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Although the role of epigenetic alterations in oncogenesis has been well studied, their prevalence in metastatic solid tumours is still poorly described. We therefore aimed at: (i) describing the presence of epigenetic gene alterations (EGA) - defined by an alteration in a gene encoding an epigenetic regulator; and (ii) evaluating their relationship with clinical characteristics and outcome in patients (pts) included in prospective molecular profiling trials.On-purpose tumour biopsies from pts with metastatic solid tumours enrolled in the Gustave Roussy-sponsored MOSCATO (NCT01566019) and MATCHR (NCT02517892) trials were molecularly profiled using whole exome sequencing (WES). Alterations in 176 epigenetic genes were assessed and classified as pathogenic variants (PV) or non-pathogenic variants by a molecular tumour board. Clinical characteristics and outcome were collected.Between Dec 2011 and Oct 2016, WES was successfully performed in 292 pts presenting various solid tumours. We found 496 epigenetic gene alterations in 134 patients (49%), including 237 pathogenic variants in 86 patients; 63 tumour samples (47%) presented ≥3 EGAs. The median number of previous treatment lines was 3 (1-10). The most frequently altered genes were KMT2D and KMT2C (16% each), ARID1A and SETD2 (10% each) and KMT2A (8%).; 31% of EGA co-occurred with a driver gene alteration (p amp;lt; 0.001). Outcome was not correlated with the presence of EGA.Epigenetic alterations occur frequently in metastatic solid tumours. With the current development of epigenetic modifiers, they increasingly represent actionable targets. Such genes should now be systematically analysed in molecular profiling studies.

The rising interest for precise characterization of the tumour immune contexture has recently brought forward the high potential of RNA sequencing (RNA-seq) in identifying molecular mechanisms engaged in the response to immunotherapy. In this review, we provide an overview of the major principles of single-cell and conventional (bulk) RNA-seq applied to onco-immunology. We describe standard preprocessing and statistical analyses of data obtained from such techniques and highlight some computational challenges relative to the sequencing of individual cells. We notably provide examples of gene expression analyses such as differential expression analysis, dimensionality reduction, clustering and enrichment analysis. Additionally, we used public data sets to exemplify how deconvolution algorithms can identify and quantify multiple immune subpopulations from either bulk or single-cell RNA-seq. We give examples of machine and deep learning models used to predict patient outcomes and treatment effect from high-dimensional data. Finally, we balance the strengths and weaknesses of single-cell and bulk RNA-seq regarding their applications in the clinic.

Purpose: Although the role of epigenetic alterations in oncogenesis has been well studied, their prevalence in metastatic solid tumors is still poorly described. We therefore aimed at: (i) describing the presence of epigenetic gene alterations (EGA) - defined by an alteration in a gene encoding an epigenetic regulator; and (ii) evaluating their relationship with clinical characteristics and outcome in patients (pts) included in prospective molecular profiling trials.Patients and methods: On-purpose tumor biopsies from pts with metastatic solid tumors enrolled in the Gustave Roussy-sponsored MOSCATO (NCT01566019) and MATCHR (NCT02517892) trials were molecularly profiled using Whole Exome Sequencing (WES). Alterations in 176 epigenetic genes were assessed and classified as pathogenic variants (PV) or non-pathogenic variants by a molecular tumor board. Clinical characteristics and outcome were collected.Results: Between Dec 2011 and Oct 2016, WES was successfully performed in 292 pts presenting various solid tumors. We found 496 epigenetic gene alterations in 134 patients (49%), including 237 pathogenic variants in 86 patients; 63 tumor samples (47%) presented ≥3 EGAs. The median number of previous treatment lines was 3 (1 – 10). The most frequently altered genes were KMT2D and KMT2C (16% each), ARID1A and SETD2 (10% each) and KMT2A (8%).; 31% of EGA co-occurred with a driver gene alteration (p < 0.001). Outcome was not correlated with the presence of EGA.Conclusions: Epigenetic alterations occur frequently in metastatic solid tumors. With the current development of epigenetic modifiers, they increasingly represent actionable targets. Such genes should now be systematically analyzed in molecular profiling studies.

