Sandrine Dudoit

Véronique Stoven

remercier chaleureusement les examinateurs

Learning from multiple genomic information in cancer for diagnosis and prognosis by Matahi Moarii De nombreuses initiatives ont été mises en places pour charactériser d'un point de vue moléculaire de grandes cohortes de cancers à partir de diverses sources biologiques dans l'espoir de comprendre les altérations majeures impliquées durant la tumorogénèse. Les données mesurées incluent l'expression des gènes, les mutations et variations de copynumber, ainsi que des signaux épigénétiques tel que la méthylation de l'ADN. De grands consortium tels que "The Cancer Genome Atlas" (TCGA) ont déjà permis de rassembler plusieurs milliers d'échantillons cancéreux mis à la disposition du public. Nous contribuons dans cette thèse à analyser d'un point de vue mathématique les relations existant entre les différentes sources biologiques, valider et/ou généraliser des phénomènes biologiques à grande échelle par une analyse intégrative de données épigénétiques et génétiques. En effet, nous avons montré dans un premier temps que la méthylation de l'ADN était un marqueur substitutif intéressant pour jauger du caractère clonal entre deux cellules et permettait ainsi de mettre en place un outil clinique des récurrences de cancer du sein plus précis et plus stable que les outils actuels, afin de permettre une meilleure prise en charge des patients. D'autre part, nous avons dans un second temps permis de quantifier d'un point de vue statistique l'impact de la méthylation sur la transcription. Nous montrons l'importance d'incorporer des hypothèses biologiques afin de pallier au faible nombre d'échantillons par rapport aux nombre de variables. Enfin, nous montrons l'existence d'un phénomène biologique lié à l'apparition d'un phénotype d'hyperméthylation dans plusieurs cancers. Pour cela, nous adaptons des méthodes de régression en utilisant la similarité entre les différentes tâches de prédictions afin d'obtenir des signatures génétiques communes prédictives du phénotypes plus précises. En conclusion, nous montrons l'importance d'une collaboration biologique et statistique afin d'établir des méthodes adaptées aux problématiques actuelles en bioinformatique.

Firstly, we show the role of DNA methylation as a surrogate biomarker of clonality between cells which would allow for a powerful clinical tool for to elaborate appropriate treatments for specific patients with breast cancer relapses.

In addition, we developed systematic statistical analyses to assess the significance of DNA methylation variations on gene expression regulation. We highlight the importance of adding prior knowledge to tackle the small number of samples in comparison with the number of variables. In return, we show the potential of bioinformatics to infer new interesting biological hypotheses.

Finally, we tackle the existence of the universal biological phenomenon related to the hypermethylator phenotype. Here, we adapt regression techniques using the similarity between the different prediction tasks to obtain robust genetic predictive signatures common to all cancers and that allow for a better prediction accuracy.

In conclusion, we highlight the importance of a biological and computational collaboration in order to establish appropriate methods to the current issues in bioinformatics that will in turn provide new biological insights. Nous nous intéressons en particulier dans cette thèse au développement de méthodes statistiques pour l'analyse de données génomiques spécifiques, les données épigénétiques, et leur lien dans le diagnostic et pronostic du cancer. Dans ce chapitre, nous introduisons les principales notions biologiques abordées dans cette thèse. La section 1.3 s'attache à introduire les perspectives actuelles liées à la thérapie du cancer. Le cancer du sein, principale pathologie traitée à l'Institut Curie, sera en particulier abordé. Dans la section 1.4, nous présentons des données épigénétiques et plus spécifiquement de la méthylation de l'ADN.

List of Figures

Preamble

After the completion of the human genome project more than ten years ago, DNA measurement technologies have witnessed dramatic progress in scope and throughput at constantly decreasing cost. This led to a flood of clinical and biological information routinely collected in hospitals and research laboratories, which impacted our fundamental understanding of biological systems, but which also required new approaches based on statistical analysis to extract biological information from large collections of data.

We focus in this thesis on a particular type of molecular data that can be measured genome-wide, namely, epigenetic data describing the methylation status of particular Chapter 1. Introduction 2 bases in DNA, and their relevance for cancer diagnosis and prognosis. In this chapter, we provide a general introduction to the main biological notions used throughout this thesis, and highlight the clinical context motivating the work. In particular, we give in section 1.3 a general introduction to cancer and the current perspectives in cancer therapy. We focus on breast cancer, the main cancer treated attheInstitutCuriewhere I worked during my PhD. In section 1.4, we touch upon epigenetics as "heritable changes not affecting the DNA coding sequence but that affect gene function" [Riggs and Porter, 1996], with a particular focus on DNA methylation. In section 1.5, finally, we summarize the main contributions of this thesis.

From a macroscopic to a molecular characterization of cancer

Cancer is a major cause of morbidity and mortality worldwide, accounting for 8.2 million deaths in 2012. It occurs when a single cell acquires the ability to reproduce aggressively and to invade other tissues. This phenomenon usually results from successive modifications that alter the function of normal cells and give them specific advantages in favor of uncontrolled proliferation and ability to spread out of the tissue of origin [Knudson, 1971, Hanahan and Weinberg, 2000, Weinberg, 2007, Hanahan and Weinberg, 2011]. Although our understanding of when and where these specific aberrations appear during tumorogenesis has greatly improved over the years, many mechanisms remain elusive.

Ad i fficulty in cancer research is the diversity of diseases it encompasses. Physicians recognize at least 200 types of cancer, with very diverse aspects and clinical implications.

Not only does cancer occur in various types of tissues, which greatly affects the patient prognosis (e.g, overall survival of breast cancer patients after 5 years is around 90%, but only around 10% for lung cancer patients), but even tumors originating from the same type of tissue can present different characteristics under a microscope and in terms of prognosis and response to treatments.

Understanding and delineating the diversity of cancer is increasingly recognized as a critical issue to improve its treatment. On the one hand, understanding which biological processes are involved during carcinogenesis for a particular subtype of cancer can improve our understanding of the disease at a molecular level, and suggest new drugs targeting new targets. On the other hand, it may also contribute, from a clinical point of view, to give a better characterization of which cancer subtypes can be associated with which specific outcome and respond to which treatment. This should help develop more personalized therapeutic strategies that could be moree fficient to a subgroup of patients than the current, still largely "one-size-fits-all"-based approach.

1.3.1 Histopathology of cancer: the premisces of personalized medicine.

Af o c u so nb r e a s tc a n c e r .

Let us focus more precisely on breast cancer, the most common cancer in women worldwide, which by itself is a very heterogeneous disease. Like most cancers, breast cancer can be divided into different categories based on different criteria, serving different purpose. A very important classification scheme is based on the histopathology of the tumor, that is, how biopsy specimens look like under the microscope. The World Health Organization (WHO) approved in 2003 a histopathological classification of breast cancers into more than 20 major tumor types and subtypes, a few of them being shown in figure 1.1. However, two classes, invasive ductal carcinomas (IDC) and invasive lobular carcinomas (ILC), account for approximately 80% of all breast cancers [START_REF] Li | Clinical characteristics of different histologic types of breast cancer[END_REF],

suggesting that this classification is not very fine-grained. In fact, the histopathological classification of breast cancer has limited prognostic and predictive value, except for some rare subtypes with clear positive (adenoid-cystic carcinomas [START_REF] Arpino | Adenoid cystic carcinoma of the breast: molecular markers, treatment, and clinical outcome[END_REF] or negative prognosis (metaplastic carcinomas [START_REF] Colleoni | Outcome of special types of luminal breast cancer[END_REF]). Patients within the major subtypes can have very diverse prognostics, while the difference between ILCs and IDCs in terms of positive or negative clinical impact is still subject to debate [START_REF] Viale | Lack of prognostic significance of "classic" lobular breast carcinoma: a matched, single institution series[END_REF].

Overall, the histopathology of cancer therefore fails to grasp the full diversity of breast cancer and has a limited clinical impact in itself, except foraf e ws p e c i fi cs u b t y p e s .

Yet it is a first, useful step towards unraveling the complexity and the heterogeneity of cancer.

Figure 1.1: Histopathology of tumors can distinguish tumors following [START_REF] Ellis | ii. histological type. relationship with survival in a large study with long-term follow-up[END_REF]. Source: Dr. Anne-Vincent Salomon (Institut Curie).

Molecular classifications of cancers: the dawn of personalized medicine

The recent progress in cancer treatments and therapies, such as breast conserving therapies or hormonotherapies, has led to rethink cancer classification in terms of risk assessment (including risk of relapse, progression, metastasis, or survival) and responsiveness to treatments, instead of the classical histopathological classification. The presence or absence of molecular markers such as the presence of estrogen (ER), progesterone (PR) and human epidermal growth factor (HER2) receptors which condition the response to targeted therapies (e.g tamoxifen for ER+/PR+ patients and trastuzumab for HER2+ patients) has reorganized the breast cancer groups in terms of treatments. Similarly, the beneficial impact of chemotherapy, which is a very toxic therapy that can often be spared to patient with very good prognosis, can be evaluated from markers of the tumor aggressivity such as the grade, its size and the level of proliferation as measured by molecular markers like Ki67. These markers are routinely assessed by immunohistochemistry on biopsy samples, and the resulting classification of cancers is therefore referred to as the immunohistochemical (IHC) classification. General guidelines and recommendations to specify the IHC classification were assessed and standardized by the American Society of Clinical Oncology (ASCO) in 2007 [START_REF] Harris | American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer[END_REF].

While of clinical use, several drawbacks to the IHC classification still remain unanswered.

First, the clinical impact of these classifications could certainly still be improved with a finer classification. In particular, patients outcome still vary greatly within each IHC tumor groups. Second, the triple-negative breast cancers (ER-/PR-/HER2-) lack all molecular components that could make them benefit from existing targeted therapies for breast cancers. Extending the features used to classify tumors is therefore necessary to assess the effect of new therapies.

1. 3.3 The overflow of -omics data and the necessity of a statistical framework

In the last 15 years, new technologies to measure thousands or millions of molecular characteristics on each given sample have emerged. Based on microarray or sequencing technologies, they have slowly but steadily transitioned cancer classification from a macroscopic to a molecular level. Being able to measure simultaneously the expression of thousands of genes has paved the way to a better understanding of cancer heterogeneity and to a new molecular classification of breast cancers [START_REF] Perou | Molecular portraits of human breast tumours[END_REF], Sørlie et al., 2001,Van't Veer et al., 2002,van de Vijver et al., 2002,Wang et al., 2005]. This classification has already impacted the patients treatments with the recent development of gene expression profiling platforms (e.g MammaPrint, OncotypeDX), which predict the risk of relapse or of treatment response of a patient by combining the expression level of a panel of genes known as a molecular signature to aid in the therapeutic strategies [START_REF] Paik | A Multigene Assay toP r e d i c tR e c u rrence of Tamoxifen-Treated, Node-Negative Breast Cancer[END_REF], Parker et al., 2009, Nielsen et al., 2010].

Yet, several statistical and biological issues remain unanswered and question the validity of such methods. [START_REF] Reyal | A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the proliferation, immune response and RNA splicing modules in breast cancer[END_REF] have shown that while of similar performances, different gene-based predictors do not share the same prognostic group assignment. Venet et al. [START_REF] Venet | Most random gene expression signatures are significantly associated with breast cancer outcome[END_REF] demonstrated that most random panels of genes are significantly associated with breast cancer outcome, questioning the biological implications of existing panels. From a statistical point of view, the analysis of such data is usually hindered by the small number of samples available, generally a few hundreds, compared to the thousands of gene expression measurements. This statistical issue commonly referred to as the "small n,b i gp" issue in the statistical community (where n refers to the sample size and p to the number of features) raises important challenges such as stability and reproducibility of the results [START_REF] Haury | The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures[END_REF], which we will discuss in more detail in chapter 2.

In summary, the heterogeneity of cancer, while adding a supplementary layer of complexity to the already difficult understanding of the biology of the disease, provides great opportunities for the specific tayloring of therapeutic strategies to the patient, and raises important methodological challenges. While a "perfect" classification from a biological and clinical point of view still remain elusive, we can expect important progress in the coming years in our ability to classify tumors and stratify patients, as we collect more data and improve our methodological approaches to analyze them. In the next chapter, we discuss the clinical and biological interest of specific biological markers, namely DNA methylation markers.

Epigenetics

The behavior of a cell mostly depends on the proteins it synthesizes, which are themselves governed by the specific regulations of gene expression. While the genome sequence of all somatic cells in an individual is virtually the same, functional variations are therefore controlled by determinants of the gene expression. Transcription factors are proteins that promote or repress gene activity by binding to promoter regions in DNA [START_REF] Vaquerizas | A census of human transcription factors: function, expression and evolution[END_REF]. Yet, several studies have underlined the insufficiency of a "transcription factors only" model to control gene expression [START_REF] Itzkovitz | Coding limits on the number of transcription factors[END_REF], Werner, 2013]. In Chapter 1. Introduction 6 particular, recent research has highlighted the crucial role played by epigenetic mechanisms in many cellular process including cell differentiation during development [Laurent et al., 2010, Smith andMeissner, 2013] but also in tumorogenesis [START_REF] Rountree | DNA methylation, chromatin inheritance, and cancer[END_REF], Ehrlich, 2002, Das and Singal, 2004, Kulis and Esteller, 2010]. The current definition of epigenetics is "the study of mitotically and/or meiotically heritable changes in gene function that cannot be explained by changes in DNA sequence" [Riggs and Porter, 1996]. The main epigenetic landmarks in mammals are histone modifications and DNA methylation, the latter being the subject of this thesis. The following subsections will therefore give an introductory description to the biological concepts of DNA methylation, and to its role in gene expression regulation particularly during tumorogenesis.

We will also discuss the clinical interest in DNA methylation as an early biomarker in cancer but also as a potential source for treatments. Finally, we will discuss the analysis of DNA methylation data from a statistical point of view.

DNA methylation

DNA methylation refers to the addition of a methyl (CH3) group to a nucleotide in the DNA sequence (figure 1.2). For mammals, this reaction, which is catalyzed by DNA methyltransferases (DNMTs), mostly occurs in the sequence context of a cytosine which is followed by a guanine noted as 5'CG3' or CpG. Three DNMTs have been identified

in mammals: DNMT1 guarantees the maintenance of methylation from the methylated parental strand to the unmethylated daughter strand during cell division [START_REF] Kho | Stalling of human dna (cytosine-5) methyltransferase at single-strand conformers from a site of dynamic mutation[END_REF], while DNMT3a and DNMT3b trigger de novo methylation or demethylation and are specifically important during embryonic development for the establishment of the epigenome [START_REF] Okano | Dna methyltransferases dnmt3a and dnmt3b are essential for de novo methylation and mammalian development[END_REF].

DNA Methylation in gene regulation

CpG sequences are greatly under-represented on average across the genome, but are specifically over-represented in condensed regions of 1 to 5 kb known as CpG Islands (CGIs) [Gardiner-Garden and Frommer, 1987]. The original definition of CGIs based on somehow arbitrary thresholds has been followed to several redefinitions based on either biological or statistical considerations [START_REF] Takai | Comprehensive analysis of CpG islands in human chromosomes 21 and 22[END_REF], Wang and Leung, 2004, Wu et al., 2010, Bock et al., 2007]. A particular interest for these regions is due to the fact that 60 to 90% of all genes are associated to CGIs, specifically in their promoter regions, [START_REF] Saxonov | A genome-wide analysis of CpG dinucleotides in the human genome distinguishes two distinct classes of promoters[END_REF]. The methylation or demethylation of a CGI is known to be related to the initiation of the transcription process of the associated gene, as shown by many studies for specific genes such as housekeeping genes [START_REF] Deaton | CpG islands and the regulation of transcription[END_REF],

imprinted genes [START_REF] Li | Role for DNA methylation in genomic imprinting[END_REF], and tissue specific genes [START_REF] Laurent | Dynamic changes in the human methylome during differentiation[END_REF]. One famous example of gene expression regulation by DNA methylation is the inactivation of one of the two copies of the X-chromosome by DNA methylation [START_REF] Pollex | Recent advances in Xchromosome inactivation research[END_REF].

Still, the precise mechanism of DNA methylation and its role in gene transcription remains largely unclear. DNA methyltransferases (DNMTs) are responsible for de novo methylation and for the maintenance of methylation after cell division [Bird, 2002].

Yet, the signals that govern the pattern of methylation accross the genome is unknown.

Moreover, methylation is tightly linked with gene expression but how it clearly regulates expression is still being debated and several hypotheses have been proposed [START_REF] Klose | Genomic DNA methylation: the mark and its mediators[END_REF],Bogdanović and Veenstra, 2009,Deaton and Bird, 2011]. A first model is that DNA methylation physically blocks the access of promoter binding sites in particular for specific transcription factors that bind preferably to unmethylated sequences [START_REF] Rodriguez | CTCF is a DNA methylation-sensitive positive regulator of the INK/ARF locus[END_REF] 1.3. A second model considers DNA methylation as the initiating mechanism for the establishment of an inactive chromatin state also known as heterochromatin.

In this case, specific proteins known as methyl-CpG binding domain proteins (MBDs) bind to region of high methylation. These MBDs, in turn, recrute histone deacetylases (HDACs) which compact the chromatin and enforce a inactive state which results in gene silencing (figure 1.3).

While gene promoter methylation has been quite extensively studied, little is known on the role of methylation outside of these regions. Gene body methylation has been positively correlated with gene expression contrary to promoter methylation [START_REF] Kulis | Intragenic DNA methylation in transcriptional regulation, normal differentiation and cancer[END_REF] and has also been associated with alternative splicing [START_REF] Maunakea | Intragenic DNA methylation modulates alternative splicingb yr e c r u i t i n gM e C P 2t o promote exon recognition[END_REF]. The role of orphan CGIs, that is CGIs far from any known genes, is yet to be elucidated but could be linked to long range epigenetic regulation [START_REF] Bert | Regional activation of the cancer genome by long-range epigenetic remodeling[END_REF] or be located in actual promoter regions of ancestral genes [START_REF] Illingworth | Orphan cpg islands identify numberous conserved promoters in the mammalian genome[END_REF]. Wiedmann: aberrant parental imprinting on chromosome 11) are strong evidence of the causal link between methylation aberrations and human diseases [Robertson, 2005].

Cancer is the epitome of those methylation-associated diseases. Our current understanding of the role of methylation aberrations in cancer points out to at least two mechanisms.

On the one hand, localized hypermethylation of promoter regions of specific genes such as tumor suppressor genes or tissue-specific genes can lead to their inactivation and lead to tumorogenesis [Baylin andHerman, 2000,Esteller et al., 2001]. On the other hand, an overall global hypomethylation can lead to genetic instability and in some cases to the activation of silenced oncogenes [Ehrlich, 2002, Ehrlich, 2009, Hon et al., 2012]. In both cases, abnormal methylation is considered a driver event for abnormal gene expression in cancer, and could therefore potentially be detected before any significant change in gene expression. Thus, understanding the general pattern of a cancer methylomes could pave the way to new schemes for early detection of cancer. This may in turn impact cancer treatment, knowing that time of diagnosis is a crucial factor in prognosis [Richards, 2009].

Several advantages of epigenetic markers over genetic markers place DNA methylation as one of the major interest in cancer research. Current measurement technologies allow for non-ambiguous mapping of methylation at well defined position on the genome contrary to, e.g, mutations of genes. In addition, the fact that DNA can be released from tumor tissues in peripheral tissues and in particular fluids [START_REF] García-Closas | Searching for blood DNA methylation markers of breast cancer risk and early detection[END_REF] such as serum, urine or plasma could allow for non-invasive early detection procedures. The reversibility of the methylation marker is also of potential interest for new treatments.

Already, demethylant agents such as the 5-aza-2'-deoxycytidine or the 5-azacytidine have already been tested and approved by the FDA to treat myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CML) [START_REF] Silverman | Randomized Controlled Trial of Azacitidine in Patients With the Myelodysplastic Syndrome: A Study of the Cancer and Leukemia Group B[END_REF].

Statistical challenges in DNA methylation analysis

Epigenome-wide analyses have become accessible with the development of microarray measurement technologies, and more recently sequencing technologies. We focus in this thesis on bisulphite-based methods such as the illumina HumanMethylation platform, which measures the methylation level of up to 450,000 CpG dinucleotides. Although the methylation of a given CpG in a given cell is a binary attribute, measurements are often issued from a mixture of cell populations with heterogeneous DNA methylation profiles. Therefore, the resulting measurements usually reflect a ratio of methylation for one specific probe as M/(M + U ) in which M represents the signal for methylated molecules and U the signal for unmethylation molecules.

This ratio lies in [0; 1] and the finite scale of DNA methylation greatly differ from the larger scale of, e.g., gene expression data. Also, DNA methylation measurements are not normally distributed and variance is greatly biased by the mean value of the probe (probes with mean methylation of 0.5 can have variance much larger than probes with mean methylation near 0 or 1). Therefore, the use of standard methods in microarray data analysis such as filtering signal with high standard deviation becomes bias-inducing in DNA methylation analysis. Such observations underly the importance of understanding the data at hand and the underlying technology to build data-driven statistical methods.

Personal contribution and organization of the thesis

This chapter presented a short overview of some of the current problematics in cancer research. Given the heterogeneity and complexity of the data now available to investigate the diversity of cancers, we chose to focus on the specific role of DNA methylation in tumorogenesis. Each of the following chapters aim at tackling a particular issue in cancer described below.

Chapter 2 introduces the different methods used in this thesis to analyse data. In particular, we discuss the use of supervised and unsupervised learning in a setting where the number n of observations is much smaller than the number p of variables studied p also known as n p, the challenges it raises and how to overcome them.

Chapter 3 tackles the issue of relapses in breast cancer. When early breast tumor are detected, an alternative to aggressive treatments such as mastectomy is tumorectomy where only the tumor is removed instead of the whole breast. Although patients survival rates are not significantly different for both therapies, tumorectomy increases the rates of tumor relapse. In case of relapse, being able to characterize the relapse as either a true recurrence or an independent tumor is essential for the treatment of the patient. A true recurrence is often synonym of an aggressive cancer and requires aggressive treatments, while an independent tumor could potentially be treated with less invasive treatments.

