and number of associated miRNAs from the signature. Significant p-values are highlighted in bold.

 A4

	Viral carcinogenesis		0.004834794	74
	Bacterial invasion of epithelial cells		0.00518036	36
	Ubiquitin mediated proteolysis		0.006448268	22
	Melanoma		0.007508105	30
	Non-small cell lung cancer		0.007831112	26
	Fatty acid metabolism		0.008055732	14
	Steroid biosynthesis		0.008281795	1
	Other types of O-glycan biosynthesis		0.01126346	13
	Protein processing in endoplasmic reticulum	0.01302932	17
	Regulation of actin cytoskeleton		0.014233	71
	Glioma		0.01423311	26
	FoxO signaling pathway		0.01449044	12
	Thyroid hormone signaling pathway		0.01501771	44
	Endometrial cancer		0.01737389	22
	Small cell lung cancer		0.03340758	13
	HTLV-I infection		0.04337413	81
	Glycosphingolipid biosynthesis -lacto and neolacto series	0.04423077	9
	Shigellosis	KEGG pathway	p-value 0.05387291	#genes 29	#miRNAs
	DNA replication		0.08411226	17
	Renal cell carcinoma	MicroT-CDS	0.09253995	26
	Mucin type O-Glycan biosynthesis Ras signaling pathway		1.184275e-12 0.1004746	5 24
	Glycosphingolipid biosynthesis -lacto and neolacto series Central carbon metabolism in cancer	3.884874e-10 0.1017866	4 27
	Biotin metabolism PI3K-Akt signaling pathway		0.0006064075 0.1389146	1 29
	Proteoglycans in cancer MAPK signaling pathway		0.0007940732 0.140083	22 76
	ErbB signaling pathway Huntington's disease		0.0008384294 0.1587988	16 2
	MicroRNAs in cancer Wnt signaling pathway		0.00138567 0.1749672	20 14
	Thyroid hormone signaling pathway Alcoholism		0.002132528 0.2882482	16 60
	Phosphatidylinositol signaling system RNA transport		0.00647907 0.2884383	12 16
	Neurotrophin signaling pathway Neurotrophin signaling pathway		0.009801489 0.3112592	22 15
	Renal cell carcinoma Progesterone-mediated oocyte maturation	0.01097907 0.4438475	13 31
	Glycosaminoglycan biosynthesis -heparan sulfate / heparin Epithelial cell signaling in Helicobacter pylori infection	0.01113265 0.4488572	4 7
	Lysine degradation HIF-1 signaling pathway		0.01605634 0.5095467	5 35
	Hippo signaling pathway Adrenergic signaling in cardiomyocytes	0.04556428 0.52571	9
	Axon guidance		0.04746173	13
	TGF-beta signaling pathway		0.05008158	8
	Glioma		0.06553687	7
	Circadian rhythm		0.06948476	5
	Sphingolipid signaling pathway		0.07745415	12
	Glycosaminoglycan biosynthesis -chondroitin sulfate / dermatan sulfate	0.1093001	2
	Choline metabolism in cancer		0.1191737	12
	MAPK signaling pathway		0.1518697	28
	Sphingolipid metabolism		0.1821781	6
	ECM-receptor interaction		0.2101148	3
	FoxO signaling pathway		0.236839	17
	Oocyte meiosis		0.248682	12
	Glycosphingolipid biosynthesis -globo series	0.2859095	3
	Small cell lung cancer		0.3219701	12
	Carbohydrate digestion and absorption	0.3981785	1
	SNARE interactions in vesicular transport	0.4719691	5
	Base excision repair		0.5364044	1
	Inflammatory mediator regulation of TRP channels	0.5608684	9
		TarBase
	Pathways in cancer		7.806205e-09	141
	MicroRNAs in cancer		3.356072e-08	36
	Proteoglycans in cancer		4.259346e-08	82
	Adherens junction		4.849975e-08	41
	Colorectal cancer		6.624356e-06	37
	Fatty acid biosynthesis		1.571663e-05	4
	p53 signaling pathway		8.629925e-05	40
	Endocytosis		0.000155911	80
	Cell cycle		0.0001862279	52
	Chronic myeloid leukemia		0.0003003506	37
	Hippo signaling pathway		0.0005622035	26
	Metabolism of xenobiotics by cytochrome P450	0.0007820423	2
	Lysine degradation		0.0007881832	7
	TGF-beta signaling pathway		0.0009288388	13
	Prostate cancer		0.001594596	39
	Hepatitis B		0.002008518	54
	Oocyte meiosis		0.002446339	16
	Bladder cancer		0.002960341	22
	Thyroid cancer		0.003013095	16
	Pancreatic cancer		0.003759289	33
	Transcriptional misregulation in cancer	0.004338518	62

Table A5 . Comparison of studies investigating miRNAs from brain samples Table A5. Comparison of studies investigating miRNAs from brain samples of FTD and/or ALS patients.