Background Recommended treatments of patients with advanced biliary tract cancer (aBTC) are limited to one chemotherapy doublet. Nevertheless, efficacy of treatment personalisation for aBTCs is supported by accumulating evidences but remains to be evaluated. Patients and methods Patients with aBTCs included in the prospective clinical trial MOSCATO-01 were treated by at least one previous systemic treatment, had an ECOG performance status of 0–1, and at least one tumour site accessible to biopsy. Multiple high-throughput molecular analysis was performed on biopsies to drive the administration of molecular targeted agents (MTAs). Results From November 2011 to March 2016, 43 patients (4%) of the 1035 adult patients included in MOSCATO-01 had aBTCs with a majority of intrahepatic localisation (67%). Successful biopsy procedures and DNA extractions led to molecular portraits for 34 patients (79%). Orientation to an appropriate early clinical trial or accessible MTA(s) was possible for 23 of these patients (68%), and 18 (53%) have received matched MTA(s). Among them, the overall response rate was 33% and the disease control rate was 88%. A PFS ≥6 months was observed in 37% and the PFS ratio was >1.3 for 50% of the patients. These patients had a lower risk for death as compared to the 20 patients not orientated to a matched MTA (HR, 0.29; 95% CI, 0.11–0.76; p = 0.008). Conclusions Within the MOSCATO-01 trial, patients with aBTCs had the highest rate of orientation to matched MTAs and derived a clear clinical benefit. A broader evaluation of these findings may improve future treatments strategies for aBTCs. Trial Registration: NCT01566019 .

Background: The COVID-19 pandemic deeply threatens the rigorous conduct of clinical trials, notably by delaying site initiation visits, patient enrolment, treatment administration, trial-associated procedures, and data monitoring. Unlike most other medical specialties, clinical trials are an integral part of patient care in oncology. Limiting access to clinical trials therefore results in a loss of chance for patients. Methods: In this retrospective single-center study, we collected clinical trial-specific items (including patient-related or trial management-related items) during the first pandemic wave (March– June 2020) and lockdown (March 17th-May 11th) at Gustave Roussy, and compared them to those of the same period in 2019. Results: In March 2020, 84 phase I (P1) and 210 phase II/III (P2/3) trials were open. During the first pandemic wave, 21 (25%) P1 and 20 (9%) P2/3 trials were temporarily halted, following a unilateral sponsor decision in virtually all cases;all but one were industry-sponsored. Despite this, all important metrics of the P1/2 trial activity remained similar to those of 2019, including the number of patients referred for inclusion (599 vs 620), inclusion consultations (215 vs 247), patients starting treatment (130 vs 130), Internal Review Board (IRB) submissions (14 vs 16), and site initiation visits (11 vs 15), all in 2020 vs 2019, respectively. The impact of the first lock-down was more marked on P2/3, with 152 patient inclusions (vs 346 in 2019), 125 randomizations (vs 278), 43 IRB submissions (vs 50) and 34 site initiation visits (vs 40). However, in parallel, 475 patients were included in three “COVID and cancer” trials. Among the 443 P1 and 2851 P2/3 patients, 198 and 628 COVID-19 PCR were performed internally, and five and 15 (2.5%) were positive, respectively. One patient with a community-based COVID-19 died after transfer in intensive care. Conclusions: Cancer clinical trials can, and must be maintained despite challenges brought by COVID-19. Sharing experiences and retrospectively evaluating the impact on patients’ safety and cancer-related outcomes will be critical to durably improve the clinical trials conduct and to anticipate at best challenges brought by future similar crises. Legal entity responsible for the study: Gustave Roussy. Funding: Has not received any funding. Disclosure: All authors have declared no conflicts of interest.

Resume L’utilisation de biomarqueurs en oncologie permet de selectionner des populations pouvant beneficier de traitements anticancereux plus efficaces et moins toxiques, et suscitent ainsi l’espoir d’une medecine de precision. L’essor de la biologie moleculaire a provoque un bouleversement du developpement des therapies moleculaires ciblees, permettant d’observer des signaux d’efficacite des les essais de phase 1 et d’accelerer ainsi l’obtention d’autorisations de mise sur le marche. A ce jour, plusieurs essais academiques, dont SHIVA, SAFIR-01 et MOSCATO-01, ont evalue l’interet de fonder le traitement oncologique sur les seules anomalies moleculaires de la tumeur, avec l’ambition a terme de supplanter les classifications anatomopathologiques et cliniques historiques. L’administration de traitements cibles a egalement permis une amelioration du pronostic de nombreux cancers, et leur recours est de plus en plus envisage en situation palliative avancee. Franchir ces frontieres permettra de tendre vers une medecine de precision en oncologie, dont l’integration en pratique clinique est desormais une attente forte des praticiens et des patients.