The monoclonality of cancer [Weinberg, 2007] suggests that being able to characterize the clonality between a primary tumor and its relapse could help tackle this issue. A clinical classification based on the concordance of the histopathological features (stage, grade, ER status, PR status, HER2 status) is used in practice but yields the same drawbacks as the clinical classification of breast cancer subtypes (see section 1.3.1).

While a few studies have investigated the use of pangenomic data to tackle breast cancer relapse classification, no method is based on DNA methylation profiles. As methylation is highly conserved in cell division, we hypothesize that it may be a good marker to assess lineage between samples. We therefore investigate the similarity of methylation profiles in different cancer samples, and propose a method based on pairwise analysis of methylation profiles to characterize clonality between samples taken at diagnosis and at relapse.

Chapter 4 tackles the role of genome-wide variations of methylation in gene expression regulations. Aberrant promoter hypermethylation has frequently been observed in cancer but its precise role in tumorogenesis has always been elusive. Epigenetic modifications have been widely studied and have been shown to be associated to gene expression repression in tumour suppressor genes. The high-coverage methylome profiles of hundreds of patients, as well as their matched gene expression and copy number profiles, now available publicly in the cancer genome atlas (TCGA) provides a comprehensive dataset to assess the extent of epigenome-wide regulation of gene expression variations.

Chapter 5 studies the existence of a methylome-based cancer classification. The CpG island methylator phenotype (CIMP), first identified in colorectal cancer, has recently become a major subject of interest and has been observed in several tissues. Yet, these characterizations of CIMP have been tissue-specific and the existence of a biological phenomenon causing the CIMP in cancer is still elusive. In addition, the clinical importance of CIMP as a predictive factor for prognosis and patients response to treatments is still being validated. We develop a pancancer genome and epigenome-wide CIMP analysis using the large TCGA datasets and demonstrate the existence of a common epigenetic signature of CIMP. Genetic profiling show that CIMP might be linked with a universal genetic signature well-documented in several CIMPs. However, clinical impact of CIMP is still lacking on the TCGA database.

Chapter 2

Methods

Résumé

Abstract

In this chapter, we introduce the statistical methods related to the work present in this manuscript. Most of the methods described here are well discussed in the literature.

Chapter 2. Methods 14 Our focus will be on their practical use for biological data analysis. This chapter is essentially composed of 2 sections.

The first section will present a few methods used in this thesis for supervised learning in the context where the number of samples is a lot smaller than the number of features also known as n p. We will focus in particular on the interpretability of the data especially in a biological context where the understanding of the underlying biological phenomenon is as important as the prediction performance of am o d e l .

In the second section, we will discuss unsupervised learningm e t h o d s . W ew i l ld e s c r i b e cluster analysis as well as dimensionality reduction techniques. We will show in particular how difficult the task of selecting objectively a model (e.g, number of clusters or optimal number of dimensions) can be in the case of real data. Finally, we will see that model selection in a biological setting, where the data are intrinsically noisy and the statistical power is usually poor (n p), generally leads to non-robust clustering. In this case, clinical significance of the method in particular for clustering to distinguish classes of different prognosis can be used to measure the importance of the model.

Supervised learning

Supervised learning refers to a set of statistical methods which try is to make sense out of a series of observations by inferring a relation between an input and an output.

The observations can usually be summarized by a set of variables also known as inputs represented by X which are usually measured (e.g the level of expression of genes), and by an outcome also known as output represented by Y which is a feature of interest.

Outputs can either be qualitative (e.g., whether or not a relapse will occur with 5 years), or quantitative (e.g, toxicity of a drug). We usually refer to a supervised learning problem with qualitative outputs as a classification problem compared to a regression problem for quantitative outputs. To summarize, the objective is to learn a model that will allow to predict Y given X only. Such a setting can be beneficial for example when measuring Y can be a lot more costly than measuring X (e.g annotating each picture on the internet is harder to obtain than the summary of a picture as a set of pixels), or more importantly when Y refers to a future event that we would like to predict in order to adapt our present strategy. Another reason to infer a predictive model is when we are interested in how the input influences the output, e.g how the genes influence the state of a patient as healthy or cancerous.

In the following, we will give the general mathematical framework of statistical supervised learning as well as the general set of notations used throughout this thesis. In particular, we will discuss the issue of learning when the number of observations is small compared to the number of training samples.

Risk minimization problem

Let's suppose that the couple (X, Y ) ∈ X × Y follow a joint probability distribution P(X, Y ). Being able to predict Y given X can be formulated as the problem of finding a function f : X → Y living in a certain space F such that Ŷ := f (X) is a good enough approximation of Y . For that, we first define a loss function l : Y × Y → R + to quantify the loss l(ŷ, y) incurred by a prediction ŷ when the true output is y .T h er i s k R : F → R of a function f is defined as the expected loss incurred by the predictions made by function on future samples under the distribution P,i . e ;

R(f )=E P [l(f (X),Y)] .
(2.1)

If P was known, then arguably we should make predictions with the function that will incur the smallest loss, i.e., the one with the smallest risk:

f * = arg min f ∈F R(f ). (2.2)
For example, a common loss function used in regression is the squared loss l(ŷ, y)= (ŷ -y) 2 , in which case the optimal predictor is

f * = arg min f ∈F E (X,Y ) (Y -f (X)) 2 = arg min f ∈F E X E Y |X (Y -f (X)) 2 |X (2.3)
which can be solved point wisely by

∀x, f * (x) = arg min c∈R E Y |X (Y -c) 2 |X = x = E [Y |X = x] .
(2.4)

The curse of dimensionality

Let's describe with an example the different problematics that we might encounter when trying to solve 2.2.

Suppose we are given a training set n of observations (x i ,y i ) i=1,...,n where the input variable x i are p-dimensional vectors in R p and the output variable y i is a real scalar in R. Our objective is to estimate a function f that is a good approximation of f * .A standard procedure known as the k-nearest neighbours to estimate f * (x) at a point x consists in averaging the observed outputs y i s for the k closest x i s in a neighboorhood of x, that is:

f (x)= 1 k i:x i ∈N k (x) y i , (2.5)
where N k (x) is the set of the k closest observations from x.

In the low-dimensional setting, when the number n of observations is very large then N k (x)t e n dt ob ev e r yc l o s et ox, making or the k-NN estimator (2.5) a good local estimator of f * (x) particularly when k is large enough to average out the noise from sufficient local neighbors. When the dimension of the input space p is large, however, observations tend to be far away from each other. To see this, suppose we have n points uniformly distributed in a unit ball in R p ; then the median distance between the origin and the closest point is given by:

d(n, p)= 1 - 1 2 1/n 1/p , (2.6)
which increases to 1 as p increases. Therefore, in large dimension data points tend to be very far from each other (Figure 2.1). Another way to see this phenomenon is to notice that the volume of a ball of radius R is proportional to R p and therefore the density is proportional to n 1/p , which means that to obtain the same density of points, the number of observed points needs to grow exponentially with the dimension. This problem, usually referred to as the curse of dimensionality [Bellman, 1961], entails that estimators like k-NN can become arbitrarily bad since k (and therefore n) should increase exponentially with p to have a good representation of the neighborhood of x.

Yet in several biological problems, the number of observed points n is generally ∼ 100 (e.g number of patients in a study) while the dimension of the input space p can be of the order of 10 4 (e.g number of genes) to 10 6 (e.g SNP data). In the following, we will discuss the different strategies to tackle the problem of learning when the dimension p is much larger than the number of samples n also known as n p.

Model selection

Let's suppose that the output Y is related to the input X by

Y = f (X)+ , (2.7)
where is a normally distributed random variable with zero mean and variance σ 2 independent of X. The expected squared prediction error of a given estimate function f at a given x 0 is given by:

Err(x 0 )=E (Y -f (x 0 )) 2 |X = x 0 , (2.8)
which can be decomposed as:

Err(x 0 )= f * (x) -E[ f (x)] 2 + E f (x) -E[ f (x)] 2 + σ 2 = Bias 2 + Variance+ Irreducible Error.
(2.9)

Amongst the three terms in the right-hand side of (2.9), two can be controlled by the choice of f , that is, the choice of the modelisation of the relationship between X and Y :

the bias and the variance terms. To have a small bias, one typically needs to have a good local estimator, like a k-NN with small k. To have a small variance, one needs to have a procedure that is not too sensitive to individual observations, like k-NN with a large k.

As suggested by the k-NN examples, bias and variance generally move in opposite way, and learning in high dimension often boils down to controlling the trade-off between bias and variance.

More generally, if we had an infinite number of observations, then we might be able to reduce both the variance and the bias terms to 0. When the number of sample n is finite, however, we do not have access to the prediction error. An intuitive estimate to estimate the risk of a candidate function f is then to take the training error, namely

1 n i (y i -f (x i )) 2
. However, simply minimizing the training error over f is not a good idea because it does not account for the complexity of the model f . As the model gets more and more complex, the training error can decrease and even tend to zero if one finds a function f that perfectly reproduces the training examples, i.e., y i = f (x i ) for all i =1,...,n. Yet, a too complicated model might not give the best output for a new observation x, since it may have large bias. On the other hand, a model too simple, with small variance, might be too biased and might also not be able to give a good prediction for a new observation. At the end, a balance between the complexity of the model and its ability fo capture robust information from the training data must be found (figure 2.2).

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Assessing the performance of a model

In the last part, we illustrated the issue of estimating the prediction error by the training error which leads to overfitting. One of the most commonly used method for estimating the prediction error is cross-validation [Stone, 1974, Allen, 1974]. Cross-validation randomly splits the data into K folds of even sizes. A model is trained on K -1 folds and an estimate of the prediction error is then obtained by taking the average error on the

K th fold.
For a given fold J,l e t ' sd e fi n e:

CV J ( f )= 1 |J| i∈J L(y i , f -J (x i )) (2.10)
where |J| is the cardinal of fold J and f -J is the fitted function computed on the dataset where the observations belonging to the J th fold are removed.

Finally, the cross validation estimate of the prediction error is given by :

CV ( f )= 1 n J∈[1;K] |J|CV J ( f ) (2.11)
A set of models f usually involves a tuning parameter α that controls the complexity.

A common model selection procedure is given by finding α that minimizes the cross validation estimate:

fCV = arg min α CV ( fα ) (2.12)
An issue that arises in cross-validation is the choice of K. Choosing K small might give a better estimation of the expected error as the training sets in each fold are very different (when K = 2 the training sets do not overlap), contrary to choosing K very large where the training sets tend to be very similar (for leave-one-out cross-validation, that is K = n the training sets in each fold differ by one observation). However, in a biological context, one also has to take into account the size n of the observations that can be relatively small. In this case, we usually choose K large to reduce the variance of the estimate.

Interpreting the data

In the following, we restrict the set of functions F to the set of affine functions that is:

f (x)=x ⊤ ω + ω 0 , (2.13)
with (ω, ω 0 ) ∈ R p+1 .T os i m p l i f y( 2.13), we usually "integrate" the constant ω 0 in x by defining artificially the new set of features x := [1; x] T ∈ R p+1 .O p t i m i z i n gi nf is therefore equivalent to finding the vector of coefficients ω.

Ordinary Least Squares.

Given a training set (x 1 ,y 1 ), ••• , (x n ,y n ), a popular estimate of the vector of coefficients ω is given by the ordinary least squares methods that is the vector of coefficients that minimizes the residual sum of squares:

RSS(ω)=∥y -Xω∥ 2 , (2.14)
where X is the matrix containing all the observations X =[ x 1 ; ••• ;

x n ] T and y is the

vector of outputs y =(y 1 ; ••• ; y n ) T .
Minimizing in ω in the case where X T X is non-singular is given by the unique solution:

ω OLS =(X T X) -1 X T y.
(2.15)

Ridge Regression. This estimate is not always an option. In a biological setting, the number of observations p is usually bigger than the number of samples n and thus X T X is singular and the number of solutions is a vector space.

One solution is to modify (2.14) by adding a penalty constraint based on the Euclidean norm of the vector ω, also known as the ridge regression [START_REF] Hoerl | Ridge Regression: Biased Estimation for Nonorthogonal Problems[END_REF]:

Ridge(ω)=∥y -Xω∥ 2 + λ∥ω∥ 2 , (2.16)
for λ > 0. The solution of (2.16) is given by: ω Ridge =(X T X + λI) -1 X T y.

(2.17)

Ridge regression has thus the interesting property to get rid of the non-singularity issue on X T X by replacing it with X T X + λI. In addition, the penalty term λ∥ω∥ 2 enforces smoothness that is the coefficients to be not too large.

Sparsity-inducing penalties.

Another important issue in a biological setting is the interpretability of the results given by the method. Suppose for example that we are interested in predicting the status of a patient (e.g, healthy or cancerous) given the expression level of all the genes. A ridge regression estimate on a training set of data containing healthy and cancerous patients might perform well on a new set of data, but may not give particular information about the biological pathways involved in tumorogenesis. The underlying assumption is that amongst the whole set of genes (∼ 25000), not all genes might be involved in defining the status of the patient. In addition, it would take too long to verify biologically all the genes one by one.

An alternative is thus to seek a good model ω with many zero coefficients, entailing that only a subset of features is used in the decision making. This would allow us, for example, to target specifically a small set of genes that can be more easily tested.

Moreover, reducing the number of features used by the model is a way to control the bias-variance trade-off and improve the generalization ability of the model. Such an approach could for example be carried out by penalizing 2.14 by the number of non-zero coefficients instead of the ridge penalty: (2.18) where ∥ω∥ 0 =# {i : ω i ̸ =0 }. However, solving (2.18) requires an exhaustive search over all possible combinations of p features, a combinatorial problem which becomes quickly intractable for p larger than a few tens. A popular approach to overcome this computational issue is to replace the l0 regularization term by the convex l1-norm, leading to the Lasso estimate: (2.19) where

l 0 (ω)=∥y -Xω∥ 2 + λ∥ω∥ 0 ,
Lasso(ω) = arg min ∥y -Xω∥ 2 + λ∥ω∥ 1 ,
∥ω∥ 1 = p i=1 |ω i |.( 2.19
) can be efficiently solved by a variety of algorithms, and leads to sparse models where the number of non-zero entries in ω is controlled by λ.

Feature selection and multiple-testing.

Another common method to select a small list of genes that should be retained in a predictive model is to assess the significance of each feature given an outcome. This is commonly done by performing univariate tests that compute a p-value representing the likeliness of a feature j to have the same distribution under different assumptions. These tests can either be parametric or nonparametric, depending on whether we have some assumptions about the distribution of the features.

The Student t-test is a popular parametric test where we suppose the data to be distributed according to a Gaussian mixture model for each features, that is, for a given feature j and an output ∈ {-1; +1}:

X j ∼ N (µ j , σ j ).
(2.20)

For each feature j,at-statistic is calculated as follows:

t j = x+1 j -x-1 j (σ +1 j ) 2 N +1 + (σ -1 j ) 2 N -1 . (2.21)
Under the null hypothesis H0 that "the feature j follows the same distribution independently of the output", t j follows a Student distribution and one can derive the p-value given by:

p right-tailed = Pr(X ≥ t j |H0) p left-tailed = Pr(X ≤ t j |H0) p two-tailed =2min Pr(X ≥ t j |H0),Pr(X ≤ t j |H0) (2.22)
In the case when the data are not normally distributed, one can instead use a nonparametric test such as the Wilcoxon rank sum test also known as the Mann-Whitney U test. The idea is to compare the ranking of the samples given the output.

Define:

R +1 j = i∈C +1 r i j , (2.23)
where C +1 is the subset of samples with positive output and r i j is the rank of the observation x j i , that is, the value of feature j for patient i. The U j -statistic is then given by:

U j = R +1 j - n +1 (n +1 + 1) 2 , (2.24)
where n +1 is the size of C +1 .U n d e rt h enull hypothesis H0, the distribution of U j is known [Wilcoxon, 1945, Mann andWhitney, 1947] and a p-value can be computed in a similar fashion as (2.22).

Applying either of the statistical tests returns a list of p-values that naturally gives a ranking of the association between each feature and the output. For a single feature, one generally applies a significance level (usually 5%). However, when the number of features is large, applying such a significance level would lead to several falsely detected features. In this case, one has to correct the p-values for multiple testing [Benjamini andHochberg, 1995, Dudoit andFridlyand, 2002].

Adding prior knowledge. While sparsity-inducing methods are useful as they allow to reduce the effective number of features, they are not alwayssufficient to overcome the problem of n p. For example, correlated variables can produce unstable signatures using lasso. Similarly, univariate tests do not take into account the joint distribution of features.

In a biological setting, one usually has access to additional information about the data such as the existence of biological pathways that can relate genes working together. One method to incorporate this prior knowledge into a model is to generalize the ridge and lasso regressions as follow:

Penalized(ω)=∥y -Xω∥ 2 + λΩ(ω), (2.25)
where Ω : R p → R + is a penalty function.

Several studies have investigated the choice of Ω such that optimal solution ω * has some specific properties:

ω * = arg min ∥y -Xω∥ 2 + λΩ(ω).
(2.26)

We have already seen that the solution of the lasso ω Lasso has the property of being sparse. As discussed above, this allows to select a subset of genes but sometimes fails to provide a stable signature as genes are often correlated. Knowing biological pathways, one can add this prior information by using a "group-lasso" penalty [Yuan andLin, 2006, Jacob et al., 2009]:

Ω(ω)= g∈G ∥ω g ∥, (2.27)
where G is a subset of P({1; ••• ; p}). For copy-number or methylation profile analysis, fused-penalties can be used as biological evidence suggest a strong correlation between close features on the genome. In addition, as profiles usually share well-defined biological traits, a joint regularization of signals can improve the detection of breakpoints [START_REF] Vert | Fast detection of multiple change-points shared by many signals using group lars[END_REF], see an illustration in figure 2.3

Unsupervised learning

Unsupervised learning, similarly to its supervised counterpart, also seeks to make sense of a series of observations. However in this case, there is no output Y and the observations are therefore summarized by an input X. The two most common objectives are: cluster analysis and dimensionality reduction.

Cluster analysis tries to summarize the set of observations by a small number ofm od e s from which the observations are drawn. In other words, the main assumption is that the set of observations is a mixture drawn from a few (generally simple) densities.

Dimensionality reduction can be associated with supervised methods. As seen previously, when the number p of features is high, the number of samples needed n needs to be very high in order to estimate Pr(X, Y ). However, while p can sometimes be large, the effective dimension can be much smaller. This is the case for example when most of the data lies in a low-dimensional manifold. In addition, this provides information about the associations between the different features.

The main issue in unsupervised learning is assessing the adequacy of the model. Contrary

to supervised learning where one could assess the effectiveness of a method by comparing Y and Ŷ , the quality of the results in the unsupervised case is often subjective.

In the following, we will discuss a few methods employed in unsupervised analysis for both cluster analysis and dimensionality reduction and illustrate them with specific examples in biology. We will in particular discuss the clinical importance of unsupervised learning despite not having clear model assessment techniques.

Cluster analysis

The main objective behind cluster analysis is to partition the set of observations into K subsets or "clusters". A natural partition is such that observations from a same cluster are more similar than observations from different clusters. These considerations naturally necessitate to introduce a similarity (or dissimilarity) measure over the set of observations.

Choosing a similarity between samples.

In general, the set of observations

(x 1 , ••• ,x n )l i ei nap-dimensional
d Pearson (x i ,x j )= 1 -r(x i ,x j ) 2 , (2.28) with r(x i ,x j )= p k=1 (x k i -xi )(x k j -xj ) p k=1 (x k i -xi ) 2 p k=1 (x k j -xj ) (2.29)
can yield significantly different results as illustrated in figure 2.4.

• • • • • • • • • • • • • • • • • • • • 1 2 3 4 5 X1 X2 X3 X4 X5 Dimension Value Figure 2
.4: Similarity between samples is subjective and therefore the clustering procedure might differ. This is a toy example representing 4 samples (red,green, blue,purple) in 5 dimension. A clustering algorithm using the euclidean distance would cluster samples 1 and 2 on one side and 3 and 4 on the other since they are close from as p a t i a lpo i n to fv i e w . H o w e v e ru s i n gaP e a r s o nd i s t a n c ew o u ld cluster samples 1 and 3o no n es i d ea n d2a n d4o nt h eo t h e rs i n c et h e i rv a r i a t i o n sa r emore coordinated (e.g the similarity in term of response to a treatment between two proteins can be better explained by the correlation of their abundance over time than by the absolute deviation between abundance over time).

K-means.

K-means is a popular clustering methods of the class of partitioning methods which, given a number K, partition the set of samples into K groups or clusters.

The goal is therefore to optimize over the set of partitions, a criterion such that the incluster similarity is large while the between-cluster similarity is small. In the case of K-means, one seeks a partition

S = {S 1 , ••• ,S k } that minimizes: K i=1 x∈S i ∥x -µ i ∥ 2 , (2.30)
where µ i is the barycenter of the samples in partition S i . Figure 2.5 illustrate an example of K-means on a toy dataset. Two main paradigms exists in hierarchical clustering:

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• bottom-up approaches, where each sample first belongs to its own cluster, and where clusters are merged together iteratively until all samples belong to a unique cluster.

• top-down approaches, conversely, start with all samples in a unique cluster and iteratively shatters clusters into two new clusters at each step until all samples are separated.

This particular set of methods yield interesting properties. In particular, visualization can be done using dendrogram, which is a representation of a binary tree where each leaf is a sample and each internal node represents the agglomerative procedure of merging two clusters together. In addition, cutting the dendrogram at a given height yields a clustering of the dataset in K clusters. A particular application in bioinformatics is to perform a clustering on the samples as well as on the features to identify a group of features (e.g genes) associated with a group of samples (e.g different clusters) (see an example in figure 2.6).

Dimensionality reduction

Another important family of unsupervised methods are dimensionality reduction techniques. Previously, we mentioned the curse of dimensionality as one important issue that constrains the number of observations to be large enough to be able to learn. In specific cases, although the samples are represented by a d-dimensional vector, the data actually lies in a much smaller space.