 A5

		Kocerha et al. 2011[1]	Chen-Plotkin et al. 2012[2]	Hébert et al., 2013[3]	Gascon et al., 2014[4]	Helferich et al. 2018[5]	Jawaid et al. 2019[6]
	Disease	FTD	FTD	FTD	FTD	ALS	FTD, ALS
	Cohort	Mixed sporadic/genetic	Mixed sporadic/genetic	Not mentioned	Sporadic	Mixed sporadic/genetic	Sporadic
	Patients, n= Discovery/replication	32 PGRN+ 8 PGRN-	5 PGRN+ 7 PGRN-	5/14	5	5 C9orf72+ 1 SOD1+ 16 sporadic	10 ALS 9/12 FTD
	Methods of analysis	Microarray	Microarray	Deep sequencing, qRT-PCR	qRT-PCR	qRT-PCR	qRT-PCR
		miR-922
	Major deregulated miRNAs	miR-516a-3p miR-571 miR-548b-5p	miR-132 miR-212	miR-132-3p	miR-124	miR-1825	miR-183/96/182
		miR-548c-5p

). Adjusted P values lower than 0.05 are shown in bold.

	miR-22-3p miR-10a-3p miR-301a-3p Supplementary	-0.17 -0.31 0.54	2.17E-01 1.98E-01 2.24E-02	3.28E-01 3.71E-01 5.83E-02
	miR-106b-5p miR-22-3p miR-144-5p	-0.27 0.19 1.50	2.34E-01 2.00E-01 2.38E-02	3.45E-01 3.71E-01 5.91E-02
	miR-4649-5p miR-320c miR-9	0.35 -0.33 0.46	2.52E-01 2.47E-01 2.45E-02	3.61E-01 4.46E-01 5.91E-02
	miR-502-3p miR-345-5p miR-451	0.30 0.22 1.18	2.56E-01 3.25E-01 2.76E-02	3.61E-01 5.70E-01 6.40E-02
	miR-18b-5p miR-4649-5p miR-26b-5p	0.26 -0.60 0.33	2.84E-01 3.47E-01 4.62E-02	3.93E-01 5.91E-01 1.03E-01
	miR-129-1-3p miR-146a-5p miR-15a-5p	-0.72 0.28 0.46	3.13E-01 3.55E-01 4.75E-02	4.24E-01 5.91E-01 1.03E-01
	miR-181a-5p miR-335-5p miR-1246	0.16 0.19 -0.82	3.58E-01 3.70E-01 5.16E-02	4.74E-01 6.02E-01 1.06E-01
	miR-15a-5p miR-1 miR-192-5p	0.16 -0.37 0.59	4.72E-01 3.91E-01 5.33E-02	6.14E-01 6.20E-01 1.06E-01
	miR-106a-5p miR-26b-5p miR-133a	0.20 0.12 0.76	4.87E-01 4.43E-01 5.52E-02	6.21E-01 6.86E-01 1.06E-01
	miR-181b-5p miR-26a-5p miR-133a-3p	0.11 -0.16 0.76	5.06E-01 4.76E-01 5.57E-02	6.33E-01 7.20E-01 1.06E-01
	miR-133a miR-1246 miR-4649-5p	-0.23 0.28 -0.95	5.36E-01 4.94E-01 8.39E-02	6.49E-01 7.30E-01 1.55E-01
	miR-133a-3p miR-1234-3p miR-502-3p	-0.23 0.41 0.50	5.39E-01 5.06E-01 8.58E-02	6.49E-01 7.32E-01 1.55E-01
	miRNA miR-29c-3p miR-320b miR-92a-3p	log-fold change -0.13 -0.18 -0.42	P value 5.71E-01 5.27E-01 9.76E-02	adjusted P value 6.74E-01 7.45E-01 1.71E-01
	Controls vs. C9orf72 presymptomatic subjects miR-133b -0.20 miR-320a -0.11 miR-106a-5p 0.49	6.39E-01 6.05E-01 1.02E-01	7.41E-01 8.36E-01 1.74E-01
	miR-34a-5p miR-4745-5p miR-9 miR-1234-3p	-1.58 -0.28 0.09 -0.99	3.72E-10 7.24E-01 6.40E-01 1.12E-01	2.42E-08 8.24E-01 8.66E-01 1.86E-01
	miR-338-3p miR-19a-3p let-7b-5p miR-26a-5p	-0.79 -0.12 -0.07 0.33	3.48E-04 7.35E-01 6.76E-01 1.38E-01	9.53E-03 8.24E-01 8.78E-01 2.22E-01