Background Molecular heterogeneity drives tumour evolution and resistance to therapies. Because of biopsy challenges in non-small cell lung cancer (NSCLC), few studies compared the molecular profile between primary tumour (PT) and metastasis (Met). Here, we compare somatic single-nucleotide variants (SNVs), tumour mutational burden (TMB) and expression profiles between PT and matched metachronous Met in NSCLC patients (pts). Methods DNA and RNA were extracted from 14 matched fresh-frozen samples and PBMCs from 7 pts treated at the Institut d’Oncologie Thoracique (Gustave Roussy, GR) between 2011 and 2015. GATK4 Mutect2 were used for SNV calling and TMB on whole exome sequencing. Salmon was used for expression counts from RNAseq; Z-scores were computed by comparing data to 158 other NSCLC RNAseq from GR molecular profiling trials; GSVA R package was use to assess single sample gene set enrichment score (ssGSEA). Results Median age was 67 yo (62-78); 4/7 metastases were biopsied after chemotherapy; histology was adenocarcinoma (6) or large cell carcinoma (1). The median number of SNVs unique to PT or Met was 50 (10-143) or 79 (36-324), respectively; it was 65 (0-110) for shared SNVs; 6/7 pts had known driver alterations (TP53, KRAS, PIK3CA) in both lesions; one Met acquired a pathogenic KRAS G35C mutation. Median TMB was 3.9 (0.3-4.3) in PT and 4.1 (2.2-8.4) in Met. Genes with top negative Z-scores (MRFAP1, EP400, RIOX1, DDX27) were shared between PT and Met in 6/7 pts; genes with top positive Z-score were unique. ssGSEA-enriched genesets were shared between PT and Met; top enriched hallmarks included Myc targets, Mitotic Spindle, DNA repair, oxidative phosphorylation and TGFb signaling. Conclusions Matched comparison of molecular profiles between PT and Met evidence shared and unique alterations; this informs on tumor evolution and may help guiding treatment choice. Legal entity responsible for the study Sophie Postel-Vinay. Funding Canceropole d’Ile de France. Disclosure A Kawachi: Funded by the DUERTECC Gustave Roussy program. L. Colmet-Daage: Funded by the INSERM ATIP-Avenir grant from SPV. L. Verlingue: Honoraria (self): Adaptherapy; Research grant / Funding (self): Bristol-Myers Squibb. D. Planchard: Honoraria (self): AstraZeneca; Honoraria (self): Bristol-Myers Squibb; Honoraria (self): Boehringer Ingelheim; Honoraria (self): Celgene; Honoraria (self): Eli Lilly; Honoraria (self): Merck; Honoraria (self): Novartis; Honoraria (self): Pfizer; Honoraria (self): prIME Oncology; Honoraria (self): Peer CME; Honoraria (self): Roche; Travel / Accommodation / Expenses: AstraZeneca; Travel / Accommodation / Expenses: Bristol-Myers Squibb; Travel / Accommodation / Expenses: Boehringer Ingelheim ; Travel / Accommodation / Expenses: Roche; Travel / Accommodation / Expenses: Merck; Travel / Accommodation / Expenses: Novartis; Travel / Accommodation / Expenses: prIME Oncology; Travel / Accommodation / Expenses: Pfizer. A. Hollebecque: Research grant / Funding (institution): AstraZeneca; Research grant / Funding (institution): BMS; Research grant / Funding (institution): Boehringer Ingelheim; Research grant / Funding (institution): Janssen Cilag; Research grant / Funding (institution): Merck; Research grant / Funding (institution): Novartis; Research grant / Funding (institution): Pfizer; Research grant / Funding (institution): Roche; Research grant / Funding (institution): Sanofi. C. Massard: Research grant / Funding (self): AstraZeneca; Research