Principal Component Analysis (PCA). This is a method to find a sequence of orthogonal vectors that captures the most information about the data by solving :

e k = arg max ∥e k ∥=1,e k ⊥{e 1 ,...,e k-1 } e T k XT k Xk e k , (2.31) with Xk = X - k-1 i=1
Xe i e T i .

(2.32) PCA, like many other dimension reduction methods, is often use to visualize highdimensional data in 2D, like for example in figure 2.7. Model selection in unsupervised learning.

We discussed previously that one particular specificity in partitioning algorithms is the choice of the parameter K.A s illustrated in 2.8, an inappropriate choice in K can result in a ill-representation of the data at hand. We showed that hierarchical clustering could circumvent this problem.

But even in this case, one generally has to choose a fixed representation (i.e,c u tt h e dendrogram at a specific height). In dimensionality reduction, one also has to make a trade-off between the number of components and how well a projection on this subspace still retain enough information about the data (figure 2.9).

Given specific hypotheses about the data (e.g, Gaussian distribution), several criteria have been proposed such as the Akaike Information Criterion (AIC) [Akaike, 1973] or the Bayesian Information Criterion (BIC) [Schwarz, 1978] to assess an optimal K.

However, these hypotheses rarely apply to real data and various heuristics have therefore been proposed too. In particular, looking at the stability of the clustering solutions obtained by perturbing the original dataset [START_REF] Dudoit | A prediction-based resampling method for estimating the number of clusters in a dataset[END_REF],Ben-Hur et al., 2002a, Monti et al., 2003] is usually the preferred choice for biological data.

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • -2 0 2 -20 2
Clinical impact of unsupervised learning. For clinical purposes, assessing statistically the number of clusters (e.g, the number of breast cancer subclasses) is often not the primary objective. The clinical importance of the clustering, such as the discovery of subclasses with a significantly worse or better prognosis presenting a particular genomic or epigenomic profile, can help clinicians in proposing the most appropriate therapies to every single patients instead of a generic therapy to all patients.

Conclusion

In this chapter, we presented a brief overview of relevant statistical methods used throughout the remaining of this thesis in a particular biological context:

• Poor statistical power (n p) leans toward simpler (linear) methods incorporating prior biological knowledge.

• A biological interpretation of the results at the cost of accuracy can sometimes be preferred as post-experimental validations can be undertaken to assess definitely the validity of the biological phenomenon.

• The performance of unsupervised methods can be assessed based on their clinical impact instead of mathematical criteria.

Chapter 3

Epigenomic alterations in breast carcinoma from primary tumor to locoregional recurrences Some content from this chapter has been published as part of a peer-reviewed article in PLoS One [START_REF] Moarii | Epigenomic Alterations in Breast Carcinoma from Primary Tumor to Locoregional Recurrences[END_REF].

Keywords: Breast cancer, recurrence, metastasis, methylation, clonality, true recurrence.

Résumé

Les modifications épigénétiques telles que les variations abnormales de la méthylation de l'ADN sont associées à l'apparition de cancers. Epigenomic alterations are well suited to study clonality and track cancer progression.

Methylation-based classification of TR and NP performed as well as clinical and copynumber based methods suggesting that these phenomenons are tightly linked.

Introduction

Breast conservative therapy, consisting in a partial mastectomy followed by whole breast irradiation, is the standard treatment for patients with early stage breast cancer. Overall survival is not significantly different from more physically and psychologically aggressive treatments such as mastectomy [Van Dongen et al., 2000]. However, patients relapse within 10 years in the same breast as the primary tumor (PT) in approximately 6% of cases [START_REF] Bartelink | impact of a higher radiation dose on local control and survival in breast-conserving therapy of early breast cancer: 10-year results of the randomized boost versus no boost EORTC 22881-10882 trial[END_REF], and within 5 years in the contralateral breast in approximately 3.5% of cases [START_REF] Vichapat | Tumor Stage Affects Risk and Prognosis of Contralateral Breast Cancer: Results From a Large Swedish Population Based Study[END_REF] or more in BRCA1/2 mutation carriers [START_REF] Metcalfe | Contralateral breast cancer in BRCA1 and BRCA2 mutation carriers[END_REF]. Moreover, at the time of diagnosis, early stage breast cancers have already spread to axillary lymph nodes in roughly 30% of cases [Jatoi, 1999].

These different types of locoregional evolutions have different implications in terms of survival and treatments. Axillary metastases (AM) is usually predictive of poor survival [START_REF] Carter | Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases[END_REF]] and is considerably worsen in triple negative breast cancers [START_REF] Borg | HER2/neu Amplification predicts poor survival in Nodepositive Breast Cancer[END_REF]. Local recurrences (LR) have been tightly linked with a greater risk of distant metastasis [START_REF] Haffty | Ipsilateral breast tumor recurrence as a predictor of distant disease: implications for systemic therapy at the time of local relapse[END_REF]. Veronesi et al. [START_REF] Veronesi | Local Recurrences and Distant Metastases After Conservative Breast Cancer Treatments: Partly Independent Events[END_REF]d i stinguished two categories of local recurrences : true recurrences (TR), corresponding to re-growth of resistant cells after initial treatment, and new primary tumors (NP), corresponding to de novo cancer. This classification is of potential interest to define adapted treatment scheme, as NP are considered to have an improved survival compared to TR [START_REF] Smith | True recurrence vs. new primary ipsilateral breast tumor relapse: An analysis of clinical and pathologic differences and their implications in natural history, prognoses, and therapeutic management[END_REF]. Contralateral breast cancers (CL) are also an heterogeneous entity depending on the synchronism with the primary tumor. Synchronous bilateral breast cancers are developed at the same time, with the same genetic, environmental and hormonal background as the PT. Metachronous CL are usually treated as new cancers [START_REF] Dawson | Evolving perspectives in contralateral breast cancer[END_REF]] although a rare portion are considered as metastases. Overall, CL are still associated with a greater risk of metastasis compared to patients without CL [START_REF] Healey | Contralateral breast cancer: clinical characteristics and impact on prognosis[END_REF].

Differences between the PT and either the AM, the LR or the CL have been studied at the genomic, transcriptomic and proteomic levels. Ellsworth et al. [START_REF] Ellsworth | Allelic Imbalance in Primary Breast Carcinomas and Metastatic Tumors of the Axillary Lymph Nodes[END_REF] showed an overall frequency of allelic imbalance greater in PT than in AM.

Weigelt et al. [START_REF] Weigelt | No common denominator for breast cancer lymph node metastasis[END_REF] explored the gene expression profile of PT and their matched AM but were not able to identify a subset of genes to discriminate them, while Feng et al. [START_REF] Feng | Differentially expressed genes between primary cancer and paired lymph node metastases predict clinical outcome of node-positive breast cancer patients[END_REF] [START_REF] Bollet | High-Resolution Mapping of DNA Breakpoints to Define True Recurrences among Ipsilateral Breast Cancers[END_REF], Ostrovnaya et al., 2010], intratumoral immune responses [START_REF] West | Intratumoral Immune Responses can Distinguish Bibliography 129 New Primary and True Recurrence Types of Ipsilateral Breast Tumor Recurrences (IBTR)[END_REF], loss of heterozigosity [START_REF] Vicini | The Use of Molecular Assays to Establish Definitively the Clonality of Ipsilateral Breast Tumor Recurrences and Patterns of In-breast Failure in Patients with Early-stage Breast Cancer Treated with Breast-conserving Therapy[END_REF], to p53 analysis [Van Der Sijp et al., 2002], or X-chromosome inactivation [START_REF] Shibata | Clonal Analysis of bilateral breast cancer[END_REF].

Finally, studies of PT and CL highlighted the role of synchronism of the CL. Similarity measures based on DNA copy number profiles [START_REF] Brommesson | Tiling array-CGH for the assessment of genomic similarities among synchronous unilateral and bilateral invasive breast cancer tumor pairs[END_REF] or allelic imbalance [START_REF] Imyanitov | Concordance of Allelic Imbalance Profiles in Synchronous and Metachronous Bilateral Breast Carcinomas[END_REF] showed a higher level of similarity between PT and synchronous CL compared to PT and metachronous CL.

Epigenetic modifications in cancer has recently been the topic of many studies. In particular the link between hypermethylation and gene silencing is well known [START_REF] Razin | DNA Methylation and Gene Function[END_REF], Tate and Bird, 1993, Bird, 2002]. Several studies have then focused to describe cancer as an epigenetic disease. Baylin et al. [START_REF] Baylin | Aberrant patterns of DNA methylation, chromatin formation and gene expression in cancer[END_REF]h a v es h o w n that aberrant hypermethylation of specific regions, dominantly CpG islands, are linked with the silencing of tumor suppressor genes and that this phenomenon is present in most cancers. Laird [START_REF] Laird | DNA methylation and cancer[END_REF], Ehrlich [Ehrlich, 2002] and Das [START_REF] Das | DNA methylation and cancer[END_REF] suggested that a global hypomethylation phenomenon was also linked with tumorogenesis. Jones [START_REF] Jones | The epigenomics of cancer[END_REF]] made a complete review of the hallmarks of epigenomics associated with cancer. Moreover, DNA methylation is conserved during cell division [Bird, 2002, Schermelleh et al., 2007] and could serve as a measure for clonality between cells in the classification of LR as either TR or NP.

In this study, epigenetic differences as well as similarities between PTs and either their AMs, LRs or CLs are analyzed. In the first part, univariate and multivariate analyzes are performed between the methylome profiles of primary tumors and their matched recurrences to observe recurrent patterns in cancer progression. Then in the second part, epigenome-wide similarity analyzes on the same samples is performed to observe clonality between tumor cells.

Chapter 3. Epigenomic alterations in breast carcinoma 35

3.4 Materials and Methods

Patients Selection

The patients selected for the study were 49 years old or younger at diagnosis of the initial tumor; all patients were premenopausal; and had no previous history of cancer, except for one nonmelanoma skin cancer. The patients' PT was either ductal or lobular invasive breast carcinoma. However, both types of tumors did not display significantly differentially methylated probes and were thus all included in this study (data not shown).

Specimens from patients with primary breast cancers and breast cancer recurrences were selected from freshly frozen samples of the Institut Curie tissue bank according to the following criteria: all patients had been treated at the Institut Curie by breastconserving surgery, including dissection of the axillary lymph nodes in most patients, followed by radiotherapy to the breast with or without a boost to the tumor bed (external beam radiotherapy or brachytherapy) and/or to the regional lymph node-bearing areas if indicated and, when required, systemic treatment as part of their initial management.

Tumor size did not correlate with the overall methylation rate (data not shown).

To ensure that the data would be informative, genomic analyzes were restricted to tumors (primary and recurrences) in which at least 50% of cancer cells had been assessed by hematoxylin, eosin, and saffron staining of sections from snap-frozen samples. All the therapies were performed posterior to the biopsies of the primary tumors. Therefore, the studied methylation profiles are not modified by any potentiale ffect of the treatments.

The 22 healthy breast tissues are taken from healthy women who underwent cosmetic plastic surgery at the Institut Curie. Part of the PT/AM cohort is identical to the cohort studied by Bollet et al. [START_REF] Bollet | High-Resolution Mapping of DNA Breakpoints to Define True Recurrences among Ipsilateral Breast Cancers[END_REF].

All experiments were performed retrospectively and in accordance with the French Bioethics Law 2004-800, the French National Institute of Cancer (INCa) Ethics Charter and after approval by the Institut Curie review board and ethics committee (Comité de Pilotage of the Groupe Sein). In the French legal context, our institutional review board waived the need for written informed consent from the participants. Moreover, women were informed of the research use of their tissues and did not declare any opposition for such researches. Data were analyzed anonymously.

Methylation profiling

For each sample the methylation status at 27,578 positions in the genome was measured with the HumanMethylation27 BeadChip of Infinium technology [ Weisenberger et al., 2008] using the standard Illumina protocol. Quality control was assessed using in-built Illumina technology.

Clinical Classification.

Histopathologic characteristics were reviewed by a single pathologist. The histological and biological properties of each sample was determined by subjecting tissue sections to immunohistochemical analysis for the estrogen receptor (clone 6F11, 1:200 dilution;

Novocastra, Newcastle Upon Tyne, England) and progesterone receptor (clone 1A6, 1 : 200 dilution; Novocastra) antibodies. Tumors were considered to be positive for these receptors if at least 10% of the invasive tumor cells in a section showed nuclear staining [START_REF] Balaton | Immunohistochemical evaluation of estrogen and progesterone receptors on paraffin sections of breast carcinomas. Practical thoughts based on the study of 368 cases[END_REF], Balaton et al., 1996]. The HER2 analysis was performed using the standard ASCO guidelines [START_REF] Wolff | Recommandations for Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Clinical Practice Guideline Update[END_REF]. In accordance with theories of the clonal evolution of tumor cell populations, LR were clinically defined as TR if they had the same histologic subtype (ductal or lobular) and a similar or increased growth rate, similar estradiol, progesterone and HER2 receptor statuses, and similar or decreased differentiation as the initial tumor [START_REF] Smith | True recurrence vs. new primary ipsilateral breast tumor relapse: An analysis of clinical and pathologic differences and their implications in natural history, prognoses, and therapeutic management[END_REF]. TR also had to share with their PT the same breast quadrant. Thus, new PT were clinically defined as such when the LR had occurred in a different location, had a distinct histologic type, or had less aggressiveness features (lower grade, presence of hormonal receptors) than the initial tumor.

Data analysis

A spatial normalization process was applied to all profiles [START_REF] Sabbah | SMETHILLIUM: Spatial normalisation METHod for ILLumina InfinIUM Human-Methylation BeadChip[END_REF]]. Among the 27,578 probes measured on each sample, 5 probes were removed due to missing values for some individuals, and all subsequent analysis was performed on the 27,573 remaining probes.

Differentially methylated probes between PT and their matched AM, LR and CL are obtained using two-sided paired and unpaired Wilcoxon tests, correcting the p-values for multiple testing with the methods of Benjamini and Hochberg [START_REF] Benjamini | Controlling the false discovery rate: a practical and powerful approach to multiple testing[END_REF]. Multivariate analysis was performed using a linear support vector machine (SVM) multidimensional classifier on either the complete methylation profile or after dimensional reduction by considering only the most significant probes based on the Wilcoxon test. A p-value was calculated to assess the significance of the predictor accuracy compared to a predictor that would predict classes randomly. Unsupervised classifications were performed with complete linkage agglomerative clustering using the MATLAB R bioinformatics toolbox, while the support vector machine implemented in LIBSVM [START_REF] Chang | LIBSVM : a library for support vector machines[END_REF] was computed with a linear kernel and nested leaveone-out cross validation for parameter selection for supervised classification.

The similarity between two copy number profiles is assessed with the partial identity score (PIS) as defined by Bollet et al. [START_REF] Bollet | High-Resolution Mapping of DNA Breakpoints to Define True Recurrences among Ipsilateral Breast Cancers[END_REF], which is based on the quantity of shared breakpoints between the two profiles and their frequencies. Following [START_REF] Bollet | High-Resolution Mapping of DNA Breakpoints to Define True Recurrences among Ipsilateral Breast Cancers[END_REF], a recurrence from a matched PT/LR pair was considered TR based on copy numbers when the PIS between the PT and LR profiles was above the 95% quantile of the empirical PIS distribution between unrelated sample pairs. Similarly, a Methylation-Similarity score (MS) is defined based on the methylation profiles of a PT and its matched LR as the inverse of the Manhattan distance between their methylation profiles considered as 27,573-dimensional vectors. LR are then classified as TR of its matched PT when the MS score is above the 95% quantile of the empirical MS distribution between unrelated pairs. As a baseline, these results were compared to the Manhattan distance between unrelated normal breast tissues.

Metastasis-free survival was estimated by the Kaplan-MeierMethod [START_REF] Kaplan | Nonparametric estimation from incomplete observation[END_REF] and compared between the group of patients who were diagnosed as TR and the group diagnosed as NP using the log-rank test. The confidence interval of the hazard ratio was obtained using a semi-parametric Cox model [START_REF] Cox | Analysis of Survival Data[END_REF].

Computation was done using MATLAB R packages Logrank [Cardillo, b] and KMPlot [Cardillo, a].

Results

Methylation differences between PT and their matched metastasis or recurrence

A collection of 17 PT/LR pairs, 11 PT/CL pairs, and 20 PT/AM pairs was analyzed.

The methylation data are available in the GEO database record number : GSE44870.

Tables 3.1, 3.2 and 3.3 detail the clinico-histopathological properties of each sample.

Some of the PT/LR samples match in part the cohort studied by Bollet et al. [START_REF] Bollet | High-Resolution Mapping of DNA Breakpoints to Define True Recurrences among Ipsilateral Breast Cancers[END_REF], and the corresponding sample numbers from both studies arep r o v i d e di n table 3.1.

Within each of the three cohorts, pairs of tumors including a PT and a metastatic or relapse sample can be used to investigate whether particular patterns in methylation profiles can serve as marker for cancer progression. Within each cohort, investigations were made to detect differences at the methylome level between PT and the corresponding matched metastasis (AM) or relapse samples (LR or CL) . Using a paired Wilcoxon test, 49 probes significantly differentially methylated were found between PT and AM samples (at a 5% FDR level). The top 10 probes (ranked by p-value) and the corresponding genes are listed in table 3.4. This suggests that a general signal characteristic of cancer progression from PT to AM might exist.

However, no probe was found significantly differentially methylated between PT and LR, and between PT and CL. This may be due to the lack of cancer progression marker at the methylation level between PT and relapse, to the fact that most relapses may Methylation Variation: Mean variation of methylation from the primary tumor to the axillary metastasis.

Methylation conservation between PT and their matched metastasis or recurrence

Instead of searching for differences between PT and their matched metastasis or recurrence, which may characterize markers for cancer progression, the study also focuses on similarities between methylation profiles, which may be useful for example to characterize clonality between a PT and a recurrence. A hierarchical clustering was first performed for all samples within each cohort to characterize the similarities between real matched pairs compared to unrelated samples. The resulting dendrograms are presented in 3.2. Interestingly we see that matched pairs of PT and metastasis/recurrence ) and in the PT/LR cohort (P-value=1.6 * 10 -6 ). This is however not true in the PT/CL cohort, where we detect no differences between correctly and randomly matched pairs (P-value=0.44). In addition, we calculated the distribution of distances between the CL tumors. We performed the same analysis between the PT tumors. We observed that the distribution were not significantly different (data not shown), as expected. This is in agreement with the assumption we made that CL tumors could be considered as new primary tumors. Finally, we also compared the distribution of distances between the healthy breast tissue i and all the other healthy breast tissues from the cohort to assess the heterogeneity between normal breast tissues.

Clonality detection based on methylation profiles

The above results suggests that methylation profiles tend to be conserved during clonal expansion (such as samples in the PT/AM cohort), but stronglydiffer between unrelated tumors in a given person (such as samples in the PT/CL cohort). Moreover, methylation seems to be a stable mechanism in normal tissues compared to cancerous ones. It is therefore tempting to use methylation distance as a tool to discriminate true recurrences from new tumors in ambiguous cases, that is, for samples in the PT/LR cohort.

As shown in Figure 3, 9 out of 17 PT/LR pairs (52%) have a MS score higher than the threshold given by the 95% percentile of the MS score between unrelated pairs (MS T hreshold =6.6 * 10 -4 ); they are therefore considered as clonal pairs from the methylation point of view. The remaining 8 pairs are considered as non-clonal, meaning that the LR may correspond to a new primary tumor.

Comparison between the methylation-based similarity measure MS score with the copynumber-based similarity measure (PIS) developed by [START_REF] Bollet | High-Resolution Mapping of DNA Breakpoints to Define True Recurrences among Ipsilateral Breast Cancers[END_REF] show a good correlation overall (ρ =0 .55, P-value=3.7 * 10 -5 , see figure 4). Table 5 gives Finally, the different classifications of LR as TR or NP were correlated with time-torecurrence and metastasis-free survivals. The differences in time-to-recurrence for the two groups defined by methylation-based classification or the clinical and histological classification were not statistically significant (P-value=0.83 and P-value=0.12). It was however significant using the partial identity score (P-value=0.03). This is interesting in the sense that one of the main criteria to distinguish TR and NP is the time-torecurrence. Therefore, methylation-based classification is based on more information than time only. the type of pairs. Each boxplot represents the distribution of Manhattan distance between matched primary and locoregional evolution ("Real"), between non-matched primary and locoregional evolution ("Artificial"), betweent w op r i m a r yt u m o r s( " P r imary") or between two locoregional evolution ("Recurrence") for each dataset.

The difference in metastasis-free survival of patients with TR and NP was not significant based on methylation (P-value=0.52, Hazard-Ratio=3.7, 5 year metastasisfree survival=75% for NP), copy-number (P-value=0.15, Hazard-Ratio=16.9, 5 year metastasis-free survival=86% for NP) or clinical features (P-value=0.17, Hazard-Ratio=6.3, 5 year metastasis-free survival=86% for NP) (figure 5).

