Paul Hofman
email: hofman.p@chu-nice.fr

Nicholas Ayache
email: nicholas.ayache@inria.fr

Pascal Barbry
email: pascal.barbry@unice.fr

Michel Barlaud
email: michel.barlaud@unice.fr

Audrey Bel
email: audrey.bel@ciusante.org

Philippe Blancou
email: philippe.blancou@unice.fr

Frédéric Checler
email: frederic.checler@unice.fr

Sylvie Chevillard
email: sylvie.chevillard@cea.fr

Gael Cristofari
email: gael.cristofari@unice.fr

Mathilde Demory
email: mathilde.demory@ville-nice.fr

Vincent Esnault
email: esnault.v@chu-nice.fr

Claire Falandry
email: claire.falandry@chu-lyon.fr

Eric Gilson
email: eric.gilson@unice.fr

Olivier Guérin
email: guerin.o@chu-nice.fr

Nicolas Glaichenhaus
email: nicolas.glaichenhaus@unice.fr

Joel Guigay
email: joel.guigay@nice.unicancer.fr

Marius Ilié
email: ilie.m@chu-nice.fr

Bernard Mari
email: bernard.mari@unice.fr

Charles-Hugo Marquette
email: marquette.c@chu-nice.fr

Véronique Paquis-Flucklinger
email: veronique.paquis@unice.fr

Frédéric Prate
email: prate.f@chu-nice.fr

Pierre Saintigny
email: pierre.saintigny@lyon.unicancer.fr

Barbara Seitz-Polsky
email: seitz-polski.b@chu-nice.fr

Taycir Skhiri
email: skhiri.t@chu-nice.fr

Ellen Van Obberghen-Schilling

Emmanuel Van Obberghen
email: ellen.van-obberghen@unice.fr

Laurent Yvan-Charvet
email: laurent.yvan-charvet@unice.fr

The OncoAge Consortium: Linking Aging and Oncology from Bench to Bedside and Back Again

Keywords: aging, cancer, optimization, research, education, elderly, well-being

It is generally accepted that carcinogenesis and aging are two biological processes, which are known to be associated. Notably, the frequency of certain cancers (including lung cancer), increases significantly with the age of patients and there is now a wealth of data showing that multiple mechanisms leading to malignant transformation and to aging are interconnected, defining the so-called common biology of aging and cancer. OncoAge, a consortium launched in 2015, brings together the multidisciplinary expertise of leading public hospital services and academic laboratories to foster the transfer of scientific knowledge rapidly acquired in the fields of cancer biology and aging into innovative medical practice and silver economy development. This is achieved through the development of shared technical platforms (for research on genome stability, (epi)genetics, biobanking, immunology, metabolism, and artificial intelligence), clinical research projects, clinical trials, and education. OncoAge focuses mainly on two pilot pathologies, which benefit from the expertise of several members, namely lung and head and neck cancers. This review outlines the broad strategic directions and key advances of OncoAge and summarizes some of the issues faced by this consortium, as well as the short-and long-term perspectives.

Introduction

Chronological age is the most important single risk factor for the development of a variety of cancers and chronic diseases that account for the majority of societal morbidity, mortality, and public health costs. Recent findings suggest that changes in certain basic biological processes are shared in physiological aging, cancer, and degenerative pathologies [START_REF] Falandry | Biology of cancer and aging: A complex association with cellular senescence[END_REF][START_REF] Kennedy | Geroscience: Linking aging to chronic disease[END_REF]. Importantly, similar processes can be altered in diseases as diverse as cancer, neurodegeneration, cardiovascular disorders, chronic obstructive pulmonary disease (COPD), osteoarthritis, and diabetes, to name a few. For instance, at the cellular level, the accumulation in tissues of senescent cells (permanent cell cycle arrest in response to various types of stress or tissue remodeling) emerges as an important contributor to aging and age-related pathologies, through both cell autonomous and non-autonomous mechanisms driving inflammation, immunosenescence, and tissue degeneration [START_REF] Grimes | Significance of cellular senescence in aging and cancer[END_REF][START_REF] Lopez-Otin | The hallmarks of aging[END_REF]. Therefore, a key challenge now is to rapidly improve our knowledge on the biological processes in common that lead to malignant transformation and degenerative pathologies [START_REF] Falandry | Biology of cancer and aging: A complex association with cellular senescence[END_REF][START_REF] Aunan | The Biology of Aging and Cancer: A Brief Overview of Shared and Divergent Molecular Hallmarks[END_REF][START_REF] Rozhok | The evolution of lifespan and age-dependent cancer risk[END_REF][START_REF] Shay | Role of Telomeres and Telomerase in Aging and Cancer[END_REF]. From a cellular standpoint, the mechanisms that drive degenerative diseases and cancer are shared at an initial phase (e.g., during the accumulation of senescent cells), before adopting a particular direction and specific genetic and epigenetic modifications that orient cells toward distinct fates (e.g., escape of cellular checkpoints for cancer cells) [START_REF] Falandry | Biology of cancer and aging: A complex association with cellular senescence[END_REF][START_REF] Aunan | The Biology of Aging and Cancer: A Brief Overview of Shared and Divergent Molecular Hallmarks[END_REF][START_REF] Rozhok | The evolution of lifespan and age-dependent cancer risk[END_REF][START_REF] Shay | Role of Telomeres and Telomerase in Aging and Cancer[END_REF][START_REF] Pawelec | Immunosenescence and cancer[END_REF]. Thus, schematically, degenerative aging and cancer can be considered as two sides of the same coin, involving many common fundamental biological mechanisms (Figure 1). Hence, the progressive degeneration of tissues can lead to transformation into cancer after activation of chronic inflammation and immunosenescence [START_REF] Barreiro | Relationships between chronic obstructive pulmonary disease and lung cancer: Biological insights[END_REF][START_REF] Biswas | Chronic obstructive pulmonary disease and lung cancer: Inter-relationships[END_REF][START_REF] Murata | Inflammation and cancer[END_REF][START_REF] Serrano | Unraveling the links between cancer and aging[END_REF]. Finally, from an epidemiological standpoint, the risk of emergence and incidence of most cancers increase with the age of the population [START_REF] Fitzmaurice | Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2016: A Systematic Analysis for the Global Burden of Disease Study[END_REF][START_REF] Micheli | Cancer prevalence in European registry areas[END_REF][START_REF] Yancik | Population aging and cancer: A cross-national concern[END_REF].

Although cancer and aging biology are closely related, they are often investigated separately. Thus, whereas a number of fundamental and translational research centers or institutes worldwide have oriented their research in the direction of aging, only a few of them have really focused their studies on the links between aging and cancer. This is the case for the Institute for Research on Cancer and Aging, Nice (IRCAN) in France, which bases its overarching strategy on combining the research developed by scientists and physicians on cancer and aging mechanisms (https://www.ircan.org). It is within this context that the OncoAge consortium was launched in Nice to facilitate the transfer of this growing knowledge on cancer and aging to medical innovation and current medical practice. This consortium was certified and recognized in 2015 as a Hospital-University Federation (HUF) by AVIESAN (https://www.aviesan.fr; https://www.oncoage.org). The global aim of the HUF program in France is to develop excellence within the university hospitals by targeting medical topics optimizing care, research, and education in these subject areas (https://www.aviesan.fr). In short, OncoAge is a HUF based on the expertise of medical and scientific teams oriented toward cancer pathologies associated with aging. The key aim of OncoAge is to improve the care of elderly patients, in particular those with cancer, to set up research projects, and develop training and educational programs in this domain (https://www.oncoage.org). These efforts should not only deepen our understanding of the mechanisms underlying cancer and aging, but also improve the daily well-being of the patients.

The aging of the world's populations has progressively modified the profile of the most frequent diseases [START_REF] Fitzmaurice | Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2016: A Systematic Analysis for the Global Burden of Disease Study[END_REF]. While infectious and cardiovascular disorders have until recently been the most frequent, and resulted in the highest number of deaths around the globe, considerable progression towards an increase in the number of certain cancers and diseases linked to aging has been observed in recent years. According to epidemiological predictions, these diseases will be among the most common in 2030, in both industrialized and non-industrialized countries. Among them, lung cancer will be the fifth cause of death in 2030, whereas according to the Global Burden of Hence, the progressive degeneration of tissues can lead to transformation into cancer after activation of chronic inflammation and immunosenescence [START_REF] Barreiro | Relationships between chronic obstructive pulmonary disease and lung cancer: Biological insights[END_REF][START_REF] Biswas | Chronic obstructive pulmonary disease and lung cancer: Inter-relationships[END_REF][START_REF] Murata | Inflammation and cancer[END_REF][START_REF] Serrano | Unraveling the links between cancer and aging[END_REF]. Finally, from an epidemiological standpoint, the risk of emergence and incidence of most cancers increase with the age of the population [START_REF] Fitzmaurice | Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2016: A Systematic Analysis for the Global Burden of Disease Study[END_REF][START_REF] Micheli | Cancer prevalence in European registry areas[END_REF][START_REF] Yancik | Population aging and cancer: A cross-national concern[END_REF].