grant / Funding (self): BMS; Research grant / Funding (self): Boehringer Ingelheim; Research grant / Funding (self): Janssen Cilag; Research grant / Funding (self): Merck, Novartis; Research grant / Funding (self): Pfizer; Research grant / Funding (self): Roche; Research grant / Funding (self): Sanofi; Advisory / Consultancy: Amgen; Advisory / Consultancy: Astellas; Advisory / Consultancy: AstraZeneca; Advisory / Consultancy: Bayer; Advisory / Consultancy: BeiGene; Advisory / Consultancy: BMS; Advisory / Consultancy: Celgene; Advisory / Consultancy: Debiopharm; Advisory / Consultancy: Genentech; Advisory / Consultancy: Ipsen; Advisory / Consultancy: Janssen; Advisory / Consultancy: Lilly. B. Besse: Research grant / Funding (self): Abbvie; Research grant / Funding (self): Amgen; Research grant / Funding (self): AstraZeneca; Research grant / Funding (self): Biogen; Research grant / Funding (self): Blueprint Medicines; Research grant / Funding (self): BMS; Research grant / Funding (self): Celgene; Research grant / Funding (self): Eli Lilly; Research grant / Funding (self): GSK; Research grant / Funding (self): Ignyta; Research grant / Funding (self): IPSEN; Research grant / Funding (self): Merck KGaA; Research grant / Funding (self): MSD; Research grant / Funding (self): Nektar; Research grant / Funding (self): Onxeo; Research grant / Funding (self): Pfizer; Research grant / Funding (self): Pharma Mar; Research grant / Funding (self): Sanofi; Research grant / Funding (self): Spectrum Pharmaceuticals; Research grant / Funding (self): Takeda. J. Soria: Advisory / Consultancy: AstraZeneca; Advisory / Consultancy: Astex; Advisory / Consultancy: Clovis; Advisory / Consultancy: GSK; Advisory / Consultancy: GamaMabs; Advisory / Consultancy: Lilly; Advisory / Consultancy: MSD; Advisory / Consultancy: Mission Therapeutics; Advisory / Consultancy: Merus; Advisory / Consultancy: Pfizer; Advisory / Consultancy: PharmaMar; Advisory / Consultancy: Pierre Fabre; Advisory / Consultancy: Roche/Genentech; Advisory / Consultancy: Sanofi; Advisory / Consultancy: Servier; Advisory / Consultancy: Symphogen; Advisory / Consultancy: Takeda; Full / Part-time employment: AstraZeneca. S. Postel Vinay: Research grant / Funding (self): AstraZeneca; Research grant / Funding (self): BMS; Research grant / Funding (self): Boehringer Ingelheim; Research grant / Funding (self): Janssen Cilag; Research grant / Funding (self): Merck; Research grant / Funding (self): Novartis; Research grant / Funding (self): Pfizer; Research grant / Funding (self): Roche; Research grant / Funding (self): Sanofi. All other authors have declared no conflicts of interest.

The ESMO/ECC congress (European Society for Medical Oncology/European Cancer Congress) took place in Vienna, Austria, September 25-29. The main topic of the conference was immunotherapies especially in advanced kidney cancer with nivolumab in phase III and in metastatic lung cancer with atezolizumab in phase II. Targeted therapies were also highlighted with cabozantinib proposed in advanced renal cancer or everolimus in differenciated neuroendocrine tumors grade 1 or 2. Furthermore the current challenges remain unchanged: improving patients' care through better selection and finding biomarkers using simple samples (blood or urine). Also early phases and personalized medicine found their place in the different presentations and were highlighted largely bringing new approaches in the treatment of metastatic patients.