Discussion

We studied alterations of methylation profiles from primary breast carcinomas and different types of recurrences, namely, axillary metastases, local recurrences and contralateral breast carcinomas. For this particular dataset, we observed significant methylation differences for 49 CpG probes, which characterizes the progression between a PT and its AM. Consistent with this result, a multivariate analysis with a linear SVM classifier using a small subset of probes perfectly distinguished PTs from AMs with a 100% accuracy. Several significantly differentially methylated probes correspond to genes involved in cancer-related mechanisms such as cell death (MCF2L, RASSF5, RASSF6, CASZ1, SLC22A18, IFI27 ), tumorogenesis (CTSZ, TP73, CTSK, PIK3R1 ), KLK11,c e l lc y c l e (PPM1G, RANBP5, VAMP8 ) and cell differentiation (SMAF1, PAX6, PAX8 ). On the contrary, for the PT/LR and PT/CL sets, univariate analyzes were not able to find significantly differentially methylated probes. This absence of specific epigenetic alterations between the primary tumors and the local recurrences or the contralateral breast recurrences was confirmed by the poor performances of linear classifiers, unable to separate PT from LR nor PT from CL significantly better than random guesses. Nevertheless, the absence of methylation markers in the PT/LR and the PT/CL groups does not necessarily mean that the primary tumor and the recurrence are independent. We cannot rule out the possibility that the recurrence arises from a specific subclone which does not match the major subclone of the primary tumor. One could for example analyze the methylation profiles of several microdissections samples of the primary tumor to study The second part of the study focused on observing stability in methylation profiles. It is interesting to note that although PTs and AMs were significantly differentiable using a subset of probes, they also have overall very similar methylation profiles indicating that the tumors might actually be clones with specific alterations characteristic of the lymph node status. The subset of genes determined in the first part, if confirmed, could be associated with bad prognosis. On the other part, although the LRs and the CLs were not significantly different from their primary tumors, they tend to have overall different methylome profiles especially for the CLs. The overall different methylome profiles for the PT/CL set was expected since CLs are usually considered to be independent tumors.

The results above suggested to use global methylation analysis as a measure of clonality to tackle the subclonal populations in the local recurrences as proposed by Veronesi et Chapter 3. Epigenomic alterations in breast carcinoma 50 al. [START_REF] Veronesi | Local Recurrences and Distant Metastases After Conservative Breast Cancer Treatments: Partly Independent Events[END_REF]. A methylation-based classification was proposed to distinguish LRs as either true recurrences of the first PT or new PT [START_REF] Smith | True recurrence vs. new primary ipsilateral breast tumor relapse: An analysis of clinical and pathologic differences and their implications in natural history, prognoses, and therapeutic management[END_REF].

A comparison with both clinical and copy-number based classifications on the same cohorts agreed on 14 out of 17 samples (82% concordance, P-value=6.410 -3 ) for both methods, although comparisons on larger cohorts are needed to assess the performance of methylation-based classification. Moreover, a good correlation between the methylationbased similarity score and the copy-number based similarity score seems to indicate a link between modifications at the genomic and epigenomic levels. Although the role of methylation in gene expression has thoroughly been studied [Bird, 2002,Razin and Riggs, 1980, Tate and Bird, 1993], the relationship between methylation and copy-number still remains unclear. Houseman et al. [START_REF] Houseman | Copy number variation has little impact on bead-array-based measures of DNA methylation[END_REF] showed no clear relationship between methylation and copy-number. On the other hand, Lauss et al. [Lauss et al., 2012b] observed associations between the two mechanisms in urothelial carcinoma, while a short report by Kwee et al. [START_REF] Kwee | Integrated DNA copy number and methylation profiling of lymphoid neoplasms using a single array[END_REF] tries to build copy number profiles from methylation profiles alone. Our study seems to validate the second hypothesis that methylation and copy-number are well connected mechanisms.

The discordances between the methylation-based classification method and the usual clinical method are discussed here for the samples 7, 8 and 14, although no actual method is a gold standard for classifying TR from NP. Sample 8 filled almost all the requirements for clinical classification as TR (location, receptor status) but failed in aggressiveness and type of tumor (PT was ductal type 2 and LR was lobular type 1). A decrease of aggressiveness of the recurrence could be explained by the use of neoadjuvant therapies.

For the change of type, Fisher et al. showed that a mixing of ductal and lobular breast carcinoma was a possibility in 6% of the patients [START_REF] Fisher | The pathology of invasive breast cancer. A syllabus derived from findings of the National Surgical Adjuvant Breast Project (protocol no. 4)[END_REF] which could explain the change in type. Sample 7 was classified as TR by clinical classification and as NP by both methylation and copy-number based classifications. This suggests some limitations to methods based only on clinical features.

An interesting question for clinical applications would have been to predict whether a primary tumor would relapse (either as AM, LR or CL) or not. However, the patient cohort used in this study does not allow to address this question. Indeed, one would require to compare the methylation profiles of patients who did not display any relapse (AM, LR and CL) to those of the current study.

Chapter 4

Changes in gene expression control by DNA methylation in cancer Some content from this chapter has been submitted to BMC Genomics.

Keywords: High-density methylation patterns, gene expression epigenetic regulation, epigenetic shift in cancer. Enfin, nous observons un lien complémentaire entre la méthylation et le copy-number dans la prédiction de l'expression des gènes.

Résumé

Nos résultats suggèrent que durant la tumorogénèse, un mécanisme de reprogrammation é p i g é n é t i q u e s ' e ffectue. Ce mécanisme n'a pas un impact direct sur l'expression des gènes associés, mais agit sur la régulation de la transcription en affectant la susceptibilité des gènes aux variations épigénétiques.

Abstract

Introduction

DNA methylation is one of the main epigenetic mechanisms, alongside histone modifications, that plays a significant role in gene silencing [START_REF] Newell-Price | DNA Methylation and Silencing of Gene Expression[END_REF], tissue differentiation [START_REF] Laurent | Dynamic changes in the human methylome during differentiation[END_REF], cellular development [START_REF] Smith | DNA methylation: roles in mammalian development[END_REF],

X-chromosome inactivation [START_REF] Pollex | Recent advances in Xchromosome inactivation research[END_REF], or genetic imprinting [Li et al.,Chapter 4. Changes in gene expression control by DNA methylation in cancer 54 1993]. Aberrant hyper-methylation of high-density CpG regions known as CpG Islands (CGIs) [Esteller, 2002] and genome-wide hypo-methylation [Ehrlich, 2002] have often been associated with cancer and there has been an increasing effort to understand the specific epigenetic modifications that contribute to carcinogenesis [START_REF] Laird | DNA methylation and cancer[END_REF], Das and Singal, 2004, Kulis and Esteller, 2010].

The possibility to measure DNA methylation genome-wide on normal and cancer tissues, with microarray or sequencing technologies, has triggered a lot of data-driven research to clarify the role of methylation in gene regulation and cancer. Several studies have highlighted a correlation between differentially methylated regions near promoter regions and gene expression changes [START_REF] Meissner | Genome-scale DNA methylation maps of pluripotent and differentiated cells[END_REF], Lister et al., 2009, Zhang et al., 2011, Hansen et al., 2012, Varley et al., 2013]. However, it has also been reported that aberrant over-methylation occurs mostly in normally down-regulated genes, questioning the role of methylation as a causal mechanism for gene repression [START_REF] Keshet | Evidence for an instructive mechanism of de novo methylation in cancer cells[END_REF], Sproul et al., 2011, Sproul et al., 2012, Sproul and Meehan, 2013]. More recently, Timp et al. have proposed a model where epigenetic aberrations contribute to carcinogenesis by dysregulating the functions of specific genes that regulate the epigenome itself [Timp andFeinberg, 2013, Timp et al., 2014]. Reddington et al. speculate that epigenetic reprogramming might lead to an altered Polycomb binding landscape which could potential impact genome regulation [START_REF] Reddington | DNA methylation reprogramming in cancer: does it act by re-configuring the binding landscape of Polycomb repressive complexes?[END_REF].

To gain further insight into the role of DNA methylation in cancer, we perform a largescale meta-analysis of methylation profiles of normal and cancerous tissues from The Cancer Genome Atlas (TCGA), focusing for each CGI on (i) how, on average, their methylation level differs between normal and cancer samples and between different tissues, and (ii) how their association with gene expression level, as estimated from interindividual variability within each sample category, differs. We show in particular that in normal tissues, most CGIs tend to be either hypo-or hyper-methylated, and that the classification is stable across tissues of origin; on cancer samples, on the other hand, a stable subset of the CGIs witness a change in their methylation status in a subset of patients. While this change in methylation has a prognostic value for patient survival in breast cancer, we did not find evidence that it directly impacts gene expression level, as most of the genes concerned are already lowly expressed both in normal and in cancerous tissues. Similar findings were already reported in [START_REF] Sproul | Transcriptionally repressed genes become aberrantly methylated and distinguish tumors of different lineages in breast cancer[END_REF]. A finer analysis of the link between CGI methylation and gene expression in the different types of tissues highlights the presence of many genes whose expression is under control of CGI methylation, particularly through changes in methylation of CpG in the flanking regions of CGIs. These genes are not the same in different tissues, and not the same in normal and cancerous tissues, but are overall enriched in transcription factors. This suggests that epigenetic reprogramming might contribute to carcinogenesis in part by modifying gene expression susceptibility to changes in DNA methylation.

Materials and Methods

Patients Selection

All data are issued from TCGA data portal. Cancer types selected are breast, colon and lung adenocarcinomas as consequent matched datasets were available for methylation, gene expression and copy number profiles. The datasets are detailed in 4.1 and the different institutions that released the data are mentioned in the acknowledgement section.

Methylation profiling

Methylation profiles were retrieved from level 2 TCGA data obtained the Illumina Hu-manMethylation450K DNA Analysis BeadChip assay, which is based on genotyping of bisulfite-converted genomic DNA at individual CpG-sites to provide a quantitative measure of DNA methylation [START_REF] Bibikova | High density DNA methylation array with single CpG site resolution[END_REF]. Following hybridization, the methylation value for a specific probe was calculated as the ratio M/(M + U )w h e r eM is the methylated signal intensity and U is the unmethylated signal intensity. 485,577 CpG methylation levels, associated with 27,176 CGIs and 21,231 genes, were measured as such accross the genome.

Following [START_REF] Irizarry | Genome-wide methylation analysis of human colon cancer reveals similar hypo-and hypermethylation at conserved tissuespecific CpG island shores[END_REF], we considered not only the CGI methylation profile but also included in the analysis proximal regions in the near vicinity (up to 4kb), namely the CGI Shores and Shelves regions in a general CGI+SS methylation profile. As we were interested in the coordinated variations of methylation, we restricted the analysis Chapter 4. Changes in gene expression control by DNA methylation in cancer 56 to CGI+SS profiles containing at least 20 probes which reduced the analysis to 1827

CGI+SS associated with 2374 genes from the original dataset.

Gene expression profiling

Gene expression profiles were retrieved from level 3 TCGA data, that is obtained from the Illumina HiSeq RNASeq technology and processed following [START_REF] Mortazavi | Mapping and quantifying mammalian transcriptomes by RNA-Seq[END_REF].

Copy number variations processing

Copy number variations were retrieved from the level 3 TCGA data infered from Affymetrix SNP6.0 data files in GenePattern following [START_REF] Reich | GenePattern 2.0[END_REF]. For each gene, we then obtained the log ratio copy number score as the segmented log ratio score for the interval containing its transcription start site.

Combined CpG island, shores and shelves pattern analysis using dynamic time warping

CGI+SS patterns were compared using dynamic time warping (DTW) [START_REF] Rabiner | Fundamentals of Speech Recognition[END_REF] as it is less sensitive to small variations than the Fréchet distance [START_REF] Efrat | Curve Matching, Time Warping, and Light Fields: New Algorithms for Computing Similarity between Curves[END_REF]u s e di n [ Vanderkraats et al., 2013]. Dynamic time warping was originally applied as a speech signal similarity measure and has been applied with success in several other fields including computer vision [START_REF] Serra | Subpixel contour matching using continuous dynamic programming[END_REF], protein structure matching [START_REF] Wu | Regression analysis of multiple protein structures[END_REF]] and time series analysis [START_REF] Keogh | Scaling up Dynamic Time Warping to Massive Datasets[END_REF].

A CGI+SS profile i can be represented as a couple of vector (X i ,Y i )=((x i 1 ,y i 1 ),...,(x i n ,y i n )) where x i k represents the position of the k th CpG associated with the CGI+SS and y i k ∈ [0; 1] represents the mean methylation level for this probe accross a given dataset.

Given two vectors of size m and n respectively. A path w is a vector The main differences between euclidean and dynamic time warping distance that is the pairing system between two signals are illustrated in 4.2. Moreover, the algorithm for DTW is described in 1. DTW is then applied for each pair of CGI+SS patterns to obtain a dissimilarity matrix that assesses the similarity in shapes between all the CGI+SS methylation profiles.

(w k 1 ,w k 2 ) (k∈[1:K]) in [1; m] × [1; n] that verifies: • w 1 ∈ {1} × [1; n] ∪ [1; m] × {1} (partial initialization) • ∀i ∈ {1; 2}, w k+1 i = w k i or w k+1 i = w k i + 1 (monotonicity and continuity) • w K ∈ {n} × [1; n] ∪ [1; m] × {n} (partial
Ward hierarchical clustering is then performed to assess the existence of characteristic patterns amongst the different datasets. The number of significant clusters is assessed through bootstrapping (n repeats = 100) on a random subset of CGI+SS of the initial dataset (ratio = 80% of the total number of CGI+SS) following Ben-Hur et al [START_REF] Ben-Hur | A stability based method for discovering structure in clustered data[END_REF].

Survival analysis

Overall survival was estimated using the Kaplan-Meier method [START_REF] Kaplan | Nonparametric estimation from incomplete observation[END_REF] to compare the survival between the group of patients with a lower level of methylation in the hemi-methylated CGI+SS compared to the group of patients with a higher level of methylation. A multivariate Cox proportional hazards regression model [START_REF] Cox | Analysis of Survival Data[END_REF] was also fitted to estimate the additional value of this classification as a predictive factor for survival compared to other clinical parameters such as age, tumor size, lymph node status, receptor status and HER2/NEU status.

Computing the predictive power of methylation

We apply ridge [START_REF] Hoerl | Ridge Regression : Applications to Nonorthogonal Problems[END_REF] and LASSO [Tibshirani, 1996] multivariate regression methods to predict gene expression using the full CGI+SS methylation profiles as well as univariate least square regression when using only the averaged methylation from the whole CGI+SS profile. Following Acharjee et al. [Acharjee, 2013], we assess the predictive power of the methylation using the predictive goodness of fit R 2 which represents the squared Pearson correlation between observed and fitted values on an independent dataset. The estimation of the predictive power for each gene is obtained through 3-fold cross-validation averaged over 100 repeats. Parameters for both lasso and ridge regression methods were obtained by minimizing the mean squared error function using nested 3-fold cross-validation on the training dataset. The use of the predictive goodness of fit instead of the classic mean squared error as a score allows to compute a comparable score between different predictions. In particular, the mean squared error is highly affected by the absolute level of gene expression while the R 2 is invariant to scaling. It is also important to note that in this case the R 2 computed for least square regression is a prediction R 2 and not just a goodness-of-fit of the given dataset and therefore provides confidence on the generalization of the score on independent datasets.

Results

Classification of genes based on their CGI methylation profiles in normal and cancerous tissues

We first assess how promoter methylation profiles differ between genes, when for each gene we consider the average methylation profile across normal or cancerous samples.

For that purpose, we collected high-density methylation datasets from the cancer genome atlas (TCGA) data portal providing more than 485K CpG methylation levels for 672 normal and cancerous samples from three tissues of origin: breast, colon and lung (4.2).

For each CGI, we combine the probes in the CGI and in the shore and shelves of the CGI, defined as the regions up to 4kb outside of the CGI [START_REF] Irizarry | Genome-wide methylation analysis of human colon cancer reveals similar hypo-and hypermethylation at conserved tissuespecific CpG island shores[END_REF], in a unique CGI, shores and shelves (CGI+SS) methylation profile. We restrict our analysis to the 1827 CGI+SS where at least 20 CpG probes are measured by the technology in order to have high enough coverage to measure the methylation variation within each CGI+SS. For each of the three tissue of origin, and each normal or cancerous set of tissues, we compute the average methylation profile of each CGI+SS by averaging the methylation values of each CpG across the samples. Hence we compute 3×2 = 6 average profile for each CGI+SS, with we refer to below as CGI+SS signatures. 2). To clarify the types of signatures captured by each cluster, we represent on a standardized CGI+SS x-axis the 10 medoid CGI+SS signatures for each cluster and each tissue (4.4).

We clearly observe that the large cluster 1, which contains about 90% of all CGI+SS, corresponds to hypo-methylated islands with hemi-methylated CGI shores and hypermethylated CGI shelves, while the smaller cluster 2 contains about 10% of CGI+SS which are fully hyper-methylated. A closer look at cluster 1 shows that, in some cases, the variation of methylation between islands and shores is unclear, in the sense that some shores are fully hypo-methylated. As CGIs, shores and shelves regions are delimited based on somehow arbitrary criteria, a systematic analysis of these signatures could lead to a refinement of currently accepted boundaries.

Performing the same unsupervised classification independently on signatures obtained from the three types of cancerous tissues leads to different results, with the apparition of a third stable cluster (4.4 panel B/D/F ). Comparing the clusters of normal and cancerous tissues shows that, for all types of tissues, the first two clusters found in cancerous tissues are mostly composed of CGI+SS of the corresponding clusters in normal tissues, while the CGI+SS in the third cluster, specifically found in cancerous tissues, tend to come evenly from both clusters in normal tissues (4.3). A look at representative signatures of each cluster (4.5,4.6,4.7) confirms that clusters 1 and 2 contain respectively hypo-and hyper-methylated profiles, just like the respective clusters in normal tissues, while cluster 3 contains CGI+SS signatures which are partly methylated. Separating the CGI+SS in cluster 3 into sub-clusters "3up" and "3down", depending on whether they are in cluster 1 or 2 in normal tissues, we further see that the level of methylation of CGI+SS signatures in the "3up" sub-cluster tends to be lower than the level of methylation of CGI+SS signatures in the "3down" sub-cluster. Interestingly, cluster 3 is mostly conserved between tissues (4.11), suggesting that these epigenetic variations might be associated with a tissue-independent carcinogenesis process.

In summary, this global analysis of methylation signatures suggests the existence of four types of CGI+SS largely conserved across tissues: the majority of them remains hypomethylated on the CGI and hyper-methylated on the shores and shelve in normal and cancerous tissues (cluster 1); a minority is hyper-methylated in normal and cancerous tissues (cluster 2); finally, a fraction of CGI+SS signatures is hypo-methylated in normal tissues and partly methylated in cancerous tissues (cluster 3up), while another fraction is hyper-methylated in normal tissues and partly methylated in cancerous ones (cluster 3down). To clarify whether these four categories or CGI+SS are associated to particular biological functions, we performed a gene functional enrichment analysis [START_REF] Yu | clusterProfiler: an R package for comparing biological themes among gene clusters[END_REF] of the genes associated to the CGI+SS in each of the four categories, for each tissue. Results are shown in 4.8, 4.9, 4.10. Restricting ourselves to Gene Ontology (GO) biological processes associated to at least 20 genes, we found that the large cluster 1 is mostly enriched in genes involved in metabolic processes, while the cancer-specific cluster 3up is enriched in genes involved in developmental processes. There was no significant functional enrichment for genes in cluster 2 and 3down.

Cancer-specific methylation does not repress gene expression but instead targets genes lowly expressed in normal tissues

CGI methylation is often associated with gene expression silencing. We therefore assess whether the CGI+SS clusters defined above, corresponding roughly to lowly methylated (clusters 1), highly methylated (cluster 2) or partially methylated in cancer (cluster 3) CGI+SS, are associated with different mean levels of gene expression. In normal breast tissues, we indeed observe that genes near hypo-methylated islands in cluster 1 are slightly but significantly less expressed than genes near an hyper-methylated islands in cluster 2 (4.12, P Breast =0 .02). There is however no significant difference between the two clusters in normal lung tissues (4.12, P Lung =0 .39), and we could not test the hypothesis on normal colon tissues since we have none with both methylation and expression data (4.1). In cancerous samples, we observe that genes near a CGI+SS in the cancer-specific cluster 3 have a significantly lower expression than other genes (4.12, P Breast ,P Lung ,P Colon < 10 -16 ), particularly for the genes near a CGI+SS in the "3up" cluster. As genes in the "3up" cluster are hypo-methylated in normal tissues, this could suggest that their cancer-specific methylation is a way to repress their expression The colorbar represents the clusters association (blue for hypomethylated cluster 1, yellow for cluster 2, dark green for cluster 3down, light green for cluster 3down). Cluster ratio (A/B): Ratio between the number of genes (A) associated with the biological process and the total number of genes (B) in a given cluster. P-val:F i s h e r ' se x a c tt e s tp -v a l u ea d j u s t e df o rm u l t i p l et e s t i n g . Cluster ratio (A/B): Ratio between the number of genes (A) associated with the biological process and the total number of genes (B) in a given cluster. P-val:F i s h e r ' se x a c tt e s tp -v a l u ea d j u s t e df o rm u l t i p l et e s t i n g . in cancer. However, a closer look at the expression of these genes in normal tissues (4.12) shows that they are already lowly expressed in normal tissues. This suggests that instead of activating CGI methylation to silence to genes, cancer cells instead activates CGI methylation of hypo-methylated genes which are already lowly expressed in normal tissues.

Cancer-specific methylation is an independent predictor of patient survival in breast cancer

Our analysis so far compares CGI+SS in terms of their mean methylation across a set of samples and does not take into account between-sample variations. CGI+SS associated with cluster 1 (resp. 3) are hypo-(resp. hyper-)methylated on average, which indicates that there is little to no variations between samples. However, signatures of CGIs in the cancer-specific cluster 3 are partly methylated, which can either hide the fact that they are hemi-methylated for most cancerous samples, or that they are highly variable between samples. We therefore assess whether the partial methylation of CGI+SS signatures in cluster 3 is related to an overall increase (for cluster 3up) or decrease (for cluster 3down) in methylation for all or most of the patients, or if this it is caused by a subset of patients that become hyper-(resp. hypo-)methylated for these CGI+SS.

For that purpose, we first summarize the methylation of each CGI+SS on each breast cancer sample by a single value, the average methylation of the probes in the CGI+SS. We then represent each sample by the vector of methylation values of the CGI+SS in cluster 3up, and perform a Ward hierarchical clustering of the cancerous samples based on this representation. The resulting clustering is shown in 4.13, where in addition we indicate the ER+, HER2 and survival information for each patient. We observe that the distribution methylation values is very bimodal, and that the hyper-methylation of a given CGI+SS from cluster 3up generally happens in a subset of patients only.