Although cancer and aging biology are closely related, they are often investigated separately. Thus, whereas a number of fundamental and translational research centers or institutes worldwide have oriented their research in the direction of aging, only a few of them have really focused their studies on the links between aging and cancer. This is the case for the Institute for Research on Cancer and Aging, Nice (IRCAN) in France, which bases its overarching strategy on combining the research developed by scientists and physicians on cancer and aging mechanisms (https://www.ircan.org). It is within this context that the OncoAge consortium was launched in Nice to facilitate the transfer of this growing knowledge on cancer and aging to medical innovation and current medical practice. This consortium was certified and recognized in 2015 as a Hospital-University Federation (HUF) by AVIESAN (https://www.aviesan.fr; https://www.oncoage.org). The global aim of the HUF program in France is to develop excellence within the university hospitals by targeting medical topics optimizing care, research, and education in these subject areas (https://www.aviesan.fr). In short, OncoAge is a HUF based on the expertise of medical and scientific teams oriented toward cancer pathologies associated with aging. The key aim of OncoAge is to improve the care of elderly patients, in particular those with cancer, to set up research projects, and develop training and educational programs in this domain (https://www.oncoage.org). These efforts should not only deepen our understanding of the mechanisms underlying cancer and aging, but also improve the daily well-being of the patients.

The aging of the world's populations has progressively modified the profile of the most frequent diseases [START_REF] Fitzmaurice | Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2016: A Systematic Analysis for the Global Burden of Disease Study[END_REF]. While infectious and cardiovascular disorders have until recently been the most frequent, and resulted in the highest number of deaths around the globe, considerable progression towards an increase in the number of certain cancers and diseases linked to aging has been observed in recent years. According to epidemiological predictions, these diseases will be among the most common in 2030, in both industrialized and non-industrialized countries. Among them, lung cancer will be the fifth cause of death in 2030, whereas according to the Global Burden of Disease (GBD), COPD is already now the third leading cause of death worldwide, a progression WHO had not predicted to occur until 2030 [START_REF] Lozano | Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: A systematic analysis for the Global Burden of Disease Study 2010[END_REF][START_REF] Quaderi | The unmet global burden of COPD[END_REF].

In this context, it is crucial to rapidly advance the molecular understanding of genetic and epigenetic mechanisms, as well as immune and metabolic abnormalities leading to the development of cancers associated with age, and to improve the care and well-being of patients with cancers that have become chronic and often invalidating. This has generated an urgent need to address many new challenges in translational projects in this field [START_REF] Partridge | Facing up to the global challenges of ageing[END_REF].

Importantly, elderly patients with lung cancer and head and neck cancer (HNC) are rarely enrolled in clinical trials, particularly in phase 1, and even less so in dedicated trials in curative or palliative settings. As an example, no standards of treatment exist for these populations, and frail elderly lung and HNC patients may be over-treated with a risk of increased toxicity while fit patients may be proposed for suboptimal treatment. It is, therefore, crucial to develop and evaluate appropriate treatments by enrolling elderly patients with cancer in a higher number of therapeutic trials. Beyond research-related concerns, OncoAge faces epidemiologic and environmental issues such as the procurement of well-controlled demographic data and the means of measuring air pollutants according to geolocalization of the patients in the Alpes-Maritimes area. Moreover, questions concerning costs (obtaining funding from public and private sources) and organization (steering multicenter efforts in the same direction) must be anticipated and managed to assure the sustainability of the consortium in the near years.

The genesis and objectives of the OncoAge consortium since its creation in 2015 at the Côte d'Azur University (Nice, France), its first accomplishments, and its future perspectives are described below.

OncoAge: The Origin of the Project

OncoAge was established in France after acceptance and certification by AVIESAN, subsequent to a national tender for HUF proposals (https://www.aviesan.fr). The application called for unique and original projects covering an aspect of health for which a program optimizing the healthcare of patients, research, and teaching in the specified domain could be addressed. The HUF OncoAge project was submitted in 2015 and selected by AVIESAN after the representatives of the project were examined by an international committee.

OncoAge at the Côte d'Azur University: Why?

The choice of setting up a HUF within the Nice Hospital of the University Côte d'Azur was motivated by several aspects, in particular, based on epidemiological arguments (Figure 2).

Disease (GBD), COPD is already now the third leading cause of death worldwide, a progression WHO had not predicted to occur until 2030 [START_REF] Lozano | Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: A systematic analysis for the Global Burden of Disease Study 2010[END_REF][START_REF] Quaderi | The unmet global burden of COPD[END_REF].

In this context, it is crucial to rapidly advance the molecular understanding of genetic and epigenetic mechanisms, as well as immune and metabolic abnormalities leading to the development of cancers associated with age, and to improve the care and well-being of patients with cancers that have become chronic and often invalidating. This has generated an urgent need to address many new challenges in translational projects in this field [START_REF] Partridge | Facing up to the global challenges of ageing[END_REF].

Importantly, elderly patients with lung cancer and head and neck cancer (HNC) are rarely enrolled in clinical trials, particularly in phase 1, and even less so in dedicated trials in curative or palliative settings. As an example, no standards of treatment exist for these populations, and frail elderly lung and HNC patients may be over-treated with a risk of increased toxicity while fit patients may be proposed for suboptimal treatment. It is, therefore, crucial to develop and evaluate appropriate treatments by enrolling elderly patients with cancer in a higher number of therapeutic trials. Beyond research-related concerns, OncoAge faces epidemiologic and environmental issues such as the procurement of well-controlled demographic data and the means of measuring air pollutants according to geolocalization of the patients in the Alpes-Maritimes area. Moreover, questions concerning costs (obtaining funding from public and private sources) and organization (steering multicenter efforts in the same direction) must be anticipated and managed to assure the sustainability of the consortium in the near years.

The genesis and objectives of the OncoAge consortium since its creation in 2015 at the Côte d'Azur University (Nice, France), its first accomplishments, and its future perspectives are described below.

OncoAge: The Origin of the Project

OncoAge was established in France after acceptance and certification by AVIESAN, subsequent to a national tender for HUF proposals (https://www.aviesan.fr). The application called for unique and original projects covering an aspect of health for which a program optimizing the healthcare of patients, research, and teaching in the specified domain could be addressed. The HUF OncoAge project was submitted in 2015 and selected by AVIESAN after the representatives of the project were examined by an international committee.

OncoAge at the Côte d'Azur University: Why?

The choice of setting up a HUF within the Nice Hospital of the University Côte d'Azur was motivated by several aspects, in particular, based on epidemiological arguments (Figure 2). According to the data from the National Institute of Statistics and Economic studies ("INSEE"), in contrast to other regions in France, the population of the Alpes-Maritimes region, where the HUF is located, is populated by a high number of elderly people (>65 years old) (https://www.insee.fr). This region has a population density above the average in France, and which is increasing yearly (https://www.insee.fr). Indeed, the Nice University Hospital already treats many patients above Cancers 2019, 11, 250 5 of 11 65 years of age, and this number is expected to increase. In this context, due to the global aging of the world population, it is estimated that the number of elderly patients admitted to this healthcare center today is similar to that which will be observed in most hospitals in the world in 2030. The high incidence of lung cancer and the above average level of atmospheric pollution in the Alpes-Maritimes region, as compared to national levels, were among the other reasons that motivated the decision to develop the OncoAge project (Figure 2). Moreover, no other HUF in France has focused on aging and cancer. Therefore, we felt that this important issue should be developed within this geographical area. Finally, the presence of IRCAN, a research center focusing on the mechanisms linking cancer and aging, provides a unique opportunity to associate the most recent biological discoveries with the health-oriented aims of OncoAge.

OncoAge at the Côte d'Azur University: How?