Background Genomic alterations of receptor tyrosine kinases (RTK) or ligands are biomarkers for some targeted therapies in a fraction of cancers, with heterogeneous responses across tumor types. A general model to predict the efficacy of a targeted treatment in a given tumor type bearing a RTK genomic alteration is lacking. Through database and literature review, we investigated whether the role of a RTK in the normal lineage could predict the response to RTK inhibitors in cancers arising from the same lineage. Methods An analysis of the literature and query of public databases on RTK function in normal cell lineages and neoplastic cells was conducted, for 16 selected RTK (EGFR, HER2, KIT, FLT3, TRKA-B-C, ROS1, ALK, RET, MET, and FGFR1-2-3-4). The physiological role of RTK in the development/maintenance of cell lineages was assessed by looking at the phenotypes resulting from the alteration of the RTK in animal models and/or known human germline mutations. The activity of TKI monotherapy in genomic-driven clinical trials was analyzed through a systematic review of published phase I-II-III studies. Efficacy was defined as a median progression-free survival > 6 months and of a response rate > 30%. A matrix was built to test the association between the physiological role and clinical activity of each RTK according to lineage contexts. Results This analysis reveals a relationship between the role in lineage development and the magnitude of clinical activity of a TKI in a cancer arising from the same lineage. For missense mutations only, the clinical activity was higher when the RTK is involved in the development or maintenance of the normal cell lineage. Conversely, in cancers harboring fusion genes and proteins involving an RTK, TKI were active regardless of cell lineage. Single agent TKI activity was consistently limited in cancers bearing RTK gene amplifications. Conclusions This analysis identifies two preferred genomic biomarkers to select candidate patient for single agent TKI, 1) fusions genes involving an RTK; 2) activating missense mutations in RTK involved in normal cell lineage physiology. Legal entity responsible for the study The authors. Funding Has not received any funding. Disclosure P.A. Cassier: Honoraria (self), Research grant / Funding (institution), Travel / Accommodation / Expenses: Novartis; Honoraria (self), Research grant / Funding (institution), Travel / Accommodation / Expenses: Roche/Genentech; Honoraria (self), Research grant / Funding (institution): Blueprint; Honoraria (self), Research grant / Funding (institution), Travel / Accommodation / Expenses: Amgen; Research grant / Funding (institution): Lilly; Research grant / Funding (institution): Bayer; Research grant / Funding (institution): AstraZeneca; Research grant / Funding (institution): Celgene; Research grant / Funding (institution): Bayer; Research grant / Funding (institution): AbbVie; Research grant / Funding (institution), Travel / Accommodation / Expenses: Bristol-Myers Squibb; Research grant / Funding (institution), Travel / Accommodation / Expenses: Merck Sharp & Dohme; Research grant / Funding (institution): Tayo; Research grant / Funding (institution): Loxo; Research grant / Funding (institution): GlaxoSmithKline; Research grant / Funding (institution): Innate. I.L. Ray-Coquard: Honoraria (self), Research grant / Funding (self): Roche; Honoraria (self), Research grant / Funding (institution): Amgen; Honoraria (self), Research grant / Funding (institution): Genmab; Honoraria (self), Research grant / Funding (institution): AstraZeneca; Honoraria (self), Research grant / Funding (institution): Novartis; Honoraria (self), Research grant / Funding (institution): Pfizer. D. Perol: Honoraria (self): AstraZeneca; Honoraria (self): Ipsen; Honoraria (self): Novartis; Honoraria (self): Lilly; Honoraria (self): Bristol-Myers Squibb. J-Y. Blay: Research grant / Funding (self): PharmaMar; Research grant / Funding (self): GlaxoSmithKline; Research grant / Funding (self): Novartis; Research grant / Funding (self): Eisai; Research grant / Funding (self): Lilly; Research grant / Funding (self): Merck Sharp and Dohme; Research grant / Funding (self): Bristol-Myers Squibb. All other authors have declared no conflicts of interest.