A B D E

Interestingly, we see that the same subset of patients tends to be simultaneously hypermethylated for all CGI+SS from cluster 3up, suggesting that hyper-methylation of these islands is a characteristics of a subset of the tumors. This allows us to divide the set of breast cancer patients in three clusters given the level of methylation in cluster 3up as either "low", "intermediate", or "high" 4.13. Interestingly, distinguishing patients given the level of methylation from the CGI+SS in cluster 3up is significantly predictive of the patient survival (4.13, log-rank, p =0.01). Surprisingly, the cluster with the lowest survival is the "intermediate" cluster encompassing a portion but not all of the triple negative breast cancers (65% in cluster 3up "low", 32% in cluster 3up "intermediate" and only 3% in cluster 3up "high"). A multivariate Cox proportional hazards regression model fitted with available clinical parameters (tumor size, lymph node status, hormone receptor status, HER2/NEU status and patient's age) further shows that this stratification of patients based on the methylation level of genes in cluster 3up adds prognostic value independently of other clinical features 4.4. These results support the existence of a CpG island methylator phenotype (CIMP) as introduced by Toyota et al. [Toyota et al., 1999a] that is clinically relevant to assess the survival of patients. More importantly, they suggest that low survival might not be associated with a positive or negative CIMP, but with an intermediate phenotype termed as CIMP-low [START_REF] Hughes | The CpG island methylator phenotype: what's in a name? Cancer research[END_REF].

A)

Figure 4.13: Cluster 3up methylation is a predictive factor for survival of patients in breast cancer patients. Panel A. Hierarchical clustering of breast cancer patients given the average methylation level of all the CGI+SS associated with cluster 3up. The row color bar represents the average methylation level for the same CGI+SS in healthy breast tissues. The column color bar gives clinical information about the patients such as ER and HER2 statuses (grey for negative and white for positive), survival information (white for positive overall survival within 5 years and red for death within 5 years). The top row of the column color bar represents the three classes distinguished by methylation profiles in cluster 3up (blue for cluster 3up "low", green for cluster 3up "intermediate" and pink for cluster 3up "high"). Panel B. Kaplan-Meier estimate of breast cancer patient survival given the cluster 3up class (blue for cluster 3up "low", green for cluster 3up "intermediate" and pink for cluster 3up "high") shows that cluster 3up "intermediate" patients have a significantly higher risk of death within 5 years than either cluster 3up "low" or "high" patients (Log-rank, p=0 . 0 1 ) .

A similar analysis on CGI+SS associated with cluster 3down is less conclusive, and does not clearly cluster patients in separate clusters (4.14). A lack of sufficient survival data for colon and lung tissues prevented a similar analysis for these tissues. Our analysis so far compares CGI+SS to one another, by looking at their average methylation profiles across collections of samples. We found no clear evidence for a correlation between mean methylation level of a CGI and mean expression level of the corresponding genes, but this may be due to the fact that many other factors impact the expression level of a gene, including biological and technical ones. Another way to assess how methylation impacts expression is to look, for each given gene, how variations in expression across samples correlates with variations in methylation of nearby CGIs. For each set of samples (split by tissue of origin and normal/cancerous state), we measure the strength of association between methylation and expression for each gene by computing a predictive goodness of fit R 2 which represents the level of gene expression variation explained by CGI+SS methylation variation. This coefficient is calculated either when the CGI+SS methylation status is summarized by the mean methylation values of all the probes, or by using the full CGI+SS methylation information of each probe.

We observe that the full CGI+SS methylation profile is predictive of gene expression for a subset of genes in each dataset, and that this predictive power is significantly higher than using only the average CGI+SS methylation (4.15, P Breast < 10 -16 , P Lung = 1.3 × 10 -16 , P Colon =3 .2 × 10 -5 ). We provide in 4.5 the list of the top 50 genes based on their predictive score in cancerous breast, colon and lung tissues. Among the 2374 genes studied, 139 genes are associated with more than one CGI+SS. For these genes, the predictive power is computed using the CGI+SS closest to the TSS. Using all the CGI+SS for these genes do not yield significant improvement over taking only the CGI+SS closest to the TSS except for breast tissues (P Breast =0.003, P Lung =0.15, P Colon =0.62). We also observe no association between the predictive goodness of fit R 2 and the CGI+SS clusters described above (P Breast =0.48, P Lung =0.47, P Colon =0.44).

Since the predictive power of multivariate models based on all CpG probes in a CGI+SS is larger than the predictive power of the mean methylation value only, we now investigate which CpG in a CGI+SS are particularly important predictors of expression. For that purpose, we measure the correlation between the methylation of individual CpG and gene expression for the 50 genes with the largest predictive R 2 , and summarize the correlation values based on the position of the probe in the CGI+SS in 4.16. As expected, we observe overall a negative correlation between methylation and gene expression, and notice that this association is stronger in CGI shores than in the CGI itself. This is coherent with results in [START_REF] Irizarry | Genome-wide methylation analysis of human colon cancer reveals similar hypo-and hypermethylation at conserved tissuespecific CpG island shores[END_REF] stating that variations in the CGI are less critical than variations in proximity regions of the CGI. Performing the same analysis by varying the number of genes selected to compute correlations from 20 to 100 gave similar results. Results in the previous section suggest that for a subset of genes, a regulation of gene expression by methylation of CpG in CGI+SS is likely. To assess whether this regulation is conserved across tissues, we compare the predictive powers of methylation for each genes when it is computed on normal or cancerous samples from different tissues. As shown in 4.17, however, we observe little correlation between the predictive power across tissues in normal and in cancer samples, suggesting that methylation regulates the expression of genes in a tissue-specific manner (R 2,N ormal Breast/Lung =0 .04, R 2,Cancerous Breast/Lung =0 .17, R 2,Cancerous Lung/Colon =0 .07, R 2,Cancerous Colon/Breast =0 .06). We also observe very little correlation between predictive powers in normal and cancerous tissues, which could suggests a shift of the epigenetic regulation mechanism during cancer development (4.18, R 2 Breast =0 .04, R 2

Lung =6× 10 -7 ).

Many mechanisms besides DNA methylation are involved in gene expression regulation.

In particular, transcription factors (TF) play a critical role in the recruitment of RNA polymerase that allows gene transcription [Struhl, 1999]. We noticed that the list of the 50 genes with the largest predictive R 2 score in each tissue is significantly enriched in TFs as collected from [START_REF] Zhang | AnimalTFDB: a comprehensive animal transcription factor database[END_REF], suggesting that methylation plays an important role in the gene regulatory process of transcription factors (P Breast =0 .03, P Lung =3× 10 -4 , P Colon =0 .02). Using the TF list obtained from [START_REF] Vaquerizas | A census of human transcription factors: function, expression and evolution[END_REF] yields similar conclusions, as well as varying the number of genes selected from 20 to 100.

A) B) C) D)
Figure 4.17: Tissue-specificity of epigenetic regulation. Scatterplot between the predictive power of DNA methylation for gene expression in normal and cancerous between different tissues (R 2,N ormal Breast/Lung =0 .04, R 2,Cancerous Breast/Lung =0 .17, R 2,Cancerous Lung/Colon = 0.07, R 2,Cancerous Colon/Breast =0.06). 4.5.6 Copy number variations in cancer is an independent factor in gene expression regulation.

In cancer, aberrant DNA copy number variations (CNVs) can have an important impact on gene expression phenotypes [START_REF] Stranger | Relative impact of nucleotide and copy number variation on gene expression phenotypes[END_REF]. Since genome-wide DNA copy number information is available for all samples analyzed in this study, we now perform an integrated analysis combining methylation, DNA copy number and gene expression.

We compute a predictive goodness of fit R 2 to represents the power of DNA copy number information alone to predict gene expression, on the one hand, and a multidimensional regression model combining both the full CGI+SS DNA methylation information and the DNA copy number information, on the other hand. We observe that combining methylation and copy number information leads to significantly better results in predicting gene expression than taking each information separately (4.19, P Breast < 10 -16 , P Lung < 10 -9 ,P Colon < 10 -8 ). Moreover, correlation analysis between predictive scores using DNA methylation only, on the one hand, and predictive scores using CNVs only, on the other hand, shows very little correlation (4.19

, R 2 Breast =7×10 -4 , R 2 Lung =1×10 -4 , R 2
Colon =1× 10 -3 ). This suggests that both methylation and DNA CNVs are important and non-redundant predictors of gene expression variations.

Discussion

DNA methylation is a well-described process in normal development and is critical in specific gene expression regulations such as X-chromosome inactivation, genomic imprinting and tissue developpment [START_REF] Laurent | Dynamic changes in the human methylome during differentiation[END_REF],Smith and Meissner, 2013,Pollex and Heard, 2012, Li et al., 1993]. Since aberrant hyper-and hypo-methylation have also been frequently observed in cancer, it has been often argued that activation of oncogenes or repression of tumor suppressor genes could be caused by these epigenetic variations [Esteller, 2002]. In the present study, we assessed the existence of characteristic CGI+SS DNA methylation signatures in normal tissues and showed a weak association between the hypermethylated signature and gene expression repression. A similar study in cancerous tissues showed the existence of a cancer-specific signature highly associated with repressed genes. However, the corresponding genes are already highly repressed in normal tissues, questioning the causal impact of methylation in gene expression regulation, as already noticed in [START_REF] Keshet | Evidence for an instructive mechanism of de novo methylation in cancer cells[END_REF], Sproul et al., 2011, Sproul and Meehan, 2013].

Using regression methods we analyzed whether differences between CGI+SS methylation across samples -independently of signatures -are predictive of gene expression variations. We showed that for certain genes, expression variations across samples can be well predicted from DNA methylation variations and that these genes are not associated with cancer-specific methylation patterns. We also showed that using the full CGI+SS methylation profiles in a multidimensional regression framework yields better predictive power than summarizing the methylation of a CpG island by one mean value, as done in previous studies [START_REF] Vanderkraats | Discovering high-resolution patterns of differential DNA methylation that correlate with gene expression changes[END_REF]. Looking at probewise methylation correlation with gene expression for the top scoring genes, we observed that the impact of a CpG methylation on gene expression is largely dependent on its location in or near the island, and that CpGs located outside of CGIs have a bigger impact on gene expression variations than CpG located within the CGI, supporting results from [START_REF] Irizarry | Genome-wide methylation analysis of human colon cancer reveals similar hypo-and hypermethylation at conserved tissuespecific CpG island shores[END_REF],van Vlodrop et al., 2011]. The impact of CGIs located outside of promoter regions, such as intragenic CGIs is still unclear as it does not seem to contribute significantly to global gene expression regulation. Yet, a few studies point at their potential role in modulating alternative promoters [START_REF] Maunakea | Intragenic DNA methylation modulates alternative splicingb yr e c r u i t i n gM e C P 2t o promote exon recognition[END_REF] or in long-range regulation [START_REF] Kulis | Intragenic DNA methylation in transcriptional regulation, normal differentiation and cancer[END_REF].

Reproducing this methodology on different datasets allowed us to compare the variations of gene expression regulation by methylation in normal and cancerous tissues but also between different types of tissues. Our results suggest that genes targeted by methylation are not only very different between different normal tissues, but more importantly that they are very different between normal and cancerous samples of a given tissue suggesting a shift of epigenetic regulation between normal and cancerous tissues. Recently, hydroxymethylation of cytosines (hmC) has been shown to be significantly present in mammalians cells [START_REF] Kriaucionis | The nuclear DNA base 5-hydroxymethylcytosine is present in Purkinje neurons and the brain[END_REF] and methylation data generated with Illumina arrays, as done here, are not able to distinguish methylation (mC) from hydroxymethylation [START_REF] Nestor | Enzymatic approaches and bisulfite sequencing cannot distinguish between 5methylcytosine and 5-hydroxymethylcytosine in DNA[END_REF]. However, hmC are significantly less present in cancer tissues [START_REF] Haffner | Global 5-hydroxymethylcytosine content is significantly reduced in tissue stem/progenitor cell compartments and in human cancers[END_REF], Jin et al., 2011]. It is therefore likely that the epigenetic information measured here is indeed cytosine methylation. we showed that their activation itself is significantly associated with DNA methylation markers, highlighting the critical role of methylation in the regulatory process. CNVs have been widely analyzed as a source of genetic variation that plays an important role in complex phenotypes such as cancer [START_REF] Stranger | Relative impact of nucleotide and copy number variation on gene expression phenotypes[END_REF], Henrichsen et al., 2009].

While CNV contribution has been characterized on a genome-wide scale, the link with other regulation mechanisms, particularly DNA methylation, is still unclear [START_REF] Houseman | Copy number variation has little impact on bead-array-based measures of DNA methylation[END_REF], Lauss et al., 2012a]. We showed that the impact of both processes in gene expression regulation seems to be non-redundant. The relatively large dataset size gives us confidence in the statistical validity of the results, which are however limited to a fraction of the total genes because of uneven coverage. Methylome sequencing has already been performed and also supports the complexity of methylation patterns but is still limited to very small datasets [START_REF] Vanderkraats | Discovering high-resolution patterns of differential DNA methylation that correlate with gene expression changes[END_REF]. Undoubtedly, larger methylome datasets available in the near future will further improve our understanding of the role of DNA methylation in gene expression regulation.

Chapter 5

Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype Some content from this chapter has been submitted to Cancer Research.

Keywords: CpG island methylator phenotype, clustering, group-lasso logistic regression, methylation, clinical impact.

Résumé

Le CpG island methylator phenotype (CIMP) a été introduit par

Abstract

The CpG island methylator phenotype (CIMP) was first characterized in colorectal cancer but since, has been extensively studied in several other tumor types such as breast, bladder, lung, gastric. CIMP is of clinical importance as it has been reported to be associated with prognosis or response to treatment. However, the identification of a universal molecular basis to define CIMP across tumors has remained elusive.

We perform a genome-wide methylation analysis of over 2,000 tumor samples from 5 cancer sites to assess the existence of a CIMP with common molecular basis across cancers. We then show that the CIMP phenotype is associated with specific gene expression variations. However, we do not find a common genetic signature in all tissues associated with CIMP.

Our results suggest the existence of a universal epigenetic and transcriptomic signature that defines the CIMP across several tumor types but does not indicate the existence of a common genetic signature of CIMP.

Introduction

Epigenetic modifications have been recognized as important players in cancer etiology and development, and constitute promising therapeutic targets for diagnosis or treatment due to their possible reversibility [START_REF] Jones | The epigenomics of cancer[END_REF],Esteller, 2008,Rodriguez-Paredes and Esteller, 2011]. In particular, aberrant methylation of CpG islands (CGIs) located in promoter regions of tumor suppressor and DNA repair genes, leading to their silencing, is now considered a hallmark of cancer playing an important role in neoplasia [Jones, 1986, Baylin and Herman, 2000, Esteller et al., 2001, Esteller, 2008, Jones and Baylin, 2007, Rodriguez-Paredes and Esteller, 2011].

Chapter 5. Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype 83

The CpG Island Methylator Phenotype (CIMP) was first defined and observed by [Toyota et al., 1999a] in a subset of colorectal cancers as the joint methylation of several promoter regions, leading to the inactivation of the corresponding genes. The stratification of patients based on CIMP was shown to be clinically relevant, as CIMP positive patients had better prognosis than CIMP negative ones, and could lead to personalized treatments. Since the identification of CIMP in colorectal cancers, many studies have tried to replicate the analysis to find CIMP in different types of cancers including but not limited to colon [START_REF] Issa | CIMP, at last[END_REF], Weisenberger et al., 2006, Estécio et al., 2007, Curtin et al., 2011, Hinoue et al., 2012], breast [START_REF] Auwera | Array-Based DNA Methylation Profiling for Breast Cancer Subtype Discrimination[END_REF], Fang et al., 2011], lung [START_REF] Suzuki | Exclusive mutation in epidermal growth factor receptor gene, HER-2, and KRAS, and synchronous methylation of nonsmall cell lung cancer[END_REF], stomach [START_REF] Chen | High CpG island methylator phenotype is associated with lymph node metastasis and prognosis in gastric cancer[END_REF] and glioblastoma [START_REF] Noushmehr | Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma[END_REF], Baysan et al., 2012, Yilmaz et al., 2012]. While most of these works concluded in the existence of a CIMP in different cancers, other studies did not yield the same conclusions [START_REF] Bae | Hypermethylation in histologically distinct classes of breast cancer[END_REF], Anacleto et al., 2005], and the genes whose promoter CGI methylation are considered to define the CIMP differ between studies. This raises the question of whether the CIMP is tissue specific or is a universal phenomenon with common biological causes affecting common genes across cancers. A recent review of CIMP-related studies across different cancers pointed out the diversity of methods and measurement technologies used to define CIMP, which hinders the establishment of a molecular basis for CIMP in spite of growing evidence linking mutations in specific genes and CIMP in several cancers [START_REF] Hughes | The CpG island methylator phenotype: what's in a name? Cancer research[END_REF].

In the present study, we investigate the existence and universality of CIMP by performing a systematic genome-wide methylation analyse on several large datasets of different cancer types simultaneously. We propose a simple methodology to assess the existence of a CIMP phenotype in each cancer, and to identify a set of genes whose promoter methylation is a marker for the CIMP. This allows us to comparet h ed i fferent cancer types in search for a cross-cancer CIMP signature, and to analyze the link between CIMP and gene expression in different cancers. Finally, we assess the clinical relevance of CIMP on the overall survival.

Material and Methods

Patients Selection

All data were retrieved from the TCGA data portal. We selected samples from bladder, breast, colon, lung and gastric adenocarcinomas because large matched datasets were available for methylation, gene expression and mutation profiles. Moreover, all these tissues were previously reported to exhibit a methylator phenotype. The datasets are Chapter 5. Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype 84 detailed in 5.1 and the different institutions that released the data are mentioned in the acknowledgement section.

Methylation profiling

Methylation profiles were retrieved from level 2 TCGA data. They were obtained with the Illumina HumanMethylation450K DNA Analysis BeadChip assay, which is based on genotyping of bisulfite-converted genomic DNA at individual CpG-sites to provide a quantitative measure of DNA methylation [START_REF] Bibikova | High density DNA methylation array with single CpG site resolution[END_REF]. Following hybridization, the methylation value for a specific probe was calculated as the ratio M/(M + U )

where M is the methylated signal intensity and U is the unmethylated signal intensity.

485,577 CpG methylation levels, associated with 27,176 CGIs and 21,231 genes, were measured as such accross the genome.

Following [START_REF] Irizarry | Genome-wide methylation analysis of human colon cancer reveals similar hypo-and hypermethylation at conserved tissuespecific CpG island shores[END_REF], we considered not only the CGI methylation profile but also included in the analysis proximal regions in the near vicinity (up to 4kb), namely the CGI Shores and Shelves regions in a general CGI+SS methylation profile.

Gene expression profiling

Gene expression profiles were retrieved from level 3 TCGA data. They were obtained from the Illumina HiSeq RNASeq technology and processed following [START_REF] Mortazavi | Mapping and quantifying mammalian transcriptomes by RNA-Seq[END_REF].

Mutation profiling

Mutations profiles were retrieved from somatic mutations profiles from level 2 TCGA data obtained through whole exome sequencing.

Chapter 5. Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype 85

CIMP analysis

To assess the existence of CIMP, we performed Ward hierarchical clustering using euclidean distance. Robustness of the clustering was obtained through consensus clustering [START_REF] Monti | Consensus Clustering : A Resampling-Based Method for Class Discovery and Visualization of Gene[END_REF]].

Predicting CIMP status from gene expression profiles

To predict CIMP using gene expression profiles, we perform logistic regression using a lasso penalty [Tibshirani, 1996]w i t hd i fferent settings.

Tissue-specific lasso

We first perform standard logistic regression using lasso to predict CIMP status using a small list of gene expression profiles for each tissue separately. Accuracy is calculated through 3-fold cross-validation averaged over 100 repeats.

Combined Lasso

For the "Combined Lasso", we pool all the samples into a single dataset independently of their tissue of origin. For cross-validation, we separate samples into training and testing by keeping a balanced proportion of samples from eacht i s s u e s .

Group Lasso

For the "Group Lasso", we predict the CIMP as described below. For sample i belonging to tissue k, we assume that the conditional probability p

β k (x i )=P β (Y =1 |x i ) follows 5.1. η β k (x i )=β 0 k +(x k i ) T β k (5.1)
Chapter 5. Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype 86 with

η β k (x i ) = log p β k (x i ) 1 -p β k (x i ) (5.
2)

The logistic lasso estimator βk λ verifies 5.3:

βk λ = arg min {-l(β)+λ∥β∥ 1 } (5.3)
where l is the log-likelihood function:

l(β)= y i * η β k (x i ) -log[1 + exp(η β k (x i ))] (5.4)
To increase the statistical power and given the similarity ofthedifferent prediction tasks, we combine the different datasets into a single prediction task as follow:

The vector of output y ∈ R p is given by:

y T =(y 1 1 , ••• ,y 1 n 1 , •••••• ,y k 1 , ••• ,y k n k ) T (5.5)
where y k i is the CIMP status for patient i in tissue k.

And the combined design matrix X is given by: 5.6) where X i ∈ R n i ×pK is the gene expression profile matrix of the p genes for the i th tissue dataset of size n i and K is the number of tissues considered.

X = ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ X 1 0 ••• 0 0 X 2 ••• 0 . . . ••• . . . 00 ••• X n ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ (
We performed group-lasso logistic regression following [START_REF] Meier | The group lasso for logistic regression[END_REF] with the groups defined as the set of features corresponding to a given gene for each tissue that is we and (e i ) ∈ R pK is the vector of zeros except for feature i.

Given the imbalanced proportion of CIMP in each datasets, we defined the "random"

predictor as a predictor that always predicts the majority class. The statistical significance of a gene expression based predictor over the "random" predictor was calculated using a Student t-test.

To determine the genetic predictive signature, genes were ranked in their frequency in appearing in the optimal lasso estimator signature averagedo v e rt h ed i fferent folds and repeats [START_REF] Meinshausen | Stability selection[END_REF]. Genes which frequency was superior to 50% were selected.