OncoAge efforts are based, for the greater part, on a limited number of "pilot" pathologies that were selected within the Nice Hospital, taking into consideration the following parameters: (i) optimal organization of the hospital sectors in the concerned domains and the potential recruitment of patients, (ii) activities of the university (publications and teaching) of the hospital departments, (iii) the organized clinical and/or biological databases, (iv) clinical and translational research performed in collaboration with scientists of the teams studying fundamental research in the specified subjects. For this, three major pathologies were initially chosen to set up the foundations of OncoAge: thoracic diseases (tumoral or non-tumoral), head and neck and thyroid pathologies, and neuromuscular degenerative disorders. Transversal studies were initiated to reinforce the fundamental knowledge on these medical questions, combining various (epi)genomics, immunology, metabolism, and artificial intelligence (AI) approaches since these topics are particularly important for aging and cancer [START_REF] Falandry | Biology of cancer and aging: A complex association with cellular senescence[END_REF][START_REF] Benz | Ageing, oxidative stress and cancer: Paradigms in parallax[END_REF][START_REF] Bernardes De | Telomerase at the intersection of cancer and aging[END_REF][START_REF] Birch | DNA damage response at telomeres contributes to lung aging and chronic obstructive pulmonary disease[END_REF][START_REF] Bottazzi | Aging, inflammation and cancer[END_REF][START_REF] Caron | DEPTOR at the Nexus of Cancer, Metabolism, and Immunity[END_REF][START_REF] Lawrence | The lysosome as a cellular centre for signalling, metabolism and quality control[END_REF][START_REF] Ovadya | Impaired immune surveillance accelerates accumulation of senescent cells and aging[END_REF][START_REF] Raffaghello | Metabolic Alterations at the Crossroad of Aging and Oncogenesis[END_REF][START_REF] Zhavoronkov | Artificial intelligence for aging and longevity research: Recent advances and perspectives[END_REF]. Actions that support common structures have been established, such as innovative programs and connection to the silver economy (https://www.france-silvereco.fr/notre-observatoire/tableau-de-bord-de-la-filiere), technological platforms (including geriatric screening tools to identify elderly cancer patients who could benefit from comprehensive geriatric assessment), and new biorepository space, training and education, dissemination of knowledge and information [START_REF] Bellera | Screening older cancer patients: First evaluation of the G-8 geriatric screening tool[END_REF][START_REF] Bellera | Modeling individual and relative accuracy of screening tools in geriatric oncology[END_REF][START_REF] Kenis | Relevance of a systematic geriatric screening and assessment in older patients with cancer: Results of a prospective multicentric study[END_REF][START_REF] Kenis | Adherence to geriatric assessment-based recommendations in older patients with cancer: A multicenter prospective cohort study in Belgium[END_REF][START_REF] Magnuson | Geriatric assessment with management in cancer care: Current evidence and potential mechanisms for future research[END_REF][START_REF] Mohile | Practical Assessment and Management of Vulnerabilities in Older Patients Receiving Chemotherapy: ASCO Guideline for Geriatric Oncology Summary[END_REF]. The participants working on these different aspects (work package leaders) interact in concert with the unique aim of building a dynamic, collaborative network.

In addition to several departments of the Nice University Hospital, the OncoAge consortium brings together a number of institutes of the Côte d'Azur University (UCA), such as the Antoine Lacassagne Comprehensive Cancer Center (CAL), the Etablissement de Santé Privé d'Intérêt Collectif (ESPIC) Hôpitaux Pédiatriques de Nice Centre Hospitalier Universitaire Fondation Lenval, and many teams of different research centers (Institut of Research and Aging, Nice (IRCAN), Centre Méditerranéen de Médecine Moléculaire (C3M), Institut de Biologie Valrose (iBV), Institut de Pharmacologie Cellulaire et Moléculaire (IPMC), Laboratoire de PhysioMédecine Moléculaire (LP2M), and Institut National de Recherche en Informatique et en Automatique (Inria)). OncoAge is not only composed of different stakeholders belonging to the Côte d'Azur University but also of teams of the Lyon University (Lyon University Hospital; Hospices Civils de Lyon; HCL; and Leon Bérard Comprehensive Cancer Center (CLB) and of the International Agency for Research on Cancer (IARC, Lyon, France), the Centre d'Energie Atomique (CEA; Fontenay aux Roses, France), and the Gustave Roussy Institute (IGR; Villejuif, France) (Figure 3). The governance of OncoAge is provided by a strategic committee and a scientific council (https://www.oncoage.org). To ensure proper functioning and to benefit from expert advice, an international scientific advisory board (SAB) composed of medical and scientific opinion leaders in the field of cancer and aging has been set up (https://www.oncoage.org).

OncoAge: Main Objectives

The overarching objective of OncoAge is to create and foster a network of expertise, and to develop collaborations and projects to improve the healthcare of elderly patients with cancer. To attain these objectives, a number of initiatives have been undertaken: (i) definition of different indicators of tracking (using publications), (ii) development of clinical and translational research projects funded by regional, national, and international bodies, (iii) introduction into the university lectures of themes developed by OncoAge, (iv) organization of workshops and conferences, and (v) communication of information to the general public and the creation of a dedicated website. These different initiatives put forward by OncoAge were first evaluated by the SAB of OncoAge in 2015, and found to be highly appropriate.

OncoAge: Main Results After Three Years of Existence (2015-2018)

Several key accomplishments both at the level of clinical and translational research projects and at the level of the structuring and dissemination of information were achieved. The clinical expertise in HNC within OncoAge is highlighted by the ELAN (ELderly Head and Neck cancer) program. These clinical studies on curative or palliative personalized treatment of elderly head and neck cancer patients after geriatric assessment (Elan Geriatric Evaluation, EGE) currently represent the only multi-center therapeutic trials dedicated to this group of patients worldwide [START_REF] Guigay | A French multicenter research program for a personalized treatment of elderly head and neck cancer patient[END_REF][START_REF] Guigay | Treatment of inoperable elderly Head and Neck cancer patients[END_REF][START_REF] Mertens | The ELAN-ONCOVAL (ELderly heAd and Neck cancer-Oncology eValuation) study: Evaluation of the feasibility of a suited geriatric assessment for use by oncologists to classify patients as fit or unfit[END_REF]. Studies were completed in 2018 and results will be presented in 2019. Moreover, an early phase clinical trial unit was set up in the Comprehensive Cancer Center Antoine Lacassagne to favor the emergence of therapeutic innovations. The governance of OncoAge is provided by a strategic committee and a scientific council (https: //www.oncoage.org). To ensure proper functioning and to benefit from expert advice, an international scientific advisory board (SAB) composed of medical and scientific opinion leaders in the field of cancer and aging has been set up (https://www.oncoage.org).

OncoAge: Main Objectives

The overarching objective of OncoAge is to create and foster a network of expertise, and to develop collaborations and projects to improve the healthcare of elderly patients with cancer. To attain these objectives, a number of initiatives have been undertaken: (i) definition of different indicators of tracking (using publications), (ii) development of clinical and translational research projects funded by regional, national, and international bodies, (iii) introduction into the university lectures of themes developed by OncoAge, (iv) organization of workshops and conferences, and (v) communication of information to the general public and the creation of a dedicated website. These different initiatives put forward by OncoAge were first evaluated by the SAB of OncoAge in 2015, and found to be highly appropriate.

OncoAge: Main Results After Three Years of Existence (2015-2018)

Several key accomplishments both at the level of clinical and translational research projects and at the level of the structuring and dissemination of information were achieved. The clinical expertise in HNC within OncoAge is highlighted by the ELAN (ELderly Head and Neck cancer) program. These clinical studies on curative or palliative personalized treatment of elderly head and neck cancer patients after geriatric assessment (Elan Geriatric Evaluation, EGE) currently represent the only multi-center therapeutic trials dedicated to this group of patients worldwide [START_REF] Guigay | A French multicenter research program for a personalized treatment of elderly head and neck cancer patient[END_REF][START_REF] Guigay | Treatment of inoperable elderly Head and Neck cancer patients[END_REF][START_REF] Mertens | The ELAN-ONCOVAL (ELderly heAd and Neck cancer-Oncology eValuation) study: Evaluation of the feasibility of a suited geriatric assessment for use by oncologists to classify patients as fit or unfit[END_REF]. Studies were completed in 2018 and results will be presented in 2019. Moreover, an early phase clinical trial unit was set up in the Comprehensive Cancer Center Antoine Lacassagne to favor the emergence of therapeutic innovations. Since 2015, OncoAge has been associated with the publication of 86 scientific articles referenced in NCBI PubMed (the "FHU OncoAge" was listed with the author's affiliation). For example, some publications are related to epidemiological, clinical, and translational research projects made in lung cancer and COPD, such as lung cancer screening and assessment of biomarkers [START_REF] Boulahssass | Predicting early death in older adults with cancer[END_REF][START_REF] Heeke | Use of the Ion PGM and the GeneReader NGS Systems in Daily Routine Practice for Advanced Lung Adenocarcinoma Patients: A Practical Point of View Reporting a Comparative Study and Assessment of 90 Patients[END_REF][START_REF] Ilie | Chromogenic Multiplex Immunohistochemistry Reveals Modulation of the Immune Microenvironment Associated with Survival in Elderly Patients with Lung Adenocarcinoma[END_REF][START_REF] Leroy | Circulating tumour cells as a potential screening tool for lung cancer (the AIR study): Protocol of a prospective multicentre cohort study in France[END_REF][START_REF] Washetine | Establishing a Dedicated Lung Cancer Biobank at the University Center Hospital of Nice (France). Why and How?[END_REF][START_REF] Giovannini-Chami | The "one airway, one disease" concept in light of Th2 inflammation[END_REF][START_REF] Falk | Effect of mutant variants of the KRAS gene on PD-L1 expression and on the immune microenvironment and association with clinical outcome in lung adenocarcinoma patients[END_REF][START_REF] Ilie | Detection of PD-L1 in circulating tumor cells and white blood cells from patients with advanced non-small-cell lung cancer[END_REF][START_REF] Kemp | A Multicenter Randomized Controlled Trial of Zephyr Endobronchial Valve Treatment in Heterogeneous Emphysema (TRANSFORM)[END_REF][START_REF] Revinski | CDC20B is required for deuterosome-mediated centriole production in multiciliated cells[END_REF]. Recently, a new project was accomplished by physicians from the Nice University Hospital and researchers belonging to the "Institut National de Recherche en Informatique et en Automatique" (Inria) who aimed to develop a lung cancer screening program based on the integration of three signatures: clinical data (leading to better risk factor assessment), chest low dose CT scan (by using computer-aided diagnosis), and biological blood signatures [START_REF] Benzaquen | Lung Cancer Screening, Towards a Multidimensional Approach: Why and How?[END_REF]. Since the HUF OncoAge was established, a strong partnership between oncologists and geriatricians belonging to the consortium was set up in order to optimize the care of the elderly cancer population. In this context, a large comprehensive geriatric assessment program using a multidimensional interdisciplinary diagnostic process was rapidly developed [START_REF] Boulahssass | The Desire to Better Understand Older Adults with Solid Tumors to Improve Management: Assessment and Guided Interventions-The French PACA EST Cohort Experience[END_REF].