	miR-142-3p miR-663a miR-181b-5p let-7g-5p	-0.82 -0.10 0.07 0.23	4.90E-04 7.51E-01 6.77E-01 1.40E-01	9.53E-03 8.28E-01 8.78E-01 2.22E-01
	miR-320a miR-1915-3p miR-223-3p miR-4745-5p	0.74 0.85 0.12 -0.95	5.87E-04 9.10E-01 6.89E-01 1.44E-01	9.53E-03 9.86E-01 8.78E-01 2.23E-01
	miR-145-5p miR-335-5p miR-127-3p miR-423-3p	-0.94 -0.02 -0.11 0.31	2.29E-03 9.29E-01 7.08E-01 1.58E-01	2.53E-02 9.90E-01 8.85E-01 2.39E-01
	miR-92a-3p miR-4530 miR-301a-3p miR-18b-5p	0.75 0.00 0.08 0.36	2.63E-03 1.00E+00 7.33E-01 1.68E-01	2.53E-02 1.00E+00 8.99E-01 2.43E-01
	let-7g-5p miR-1825 miR-145-5p let-7e	-0.46 0.00 0.10 -0.38	2.73E-03 1.00E+00 7.48E-01 1.71E-01	2.53E-02 1.00E+00 9.01E-01 2.43E-01
	miR-199a-5p miR-4299 miR-423-3p miR-129-1-3p	-1.13 0.00 -0.06 -0.83	3.62E-03 1.00E+00 7.90E-01 1.72E-01	2.62E-02 1.00E+00 9.33E-01 2.43E-01
	miR-206 miR-3665 miR-181a-5p miR-7	2.04 0.00 0.04 0.26	3.62E-03 1.00E+00 8.21E-01 1.86E-01	2.62E-02 1.00E+00 9.53E-01 2.57E-01
	miR-30b-5p Controls vs. C9orf72 patients -1.17 miR-142-3p -0.03 miR-151a-5p 0.21	4.45E-03 8.89E-01 3.40E-01	2.89E-02 9.97E-01 4.53E-01
	miR-191-5p miR-34a-5p miR-338-3p miR-335-5p	-0.44 -1.49 -0.03 0.21	5.34E-03 7.78E-08 8.90E-01 3.41E-01	3.00E-02 5.06E-06 9.97E-01 4.53E-01
	miR-27a miR-451 miR-7 miR-127-3p	-0.89 2.20 -0.01 0.26	5.53E-03 2.87E-05 9.42E-01 3.94E-01	3.00E-02 6.80E-04 1.00E+00 5.12E-01
	miR-320b miR-194-5p miR-663a miR-191-5p	0.76 1.55 -0.18 0.10	7.88E-03 3.21E-05 9.51E-01 5.21E-01	3.94E-02 6.80E-04 1.00E+00 6.46E-01
	miR-143-3p miR-144-5p miR-143-3p miR-181a-5p	-0.67 2.68 0.01 -0.12	9.46E-03 4.19E-05 9.56E-01 5.22E-01	4.22E-02 6.80E-04 1.00E+00 6.46E-01
	miR-1246 miR-29b-3p miR-4530 miR-144-3p	1.10 1.01 0.00 -0.22	9.73E-03 2.86E-04 1.00E+00 5.27E-01	4.22E-02 3.72E-03 1.00E+00 6.46E-01
	miR-223-3p miR-29c-3p miR-1825 miR-1	-0.70 0.80 0.00 0.20	1.08E-02 6.61E-04 1.00E+00 6.86E-01	4.38E-02 7.16E-03 1.00E+00 8.21E-01
	miR-144-3p miR-192-5p miR-4299 miR-34a-5p	0.87 0.99 0.00 0.09	1.17E-02 9.39E-04 1.00E+00 6.95E-01	4.46E-02 8.72E-03 1.00E+00 8.21E-01
	let-7b-5p miR-19a-3p miR-3665 miR-425-5p	0.39 1.25 0.00 -0.05	1.52E-02 1.32E-03 1.00E+00 7.35E-01	5.50E-02 1.08E-02 1.00E+00 8.54E-01
	miR-151a-5p miR-502-3p C9orf72 presymptomatic subjects vs. C9orf72 patients -0.50 0.80 miR-10a-3p 0.06	1.83E-02 4.84E-03 7.93E-01	6.25E-02 3.50E-02 9.04E-01
	miR-1234-3p miR-15a-5p miR-206 miR-181b-5p	1.40 0.62 -3.85 -0.04	2.01E-02 6.70E-03 2.35E-06 8.34E-01	6.52E-02 4.36E-02 9.04E-05 9.35E-01