Survival analysis

Overall survival was estimated using the Kaplan-Meier method [START_REF] Kaplan | Nonparametric estimation from incomplete observation[END_REF] to compare the survival between CIMP positive and CIMP negative tumors. A multivariate Cox proportional hazards regression model [START_REF] Cox | Analysis of Survival Data[END_REF] was also fitted.

Results

Ac r o s s -c a n c e rC I M Ps i g n a t u r e

We first assess with a common methodology whether a CIMP can be detected on different cancers, and whether CIMP in different cancers share a common signature in terms of which gene promoters are hypermethylated in CIMP positive patients. For that purpose, we collected high-density methylation datasets from the cancer genome atlas (TCGA) data portal providing more than 485,000 CpG methylation levels for more than 2,000 samples from five tissues of origin: bladder, breast, colon, lung and stomach (Table 5.1).

For each sample, we aggregate the methylation levels of CpG probes by CGI, including the CGI itself and its shores and shelves, resulting in a single methylation level for each of 21,176 CGIs in each sample.

Chapter 5. Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype 88

A CIMP corresponds to the joint hypermethylation of a subset of CGIs in a subset of samples [Toyota et al., 1999a]. To characterize from whole-genome methylation data whether a CIMP exists for a cancer, and which CGIs characterize it, we follow a standard methodology: (i) select the 5% most variant CGIs in the set of samples, which we call the CIMP signature, and (ii) check by unsupervised classification whether the samples cluster into two main clusters (CIMP positive and negative clusters) when we restrict them to the methylation values they take on the CGIs in the CIMP signature.

We apply this methodology to each of the five families of tumors, cutting the tree obtained by hierarchical clustering to two clusters in order to enforce a classification of all samples into two subgroups based on the methylation of CGIs in the CIMP signature.

Interestingly, in all five cases, one of the two clusters is clearly characterized by an overall hypermethylation of most CGIs in the signature compared to the second cluster, allowing us to characterize it as the CIMP positive cluster, the second one being the CIMP negative cluster (5.1). The proportion of CIMP positive samples according to this definition varies from about 20% for breast and colon cancers to 30% for bladder and about 60% and 70% for stomach and lung cancers respectively (Table 5.2). Proportion of the CIMP-positive group in each tissue is similar to previously reported studies [START_REF] Hughes | The CpG island methylator phenotype: what's in a name? Cancer research[END_REF]. Varying the size of the CIMP signature from 1% to 10% of all CGIs had a small impact on the clustering stability (5.2).

Comparing the epigenetic signatures that defines CIMP for each tissue, we find a common set of 89 CGIs associated with 51 genes (Figure 5.3, panel B). If the signatures were random subsets of 5% of all CGIs independent from each other, the overlap would contain on average (5%) 5 ≃ 3.10 -5 % of all CGIs, namely 0.006 CGI. This provides a strong evidence that a common set of genes is involved in CIMP in different cancers. We call these 89 CGIs the cross-cancer CIMP signature (Table 5. Several studies suggest the existence of a third class in CIMP phenotype that corresponds to an intermediate level of methylation [START_REF] Ogino | CpG island methylator phenotype-low (CIMP-low) in colorectal cancer: possible associations with male sex and KRAS mutations[END_REF], Shen et al., 2007, Hinoue Chapter 5. Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype 89 et al., 2012]. While we enforced an analysis with 2 classes to define the CIMP of each sample as positive or negative in the previous section, we now examine whether the data call for a third class. Following [START_REF] Monti | Consensus Clustering : A Resampling-Based Method for Class Discovery and Visualization of Gene[END_REF], we assess the existence of an intermediate CIMP phenotype for each tissue by comparing the increase in empirical cumulative distributive distribution ∆(K) for different values of K =2,...,5w h e r eK is the number of clusters considered for CIMP.

Figure 5.4 shows how ∆(K) varies as a function of K for each cancer, suggesting how many clusters exist in each case. We observe that the existence of a third class is not clear-cut. While colon and breast tissues show a significant increase in ∆(K) for K =3 suggesting a possible third cluster in CIMP, bladder is flat between 2 and 3 clusters, while lung and gastric cancers do not support the presence of 3 classes. In addition, we assess the stability of 3 clusters by varying the number of CGIs that define CIMP and observed that while CIMP clusters are highly robust for K = 2, there is some high variability in the cluster definitions for K =3( 5.2). In summary, the presence of 2 clusters is well supported by the data in all cancers, while the third cluster is much more debatable.

Similar gene expression variations are predictive of CIMP.

To shed light on the relationship between methylation and transcription, we now assess to what extent a transcriptomic signature can classify the samples as CIMP positive or negative. For that purpose, we collected for each family of cancer samples with both methylation and gene expression data available, leading to a subset of samples with an overall proportion of CIMP positive samples comparable to that of the original dataset (Table 5.4). We measure by cross-validation how well expression data alone can recover the two CIMP classes. We first perform a multivariate regression analysis using the lasso technique to assess whether gene expression of a few genes can be predictive of the CIMP status for each tissue separately. The cross-validation accuracies for each family of cancer are shown in Table 5.5. We observe that while a classifier based on gene expression performs significantly better than random to recover CIMP positive samples in breast, lung and stomach cancers, the performance on bladder and colon is not different from a random classifier. Moreover, we compare the lists of genes selected in the transcriptomic signature after bootstrap resampling of the samples in order to assess their robustness and potential biological significance (Figure 5.5, panel C). We observe that very few genes are robustly selected in the signatures, and in particular that no gene is associated with Chapter 5. Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype 95 BLCA-CIMP and COAD-CIMP prediction in more than 15% of the bootstrap resampling. In addition, the transcriptomic signatures of different cancers are very diverse, and no gene is present in all of them (Figure 5.5, panel B). Overall, these results suggest that there is information in the transcriptome related to the CIMP status, but that a robust signature across cancers is difficult to obtain.

Chapter 5. Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype 100 5.5.4 Clinical impact of CIMP.

Survival analysis in several CIMP studies has often shown distinct outcome between CIMP positive and negative tumors. However, there is no consensus in the general survival associated with CIMP: while CIMP has been associated with improved survival and lower risk of metastasis in breast [START_REF] Fang | Breast cancer methylomes establish an epigenomic foundation for metastasis[END_REF], colorectal [START_REF] Weisenberger | CpG island methylator phenotype underlies sporadic microsatellite instability and is tightly associated with BRAF mutation in colorectal cancer[END_REF], leukemia [START_REF] Toyota | Methylation profiling in acute myeloid leukemia[END_REF], Garcia-Manero et al., 2002[START_REF]Lack of CpG island methylator phenotype defines a clinical subtype of T-cell acute lymphoblastic leukemia associated with good prognosis[END_REF][START_REF]CpG island methylator phenotype redefines the prognostic effect of t(12;21) in childhood acute lymphoblastic leukemia[END_REF] or gliomas [START_REF] Noushmehr | Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma[END_REF], it has also been reportedly associated with poor survival for bladder [START_REF] Maruyama | Aberrant promoter methylation profile of bladder cancer and its relationship to clinicopathological features[END_REF], lung [START_REF] Suzuki | Exclusive mutation in epidermal growth factor receptor gene, HER-2, and KRAS, and synchronous methylation of nonsmall cell lung cancer[END_REF], Liu et al., 2008] or prostate cancers [START_REF] Maruyama | Aberrant promoter methylation profile of prostate cancers and its relationship to clinicopathological features[END_REF], and prognosis even remains unclear for gastric cancers [Toyota et al., 1999b,Oue et al., 2003,Kim et al., 2003, Etoh et al., 2004, Kusano et al., 2006].

We perform a systematic survival analysis on the different tissues to assess the clinical impact of CIMP. However, we observe no significant association between CIMP and survival, in any of the tissues (Table 5.7 and 5.8).

Discussion

CIMP has been thoroughly studied over the past few years in several tissue types but the heterogeneity of the methods and measurement technologies has hindered the assessment of a common epigenetic and genetic signature predictive of CIMP across all cancer sites [START_REF] Hughes | The CpG island methylator phenotype: what's in a name? Cancer research[END_REF]. In the present study, we analyze a large dataset of over 2,000 tumor methylation profiles measured with a single technology from 5 different tissues types. We observe a universal epigenetic signature that defines CIMP independently from the tissue of origin, which might suggest a common molecular basis to CIMP across tissues. Genes associated with these CGIs are enriched in several biological pathways linked to organ development, and include several interesting genes such as CDKN2A coding for p16, a well-characterized tumor suppressor protein [START_REF] Nobori | Deletions of the cyclin-dependent kinase-4 inhibitor gene in multiple human cancers[END_REF], which is aberrantly hypermethylated in CIMP positive tumors and might contribute to tumor which is known to reprogram the chromatin state and is associated with breast cancer metastasis [START_REF] Gupta | Long noncoding RNA HOTAIR reprograms chromatin state to promote cancer metastasis[END_REF], might on the contrary be repressed in CIMP tumors and be linked with a better prognosis for breast cancerous patients. GREM1 is another gene present in the CIMP signature and is associated with tumor cell proliferation [START_REF] Sneddon | Bone morphogenetic protein antagonist gremlin 1 is widely expressed by cancer-associated stromal cells and can promote tumor cell proliferation[END_REF]. Less documented genes present in the CIMP signature could potentially be investigated for a biological validation of their role in tumor development.

Recent studies have pointed out that epigenetic aberrations could be derived from genetic aberrations [START_REF] Reddington | DNA methylation reprogramming in cancer: does it act by re-configuring the binding landscape of Polycomb repressive complexes?[END_REF]. By combining the different datasets into a single prediction task, we are able to identify a common set of genes whose expression levels can predict the CIMP status for each tissue. This gene list is enriched mostly in genetic regulatory pathways, suggesting that the epigenetic reprogramming and thus CIMP might be an intermediate step in the regulatory mechanism. Among the genes contained in the signature, ZIC2, which is robustly selected in each bootstrap of the CIMP prediction task and is significantly more expressed in CIMP positive tumors for each tissue, has been known to act as a Wnt/β-catenin signalling inhibitor [START_REF] Pourebrahim | Transcription factor Zic2 inhibits Wnt/betacatenin protein signaling[END_REF] which is usually upregulated in several cancers. Another interesting characteristic of this genetic predictive signature from a clinical point of view is the recurrence of cancer/testis antigens (CTAs) such as MAGEC2 [von Boehmer et al., 2011, Yang et al., 2014, Reinhard et al., 2014], MAGEA12 [START_REF] Heidecker | Cytolytic T lymphocytes raised against a human bladder carcinoma recognize an antigen encoded by gene MAGE-A12[END_REF], Mollaoglu et al., 2008], MAGEA2 [START_REF] Peche | MageA2 restrains cellular senescence by targeting the function of PMLIV/p53 axis at the PML-NBs[END_REF], LDHC [START_REF] Tang | Homo sapiens lactate dehydrogenase c (Ldhc) gene expression in cancer cells is regulated by transcription factor Sp1, CREB, and CpG island methylation[END_REF], which are interesting targets for cancer immunotherapy [START_REF] Scanlan | Cancer/testis antigens: an expanding family of targets for cancer immunotherapy[END_REF] and are consistently under-expressed in CIMP positive tumors. Recently Gevaert et al. [Gevaert, 2015] also showed a strong association between MAGEA4 hypomethylation and CIMP positive tumors which further supports the link between CTAs and the absence of a methylator phenotype.

Mutation analyses are not very conclusive in defining a set of specific somatic mutations significantly associated with CIMP. In particular, lowly mutated cancer sites such as bladder, breast or even lung do not show any mutations significantly associated with CIMP. For highly mutated cancer sites such as colon or stomach, our results confirm a strong association between BRAF mutation and COAD-CIMP [START_REF] Weisenberger | CpG island methylator phenotype underlies sporadic microsatellite instability and is tightly associated with BRAF mutation in colorectal cancer[END_REF] but do not show any particular associations with IDH1/2,w h i c hh a v eb e e nr eported to be causal in gliomas and leukemia [START_REF] Yilmaz | IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype[END_REF], Figueroa et al., 2010].

There is a strong association between COAD and STAD-CIMP, andt h es p e c i fi cm utations of genes related with extracellular matrix and cell adhesion, both reported to be strongly associated with metastasis [START_REF] Gilkes | Hypoxia and the extracellular matrix: drivers of tumour metastasis[END_REF], Lu et al., 2012, Bendas and Borsig, 2012, Okegawa et al., 2004]. Interestingly, neuronal developmental processes are highly enriched but affecting different genes from the universal epigenetic signature.

Chapter 5. Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype 103

Associations with neuronal development were already mentioned in [START_REF] Noushmehr | Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma[END_REF].

Studies have often reported a clear distinct clinical prognosis associated with CIMP [START_REF] Fang | Breast cancer methylomes establish an epigenomic foundation for metastasis[END_REF],Weisenberger et al., 2006,Toyota et al., 2001,Noushmehr et al., 2010].

This reiterates that a main reason for defining CIMP in each tissue site is its potential use as a prognosis marker. However, CIMP could be associated with a good or bad prognosis depending on the type of tumors. In the current study, we do not observe a significant association with any good nor bad prognosis linked with CIMP.

Conclusion

This meta-analysis of more than 2,000 samples sheds new light on CIMP across cancers, its link with gene expression, and its clinical relevance. We found strong evidence that a panel of genes, which we call the pan-cancer CIMP signature, is involved simultaneously in the establishment of the CIMP in various cancer sites, which might be an indicator of a universal biological process behind CIMP. We found that differences in the CIMP status of a sample is associated to differences in the transcriptome, and also found a core set of genes whose expression levels differentiates CIMP positive and negative samples, in all cancers studied. Finally, we found little evidence of association between CIMP and mutations, except for the well-known BRAF mutation in colon cancer, and also little association with patient survival.

Chapter 6

Discussion

The main objective of the projects developed in this thesis is the use of computational tools to describe a biological phenomenon. We focused on the role of epigenetics, more precisely DNA methylation, and its entanglement with other biological sources such as gene expression, copy-number or mutations. For that, the use of prior information is critical to conform to the current biological knowledge but also to reduce the complexity of the problem. Rigourous approaches thus allow to formalize the extent of the validity of a biological hypothesis but also to generalize to the whole genome a gene-specific observation.

6.1 "DNA methylation in cancer: too much, but also too little" . . . and more.

The initial observation from Gardiner-Garden and Frommer [Gardiner-Garden and Frommer, 1987] on the role of DNA methylation in mammals has brought a lot of attention on the study of CpG Islands. The study showed that these small regions with high G+C content and generally located close to the promoter region of genes, could be linked with transcriptional and post-transcriptional repression of gene expression.

Ten years ago, Ehrlich [Ehrlich, 2002]r e v i e w e di n" DNA methylation in cancer: too much, but also too little" the role of DNA methylation in cancer. She brought the attention in particular on the fact that researchers were at that time too focused on aberrant targeted hypermethylation of CpG Islands in cancer and were probably missing out on the critical role of global hypomethylation in cancer.

More recently, Irizarry et al. [START_REF] Irizarry | Genome-wide methylation analysis of human colon cancer reveals similar hypo-and hypermethylation at conserved tissuespecific CpG island shores[END_REF] confronted the original assumption that CpG Island methylation was the most important epigenetic feature in gene regulation and identified neighboring but non-CGI regions for which DNA methylation had an even higher association with transcription.

Finally, Sproul et al. [START_REF] Sproul | Transcriptionally repressed genes become aberrantly methylated and distinguish tumors of different lineages in breast cancer[END_REF] revisited the role of DNA methylation in gene transcriptional regulation and suggested that the initial postulate might not always be true.

The evolution of the scientific community knowledge regarding epigenetics, reviewed here, is characteristic of the subtle trade-off between introducing bias and aiding the computational task.

6.2 "All models are wrong but are some of them actually useful?".

As previously discussed, the technological breakthroughs in biology have accelerated the acquisition of large datasets. Yet, although we can have access to millions of genomic features about a patient, we are still limited by the small number of patients. From a statistical point of view, it is important to make specific assumptions about the data in order to reduce the complexity of the problem. Here, we discuss how relevant it is to make valid biological assumptions about the data and how this can actually affect our results.

In Chapter 3, we used biological properties of DNA methylation, that is the robustness of DNA measurements, in comparison to RNA, and its stability over time, to develop a surrogate marker of the clonality between cells. This straightforward analysis has potentially important implications in the patients therapeutic strategies and illustrate the direct impact of computational tools to the clinic.

However, the other chapters do not share the same straightforwardness in the results.

In Chapter 4, we confront the original postulate regarding the causal role of methylation challenged by [START_REF] Sproul | Transcriptionally repressed genes become aberrantly methylated and distinguish tumors of different lineages in breast cancer[END_REF]. Our results support the original postulate to some extent:

• The relationship between methylation and gene expression is more complex than originally stipulated.

• There is a poor generalization to the whole-genome.

In addition, we can quickly check for new hypotheses:

Chapter 6. Discussion 107

• How does genome-wide methylation impacts a single gene expression instead of simply its promoter methylation?

• How related is the level of regulation between normal and cancerous tissues?

Chapter 5 is also related to the validation of a biological observation. The hypermethylator phenotype was observed in several cancer but there was no causal biological phenomenon common to tissue specific phenotypes. We showed that adapted regression techniques using similarity between datasets could circumvent the instability of predictive signatures.

While bioinformatics will not replace biological validation, there is an important contribution related to guiding the focus of future experiments. In return, biological knowledge allows to adapt generic models to tackle the n p issue.

6.3 Perspectives in the use of computational tools for epigenetics and biology.

In this last section, we discuss the relevant perspectives to computational analysis in particular for epigenetics. During this thesis, we focused on the validation and generalization of biological phenomena using statistical methods. The recent problematics that arose in biology provide future directions to our results:

Tumor heterogeneity. Clonality between cells as discussed in Chapter 3 do not take into account the existence of several subclones. New methods to combine a deconvolution problem with clonality assessment could allow to discuss the evolution of tumor cells from its diagnosis to its relapse and help characterize the patients response to specific treatments.

Alternative splicing. The existence of orphan CGIs and more generally of non-CGI DNA methylation could be related to more subtle transcriptional regulatory mechanisms than those discussed in Chapter 4. Alternative splicing is an ongoing research subject that could benefit from methylation information.

Long Range epigenetic regulation.

Several studies suggest the importance of DNA methylation for long range activation or repression of genes. This could relate to the 3D structure of the DNA generally not taken into account in longitudinal studies. While the 3D structure reconstruction of the DNA is still an ongoing topic, the integration of such knowledge into further epigenetic analyses could prove useful.

1. 1

 1 Histopathological sections of breast cancer .................. 1.2 Methylation ................................... 1.3 Methylation regulatory mechanism ...................... 2.1 Curse of dimensionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Bias-Variance Tradeoff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Joint Regularization of methylome profiles . . . . . . . . . . . . . . . . . . 2.4 Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 K-means clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Hierarchical Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 PCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8 K-means clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.9 Trade-off in dimensionality reduction . . . . . . . . . . . . . . . . . . . . . 3.1 Accuracy of the SVM classifier as a function of the number of probes selected . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Study of similarity between matched primary tumors and recurrences by hierarchical clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Pairwise methylome distance for each samples . . . . . . . . . . . . . . . . 3.4 Distribution of methylation similarity between samples given the type of pairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Histogram of the distribution of methylome-similarity score (MS) between unrelated PT/LR pairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Correlation between methylation and copy-number scores . . . . . . . . . 3.7 Kaplan-Meier estimates of the metastasis-free survival between TR and NP for the different classification methods . . . . . . . . . . . . . . . . . . 4.1 Standard pairing between two signals . . . . . . . . . . . . . . . . . . . . . 4.2 Dynamic time warping pairing between two signals . . . . . . . . . . . . . 4.3 Distance matrix between signal 1 and signal 2 . . . . . . . . . . . . . . . . 4.4 CGI+SS patterns in breast tissues . . . . . . . . . . . . . . . . . . . . . . 4.5 Characteristic profiles for each clusters . . . . . . . . . . . . . . . . . . . . 4.6 Characteristic profiles for each clusters . . . . . . . . . . . . . . . . . . . . 4.7 Characteristic profiles for each clusters . . . . . . . . . . . . . . . . . . . . 4.8 Gene Ontology analysis given the cluster assignment for cancerous breast tissues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.9 Gene Ontology analysis given the cluster assignment for cancerous colon tissues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x List of Figures xi 4.10 Gene Ontology analysis given the cluster assignment for cancerous lung tissues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.11 Inter-tissue stability of the cancerous-specific cluster . . . . . . . . . . . . 4.12 cluster characteristics analysis in breast tissues . . . . . . . . . . . . . . . 4.13 Clinical relevance of the CGI+SS clusters for survival prognosis in breast cancer patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.14 Hierarchical clustering of breast cancer patients based on the average methylation level of CGI+SS associated with cluster 3down . . . . . . . . 4.15 Impact of DNA methylation in gene expression prediction . . . . . . . . . 4.16 Methylation association with gene expression by regions . . . . . . . . . . 4.17Tissue-specificity of epigenetic regulation . . . . . . . . . . . . . . . . . . . 4.18 Shift of epigenetic regulation in cancer . . . . . . . . . . . . . . . . . . . . 4.19 Association between predictive power of methylation and copy-number variations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Methylation profiles hierarchical clustering for each tissue based on the most variant probes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Stability of CIMP clusters given the proportion of variant CGIs . . . . . . 5.3 Universal epigenetic signature for CIMP . . . . . . . . . . . . . . . . . . . 5.4 Stability of CIMP given the number of clusters . . . . . . . . . . . . . . . 5.5 Gene expression variations predictive of CIMP . . . . . . . . . . . . . . . 5.6 Analysis of a genetic signature associated with CIMP . . . . . . . . . . . 5.7 Comparison of genetic signatures associated with CIMP for colon and gastric cancers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.8 Clinical impact of CIMP on the patient survival . . . . . . . . . . . . . . . La complétion du "Human Genome Project" a accéléré le développement de nouvelles technologies de mesures liées au génome humain. Ce flux d'information biologique et clinique a considérablement impacté notre manière d'aborder une hypothèse biologique.