Other specific studies concern the head and neck pilot pathology [START_REF] Harrington | Nivolumab versus standard, single-agent therapy of investigator's choice in recurrent or metastatic squamous cell carcinoma of the head and neck (CheckMate 141): Health-related quality-of-life results from a randomised, phase 3 trial[END_REF][START_REF] Bozec | MiR-223-3p inhibits angiogenesis and promotes resistance to cetuximab in head and neck squamous cell carcinoma[END_REF]. Several scientific projects managed by leaders of OncoAge were financed by different organizations, including the Institut National du Cancer (the French NCI), l'Agence Nationale de la Recherche, la Fondation de l'Association de la Recherche contre le Cancer, le Cancéropôle Provence Alpes Côte d'Azur" (PACA), and the Infrastructure en Biologie Santé et Agronomie" (IBiSA). A master's program on "Biobanks and Complex Data Management" was set up through the association of the Côte d'Azur University and the Nice Hospital (https://MScbiobanks-complex-data) [START_REF] Ilie | Fueling the sustainability of Next Generation Biobanks by training. A New Master "Biobanks and Complex Data Management[END_REF]. The Laboratory of Clinical and Experimental Pathology within OncoAge has been selected by the European Society of Pathology to serve as an advanced training center for molecular pathology with an emphasis on liquid biopsy. Since 2015, the master's program has enrolled students from all around the world and so far, three classes of students have been trained. This master's is supported by the biobank of the Nice University Hospital (BB-0033-0025), which has benefited since 2015 from new infrastructures and developments (http://univ-cotedazur.fr/en/ education/informations-utiles/les-informations-utiles/biobanks-complex-data/#.XGlsb7jjJ4E) [START_REF] Ilie | Fueling the sustainability of Next Generation Biobanks by training. A New Master "Biobanks and Complex Data Management[END_REF].

This biobank has integrated the technological platform of OncoAge and the biological specimens associated with the clinical data and is available to the teams of OncoAge after a material transfer agreement has been signed. The visibility of OncoAge has been ensured through the creation of a website and the organization of several symposiums, including the first joint meeting on lung cancer associating the MD Anderson Cancer Center and the HUF OncoAge (https://www.oncoage.org/ news-and-events-2/3/). Moreover, recently, on behalf of the HUF OncoAge, different actors of the consortium have had the opportunity to participate in the writing of a next Encyclopledia of Aging and Population Aging, edited by Springer, which will be available at the end of 2019. The HUF OncoAge will lead the "Cancer and Treatment" section of this encyclopedia.

The different actions accomplished by OncoAge were favorably evaluated by the SAB of OncoAge at the end of 2018.

OncoAge: Current Developments and Perspectives

A number of perspectives have been envisioned for the short term (2020). First, broadening the pathologies that OncoAge intends to investigate, in particular, skin cancers (including melanoma), will be integrated in 2019. New clinical-biological collections will be built, either as a complement to existing collections or from a new population of patients. Concerning the complementary collections (from patients with lung cancer and chronic obstructive pulmonary disease), the samples will include urine, total blood, and peripheral blood mononuclear cells (PBMCs). A new collection of blood (plasma, PBMCs, and total blood) obtained from healthy individuals older than 80 years and residents of the Alpes-Maritimes region will be assembled. Finally, a collection of bronchial and transthoracic biopsies (tissues fixed and paraffin embedded) obtained from lung cancer patients will be set up for future translational research projects. Several other objectives have been defined. Amongst these, an international master's degree on aging, dedicated to researching questions concerning aging and associated diseases, should be created. Further, a national project implicating private-public partners targeting innovation in the domain of aging should initiate several national and international collaborations (http://www.agence-nationalerecherche.fr/). Finally, new clinical trials, all with translational studies (including mechanisms of resistance to immunotherapy), have been launched or are in advanced phases of discussion with academic institutional groups and pharmacological companies. Indeed, immunotherapy in elderly patients has become a promising treatment alternative and put in the limelight [START_REF] Casaluce | Lung cancer, elderly and immune checkpoint inhibitors[END_REF][START_REF] Daste | Immune checkpoint inhibitors and elderly people: A review[END_REF][START_REF] Elias | Aging, immune senescence, and immunotherapy: A comprehensive review[END_REF][START_REF] Helissey | The development of immunotherapy in older adults: New treatments, new toxicities?[END_REF][START_REF] Kanesvaran | Immunotherapy in Older Adults With Advanced Cancers: Implications for Clinical Decision-Making and Future Research[END_REF]. In this context, the development and assessment of biomarkers of senescence will be associated with clinical trials thanks to the biobank (BB-0033-00025) and the OncoAge research teams [START_REF] Falandry | Are aging biomarkers clinically relevant in oncogeriatrics?[END_REF][START_REF] Ferrara | Immunosenescence and immunecheckpoint inhibitors in non-small cell lung cancer patients: Does age really matter?[END_REF]. These studies will benefit from the acquisition by OncoAge of technical platforms of state-of-the-art equipment and AI-based software (HALO AI TM , Indica Labs, London, UK) for high-speed whole slide imaging and quantitative multiplexing.

Conclusions

The most prominent feature of aging is a gradual deterioration/loss of cells that is associated with organ dysfunction and the rise of age-related chronic pathologies. Amongst these, cancer stands out as its occurrence significantly increases with age and has a devastating human and public health cost. Deciphering the clinical features, the biological markers, and the lifestyle and environmental factors that are shared between common chronic age-related pathologies and cancer should lead to the development of new clinical approaches, including the validation of surrogate biomarkers of frailty and predisposition. A deeper understanding of the common mechanisms involved both in aging and cancer is expected to considerably improve our knowledge on how to prevent age-related pathologies and how to optimize the care of elderly patients. In this context, OncoAge is a unique consortium composed of more than 1000 participants and actors located in Nice, Lyon, and Paris with the exclusive ambition of working together on clinical-biological and medical-scientific projects that aim to improve the care of elderly patients with cancer. This consortium has actively developed translational and clinical projects and has created innovation in the domain of geriatric oncology. The increase in the age of the world's populations has created new urgent demands on healthcare, as well as major strategic and economic issues. Improving the autonomy of elderly patients with cancer, avoiding repeated and long hospitalizations, performing early screening for certain cancers, and predicting, as well as preventing complications, are all objectives set out by OncoAge. Moreover, understanding the relationships between the aging phenomenon and cancer is a timely and multifaceted challenge where high-level research efforts in medicine, genomics, and biology have to be combined with societal approaches focused on individuals. In this context, OncoAge has designed and made operational an original holistic approach combining genotype and phenotype analyses of the aging and cancer processes.

To conclude, our expert multipronged approach is consolidated by the enthusiasm of the many physicians and scientists of several leading hospitals and strong research centers and warrants the future of OncoAge.

Figure 1 .

 1 Figure 1. Common mechanisms that drive degenerative diseases and cancer. OncoAge is dedicated to three major pathologies: thoracic diseases (tumoral or non-tumoral), head and neck and thyroid pathologies, and neuromuscular degenerative disorders.

Figure 1 .

 1 Figure 1. Common mechanisms that drive degenerative diseases and cancer. OncoAge is dedicated to three major pathologies: thoracic diseases (tumoral or non-tumoral), head and neck and thyroid pathologies, and neuromuscular degenerative disorders.

Figure 2 .

 2 Figure 2. (a) High population density within the Alpes-Maritimes area (purple arrow) (INSEE data). (b) Age pyramid within the Alpes-Maritimes area in 2013 and pre-visions for 2050 (INSEE data). (c) Air pollution levels (in red, very high air pollution) within the Alpes-Maritimes area (purple arrow; www.prevair.org).

Figure 2 .

 2 Figure 2. (a) High population density within the Alpes-Maritimes area (purple arrow) (INSEE data). (b) Age pyramid within the Alpes-Maritimes area in 2013 and pre-visions for 2050 (INSEE data). (c) Air pollution levels (in red, very high air pollution) within the Alpes-Maritimes area (purple arrow; www.prevair.org).