	miR-26a-5p miR-206 miR-29b-3p miR-200c-3p	-0.49 -1.81 1.42 -0.03	2.11E-02 7.92E-03 2.78E-06 8.72E-01	6.52E-02 4.68E-02 9.04E-05 9.61E-01
	miR-374b-5p let-7e miR-30b-5p miR-663a	-0.80 -0.74 1.90 -0.07	2.45E-02 9.91E-03 3.15E-05 9.82E-01	7.01E-02 5.37E-02 6.83E-04 1.00E+00
	miR-146a-5p miR-133b miR-199a-5p miR-1915-3p	-0.68 1.11 1.70 0.59	2.48E-02 1.45E-02 6.58E-05 1.00E+00	7.01E-02 7.26E-02 8.93E-04 1.00E+00
	miR-320c miR-18b-5p miR-27a miR-4530	0.58 0.62 1.41 0.00	3.60E-02 1.59E-02 6.87E-05 1.00E+00	9.37E-02 7.37E-02 8.93E-04 1.00E+00
	miR-301a-3p miR-106a-5p miR-29c-3p miR-1825	-0.46 0.69 0.92 0.00	3.60E-02 1.88E-02 1.86E-04 1.00E+00	9.37E-02 8.17E-02 2.01E-03 1.00E+00
	miR-144-5p miR-1915-3p miR-320a miR-4299	1.18 1.44 -0.85 0.00	4.82E-02 2.04E-02 2.17E-04 1.00E+00	1.20E-01 8.29E-02 2.02E-03 1.00E+00
	miR-7-1-3p miR-129-1-3p miR-374b-5p miR-3665	-0.55 -1.54 1.42 0.00	4.99E-02 3.45E-02 2.87E-04 1.00E+00	1.20E-01 1.32E-01 2.33E-03 1.00E+00
	miR-425-5p miR-191-5p miR-7-1-3p	0.29 -0.33 1.08	5.39E-02 4.58E-02 4.35E-04	1.23E-01 1.64E-01 3.14E-03
	miR-9 miR-106b-5p miR-19a-3p	-0.37 0.46 1.37	5.51E-02 4.79E-02 6.33E-04	1.23E-01 1.64E-01 4.11E-03
	miR-345-5p miR-144-3p miR-338-3p	-0.40 0.65 0.76	5.72E-02 5.17E-02 1.26E-03	1.24E-01 1.68E-01 7.43E-03
	miR-451 miR-7-1-3p miR-145-5p	1.02 0.53 1.04	5.90E-02 6.26E-02 1.41E-03	1.24E-01 1.94E-01 7.61E-03
	miR-423-3p miR-30b-5p miR-142-3p	-0.37 0.73 0.79	7.56E-02 8.19E-02 1.57E-03	1.53E-01 2.40E-01 7.83E-03
	miR-10a-3p miR-374b-5p miR-320b	-0.38 0.62 -0.94	1.05E-01 8.50E-02 1.86E-03	2.08E-01 2.40E-01 8.61E-03
	miR-29b-3p miR-4745-5p miR-320c	-0.41 -1.23 -0.92	1.23E-01 9.18E-02 2.09E-03	2.34E-01 2.49E-01 9.06E-03
	miR-194-5p miR-27a miR-106b-5p	0.55 0.51 0.72	1.27E-01 1.19E-01 2.97E-03	2.36E-01 3.11E-01 1.21E-02
	miR-7 miR-425-5p miR-146a-5p	-0.28 0.24 0.96	1.44E-01 1.27E-01 3.36E-03	2.60E-01 3.18E-01 1.28E-02
	miR-192-5p miR-199a-5p miR-133b	0.39 0.57 1.31	1.67E-01 1.45E-01 6.20E-03	2.93E-01 3.39E-01 2.23E-02
	miR-200c-3p miR-200c-3p miR-223-3p	-0.26 -0.29 0.82	1.79E-01 1.51E-01 6.53E-03	3.01E-01 3.39E-01 2.23E-02
	let-7e let-7g-5p let-7b-5p	-0.35 -0.22 -0.46	1.82E-01 1.56E-01 7.16E-03	3.01E-01 3.39E-01 2.33E-02
	miR-26b-5p miR-133a-3p miR-345-5p	-0.20 0.53 0.61	1.88E-01 1.62E-01 7.69E-03	3.01E-01 3.39E-01 2.38E-02
	miR-127-3p miR-133a miR-194-5p	-0.37 0.53 1.01	1.90E-01 1.62E-01 8.56E-03	3.01E-01 3.39E-01 2.53E-02
	miR-1 miR-92a-3p miR-143-3p	-0.57 0.33 0.68	2.13E-01 1.74E-01 1.13E-02	3.28E-01 3.53E-01 3.19E-02
	miR-151a-5p miR-22-3p	-0.28 0.36	1.97E-01 1.77E-02	3.71E-01 4.80E-02