Figure 1 . 2 :

 12 Figure 1.2: DNA Methylation of a cytosine by DNMT. SAM: S-adenosylmethionine is ametabolitepresentincellsandusedasacoenzymeinthetransfer of the methyl-group.Source: Alice Pinheiro (Institut Curie).

Figure 1 . 3 :

 13 Figure 1.3: Panel A. DNA methylation physically blocks the access of promoter binding sites and prevent the binding of transcription factors. Panel B. Methyl-CpG binding domain proteins (MBDs) bind to region of high methylation which in turn, recruit histone deacetylases (HDACs) which compact the chromatin into an inactive state. Source: [Ling and Groop, 2009]( C o p y r i g h t : C CB Y -N C -N D3 . 0 )

Figure 2 . 3 :

 23 Figure 2.3: Joint Regularization of methylation profiles of chromosome 1u s i n ga total-variation penalty show similar breakpoints of hyper and hypo-methylated blocks but different levels for healthy patients (blue) and breast cancer patients (red).

  space and one natural dissimilarity measure between two observations is the Euclidean distance between the two vectors in R p .H o w e v e r other dissimilarity measures exist and can lead to very different clustering results. For example, one can define a distance using the Pearson correlation r as:

  Figure 2.5: K-means clustering (k=5) on simulated data sampled following 5 normal distributions. Different colors represent the cluster assignment of each observations while the triangles represents the center of each cluster.

Figure 2 . 6 :

 26 Figure 2.6: Hierarchical clustering of breast samples (columns) using CpG methylation as features (rows). The column color panel gives a distinction of the breast samples considered (red=normal tissue, cyan= cell line, dark blue= ductal carcinoma in situ, green= infiltrating ductal carcinoma). The row colorp a n e lg i v e si n f o r m a t i o n about the CpGs measured (blue= belonging to a CGI, yellow=outside of a CGI). This bi-clustering is able to distinguish the different types of tissues but also the types of CpGs measured.

Figure 2 . 7 :

 27 Figure 2.7: Projection of breast methylation profiles on the first two principal components (31% of the total variance explained). This representation shows that normal tissues are clustered together. Other methylation profiles come from breast primary tumors (Set 1) and are linked with their matched locoregional recurrence (Set2) given the localization (AH= axillary metastasis, RL= ipsilateral relapse, CL= controlateral relapse). See chapter 3 for more information about the data.

Figure 2

 2 Figure 2.8: K-means clustering when K is not well suited for the data (K=3).

  Cor (Correspondence): correspondence number with the Bollet/Servant cohort from[START_REF] Bollet | High-Resolution Mapping of DNA Breakpoints to Define True Recurrences among Ipsilateral Breast Cancers[END_REF], Type : histological type of the tumor (D= ductal, L= lobular), Grade : Aggressiveness of the tumor (1 to 3), ER : percentage of estrogen receptor present in the sample, PR : percentage of progesterone receptor present in the sample, Loc (Location): 1 if the recurrence was located less than 4cm fromt h eP T .

Figure 3 . 1 :

 31 Figure 3.1: Accuracy of the paired-SVM classifier as a function of the number of probes selected obtained through leave-one-out cross-validation for each dataset (orange= PT/AM, blue= PT/LR, pink=PT/CL) .

  a comparison of the outcomes given by methylation-based, copy-number based and clinicalbased classification of LR as TR or NP. The methylation-based classification method agreed with the copy-number based PIS classification method on 14 out of 17 pairs (concordance=82%, P-value=6.3 * 10 -3 ) and agreed with the clinical-based classification on 14 out of 17 pairs (concordance=82%, P-value=6.3 * 10 -3 ).

Figure 3 . 2 :

 32 Figure 3.2: Study of similarity between matched primary tumors and recurrences by hierarchical clustering. Hierarchical clustering based on the manhattan distance between methylome profiles with complete linkage was performed. Real pairs that are closer to each other than to any other samples are underlined. Panel A (resp. B, resp. C) represents the PT/AM (resp. PT/LR, resp. PT/CL) set.

Figure 3 . 3 :

 33 Figure 3.3: Pairwise methylome distance for each samples. Each boxplot represents the Manhattan distance between primary tumor i and an unrelated locoregional evolution, or the Manhattan distance between locoregional evolution i and an unrelated primary tumor. The black square represent the Manhattan distance between the matched primary tumor and locoregional evolution from sample i.T h ey e l l o w( r e s p . blue, resp. pink) panel represents the PT/AM (resp. PT/LR, resp. PT/CL) set. The last panel represents the distribution of distances betweent h eh e a l t h yb r e a s tt i s s u ei and all the other healthy breast tissues from the cohort.

Figure 3 . 4 :

 34 Figure 3.4: Distribution of methylation similiarity between samples given the type of pairs. Each boxplot represents the distribution of Manhattan distance between matched primary and locoregional evolution ("Real"), between non-matched primary and locoregional evolution ("Artificial"), betweent w op r i m a r yt u m o r s( " P r imary") or between two locoregional evolution ("Recurrence") for each dataset.

Figure 3 . 6 :

 36 Figure 3.6: Correlation between methylation and copy-number scores. The horizontal red line (resp. vertical dashed blue line) corresponds to the 95% quantile of the distribution of the methylation-scores (resp. partial identity scores) for the unrelated pairs : MS T hreshold =6.6 * 10 -4 (resp. PIS T hreshold =0.12). PT/AM (resp. PT/LR, resp. PT/CL) pairs are colored in yellow (resp. blue, resp. pink). The black line corresponds to the linear regression between methylation and copy-number scores for all the datasets.

Figure 3 . 7 :

 37 Figure 3.7: Kaplan-Meier estimates of the metastasis-free survival between TR and NP for the different classification methods. The full black (resp. green) line corresponds to the survival for samples classified as TR (resp. NP) and the corresponding dashed lines correspond to upper and lower 95% CI. The red crosses represent censored data. Panel A (resp. B, resp. C) represent the methylation-based (resp. copynumber based, resp. clinical based) classification.

Chapter 4 .

 4 boundary condition) Changes in gene expression control by DNA methylation in cancer 57 For two CGI+SS profiles, we thus compute the DTW distance as: DT W (CGI 1 ,CGI 2

Figure 4 . 1 :

 41 Figure 4.1: Standard pairing between two signals.

Figure 4 . 2 :Chapter 4 .Figure 4 . 3 :

 42443 Figure 4.2: Dynamic time warping pairing between two signals.

Chapter 4 .

 4 Changes in gene expression control by DNA methylation in cancer 59

Figure 4 . 4 :

 44 Figure 4.4: CGI+SS patterns in breast tissues. Hierarchical clustering of CGI+SS DNA methylation patterns for breast normal tissues (panel A) and breast cancerous tissues (panel B) using DTW as a distance metric anda" W a r d "l i n k a g e .The colorbar represents the clusters association (blue for hypomethylated cluster 1, yellow for cluster 2, dark green for cluster 3down, light green for cluster 3down).

Figure 4 . 7 :

 47 Figure 4.7: Characteristic profiles for each clusters. Visualization of the CGI+SS DNA methylation signatures as condensed profiles fromt h e1 0m e d o i d sp r ofiles for each clusters in lung normal (panel A) or cancerous (panel B) tissues. The two orange dashed lines represent the normalized 1kb long CGI region while the two blue lines represent the 2kb limit between shores and shelves regions.

Figure 4 . 8 :

 48 Figure 4.8: Gene Ontology analysis given the cluster assignment for cancerous breast tissues. Cluster:C l u s t e ra s s i g n m e n to fag e n et h eC G I + S Sm e t h y l a t i o n pattern . Description:D e s c r i p t i o no ft h eb i o l o g i c a lp r o c e s s e se n r i c h e d( t o p1 0r anked by cluster ratio).Cluster ratio (A/B): Ratio between the number of genes (A) associated with the biological process and the total number of genes (B) in a given cluster. P-val:F i s h e r ' se x a c tt e s tp -v a l u ea d j u s t e df o rm u l t i p l et e s t i n g .

Figure 4 . 9 :

 49 Figure 4.9: Gene Ontology analysis given the cluster assignment for cancerous colon tissues. Cluster:C l u s t e ra s s i g n m e n to fag e n et h eC G I + S Sm e t h y l a t i o n pattern . Description:D e s c r i p t i o no ft h eb i o l o g i c a lp r o c e s s e se n r i c h e d( t o p1 0r anked by cluster ratio).Cluster ratio (A/B): Ratio between the number of genes (A) associated with the biological process and the total number of genes (B) in a given cluster. P-val:F i s h e r ' se x a c tt e s tp -v a l u ea d j u s t e df o rm u l t i p l et e s t i n g .

Figure 4 .Figure 4 .

 44 Figure 4.10: Gene Ontology analysis given the cluster assignment for cancerous lung tissues. Cluster:C l u s t e ra s s i g n m e n to fag e n et h eC G I + S Sm e t h y l a t i o n pattern . Description:D e s c r i p t i o no ft h eb i o l o g i c a lp r o c e s s e se n r i c h e d( t o p1 0r anked by cluster ratio).Cluster ratio (A/B): Ratio between the number of genes (A) associated with the biological process and the total number of genes (B) in a given cluster. P-val:F i s h e r ' se x a c tt e s tp -v a l u ea d j u s t e df o rm u l t i p l et e s t i n g .

Figure 4 .

 4 Figure 4.12: cluster characteristics analysis in breast tissues. Gene expression distribution for genes based on the cluster assignment of their associated CGI+SS. Panel A/D. Gene expression distribution in normal tissues shows a slight repression for genes associated with cluster 2 (hypermethylated CGI+SS profiles). "Ref" represents the genome-wide gene expression distribution (Panel A=breast, Panel D=lung) PanelB/E. Gene expression profiles in cancerous tissues shows high repression for genes associated with cluster 3 and specifically cluster "3up" (hemi-methylated CGI+SS profiles) (Panel B=breast, Panel E=lung). Panel C/F. Gene expression profiles in both normal and cancerous tissues using the cluster assignement in cancerous tissues shows that genes associated with cluster "3up" in cancerous tissues define a cluster of genes already repressed in normal tissues (Panel C=breast, Panel F=lung).

Figure 4 . 14 :

 414 Figure 4.14: Hierarchical clustering of breast cancer patients based on the average methylation level of CGI+SS associated with cluster3 d o w n .

4. 5 . 4

 54 Methylation of CpG in CGI shores is negatively correlated with gene expression.

Figure 4 .

 4 Figure 4.15: Impact of DNA methylation in gene expression prediction.Predictive power distribution of DNA methylation for gene expression using either the average CGI methylation and least squares (orange) or the full CGI+SS profile and lasso regression (purple) shows that a more complex model allows to better predict gene expression variations in both normal and cancerous tissues (panel A= breast, panel B= colon, panel C=lung).

4. 5 . 5

 55 Regulation of gene expression by DNA methylation is tissuespecific and the process is altered in cancer tissues but overall targets transcription factors.

Figure 4 . 16 :

 416 Figure 4.16: Methylation association with gene expression by regions. Distribution of the correlation between individual probes and gene expression variation for breast top 50 genes ranked by their predictive score by regions related to the CGI exhibits a stronger association for probes located outside of the CGI particularly in shores regions (panel A= normal breast tissues, panel B= cancerous breast tissues, panel C= cancerous colon tissues, panel D= lung normal tissues, panelE =l u n gc a n c e r o u st i ssues).

Figure 4 .

 4 Figure 4.18: Shift of epigenetic regulation in cancer. Scatterplot between the predictive power of DNA methylation for gene expression in normal and cancerous for breast lung tissues (left: R 2 breast =0.04, right: R 2 Lung =6× 10 -7 )

Figure 4 .

 4 Figure 4.19: Association between predictive power of methylation and copynumber variations. Panels A/C/E. Predictive power distribution using either CNV data only with least squares, DNA methylation data only with lasso regression or both CNV and DNA methylation data with lasso regression. Combined methylation and CNV information yield significantly higher predictive power(panelA=breastcancerous tissues, panel C= colon cancerous tissues, panel E= lung cancerous tissues). Panels B/D/F. Scatterplot of predictive power using DNA methylation only and copy-number information only shows that both regulation mechanisms operate exclusively on genes (panel B= breast cancerous tissues, panel D=colon canceroust i s s u e s ,p a n e lE =l u n g cancerous tissues).

Chapter 4 .

 4 Changes in gene expression control by DNA methylation in cancer 79In addition, the association between DNA methylation and other important regulation mechanisms widens our understanding of the role of methylation in the whole gene expression regulation process. While TFs are centric in controlling gene expression,

Chapter 5 .

 5 Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype 87 where G := g i =(e i ,e i+p , ••• ,e i+(K-1)p ) ∀i ∈ [1; p] (5.8)

  3). A hierarchical clustering on all samples restricted to this cross-cancer CIMP signature is able to cluster CIMPpositive and CIMP-negative patients independently of the tissue of origin (Figure 5.3, panel A), suggesting that CIMP observed in each individual cancer share in common a significant proportion of genes whose promoter CGIs are hypermethylated in all CIMP positive cancers. A functional enrichment analysis of the cross-cancer CIMP signature reveals that it is significantly enriched in genes involved inc e l ld i fferentiation, neuronal developmental and immune response processes (Figure 5.3, panel C).

5. 5 . 1

 51 Are there 2 or 3 CIMP classes?

Figure 5 . 1 :Chapter 5 .

 515 Figure 5.1: Methylation profiles hierarchical clustering for each tissue based on the most variant probes. Heatmaps range from hypomethylated (blue) to hypermethylated (yellow). The column colorbar represents the CIMP assignment (yellow= CIMP-positive, blue= CIMP-negative). Panel A. Bladder Panel B. Breast Panel C. Colon Panel D. Lung Panel E. Stomach.

Figure 5 . 2 :

 52 Figure 5.2: Stability of CIMP clusters given the proportion of variant CGIs. Robustness of cluster assignment for each sample (columns) as a function of the proportion of variant CGIs considered from 1 to 10 % (rows) and given the number of CIMP clusters considered (left panels: K=2, right panels: K=3, yellow=CIMP-positive, blue=CIMP-negative, black=CIMP-low) for bladder (panel A/B), breast (panel C/D), colon (panel E/F), lung (panel G/H), stomach (panel I/J). Panel K.Table summarizing the stability of the cluster assignments for each tissue and different number of CIMP clusters considered.

Chapter 5 .Figure 5 . 4 :

 554 Figure 5.4: Stability of CIMP given the number of clusters.

Chapter 5 .

 5 Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype 101

Figure 5 . 8 :

 58 Figure 5.8: Clinical impact of CIMP on the patient survival. Panel A. Bladder Panel B. Breast Panel C. Colon Panel D. Lung Panel E. Stomach.

  Epigenomic alterations in breast carcinoma32Nos résultats montrent qu'un nombre restreint de sondes (49 sondes sur 27000) semblent être caractéristiques de la progression tumorale vers une métastase axillaire. Toutefois, aucune différence consistente n'a observée entre tumeurs primaires et récidive locale ou contralatérale, ce qui témoigne d'un lien moins marqué voire absent entre ces groupes.

	Dans un second temps, nous observons que les tumeurs primaires sont associées dans
	la majorités des cas à leur métastase respective (75%) alors que les tumeurs primaires
	et les récidives contralatérales ne montrent pas plus de similarité que deux tumeurs
	indépendantes. Ce résultat valide l'utilisation de la méthylation comme marqueur de
	clonalité entre deux échantillons et nous élaborons un score pour classifier les récidives
	locales. Cette classification valide la tendance (non-significative) des vraies récidives	à
	être de moins bon pronostic et apporte un intérêt clinique dans l'apport décisionnel pour
	le traitement des patients.
	3.2 Abstract
	Pour cela, nous disposons du profils de méthylation de 48 tumeurs primaires ainsi que du
	profil de méthylation de leur métastase axillaire associée (20 cas), de leur récidive locale
	i.e dans le même sein (17 cas) ou de leur récidive contralatérale i.e dans le sein opposé were evenly split between high and low MS score, suggesting two groups : true recur-
	(11 cas). Dans un premier temps, des méthodes d'analyses univariées et multivariées rences (TR) and new primary tumors (NP). CL were classified as new tumors. MS score
	ont permises de determiner des sondes significativement différentiellement méthylées et was significantly correlated with copy-number based scores. There was no significant
	difference between the metastatic-free survival of groups of patients based on different
	classifications.

Comment ce mécanisme influt sur la progression tumorale est cependant encore floue. En comparant le méthylome initial de cancer du sein au méthylome des récidives de cancer chez ces mêmes patients, nous cherchons à déterminer des marqueurs de la progression tumorale dans le cancer du sein. marqueurs de la progression tumorale. Dans un second temps, nous établissons à partir des profils de méthylation un score de similarité entre deux échantillons, ce qui nous permet d'établir le caractère clonal entre une tumeur primaire et sa récidive locale, primordial dans la stratégie thérapeutique à employer. Chapter 3. Epigenetic modifications such as aberrant DNA methylation has long been associated with tumorogenesis. Little is known, however, about how these modifications appear in cancer progres-sion. Comparing the methylome of breast carcinomas and locoregional evolutions could shed light on this process. We propose to analyze the methylome profiles of 48 primary breast carcinomas (PT) and their matched axillary metastases (PT/AM pairs, 20 cases), local recurrences (PT/LR pairs, 17 cases) or contralateral breast carcinomas (PT/CL pairs, 11 cases) were analyzed. Univariate and multivariate analyzes were performedt od e t e r m i n ed i fferentially methylated probes (DMPs), and a similarity score was defined to compare methylation profiles. Correlation with copy-number based score was calculated and metastatic-free survival was compared between methods. 49 DMPs were found for the PT/AM set, but none for the others (FDR < 5%). Hierarchical clustering clustered 75% of the PT/AM, 47% of the PT/LR, and none of the PT/CL pairs together. A methylation-based score (MS) was defined as a clonality measure. The PT/AM set contained a high proportion of clonal pairs while PT/LR pairs

  identified a set of 79 genes able to differentiate PT from matched AM. Studies between PT and LR have mainly focused on distinguishing TR and NP. A criterion based on clinical and pathological features was first established but

	judged insufficiently robust for most clinical applications. Several studies investigated
	the difference between TR and NP based on pangenomic analyzes of DNA copy number
	alterations (CNA)

Table 3 .

 3 1: PT/LR clinical and histological features.

	PT	Local Recurrence

Table 3 .

 3 2: PT/CL clinical and histological features. Type : histological type of the tumor (D= ductal, L= lobular, Med=Medullary, Meta=Metaplasic), Grade : Aggressiveness of the tumor (1 to 3), ER : percentage of estrogen receptor present in the sample, PR : percentage of progesterone receptor present in the sample.

					PT			Contralateral Recurrence
	Pair Age Type Grade ER PR HER2 Type Grade ER PR HER2
	1	46.6	L	3	80	80	0	NA	NA	90	20	0
	2	46.9	D	2	60	100	0	D	2	30 100	0
	3	48.4	D	3	70	60	0	D	3	100 10	0
	4	42.6	D	2	0	0	0	D-L	2	100 70	0
	5	48.5	D	2	70	20	0	D	3	10	20	0
	6	44.5	D	2	≥10 ≥10	0	Med	2	0	0	0
	7	46	D	2	80	30	0	D	1	40	95	0
	8	48.9	D	3	90	20	0	Meta	3	0	0	0
	9	38.9	D	3	0	0	0	D	3	100 40	0
	10	31	D	3	0	0	0	D	3	0	0	0
	11	36.3	D	3	10	5	0	D	3	0	0	0

Table 3 . 3 :

 33 PT/AM clinical and histological features. Age of the patient at diagnosis of the primary tumor in years, Type : be biologically related to the PT, or to the small size of the cohort which limits the power of statistical tests.On the PT/AM cohort, the SVM model correctly identified the PT and AM in 18 out of 20 held-out pairs (90% success rate, P-value=2.0 * 10 -4 ) when considering the whole methylation profile probes. The SVM model obtained after dimensionality reduction by filtering the 22 most significant probes selected according to a Wilcoxon test gave a 100% accuracy. As illustrated in 3.1, good accuracy was still achieved when considering an increasing number of probes (Accuracy ∼ 90%). On the PT/LR and PT/CL cohorts, however, the success rate was respectively 58% (10 out of 17 pairs, P-value=0.31) and 27% (3 out of 11 pairs, P-value=0.11) when taking all probes into account. Note that these values are not significantly different from random guess.

	Pair Age Type Grade ER	PR HER2
	1	45.9	D	3	70	70	0
	2	NA	D	3	90	20	0
	3	NA	NA	NA	95	30	0
	4	48.8	D	1	60	90	0
	5	43.6	D	3	0	0	0
	6	35.3	D	2	20	70	0
	7	45.1	D	3	10	25	0
	8	41.9	D	2	70	40	NA
	9	43.5	D	1	≥ 10 ≥ 10	0
	10	43.7	D	3	80	50	NA
	11	44.9	D	2	0	0	0
	12	43.6	D	1	≥ 10	0	0
	13	40.2	D	3	0	0	1
	14	32.5	L	3	40	60	1
	15	38.5	D	2	0	10	0
	16	37.5	D	3	40	50	0
	17	39.3	D	3	80	90	0
	18	37.6	D	3	0	0	0
	19	36.6	D	3	10	50	1
	20	35.4	D	3	0	30	0
	Age:

histological type of the tumor (D= ductal, L= lobular, Meta=Metaplasia), Grade : Aggressiveness of the tumor (1 to 3), ER : percentage of estrogen receptor present in the sample, PR : percentage of progesterone receptor present in the sample.

not

Table 3 . 4 :

 34 Most significantly differentially methylated genes between PT and AM samples (Top 10).

	CpG	Gene	Pvalue Methylation Variation
	cg04619381 LOC222171	0.013	-0.048
	cg18140857	RDHE2	0.013	0.102
	cg23698969 SLC22A18	0.013	0.042
	cg20161089	IFI27	0.013	0.238
	cg24959428	GBP6	0.02	0.126
	cg22630748	INHBE	0.02	0.1
	cg03623878	MCF2L	0.02	-0.05
	cg16179125	CTSZ	0.02	0.182
	cg25115460	TP73	0.022	0.109
	cg11946165	CTSK	0.022	0.098

CpG: CpG probe name. Gene: Associated gene. Pvalue: FDR corrected p-value.