Figure 3 .

 3 Figure 3. The OncoAge consortium members. CEA: Centre d'Energie Atomique; IGR: Gustave Roussy Institute; CLB: Léon Bérard Comprehensive Cancer Center; IARC: International Agency for Resarch on Cancer; HCL: Hospices Civils de Lyon; CAL: Antoine Lacassagne Comprehensive Cancer Center; CHUN: Centre Hospitalier Universitaire de Nice; UCA: Université Côte d'Azur; IRCAN: Institut of Research and Aging, Nice; IBV: Institut de Biologie Valrose; LP2M: Laboratoire de PhysioMédecine Moléculaire; ESPIC: Etablissement de Santé Privé d'Intérêt Collectif Hôpitaux Pédiatriques de Nice Centre Hospitalier Universitaire Fondation Lenval; IPMC: Institut de Pharmacologie Cellulaire et Moléculaire; INRIA: Institut National de Recherche en Informatique et en Automatique.

Figure 3 .

 3 Figure 3. The OncoAge consortium members. CEA: Centre d'Energie Atomique; IGR: Gustave Roussy Institute; CLB: Léon Bérard Comprehensive Cancer Center; IARC: International Agency for Resarch on Cancer; HCL: Hospices Civils de Lyon; CAL: Antoine Lacassagne Comprehensive Cancer Center; CHUN: Centre Hospitalier Universitaire de Nice; UCA: Université Côte d'Azur; IRCAN: Institut of Research and Aging, Nice; IBV: Institut de Biologie Valrose; LP2M: Laboratoire de PhysioMédecine Moléculaire; ESPIC: Etablissement de Santé Privé d'Intérêt Collectif Hôpitaux Pédiatriques de Nice Centre Hospitalier Universitaire Fondation Lenval; IPMC: Institut de Pharmacologie Cellulaire et Moléculaire; INRIA: Institut National de Recherche en Informatique et en Automatique.

Conflicts of Interest:

The authors declare no conflict of interest.


Zilong Zhao
email: zilong.zhao@gipsa-lab.fr

Sophie Cerf
email: sophie.cerf@gipsa-lab.fr

Robert Birke
email: robert.birke@ch.abb.com

Bogdan Robu
email: bogdan.robu@gipsa-lab.fr

Sara Bouchenak
email: sara.bouchenak@insa-lyon.fr

Sonia Ben Mokhtar
email: sonia.benmokhtar@insa-lyon.fr

Lydia Y Chen

Active Learning from Unreliable Data

Keywords: Unreliable Data, Images, Attacks, Machine Learning, Deep Neural Network

Classification algorithms have been widely adopted in big recommendation systems, e.g., products, images and advertisements, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the field can be unreliable due to careless annotations or malicious data transformation. In our previous work, we proposed a two-layer learning framework for continuous learning in the presence of unreliable anomaly labels, it worked perfectly for two use cases, (i) detecting 10 classes of IoT attacks and (ii) predicting 4 classes of task failures of big data jobs. To continue this study, now we will challenge our framework with image dataset.

The first layer of quality model filters the suspicious data, where the second layer of classification model predicts data instance's class. As we focus on the case of images, we will use widely studied datasets: MNIST, Cifar10, Cifar100 and Ima-geNet. Deep Neural Network (DNN) has demonstrated excellent performances in solving images classification problems, we will show that two collaborating DNN could construct a more robust and high accuracy model.

I. INTRODUCTION

A large amount of user-generated data powers up machine learning based applications in our daily life. Labeled data (especially for images) from search engine provides a fast and cheap way to build the large-scale dataset. But it also inevitably introduces some incorrect labels. For examples, if we want to construct an image dataset of "airplane", with keyword "airplane", Google Image can give us a very good first result. But we can also notice that there are not only airplane images, they have also images: airplane interior, airplane runway, airplane structure sketch and Cartoon airplane.

To solve this problem, a simple way to clean the data, it's to find a domain expert to remove or relabel the suspect data in a preprocessing stage. However large-scale annotated datasets with high-quality label annotations are not always available for new domain, due to the significant time and efforts it takes, not to mention that for some online-tuning systems, the user-generated data can be infinite. There is no doubt that DNN is delivering superior results on image classification, but this success is highly tied to the availability of large-scale annotated datasets. So we can see a big contradiction here.

Standard machine learning algorithms typically assume clean labels and overlook the risk of noisy labels. But recent studies have shown that learning from high proportion noisy labels can significantly degrade the DNN's classification accuracy [START_REF] Vadim | Problem of knowledge discovery in noisy databases[END_REF].

Existing works of learning from noisy data can be roughly divided into two aspects: (1) filtering out noisy labels and learning only from the predicted clean data, (2) designing noise-aware classification algorithms. The first builds one or multiple filter models, e.g., SVM [START_REF] Segata | Noise reduction for instance-based learning with a local maximal margin approach[END_REF] to clean the data, and only the data instances that predicted label by one or more filters matches its original label, we believe this data instance is clean and can be used to train the classification model. The second type of approach can be summarized by several works, one proposed method is to correct noisy labels to their true labels via a clean label inference step. These methods assume the availability of a small clean dataset to be used [START_REF] Li | Learning from noisy labels with distillation[END_REF], [START_REF] Vahdat | Toward robustness against label noise in training deep discriminative neural networks[END_REF]. A different approach is to learn directly through noisy data, and in parallel, running a local intrinsic Dimensionality [START_REF] Michael | Local intrinsic dimensionality I: an extreme-valuetheoretic foundation for similarity applications[END_REF] measurement to monitor the stage of training process, to make sure at the end, the test accuracy stabilizes at its highest level [START_REF] Ma | Dimensionality-driven learning with noisy labels[END_REF].

While the recent state-of-the-art solution can have a very good result on noisy label issues, little focus has been given to the online setting with data instances having fluctuating noise levels. Our study presents the initial results on how to build a classifier by selectively and continuously learn from high quality data that leads to a strong classifier.

Figure 1 shows training and predicting process of our system. Training process is triggered by D i : ith training batch, the quality model will predict labels for each data instance, if the predicted label matches its original label, we believe this data instance is clean, and will pass it to train classification model, these "clean" data will also be used to train quality model itself, if the predicted label doesn't match its original label, we will throw the data instance. From P i to Ŷ P i represents predicting process, only classification model is involved in this process. As the system for image classification is on developing, we list two study cases from our previous work, to demonstrate the impact of noisy data on anomaly classification detection.

• Detecting IoT device attacks from inspecting network traffic data collected from commercial IoT devices [START_REF] Meidan | N-baiotnetworkbased detection of iot botnet attacks using deep autoencoders[END_REF]. This dataset contains nine types of IoT devices which are subject to ten types of attacks. Specifically, we focus on the Ecobee thermostat device that may be infected by Mirai malware and BASHLITE malware. Here we focus on the scenario of detecting and differentiating between ten attacks. It is important to detect those attacks with high accuracy against all load conditions and data quality. • Predicting task execution failures for big data jobs running at Google cluster [START_REF] Reiss | Google clusterusage traces: format+ schema[END_REF], [START_REF] Rosà | Failure analysis and prediction for big-data systems[END_REF]. This trace contains a month-long jobs execution record from Google clusters. Each job contains multiple tasks, which can be terminated into four different states: finish, fail, evict, or kill. The last three states are considered as anomaly states. To minimise the computational resource waste due to anomaly states, it is imperative to predict the final execution state of task upon their arrivals. Iot and Cluster datasets have perspectively 115 and 27 features, 11 and 4 classes . To detect anomalies in each case, related studies have applied machine learning classification algorithms, e.g., k-nearest neighbor (KNN), nearest centroid and multilayer perceptron (MLP) (a.k.a feed-forward deep neural networks), under the scenario where different levels of label noise are present. Here, we evaluate how the detection accuracy changes relative to different levels of noises. We focus on offline scenarios where classification models are learned from 14000 records and evaluated on a clean testing dataset of 6000 records. We specifically apply KNN, nearest centroid and MLP on IoT device attacks and cluster task failures respectively, and summarize the accuracy results in Figure 2a and Figure 2b.

One can see that noisy labels clearly deteriorate the detection results for both IoT attacks and task failures, across all three classification algorithms. For standard classifiers, like KNN and nearest centroid, the detection accuracy decays faster than MLP that is more robust to the noisy labels. Such an observation holds for both uses cases. In IoT attacks, MLP can even achieve a similar accuracy as the case of no label noises, when there is 50 percent of label classes are altered.

III. PERSPECTIVE

From our new study result, image dataset like cifar-10 (a natural image data set with 10 categories [START_REF] Alex | Learning multiple layers of features from tiny images[END_REF]) can also follow the trend with variation of noise level as in Figure 2, the only difference is for image data, the performance of KNN and Nearest centroid algorithms are much worse, only using Convolutional Neural Network (CNN) with specific configuration could maintain the curve as MLP in Figure 2.