Background Primary brain tumors are highly heterogeneous and current therapy confers only modest clinical benefit. There is a tremendous need for new therapeutic options and genomic profiling could help defining new strategies. Methods Data from patients with advanced primary brain tumors enrolled in the prospective clinical trial MOSCATO at the Drug Development Department (DITEP) at Gustave Roussy Cancer Center were retrospectively reviewed. Multiple high throughput molecular techniques were used to identify genetic mutations: Next Generation Sequencing (NGS), comparative genomic hybridization array (CGHa) and Foundation one CDx (FMI). Matched therapy was decided accordingly for patients who had targeted molecular alterations. Results Between April 2016 and December 2018, 103 patients with primary brain tumors were enrolled. Median age was 48 years (range, 19-75), median number of previous systemic therapies was 2 (range, 0–5), 98% had an ECOG performance status 0 or 1. The most prevalent histology was glioblastoma (70 %). Ten patients were screen failure due to unavailability of tumor tissue and no possibility of new brain biopsy. Eighty-nine molecular analyses were successful (CGH: N = 45, NGS: N = 47, FMI: N = 42). Median time between consent and results was 49 days (range, 18-235). Tumor mutational burden (TMB) was available in 42 pts and was considered low ( Conclusions Molecular profiling in patients with primary brain tumors is feasible and can lead to orientation in clinical trials and/or treatment with targeted therapies. However the reasons for the small number of patients finally treated are currently under investigations and will be presented at the conference. Legal entity responsible for the study Christophe Massard. Funding Institut Gustave Roussy. Disclosure C. Baldini: Speaker Bureau / Expert testimony: BMS; Speaker Bureau / Expert testimony: Abbvie; Travel / Accommodation / Expenses: Roche; Travel / Accommodation / Expenses: Amgen; Speaker Bureau / Expert testimony: Sanofi. L. Verlingue: Speaker Bureau / Expert testimony: Adaptherapy; Speaker Bureau / Expert testimony: Pierre Fabre; Research grant / Funding (self): BMS. E. Angevin: Advisory / Consultancy: Merck; Advisory / Consultancy: GSK; Advisory / Consultancy: Celgene; Travel / Accommodation / Expenses: Abbvie; Travel / Accommodation / Expenses: Roche; Travel / Accommodation / Expenses: Sanofi; Travel / Accommodation / Expenses: Pfizer. A. Varga: Speaker Bureau / Expert testimony: Roche; Speaker Bureau / Expert testimony: Astra Zeeneca; Speaker Bureau / Expert testimony: MSD. S. Postel Vinay: Research grant / Funding (self): Merck; Research grant / Funding (self): Boehringer; Research grant / Funding (self): Roche; Research grant / Funding (self): AstraZeneca; Speaker Bureau / Expert testimony: Merck; Speaker Bureau / Expert testimony: AstraZeneca; Travel / Accommodation / Expenses: AstraZeneca. A. Gazzah: Travel / Accommodation / Expenses: Boehringer; Travel / Accommodation / Expenses: Novartis; Travel / Accommodation / Expenses: Pfizer; Travel / Accommodation / Expenses: Roche; Advisory / Consultancy: Novartis. R. Bahleda: Travel / Accommodation / Expenses: Taiho; Travel / Accommodation / Expenses: Janssen; Travel / Accommodation / Expenses: Pfizer; Travel / Accommodation / Expenses: Boehringer. A. Marabelle: Advisory / Consultancy: Roche; Advisory / Consultancy: Pierre Fabre; Advisory / Consultancy: Onxeo; Advisory / Consultancy: EISAI; Travel / Accommodation / Expenses: Bayer; Advisory / Consultancy: Genticel; Advisory / Consultancy: Rigontec; Advisory / Consultancy: Diachii; Advisory / Consultancy: Imaxio; Advisory / Consultancy: Sanofi; Advisory / Consultancy: BioNtech; Advisory / Consultancy: Corvus; Advisory / Consultancy: Deerfield; Advisory / Consultancy: Bioncotech; Research grant / Funding (self): Merus; Research grant / Funding (self): BMS; Research grant / Funding (self): Boehringer; Research grant / Funding (self): Transgene. V. Ribrag: Advisory / Consultancy, Travel / Accommodation / Expenses: BMS; Honoraria (self), Advisory / Consultancy: Pharmamar; Advisory / Consultancy: Incyte; Advisory / Consultancy: MSD; Advisory / Consultancy, Travel / Accommodation / Expenses: Roche; Advisory / Consultancy: Nanostring Technologies; Speaker Bureau / Expert