Table 3 . 5 :

 35 Comparison of classification methods for clonality between pairs in the PT/LR cohort.

					Panel A
			Scores			Classification
	Pair Cor PIS	MS	Time	PIS MS Clinical Divergence
				(×10 -4 ) ( Years)
	1	1	0.019	4.42	6.5	NP NP	NP
	2	3	0.435	7.82	3.2	TR TR	TR
	3	11 0.018	3.95	6.4	NP NP	NP
	4	16 0.303	5.59	3.8	TR NP	NP	PIS
	5	12 0.113	6.11	3.4	NP NP	NP
	6	13 0.214	9.29	4.6	TR TR	TR
	7	15 0.105	5.36	3.2	NP NP	TR	Clinical
	8	2	0	7.57	5.2	NP TR	NP	MS
	9	4	0.203	9.76	3.5	TR TR	TR
	10	14 0.321	6.64	2.4	TR TR	TR
	11	18 0.003	5.44	2.2	NP NP	NP
	12	20 0.103	5.60	1.4	NP NP	NP
	13	21 0.356	6.82	4.2	TR TR	TR
	14	23 0.328	5.37	0.9	TR NP	TR	MS
	15	24 0.312	6.69	1.4	TR TR	TR
	16	25 0.357	9.09	2.7	TR TR	TR
	17	26 0.493	8.69	2.0	TR TR	TR

Cor (Correspondence): correspondence number with the Bollet/Servant cohort. scores: scores obtained with partial identity (PIS) or methylation( M S ) .Time:t i m e elapsed between diagnosis of the PT and diagnosis of the recurrence. Classification: classification of the recurrence based on copy number (PIS), methylation (MS) or clinical features (clinical). Divergence: which method deviated from the others. potential heterogeneity.

  Changes in gene expression control by DNA methylation in cancer 52 statistiques, nous analysons le lien entre les variations de méthylation des ilôts CpG et les variations d'expression des gènes associés pour comprendre l'ampleur de la méthylation dans le mécanisme de régulation des gènes.

	Nous montrons dans ce chapitre que les profils de méthylation des ilôts CpG chez les pa-
	tients sains peuvent se résumer par 2 profils caractéristiques : le premier est associé à un
	ilôt CpG hypométhylé dont les régions voisines ("shores" et "shelves") sont généralement
	hyperméthylées et le second est associé à une région globalement hyperméthylée. De
	plus, l'assignation d'un ilôt à un profil caractéristique est globalement conservée entre
	les différents tissus, ce qui met en évidence la stabilité d'un profil de méthylation associé
	à un gène donné. Nous observons par ailleurs chez les patients cancéreux l'existence d'un
	profil caractéristique supplémentaire associé à une région globalement hémi-méthylée.
	La méthylation de régions à forte densité en CpGs, communément appelées ilôts CpGs,
	est un mécanisme associé à la régulation du niveau d'expression des gènes dans des
	cas bien précis. Certaines altérations spécifiques telles que l'hyperméthylation de tels
	ilôts proches de certains gènes suppresseurs de tumeurs, entrainant leur inactivation, ou
	encore l'hypométhylation d'ilôts associés à certains oncogènes particuliers, entrainant
	leur réactivation, sont fréquemment observées dans plusieurs types de cancer. Cepen-
	dant, le rôle de la méthylation dans la régulation de la transcription de l'ensemble du
	génôme est encore très peu connue. En particulier, de récentes études ont montré que
	l'hyperméthylation de certains ilôts CpG n'était pas causal à la répression des gènes
	mais agissait comme un verrou supplémentaire.

L'analyse des données publiques à grandes échelles disponibles sur "The Cancer Genome Atlas" (TCGA) nous permet aujourd'hui de combiner les données d'expressions de gènes, de méthylation à haute densité, mais également de copy-number pour 672 échantillons sains et cancéreux dans 3 types de cancers diffé r e n t s . A l ' a ide de diverses méthodes Chapter 4.

La distribution de l'expression des gènes en fonction de l'appartenance de l'ilôt CpG correspondant à un profil caractéristique montre que de manière générale, le caractère hypo-ou hyperméthylé de l'ilôt CpG n'est pas associé à un niveau plus ou moins élevé de l'expression des gènes. L'expression des gènes associés aux ilôts hémi-méthylés observés uniquement, bien que très fortement réprimés dans les tissus cancéreux, sont également réprimés dans les tissus sains, ce qui remet en question le rôle causal de la méthylation dans la régulation de l'expression. Bien que les profils précédemment décrits, basés sur le niveau moyen de méthylation par sonde à l'échelle d'un sous-groupe de patients n'ai pas montré d'association avec le niveau d'expression, une analyse à l'échelle de chaque individu montre que certaines variations -localisées spécifiquement dans les régions périphériques de l'ilôt CpG (CGI shores) -sont fortement négativement corrélées à la régulation de l'expression du gène associé. Ces gènes, pour lesquels une forte association existe entre la méthylation et l'expression, semblent différer d'un tissu à l'autre mais surtout, entre un tissu sain et un même tissu cancéreux. Une forte association est observée entre ces gènes fortement régulés par la méthylation et les facteurs de transcriptions, ce qui souligne le rôle majeur de la méthylation dans le mécanisme de régulation.

Table 4 .

 4 1: Patients Dataset. Original dataset sizes for methylation (Meth), gene expression (GE) and CNV profiles for normal (N) or cancerous (C) tissues. The "Matched" column represents the final dataset containing samples with matched methylation, gene expression and copy number profiles.

		Meth		GE	CNV	Matched
		N	C	N	C	N	C	N	C
	Breast	97	626 100 778 1073 1041 70 474
	Colon	38	291	0	193	0	470	0	33
	Lung	32	452	37	125	568	516	13	82
	Total	167 1370 137 1096 1641 1981 83 589

Table 4 . 2 :

 42 Concordance analysis of CGI+SS patterns clusters between normal tissues.

	Clusters	Colon
	Breast	1	2
	1	1560	9
	2	113 145
	Clusters	Lung
	Colon	1	2
	1	1610	7
	2	63	147
	Clusters	Breast
	Lung	1	2
	1	1549 20
	2	68	190
	To assess the diversity of CGI+SS signatures across genes, we perform an unsupervised
	classification of all signatures for each of the 6 types of samples, using Ward hierarchical
	clustering. Since different CGI+SS may contain a different number of GpG probes, we

use a specific distance based on dynamic time warping to compare signatures of different lengths. 4.4 (panel A/C/E) shows the CGI+SS clustering obtained for signatures measured on normal samples from breast (resp. lung and colon) samples. We observe two stable clusters, which are largely conserved across the 3 tissues of origin (Table

Table 4 . 3 :

 43 Concordance analysis of CGI+SS patterns clusters from normal to cancerous tissues. Each table represents the concordance of clusters between normal and cancerous clustering analysis. Bold numbers in the diagonal shows the stability of clusters between normal and cancerous tissues.

	Breast	Normal
	Cancerous	1	2
	1	1231 21
	2	9	109
	3	329	128
	Lung	Normal
	Cancerous	1	2
	1	1128 12
	2	18	168
	3	471	30
	Colon	Normal
	Cancerous	1	2
	1	1112 11
	2	13	106
	3	548	37

Table 4 . 4 :

 44 Multivariate Cox regression analysis including the level ofm e t h y l a t i o ni n the cancer-specific cluster "3up" in addition to significant clinical variables for breast cancer.

	Clinical variable (Reference)	HR (95% CI)	p-value
	Cluster 3up (Low vs intermediate) 3.44 (1.44-8.23)	0.007
	Cluster 3up (Low vs high)	1.92 (0.50-7.34)	0.34
	(ER,HER2) (-/-vs +/-)	0.37 (0.15-0.88)	0.026
	(ER,HER2) (-/-vs -/+)	1 × 10 -8 (0-Inf)	1
	(ER,HER2) (-/-vs +/+)	0.53 (0.09-2.94)	0.46
	Lymph Node (Negative)	4.51 (1.63-12.44)	0.004
	Color Key
	0.2	0.6
	Methylation
		Cluster 3up
		Survival

Table 4 . 5 :

 45 Genes regulated by methylation in different cancerous tissues. Gene:T o ps c o r i n gg e n e sr a n k e db yt h ep r e d i c t i v ep o w e ro fm e t h y l a tion to predict gene expression variation. Score: R 2 score associated.

	Breast		Colon		Lung
	Gene	Score	Gene	Score	Gene	Score
	DQX1	0.699	C11orf93	0.786	PTPRCAP 0.639
	IRS2	0.692	FAM24B	0.695	HOXB2	0.620
	GPSM3	0.669	SCAND3	0.679 LOC254559 0.606
	FOXC1	0.642	CLIC6	0.667	KLC4	0.598
	PSMB9	0.624	TBX18	0.639	SEMA4G	0.597
	HOXC10	0.623	C11orf92	0.617	COL25A1	0.596
	NDRG2	0.623	FOXD2	0.601	HOXC13	0.591
	MAPT	0.607	ACSF3	0.586	SOX9	0.580
	STC2	0.606	FKBP10	0.583	DUSP4	0.579
	ZNF502 †	0.585	TACSTD2	0.576	HOXA10	0.578
	PTPRCAP	0.583	TMEM176B	0.573	SIM2	0.574
	SCAND3	0.583	TMEM176A	0.568	FKBP10	0.568
	SLC1A4	0.580	FAM50B	0.563	VA X 2	0.563
	TAP1	0.576	SC65	0.563	FAM50B	0.563
	DBNDD2	0.565	ZIC5	0.555	TPD52L1	0.560
	OTX1	0.564	EFNA3	0.535	DQX1	0.554
	TCF7	0.561 SYS1-DBNDD2 0.532	FAM24B	0.547
	LY6G6C	0.561	DLX6AS	0.528	ZNF502	0.539
	FERMT3	0.560	HOXB6	0.525	CSNK1E	0.531
	ZIC4	0.559	C5orf38	0.523	IRX2	0.528
	HLA-B	0.556	H19	0.515	KCTD1	0.527
	GDF9	0.551	PCDHGA5	0.512	ENO3	0.524
	SOX9	0.551	ME3	0.502	ISL2	0.506
	CELSR1	0.550	CHFR	0.501	STMN1	0.503
	SYS1-DBNDD2 0.549	GPR120	0.499	TRIM15	0.501
	HLA-E	0.549	SLC35C1	0.497	HLTF	0.500
	CYP1B1	0.541	SLC5A6	0.487	DMRTA2	0.497
	RUNX3	0.540	RGL2	0.481	ZIC4	0.497
	KIAA1949	0.537	HOXB2	0.481	ALX3	0.496
	RIPK4	0.531	MGMT	0.477	IRS2	0.494
	TPPP2	0.530	TAP1	0.474	SC65	0.488
	HLA-F	0.530	ETV4	0.474	DCLRE1A	0.485
	PPP1R3C	0.529	PCDHGA12	0.466	LIME1	0.482
	HOXB5	0.528	HOXD9	0.461	H2AFY2	0.469
	CELSR3	0.527	DBNDD2	0.458	KIAA1949	0.468
	B3GNT5	0.525	GPSM3	0.456	ZIC5	0.456
	ME3	0.524	KLC4	0.454	BMI1	0.453
	TMC8	0.523	FARP1	0.452	IRX4	0.448
	AIF1	0.522	FTH1	0.450	C11orf93	0.443
	SLC39A6	0.521	HSPA1L	0.443	DNTTIP1	0.442
	HOXC11	0.512	FSCN1	0.441	GATA6	0.440
	ERBB2	0.505	MUC12	0.441	HIST3H2A 0.434
	TBC1D10C	0.503	WIT1	0.440	PIK3R3	0.433
	SIM2	0.503	SS18L1	0.439	PIM3	0.431
	CAMK2N1	0.502	HOXA1	0.439	FAT 1	0.431
	RGMA	0.499	AMH	0.438	HOXC9	0.430
	LOC100132215 0.497	HOXA5	0.433	SOCS2	0.429
	PAX6	0.497	ZNF518B	0.430	MGC29506 0.426
	VA N G L 2	0.496	EMX1	0.430	RDH5	0.425
	DDHD2	0.487	PDX1	0.429	CHST11	0.424

  Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype 82 échantillons tumoraux dans 5 tissus différents et nous montrons l'existence d'une signature épigénétique commune à tous les cancers déterminant du phénotype CIMP. De plus, une analyse intégrative des profils d'expression révèle qu'une signature transcriptomique est également en mesure de prédire ce phénotype avec une très grande précision.

	Chapter 5. Nos résultats soutiennent l'existence d'un phénomène biologique commun associé au
	CIMP marqué par la présence d'une signature épigénétique et génétique commune	à
	tous les cancers.
	Toyota et al. dans
	le cancer du colon, pour caractériser une sous-population de cancers avec desprofils
	épigénétiques particuliers marqués par une hyperméthylation coordonnée d'un certains
	nombres d'ilôts CpG. Depuis, ce phénotype a été étendu à d i fférents profils de tumeurs
	dont, entre autres, le sein, la vessie, le poumon ou encore l'estomac. Le CIMP a un
	intérêt clinique majeur car il est associé à un niveau de réponse au traitement diffé r e n t
	mais également à un pronostic de survie particulier. Cependant, l'absence d'une base
	moléculaire au CIMP, commune à tous les cancers pose toujours des questions: est-ce
	que le CIMP est associé à un phénomène biologique réel ou est-ce qu'il s'agit simplement
	d'aberrations épigénétiques propres à chaque cancer?
	Nous avons analysé de manière systématique les profils de méthylations pangénomique
	issus d'une technologie unique (Illumina HumanMethylation450K) sur plus de 2000

Table 5

 5

		.1: Patients Dataset. Original dataset sizes for methylation (Meth), gene
	expression (GE) and mutation profiles for cancerous tissues.T h e" M a t c h e d "c o l u m n
	represents the number of available samples both methylation and gene expression pro-
				files.
		Meth GE	Meth/GE Meth/Mutations
	Bladder	373	56	43	28
	Breast	626	778	478	468
	Colon	291	193	34	219
	Lung	452	125	82	411
	Stomach	338	373	309	199
	Overall	2090 1525	941	1325

Table 5 .

 5 2: CIMP Proportion.

		Negative Positive Ratio
	Bladder	262	111	30%
	Breast	509	117	19%
	Colon	232	59	20%
	Lung	136	316	70%
	Stomach	144	194	57%
	Overall	1283	797	38%

Table 5 . 3 :

 53 Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype 93 List of genes associated with the common set of CGIs that define CIMP in each tissue.

		LOC339524, GSTM1, CD1D, LMX1A
		CACNA1E ,NR5A2, WNT3A, GNG4
		EMX1, CTNNA2 ,LRRTM1, DLX1
		EVX2, HOXD13, GBX2, SYN2
		HAND2, NBLA00301, EBF1, HIST1H2BB
	Epigenetic HIST1H3C, HLA-DRB1, C6orf186, IKZF1
	Signature	p16, HMX3, KNDC1, KLHL35
		HOTAIR, SLC6A15, ALX1, RFX4
		CLDN10, ADCY4, RIPK3, NID2
		OTX2, OTX2OS1, GSC, KIF26A
		GREM1, SEC14L5, HS3ST3B1, IGF2BP1
		HOOK2, NFIX, ZNF577, ZNF649
		CPXM1, CDH22, CHRNA4

Table summarizing the stability of the cluster assignments for each tissue and different number of CIMP clusters considered.

Chapter 5. Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype 92 Figure 5.3: Universal epigenetic signature for CIMP.

Chapter 5.

Table 5 . 4 :

 54 Matched Meth/GE samples CIMP Proportion.

		Negative Positive Ratio
	Bladder	27	16	37%
	Breast	385	93	20%
	Colon	27	7	20%
	Lung	22	60	75%
	Stomach	131	178	58%
	Overall	592	354	37%

Table 5 .

 5 7: Clinical impact of CIMP. Overall survival proportion given the CIMP phenotype and the p-value associated with the survival analysis (logrank test).

	Tissue	Event	P-value
		CIMP-CIMP+
	BLCA 47/214	21/96	0.74
	BRCA 29/495	9/114	0.20
	COAD 28/218	6/54	0.57
	LUAD 24/127 67/295	0.49
	STAD	26/141 20/193	0.29

Figure 3.5: Histogram of the distribution of methylome-similarity score (MS) between unrelated PT/LR pairs. MS score for matched pairs is represented by circles. The vertical dashed line corresponds to the 94% quantile of the distribution of the MS scores for the unrelated pairs, used as a threshold tod e fi n ec l o n a lp a i r s (MS T hreshold =6.6 * 10 -4 ).

Acknowledgements

List of Tables

BLCA BRCA COAD LUAD STAD sso Distribution of the accuracy of the CIMP-phenotype prediction taskg i v e nt h ep a t i e n tg e n e expression profile using n =100bootstrapand3-foldcross-v alidationforsev eralmethods (pink= "tissue-specific" lasso, green= "Combined-Lasso", blue= "Group-Lasso", red star= random prediction). Panel B. Venn diagram of the tissue-sp ecific gene signatures using lasso for each tissue separately. Panel C. Venn diagram representing the intersection between the "Combined" and "Group" lasso gene signatures. Panel D. Stability of each gene signature for each tissue-specific CIMP prediction as well as the "Combined-Lasso" and the "Group-Lasso" CIMP prediction task obtained and ranked by frequency of appearance using bootstrap (n =1 0 0r e p e a t s ) . F o rb l a d d e r and colon CIMP prediction task, the signature was non robust (frequency of the most redundant gene inferior to 10%). The combined prediction task signature outperforms the tissue-specific signatures in robustness.

Chapter 5. Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype 96 However, the poor accuracy as well as the non-robustness of genetic signatures to predict CIMP may be due to the small size of some datasets (n BLCA = 43, n COAD = 34). To overcome the lack of statistical power due to small sample size, we combine in a second analysis the different datasets into a single multivariate regression analysis, based on the assumption that the CIMP signatures of different cancers may share the same genes. We train classifiers to predict CIMP status from gene expression data jointly across cancers using two methods, based on two different assumptions: (i) assuming that all tissues share the same gene signature and coefficients for the prediction task, we run a single Lasso classification on the combined datasets ("Combined-Lasso" prediction) or (ii) assuming that all tissues share the same gene signature but with different coefficients, we jointly train several models with a group Lasso approach to constrain the selected genes to be the same across cancers without imposing their coefficients to coincide ("Group-Lasso" prediction). The rationale for the group lasso approach is that while CIMP may be caused by a common subset of genes, but their impact may varybetweentissues. Our results show that both methods significantly outperforms the tissue-specific predictions (P ≤ 2.10 -16 ,F i g u r e5.5 panel A, 5.6) in particular for bladder and colon where the size of the initial datasets could not give sufficient statistical power to predict CIMP accurately. There is overall little difference between both methods, with the notable exception of lung cancer where the combined lasso approach is significantly worse than the group lasso (and even the single lasso) model, suggesting that in that case the weights of the genes in the CIMP signature may differ from other cancers. More importantly, each method allows to identify a common genetic signature (51 genes for the "Combined" prediction and 58 genes for the "Group-Lasso" prediction) that distinguishes CIMPpositive and CIMP-negative class for each tumors which is more robust than all the tissue-specific signatures (Figure 5.5 panel C). In addition, these signatures share a large common set of genes (25 common genes). We perform gene ontology analysis on the intersection of the two predictive gene signatures and find specific enrichment only for genetic regulatory processes.

Chapter 5. Integrative DNA methylation and gene expression profiles to assess the universality of the CpG island methylator phenotype 97 Several somatic mutations have been found to be tightly associated with epigenetic aberrations in CIMP. Recent studies have pointed out the causal role of IDH1 mutations in Glioblastoma-CIMP [START_REF] Noushmehr | Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma[END_REF], Yilmaz et al., 2012] and tight associations between IDH2 and TET2 mutations with other CIMPs (leukemia [START_REF] Figueroa | Leukemic IDH1 and IDH2 mutations result in a hypermethylation phenotype, disrupt TET2 function, and impair hematopoieticd i fferentiation[END_REF],

enchondroma and spindle cell hemangioma [START_REF] Amary | Ollier disease and Maffucci syndrome are caused by somatic mosaic mutations of IDH1 and IDH2[END_REF], Pansuriya et al., 2011]).

In colon, BRAF and KRAS mutations are associated with microsatellite instability and COAD-CIMP [START_REF] Weisenberger | CpG island methylator phenotype underlies sporadic microsatellite instability and is tightly associated with BRAF mutation in colorectal cancer[END_REF].

We re-assess the association between mutations in these genes and CIMP in the different types of cancers (Figure 5.6, panel A). We recover a strong association between BRAF mutation and CIMP-positive colon tumors but no specific association with other tumor types. We also find no coordinated association between IDH1, IDH2, KRAS, BRAF or TET2 mutations and CIMP phenotypes for all tissues. In addition we perform genome-wide mutation analysis to assess whether specific gene mutations are associated with CIMP. We find no significant gene mutation association for bladder, breast nor lung CIMPs. For colon and gastric cancer, we find respectively 459 and 1070 gene mutations associated with CIMP with a common intersection of 195 genes (5.7). Gene ontology analysis of this set of genes shows significant enrichment for extracellular matrix organization and cell adhesion but also neuronal developmental processes (5.7).

Finally, we also look at the rate of mutations in each tissue given the CIMP phenotype.

We observe a significant association between the number of mutations and the CIMP status for colon and gastric cancer (Figure 5.6 panel B), in accordance with the tight association between CIMP and microsatellite instability for these two tissues [START_REF] Herman | Incidence and functional consequences of hMLH1 promoter hypermethylation in colorectal carcinoma[END_REF],Weisenberger et al., 2006,Jones et al., 2012,Zang et al., 2012]. However, the same observation could not be made for bladder, breast and lung.