From our current result on implementing our two-layer system on Iot and Cluster dataset, we have concluded that the first layer quality model's accuracy is very important for online learning scenario. In our framework, the quality model plays the role of domain expert as we mentioned in section I. The ongoing work is to find out what is the minimum initial clean images to learn, so that we can have a reasonable accuracy for quality model to kick-start our system. To evaluate our system, we need to prepare a testing dataset with all clean data, to see how the classification model's accuracy changes over time.

In a first stage, the result we want to see is that we could use as few as possible initial training data to launch our system, and the classification model's accuracy could converge to the same level just as we train the classification model without noisy data. Secondly, if the result works as we want, we could still make it better by accelerating the converging speed of classification model. As we described in section I, the data instance, which is predicted as not clean by quality model, is thrown away. That decreases the total training data used for classification model. Quality model is not 100% correct, and its accuracy could also increase over time, we should give a second chance to these thrown data instances in later batches. Furthermore, as considering the problem of overfitting, passing only clean data to classification model may not be the best solution, adding a small proportion of noisy data within clean data could also be worth to explore.

Fig. 1 :Fig. 2 :

 12 Fig. 1: System framework. The training process is triggered by every batch of D i . Each bloc represents a machine learning algorithm, the colored arrows on top of bloc represent the coming data used to train the algorithm. P i represents a batch of data need to be predicted.


Jonathan Benzaquen
email: benzaquen.j@chu-nice.fr

Jacques Boutros
email: boutros.j@chu-nice.fr

Charles Marquette
email: marquette.c@chu-nice.fr

Hervé Delingette
email: herve.delingette@inria.fr

Paul Hofman
email: hofman.p@chu-nice.fr

Lung Cancer Screening, Towards a Multidimensional Approach: Why and How?

Keywords: lung cancer, artificial intelligence, screening

Early-stage treatment improves prognosis of lung cancer and two large randomized controlled trials have shown that early detection with low-dose computed tomography (LDCT) reduces mortality. Despite this, lung cancer screening (LCS) remains challenging. In the context of a global shortage of radiologists, the high rate of false-positive LDCT results in overloading of existing lung cancer clinics and multidisciplinary teams. Thus, to provide patients with earlier access to life-saving surgical interventions, there is an urgent need to improve LDCT-based LCS and especially to reduce the false-positive rate that plagues the current detection technology. In this context, LCS can be improved in three ways: (1) by refining selection criteria (risk factor assessment), (2) by using Computer Aided Diagnosis (CAD) to make it easier to interpret chest CTs, and (3) by using biological blood signatures for early cancer detection, to both spot the optimal target population and help classify lung nodules. These three main ways of improving LCS are discussed in this review.

Introduction

Lung cancer (LC) is the leading cause of death from cancer, but early-stage treatment improves LC prognosis. The National Lung Screening Trial (NLST) demonstrated that annual LC screening (LCS) with low-dose computed tomography (LDCT) reduced mortality by 20% compared to controls controls [START_REF] Lung | Reduced lung-cancer mortality with low-dose computed tomographic screening[END_REF] (Table 1). More recently, the Dutch-Belgian NELSON lung cancer screening trial presented in September 2018 at the International Association for the Study of Lung Cancer (IASLC) 19 th World Conference on Lung Cancer (WCLC) in Toronto, Canada, showed reduced mortality by more than 25% in the LDCT arm compared to the control arm [START_REF] De Koning | Effects of Volume CT Lung Cancer Screening: Mortality Results of the NELSON Randomised-Controlled Population Based Trial[END_REF] (Table 1). Based on the NLST results, the United States Preventive Services Task Force (UPSTF) issued recommendations for LCS of people meeting the NLST criteria. The Centers for Medicare & Medicaid Services (CMS) decided to provide coverage for LCS in smokers aged 55 to 77 years with more than a 30-pack-years smoking smoking history and who had not quit within the last 15 years [START_REF] Moyer | Screening for lung cancer: U.S. preventive services task force recommendation statement[END_REF]4]. Low-dose computed tomography is now the cornerstone of LCS in North America and Australia. Given the confirmatory results of the NELSON screening trial [START_REF] De Koning | Effects of Volume CT Lung Cancer Screening: Mortality Results of the NELSON Randomised-Controlled Population Based Trial[END_REF], it can be assumed that LDCT screening will be approved in Europe and that health authorities will very soon provide coverage for LDCT-based LCS [START_REF] Oudkerk | European position statement on lung cancer screening[END_REF][START_REF]LdKR99ek3D2cGKlII4fA8s7IcaW6jhefdxHSYVPJAgkYn1ER8RW0MWOk[END_REF], as for breast cancer (mammography) and colon cancer (colonoscopy). However, despite Medicare and Medicaid coverage, the take-up of LCS in the US remains very low (i.e., below 4%) [START_REF] Kinsinger | Implementation of lung cancer screening in the Veterans Health Administration[END_REF][START_REF] Pham | Lung cancer screening rates: Data from the lung cancer screening registry[END_REF][START_REF] Tonge | To know or not to know…?" Push and pull in ever smokers lung screening uptake decision-making intentions[END_REF]. The reasons reasons for such a low take-up of LCS include: [START_REF] Lung | Reduced lung-cancer mortality with low-dose computed tomographic screening[END_REF] patients not wanting screening (fatalism mentality mentality in the elderly, stigma associated with LC, poor lifestyle choice); (2) patients' awareness (i.e., less than breast cancer screening); (3) physicians not referring (difficult recall of smoking history, controversies among primary care societies, controversies among health agencies); and (4) a high false-positive rate requiring cumbersome follow-up [START_REF] Pham | Lung cancer screening rates: Data from the lung cancer screening registry[END_REF][START_REF] Tonge | To know or not to know…?" Push and pull in ever smokers lung screening uptake decision-making intentions[END_REF][10]. Among these reasons, some are related to the practicality of LCS. In this respect, the need for repeated imaging and downstream diagnostic evaluations related to a high false-positive rate of LDCT (ranging from 26 to 58%) [START_REF] Lung | Reduced lung-cancer mortality with low-dose computed tomographic screening[END_REF][START_REF] Kinsinger | Implementation of lung cancer screening in the Veterans Health Administration[END_REF] is responsible for needless anxiety of patients and their family. In the Veterans Health Affairs (VHA) study, up to 52% of the screened patients who did not have LC required downstream diagnostic procedures [START_REF] Kinsinger | Implementation of lung cancer screening in the Veterans Health Administration[END_REF]. The global shortage of radiologists facing a growing and aging population in Europe will quickly overload existing LC clinics and multidisciplinary teams. In addition, the high rate of false-positive results will lead to cumbersome follow-up and surveillance of incidental pulmonary nodules. Thus, there is urgent need to improve LDCT-based LCS, and especially to reduce the false-positive rate that plagues the current detection technology, to provide patients earlier access to life-saving intervention.

Lung Cancer Screening Can Be Improved

Lung cancer screening can be improved in several ways: (1) refine selection criteria (risk factor assessment); (2) use Computer-Aided Diagnosis (CAD) to make it easier to interpret chest CTs; (3) use biomarkers to detect early-stage LC, to spot the optimal target population or to help classify lung nodules; and (4) use highly sensitive bronchoscopic techniques to enhance the detection rate of central airway lesions.

Refine Selection Criteria to Improve the Effectiveness and Efficiency of Lung Cancer Screening

The following terms must be defined here: 1) screening effectiveness, which is the number needed to screen (NNS) per LC death prevented, and 2) screening efficiency, which is the number of false-positive results and downstream diagnostic procedures per LC death prevented (a surrogate of harm-to-benefit ratio).

Risk-based selection improved LCS effectiveness by 17% as compared to UPSTF screening criteria [START_REF] Katki | Development and validation of risk models to select ever-smokers for ct lung cancer screening[END_REF]. A similar conclusion was drawn by Caverly et al., who relied on the Bach risk model [START_REF] Caverly | Comparison of Observed Harms and Expected Mortality Benefit for Persons in the Veterans Health Affairs Lung Cancer Screening Demonstration Project[END_REF][START_REF] Bach | Benchmarking lung cancer mortality rates in current and former smokers[END_REF] in the VHA study and found an NNS per LC death prevented ranging from 687 in the highest risk quintile to 6903 in the lowest risk quintile [START_REF] Caverly | Comparison of Observed Harms and Expected Mortality Benefit for Persons in the Veterans Health Affairs Lung Cancer Screening Demonstration Project[END_REF]. Risk-based selection can also improve the LCS efficiency. In the VHA study, although the harm (overall rates of false positives requiring tracking or requiring downstream evaluations) did not differ between low-and high-risk quintiles, LCS was much more efficient in the high-risk quintile [START_REF] Kinsinger | Implementation of lung cancer screening in the Veterans Health Administration[END_REF][START_REF] Caverly | Comparison of Observed Harms and Expected Mortality Benefit for Persons in the Veterans Health Affairs Lung Cancer Screening Demonstration Project[END_REF].