Table 2 . Complete output from differential expression analyses in the GRN cohort, for each pairwise comparison between clinical groups. The columns show the 30 studied miRNAs, the log-fold change when comparing the clinical groups, the unadjusted P values, and finally the adjusted P values after Benjamini-Hochberg. For each pairwise comparison, a positive log-fold change means that the miRNA is overexpressed in the first group. Controls (n=31), GRN presymptomatic subjects (n=30), GRN patients (n=28). Adjusted P values lower than 0.05 are shown in bold.

 2 Synthetic datasets generationInput: number of subjects n, number of features m, disease progression scores values v i (i=1,...,n).Output: set L containing the datasets D ∈ R n×m L = {} for f in {0,2, 5, 10, 15, 20, 25, 30, 35, 40} do for s in {0.001, 0.2, 0.5, 0.8, 1, 5} doC 1,..., m 2 ← NB(r = 3000, p = 0.75, size = (n, m 2 )) C m 2 +1,...,m ← N (µ=1000, σ=200, size=(n, m2)) /* f features from each modality positively correlate with disease progression */ for j in {1, ..., f} ∪ { m 2 + 1, ..., m 2 + 1 + f } do C j ← v ⊙ C j end for /* the next f features are negatively correlated */ for j in {f, ..., 2f } ∪ { m 2 + 1 + f, ..., m 2 + 1 + 2f } do C j ← 1v ⊙ C j end for /* normalize and add zero-mean Gaussian noise */ for j in {1,...,m} do C j ←