testimony: Servier; Honoraria (self): Eisai. S. Champiat: Speaker Bureau / Expert testimony: AstraZeneca; Speaker Bureau / Expert testimony: BMS; Speaker Bureau / Expert testimony: MSD; Speaker Bureau / Expert testimony: Roche; Speaker Bureau / Expert testimony: Novartis; Speaker Bureau / Expert testimony: Janssen. J. Michot: Speaker Bureau / Expert testimony: BMS; Speaker Bureau / Expert testimony: AstraZeneca; Speaker Bureau / Expert testimony: Janssen. A. Hollebecque: Advisory / Consultancy: Amgen; Advisory / Consultancy: Spectrum Pharmaceuticals; Advisory / Consultancy: Lilly; Travel / Accommodation / Expenses: Servier; Travel / Accommodation / Expenses: Amgen; Travel / Accommodation / Expenses: Lilly; Speaker Bureau / Expert testimony: Bayer. J. Soria: Full / Part-time employment: Medimmune. C. Massard: Advisory / Consultancy: Amgen; Advisory / Consultancy: Astellas; Advisory / Consultancy: AstraZeneca; Advisory / Consultancy: Bayer; Advisory / Consultancy: Beigene; Advisory / Consultancy: BMS; Advisory / Consultancy: Celgene; Advisory / Consultancy: Debiopharm; Advisory / Consultancy: Roche/Genentech; Advisory / Consultancy: Lilly; Advisory / Consultancy: Ipsen; Advisory / Consultancy: Janssen; Advisory / Consultancy: Medimmune; Advisory / Consultancy: Novartis; Advisory / Consultancy: Pfizer; Advisory / Consultancy: Sanofi. All other authors have declared no conflicts of interest.

Background Deep learning (DL) is one of the best approaches to predict nonlinear behaviors from high dimensional data. Nevertheless predicting the outcome of patients affected by cancers from transcriptomic data has shown limited performance, even with DL (C-index usually Methods The model was a Multi-Mayer Perceptron (MLP) with 22913 inputs corresponding to genes bulk tumor whole genome RNAseq expression analysis. An important restriction was applied to the number of units at second layer (N=100), with further linear decrease across subsequent layers. Architecture of the model (number of layers, skip connections), L1 normalization value and learning rate were optimized by grid search on 30 parallel models. Training was performed using Keras package in R. Data were split into 70% training, 15% cross validation, 15% validation for each step, without contamination between the 2 transfer learning steps. The pre-training step consisted in predicting the organs of sample origin using 17.487 public RNAseq data of normal & cancer tissues (GTEX from gtexportal.org & TCGA from cBioportal.org). Fine-tuning on patients survival used 6401 training tumors. The model's performance on survival prediction was evaluated by C-index and the area under the survival receiver-operating characteristic curve (AUROC). Results The pre-training using GTEx and TCGA reached very high performance with validation accuracy of 0.96 to predict organ of origins for the best model (all models had validation accuracy > 0.9). Fine-tuning on survival, the prognostic performance of the best model on the validation cohort was C-index=0.74 and AUROC=0.81 (80% of models had a C-index > 0.6). The best model had 8 hidden layers and a small penalization value. Conclusions Thanks to this original transfer learning method, we achieved a high performance to estimate cancer patients' prognostic from whole genome expression, a classically challenging task. Learning on public databases is a valuable method of DL for personalized cancer care. Legal entity responsible for the study The authors. Funding Has not received any funding. Disclosure E. Angevin: Advisory / Consultancy: Amgen; Advisory / Consultancy: Astellas; Advisory / Consultancy: AstraZeneca; Advisory / Consultancy: Bayer; Advisory / Consultancy: BeiGene; Advisory / Consultancy: BMS; Advisory / Consultancy: Celgene; Advisory / Consultancy: DebioPharma; Advisory / Consultancy: Genentech; Advisory / Consultancy: Ipsen; Advisory / Consultancy: Janssen; Advisory / Consultancy: Lilly; Advisory / Consultancy: MedImmune; Advisory / Consultancy: Novartis; Advisory / Consultancy: Pfizer; Advisory / Consultancy: Roche; Advisory / Consultancy: Sanofi; Advisory / Consultancy: Orion. A. Hollebecque: Advisory / Consultancy: Amgen; Advisory / Consultancy: Spectrum Pharmaceuticals; Advisory / Consultancy: Lilly; Advisory / Consultancy: Debiopharm; Travel / Accommodation / Expenses: Servier; Travel / Accommodation / Expenses: Amgen; Travel / Accommodation / Expenses: Lilly; Travel / Accommodation / Expenses: Incyte; Travel / Accommodation / Expenses: Debiopharm. E. Deutsch: Advisory / Consultancy: Boehringer; Advisory / Consultancy: Medimune; Advisory / Consultancy: Amgen; Research grant / Funding (self): AstraZeneca; Research grant / Funding (self): biotrachea; Research grant / Funding (institution): BristolMyersSquidd; Research grant / Funding (self): Clevelex; Research grant / Funding (self): EDF; Research grant / Funding (self): Lilly; Research grant / Funding (self): GlaxoSmisthKline; Research grant / Funding (self): Merk; Research grant / Funding (self): Nanobiotix; Research grant / Funding (self): Oseo; Research grant / Funding (self): Ray Search Laboratory; Research grant / Funding (self): Roche; Research grant / Funding (self): Ipsen; Research grant / Funding (self): Servier; Research grant / Funding (self): Takeda. C. Massard: Advisory / Consultancy: Amgen; Advisory / Consultancy: Astellas; Advisory / Consultancy: AstraZeneca; Advisory / Consultancy: Bayer; Advisory / Consultancy: BeiGene; Advisory / Consultancy: BMS; Advisory / Consultancy: Celgene; Advisory / Consultancy: DebioPharma; Advisory / Consultancy: Genentech; Advisory / Consultancy: Ipsen; Advisory / Consultancy: Janssen; Advisory / Consultancy: Lilly; Advisory / Consultancy: MedImmune; Advisory / Consultancy: Novartis; Advisory / Consultancy: Pfizer; Advisory / Consultancy: Roche; Advisory / Consultancy: Sanofi; Advisory / Consultancy: Orion. L. Verlingue: Research grant / Funding (self): Bristol-Myers Squibb; Advisory / Consultancy: Pierre Fabre; Advisory / Consultancy: Adaptherapy. All other authors have declared no conflicts of interest.

Resume Le cancer du poumon est la premiere cause de mortalite par cancer en France. 39 500 nouveaux cas de cancer du poumon etaient attendus en 2011, soit 27 500 chez l’homme et 12 000 chez la femme, representant 11 % de l’ensemble des nouveaux cas de cancer ; 29 100 deces etaient attendus, dont 21 000 chez l’homme et 8 100 chez la femme, representant 20 % de l’ensemble des deces par cancer. Sa presentation (âge, sexe, type histologique) s’est recemment modifiee, essentiellement en raison des changements des pratiques tabagiques. Les expositions aux cancerigenes en milieu professionnel sont aussi plus largement prises en compte. En depit de l’introduction de nouveaux medicaments et de nouvelles strategies therapeutiques, la survie globale des patients s’est relativement peu amelioree au cours des dernieres annees.

Aim: R/M cancer patients may have prolonged survival, and R/M cancer can be considered as a chronic disease. To our knowledge, there are few data describing these patients. The main goal of the present study was to describe the clinico-biological features of patients surviving more than two years with R/M cancer. Methods: During 4 months, we conducted a national multicentric survey about patients aged ≥ 18 with R/M cancer for more than 24 months. Clinico-biological data were collected in 39 French centers. Preliminary results of the first 200 patients are presented. Results: Most of them were women (70%) with breast cancer (44%) but a wide variety of other cancers were represented. Median age at diagnosis was 58 [range: 20-85]. Median time between primary tumor and R/M disease was 19 months [range: 0-312] with 39% of patients with a R/M disease at presentation. Median time from R/M disease was 46 months [range: 24-241]. At the time of R/M diagnosis, 72% of patients were not single; 48% were working while 37% were retired. At data collection, 88% of non-single patients were not separated, but only 32% of working patients were still working. At the time of R/M diagnosis, patients presented with good performance status (ECOG performance status of 0 or 1 in 88% of cases), without malnutrition (Body Mass Index Conclusions: Our preliminary results suggest that an important proportion of R/M cancer patients who live more than 2 years have been treated with a targeted therapy and have participated in a clinical trial. However, a majority of them have stopped their job and few of them received palliative care. Disclosure: All authors have declared no conflicts of interest.