Use Computer Aided Diagnosis for Low-Dose Computed Tomography Interpretation to Facilitate Lung Cancer Screeningand Lessen the False-Positive Rate

The interpretation of LDCT may be difficult in the setting of LCS. The simple algorithm design should be based on two questions surrounding the key lesion detected with LDCT (i.e., lung nodule): (1) "Does this individual have a nodule?" If the answer is "no", then he/she will be given an appointment for the next screening round; ( 2) if the answer is yes, then the second question is "Is this nodule cancerous?" Depending on its features, the nodule will be classified as malignant (M), benign (B), or indeterminate (I) (Figure 1). Figure 2 exemplifies the range of difficulties encountered by physicians of LC clinics in the setting of LCS. Lung cancer screening takes time when relying on LDCT alone. Indeed, most decision-making algorithms for lung nodules advocate a repeat CT to study the volume-doubling time (VDT), a datum which, combined with the morphology of the nodule, has the most determinant weight to decide whether or not to go to invasive procedures including surgery [START_REF] Wood | Lung cancer screening, version 3.2018[END_REF]. To exemplify the stress and anxiety generated by the discovery of a lung nodule, one can look at the VHA experience in which 56% of the nonmalignant nodules required tracking and took an average of more than a year for the patient to be reassured (or not) of the nature of their nodule.

Deep convolutional neural networks (CNNs) have been successfully developed in the field of medical image analysis over the past five years and can be specifically trained for lung nodule detection and reduction of false positivity. CAD systems for LCS involve two steps: (1) detection of pulmonary nodules (which often includes lung segmentation, nodule detection, and segmentation); and (2) diagnosis of their malignancy based on the analysis of a set of features, such as volume, shape, VDT, and density gradient of each nodule (Figure 3). Currently, there are many studies about the first step, but few about the second step [START_REF] El-Baz | Computer-aided diagnosis systems for lung cancer: Challenges and methodologies[END_REF][START_REF] Murphy | The utilisation of convolutional neural networks in detecting pulmonary nodules: A review[END_REF]. Convolutional neural networks are trained on publicly available databases (Table 2) and then tested on different datasets.

Training databases include: (1) chest CTs with annotated nodules, such as the Lung Nodule Analysis 2016 (LUNA16) dataset, which is a collection of 888 axial CT scans of the patients' chest cavities taken from the Lung Image Database Consortium image collection (LIDC/IDRI) database [START_REF] Armato 3rd | The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans[END_REF]; in total, 1186 nodules were annotated across 601 patients; and (2) chest CTs labeled as "with cancer" if the associated patient was diagnosed with cancer within one year of the scan, and "without cancer" otherwise. Once trained, the CNN output provided a probability of malignancy between 0 and 1 (Figure 4). The 2017 Kaggle Data Science Bowl was a critical milestone in support of the National Cancer Institute Cancer Moonshot by convening the data science and medical communities to develop LCS algorithms [START_REF] Kaggle | Kaggle Data Science Bowl[END_REF]. Using a dataset of 2101 high-resolution lung scans provided by the National Cancer Institute and labeled as "with" or "without cancer", the 1972 competing teams have developed algorithms to accurately determine when lesions in the lungs were cancerous. Liao et al. won this 2017 Data Science Bowl by proposing the first volumetric end-to-end 3D CNN for 3D lung nodule detection and characterization with an AUC of 87% on the blinded test set [START_REF] Liao | Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network[END_REF].

Use Blood Biomarkers in the Setting of Lung Cancer Screening

Different tumor-derived components can be detected and isolated from blood samples, including circulating tumor cells (CTCs), circulating cell-free tumor DNA (cftDNA), cell-free tumor RNA (cftRNA), exosomes, and tumor-educated platelets (TEP) [START_REF] Santarpia | Liquid biopsy for lung cancer early detection[END_REF][START_REF] Hofman | Liquid biopsy for early detection of lung cancer[END_REF] (Figure 5). These components can be used as biomarkers: (1) to detect early stage LC; (2) to spot the optimal target population for LCS; or (3) to help classify indeterminate lung nodules. We previously showed that in high-risk patients (i.e., Chronic Obstructive Pulmonary Disease (COPD) and heavy smokers), circulating tumor cells (CTCs) detected with the isolation by size of epithelial tumor cell (ISET) technique (RARECELLS, Paris, France) could be detected in patients with COPD without clinically detectable LC up to four years before LC was identified on LDCT [START_REF] Ilie | Sentinel" circulating tumor cells allow early diagnosis of lung cancer in patients with Chronic obstructive pulmonary disease[END_REF]. The CTCs detected had a heterogeneous expression of epithelial and mesenchymal markers, and some specific antigens (such as TTF1), which were similar to the corresponding phenotype of the lung tumor [START_REF] Hofman | Detection and characterization of circulating tumor cells in lung cancer: Why and how?[END_REF]. No CTCs were detected in control smoking and nonsmoking healthy individuals. From these preliminary results, we demonstrated for the first time that in high-risk patients, CTCs can be detected very early in the course of LC. We therefore launched a national prospective cohort study (the AIR study) to assess the role of CTCs in LCS in a high-risk population, that is, patients with COPD, heavy smokers, and >55-year-old patients (NCT02500693) [START_REF] Leroy | Circulating tumour cells as a potential screening tool for lung cancer (the AIR study): Protocol of a prospective multicentre cohort study in France[END_REF].

In addition to CTCs, a more or less complex signature of the plasma microRNA (miRNA) has been shown to be associated with localized or metastatic LC [START_REF] Wozniak | Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer[END_REF][START_REF] Sanfiorenzo | Two Panels of Plasma MicroRNAs as Non-Invasive Biomarkers for Prediction of Recurrence in Resectable NSCLC[END_REF][START_REF] Boeri | MicroRNA signatures in tissues and plasma predict development and prognosis of computed tomography detected lung cancer[END_REF]. More recently, studies have been performed on populations with a high risk of developing LC but without a known cancer. In particular, Sozzi et al. identified a signature of plasma microRNAs that showed an excellent predictive value for LC in a high-risk population [START_REF] Sozzi | Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: A correlative MILD trial study[END_REF]. In this latter study, the authors showed that the addition of a 24-microRNA signature classifier (MSC) to LDCT could raise LC detection sensitivity to 98% [START_REF] Sozzi | Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: A correlative MILD trial study[END_REF].

Montani et al. identified an LC-predictive signature of 13 microRNAs for high-risk individuals with a sensitivity of 77.8% and a negative predictive value greater than 99%, similar to LDCT test performance, suggesting the eventuality to use first miRNA tests in this population of patients [START_REF] Montani | MiR-test: A blood test for lung cancer early detection[END_REF].

In addition to CTC and miRNA, Cohen et al. described the CancerSEEK test, which utilizes combined assays for genetic alterations (mutation present in plasma circulating tumor DNA) and protein biomarkers. Not only does this test have the capacity to identify the presence of stage I to III cancers of the ovary, liver, stomach, pancreas, esophagus, colo-rectum, lung, or breast, but also to localize the organ of origin of these cancers [START_REF] Cohen | Detection and localization of surgically resectable cancers with a multi-analyte blood test[END_REF].

Although still exploratory, gene expression profiling in the respiratory epithelium may help assess LC risk in the setting of detection of early-stage LC. Indeed, there is some evidence that nontumor adjacent cells share some molecular characteristics with tumor cells. In this context, it has been recently demonstrated that most genetic alterations expressed in smoker patients are not only found in bronchial but also in the nasal epithelium [START_REF] Billatos | The airway transcriptome as a biomarker for early lung cancer detection[END_REF]. Thus, LC-associated gene expression assessment in nontumor respiratory epithelium may represent a promising field of development to optimize LC risk evaluation and, thus, to better understand its pathogenesis [START_REF] Billatos | The airway transcriptome as a biomarker for early lung cancer detection[END_REF].

Biomarkers to Identify High-Risk Individuals and to Spot the Optimal Target Population for Lung Cancer Screening

Several markers were investigated in large prospective LCS programs, such as the Continuous Observation of Smoking Subjects (COSMOS) and the Multicenter Italian Lung Detection (MILD) trial; in interventional programs, such as the Carotene and Retinol Efficacy Trial (CARET); and in observational cohorts, such as the European Prospective Investigation into Cancer and Nutrition (EPIC) and the Northern Sweden Health and Disease Study (NSHDS) [START_REF] Sozzi | Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: A correlative MILD trial study[END_REF][START_REF] Montani | MiR-test: A blood test for lung cancer early detection[END_REF][START_REF] Guida | and the Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium for Early Detection of Lung Cancer. Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins[END_REF]. The miRNA signature [START_REF] Sozzi | Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: A correlative MILD trial study[END_REF][START_REF] Montani | MiR-test: A blood test for lung cancer early detection[END_REF] and serum proteins [START_REF] Guida | and the Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium for Early Detection of Lung Cancer. Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins[END_REF] were evaluated and performed well in determining a risk score for developing LC.