	miRNA	log-fold change	P value	adjusted P value
	Controls vs. GRN presymptomatic subjects
	miR-502-3p	0.53	1.58E-02	3.71E-01
	miR-451	0.78	3.88E-02	3.71E-01
	let-7e	-0.39	4.87E-02	3.71E-01
	miR-206	1.01	4.95E-02	3.71E-01
	miR-92a-3p	0.27	1.15E-01	6.04E-01
	miR-345-5p	0.26	1.21E-01	6.04E-01
	miR-15a-5p	0.22	2.23E-01	7.11E-01
	let-7g-5p	-0.13	2.40E-01	7.11E-01
	miR-320a	0.18	2.55E-01	7.11E-01
	miR-30b-5p	0.33	2.65E-01	7.11E-01
	miR-22-3p	0.11	2.70E-01	7.11E-01
	miR-223-3p	0.21	2.87E-01	7.11E-01
	miR-301a-3p	-0.17	3.25E-01	7.11E-01
	miR-10a-3p	-0.19	3.32E-01	7.11E-01
	miR-320b	0.20	3.62E-01	7.20E-01
	miR-1246	0.32	3.91E-01	7.20E-01
	miR-34a-5p	-0.13	4.21E-01	7.20E-01
	let-7b-5p	0.10	4.32E-01	7.20E-01
	miR-200c-3p	-0.09	4.60E-01	7.26E-01
	miR-146a-5p	0.13	5.84E-01	8.76E-01
	miR-26a-5p	-0.07	6.22E-01	8.89E-01
	miR-335-5p	-0.07	6.83E-01	9.31E-01
	miR-663a	0.13	7.30E-01	9.31E-01
	miR-7	-0.05	7.45E-01	9.31E-01
	miR-9	-0.03	8.77E-01	9.79E-01
	miR-106b-5p	0.02	8.92E-01	9.79E-01
	miR-106a-5p	0.03	9.05E-01	9.79E-01
	miR-18b-5p	-0.02	9.16E-01	9.79E-01
	miR-127-3p	0.01	9.60E-01	9.79E-01
	miR-26b-5p	0.00	9.79E-01	9.79E-01
	Controls vs. GRN patients
	miR-451	2.23	2.65E-06	7.96E-05
	miR-15a-5p	0.77	3.03E-04	4.54E-03
	miR-502-3p	0.82	1.73E-03	1.73E-02
	miR-7	0.56	4.56E-03	3.42E-02
	miR-18b-5p	0.64	7.39E-03	4.44E-02
	miR-106a-5p	0.68	1.14E-02	5.72E-02
	miR-92a-3p	0.51	1.57E-02	6.72E-02
	miR-106b-5p	0.47	2.67E-02	9.44E-02
	let-7b-5p	0.33	2.83E-02	9.44E-02
	miR-223-3p	-0.35	1.24E-01	3.72E-01
	miR-320a	0.24	2.17E-01	5.93E-01

https://www.bioinformatics.babraham.ac.uk/projects/fastqc

https://www.mirbase.org

http://fmri.ucsd.edu/Howto/3T/structure.html

https://surfer.nmr.mgh.harvard.edu/

https://fsl.fmrib.ox.ac.uk/fsl/fslwiki

https://www.fil.ion.ucl.ac.uk/spm/

https://itk.org/

https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT

https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FNIRT

https://adni.loni.usc.edu/

https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga

https://clinicaltrials.gov/ct2/show/NCT02590276

https://www.mirbase.org

https://clinicaltrials.gov/ct2/show/NCT040146733.2. Neuroimaging dataset

http:// www.bioinformatics.babraham.ac.uk/projects/fastqc

https://pypi.org/project/pcurvepy/

(A) Training data projected in the latent space (B) Test data projected in the latent space (C) Computed scores for the test data

https://pypi.org/project/pyebm/

https:// osf.io

https://adni.loni.usc.edu/

Acknowledgements

progression scores from cross-sectional multimodal data, and tested it with miRNA and neuroimaging features extracted from a cohort of C9orf72 expansion carriers.

Taken together, the findings of this thesis represent an encouraging step in improving the design of clinical trials for these rare but devastating neurodegenerative disorders. • Kmetzsch, V., Anquetil, V., Saracino, D., Rinaldi, D., Camuzat, A., Gareau, T., Jornea, L., Forlani, S., Couratier, P., Wallon, D., Pasquier, F., Robil, N., de la Grange, P., Moszer, I., Le Ber, I., Colliot, O., Becker, E., PREV-DEMALS study group, "Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis", Journal of Neurology, Neurosurgery & Psychiatry, 92 [START_REF] Helferich | Dysregulation of a novel miR-1825/TBCB/TUBA4A pathway in sporadic and familial ALS[END_REF]: 485-493 (2021). doi: 10.1136/jnnp-2020-324647 -hal-03046771.

Appendix

SUPPLEMENTARY MATERIAL

Method A1: Neuropsychological protocol. Method A2: Description of the four cases at the transitional stage. Table A1: Clinical status and center proportion in each batch. Table A2: Complete output from EdgeR. Table A3: List of the putative target genes. Table A4: Complete output from DIANA-miRPath v.3. Table A5: Comparison of studies investigating miRNAs from brain samples.