Other investigations, such as methylation of free plasma DNA, are potential options for identifying individuals at high risk of developing LC [START_REF] Jiang | Role of circulating-tumor DNA analysis in non-small cell lung cancer[END_REF][START_REF] Lehmann-Werman | Identification of tissue-specific cell death using methylation patterns of circulating DNA[END_REF][START_REF] Tomasetti | Circulating epigenetic biomarkers in lung malignanciesFrom early diagnosis to therapy[END_REF][START_REF] Levine | DNA methylation age of blood predicts future onset of lung cancer in the women's health initiative[END_REF][START_REF] Warton | Methylation of cell-free circulating DNA in the diagnosis of cancer[END_REF][START_REF] Hulbert | Early detection of lung cancer using DNA promoter hypermethylation in plasma and sputum[END_REF] but still need to be evaluated in validation studies.

Biomarkers to Help Classify Indeterminate Lung Nodules

Appropriate management of indeterminate lung nodules is one of the key factors of success in LCS implementation. Several biological signatures have been studied or are under investigation to help classify indeterminate lung nodules. Among them, plasma protein biomarkers combined with clinical risk factors (age, smoking history, nodule diameter, nodule edge characteristics, and nodule location) in an "integrated classifier" performed well in identifying benign nodules among nodules classified as indeterminate [START_REF] Silvestri | Assessment of Plasma Proteomics Biomarker's Ability to Distinguish Benign from Malignant Lung Nodules: Results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial[END_REF].

The presence of serum antibodies to a panel of seven LC-associated antigens distinguished malignant from benign nodules in a prospective registry [START_REF] Massion | Autoantibody Signature Enhances the Positive Predictive Power of Computed Tomography and Nodule-Based Risk Models for Detection of Lung Cancer[END_REF]. In this study, patients harboring a 4-20 mm lung nodule with a positive antibodies panel test (EarlyCDT-Lung Test, (ECLS), Oncimmune, De Sotto, MO, USA) had a twofold increased relative risk to develop an LC than patients with a negative test [START_REF] Massion | Autoantibody Signature Enhances the Positive Predictive Power of Computed Tomography and Nodule-Based Risk Models for Detection of Lung Cancer[END_REF]. A combined strategy using ECLS test and risk model integration showed a high specificity (>92%) and a positive predictive value of >70% for LC detection. The capacity of this tumor-associated antigen test, combined with LDCT, to reduce the incidence of late-stage LC at presentation is presently being investigated in a randomized controlled trial [START_REF] Sullivan | Detection in blood of autoantibodies to tumour antigens as a case-finding method in lung cancer using the EarlyCDT ® -Lung Test (ECLS): Study protocol for a randomized controlled trial[END_REF]. This interventional study includes 12,000 Scottish patients, aged 50-75 years, current or former smokers (with at least 20 pack-years or with less than 20 pack-years plus a family history of LC), tested with ECLS, X-ray chest, and CT scan, and with a follow-up of 24 months [START_REF] Sullivan | Detection in blood of autoantibodies to tumour antigens as a case-finding method in lung cancer using the EarlyCDT ® -Lung Test (ECLS): Study protocol for a randomized controlled trial[END_REF].

Other immune biological signatures, such as C4d-specific antibodies, have been investigated to diagnose indeterminate lung nodules and showed equivocal results [START_REF] Ajona | Complement C4d-specific antibodies for the diagnosis of lung cancer[END_REF].

Finally, a prespecified miRNA signature showed its ability to distinguish LC from the large majority of benign LDCT-detected pulmonary nodules. The combination of MSC/LDCT could reduce LC false-positive rate detection fivefold (19.7% vs 3.7% for LDCT and MSC/LDCT, respectively) [START_REF] Sozzi | Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: A correlative MILD trial study[END_REF].

Highly Sensitive Bronchoscopic Techniques to Enhance the Detection Rate of Central Airway Lesions

Low-dose computed tomography has a very low detection rate for central airway lesions that are more commonly squamous cell carcinomas (SqCC) and for preinvasive lesions. Therefore, enhancing LDCT detection rate using bronchoscopic techniques, such as autofluorescence bronchoscopy (AFB), narrow band imaging, or high magnification bronchovideoscopy, can be promising [START_REF] Andolfi | The role of bronchoscopy in the diagnosis of early lung cancer: A review[END_REF]. Some authors have incorporated endoscopic techniques in LCS strategies. McWilliams et al. showed promising results when combining sputum atypia with LDCT and AFB [44,[START_REF] Mcwilliams | Lung cancer screening using multi-slice thin-section computed tomography and autofluorescence bronchoscopy[END_REF]. The benefit of combining AFB with LDCT in LCS was not confirmed in the large-scale trial performed by Tremblay et. al [START_REF] Tremblay | Low Prevalence of High-Grade Lesions Detected with Autofluorescence Bronchoscopy in the Setting of Lung Cancer Screening in the Pan-Canadian Lung Cancer Screening Study[END_REF] in which AFB detected too few CT-occult cancers, and thus failed to show any benefit in high-LC-risk patient screening. Furthermore, due to the decreasing incidence of SqCC, and its precursors, that is, dysplasias and SqCC in situ, relative to adenocarcinoma, and in the absence of a clear survival benefit to detecting precancerous central airway lesions, AFB does not seem to have a place in today's LCS strategies [START_REF] Epelbaum | Autofluorescence bronchoscopy for lung cancer screening: A time to reflect[END_REF].

Deep Learning for Early Cancer Detection

As yet, in the setting of early cancer diagnosis, deep learning has essentially been applied to chest imaging interpretation. However, the complexity of the approach of deep learning techniques can now be considered, as soon as one simultaneously analyzes parameters that appear to be completely independent. For instance, when developing their CancerSEEK test, Cohen et al. used supervised machine learning to predict the underlying cancer type. The input algorithm took into account the circulating tumor DNA (ctDNA) and protein biomarker levels as well as the gender of the patient [START_REF] Montani | MiR-test: A blood test for lung cancer early detection[END_REF].

Conclusion

It can be reasonably assumed that very soon, European health authorities will provide coverage for LDCT-based LCS. This, added to the global shortage of radiologists, will result in a large number of anxious patients with "indeterminate lung nodules" overloading lung clinics, waiting for repeat chest imaging and invasive tests to obtain a definite answer.

In this context, we strongly believe that there is room for using, as a first reading approach, a CNN-driven LDCT with a predefined detection threshold to label all "nodule-free" examinations as reassuring, and then to incorporate the trilogy of chest imaging, risk factors, and biological signatures into machine learning algorithms to classify the nodules that have been detected according to the level of suspicion. For health care professionals, CNNs are often considered as a black box. Thus, to avoid this pitfall, one will also have to demystify the decision tree and to report on the respective weight of each clinical, radiological, and biological input that led to nodule classification (Figure 6).

Conflicts of Interest:

The authors declare no conflict of interest.

Figure 1 .

 1 Figure 1. "I" nodules, the "grey zone" of lung cancer screening.

Figure 2 .

 2 Figure 2. Six lung cancer screening cases illustrating to what extent interpretation of low-dose computed tomography may be difficult. NSCLC: non-small cell lung cancer; GGO:ground glass opacities

Figure 3 .

 3 Figure 3. Architecture of computer-aided diagnosis systems for lung cancer screening. t1: first screening round. t2: follow-up

Figure 4 .

 4 Figure 4. Training CNN for lung cancer screening.

Figure 5 .

 5 Figure 5. Tumor-derived components that can be used in the setting of lung cancer screening. TEP: tumor-educated platelets; miRNA: microRNA

Figure 6 .

 6 Figure 6. Workflow of lung cancer screening.

Funding:

  The authors wish to thank the Ligue Départementale des Alpes Maritimes de Lutte contre le Cancer, the Conseil Départemental des Alpes Maritimes, and the Maskini foundation for their funding support.

Table 1 .

 1 Summary of the National Lung Screening Trial (NLST) and the NELSON trials.

Table 2 .

 2 Publicly available databases for lung cancer screening.

		Kaggle	Luna16	NLST	COPDG gene	LTRC
	Number	1397	888	>1000	>1000	>1000
	Date			2002-2004 to 2009	Start 2008
	Available
	without	Yes	Yes	No	No	No
	registration
	COPD* cases				Yes	Yes
			x, y, z
		Cancer/no	coordinates
	Ground truth	cancer one year	and
		after the CT scan	diameter of
			nodules
	Image data	Yes	Yes	Yes	Yes	Yes
	Cohort level	"high-risk		Age 55-74	Age 45-80	"Most donor subjects have
		patient"		>30 years smoking history	>10 pack-years	interstitial fibrotic lung
				<15 years since quitting	smoking history	disease or COPD"
						Average age 60
	Individual level			Questionnaire: living	Subject phenotype:	Clinical and pathological
				condition, family history.	living condition,	diagnoses, pulmonary
				Cancer diagnosis:	gender, medical	function tests, living
				location/tumor size	history,	condition, exercises tests…
					comorbidities,
					physical
					characteristics…
	Biological data			Lung tissues	SNP genotype	Blood and lung tissues

*Chronic obstructive pulmonary disease (COPD); CT: computed tomography; SNP: single nucleotide polymorphisms.