Loic Verlingue

Hypermutated tumours in the era of immunotherapy: The paradigm of personalised medicine

Immune checkpoint inhibitors have demonstrated unprecedented clinical activity in a wide range of cancers. Significant therapeutic responses have recently been observed in patients presenting mismatch repair-deficient (MMRD) tumours. MMRD cancers exhibit a remarkably high rate of mutations, which can result in the formation of neoantigens, hypothesised to enhance the antitumour immune response. In addition to MMRD tumours, cancers mutated in the exonuclease domain of the catalytic subunit of the DNA polymerase epsilon (POLE) also exhibit an ultramutated genome and are thus likely to benefit from immunotherapy. In this review, we provide an overview of recent data on hypermutated tumours, including MMRD and POLE-mutated cancers, with a focus on their distinctive clinicopathological and molecular characteristics as well as their immune environment. We also discuss the emergence of immune therapy to treat these hypermutated cancers, and we comment on the recent Food and Drug Administration approval of an immune checkpoint inhibitor, the programmed cell death 1 antibody (pembrolizumab, Keytruda), for the treatment of patients with metastatic MMRD cancers regardless of the tumour type. This breakthrough represents a turning point in the management of these hypermutated tumours and paves the way for broader strategies in immunoprecision medicine.

A comprehensive approach to the molecular determinants of lifespan using a Boolean model of geroconversion

Altered molecular responses to insulin and growth factors (GF) are responsible for late‐life shortening diseases such as type‐2 diabetes mellitus (T2DM) and cancers. We have built a network of the signaling pathways that control S‐phase entry and a specific type of senescence called geroconversion. We have translated this network into a Boolean model to study possible cell phenotype outcomes under diverse molecular signaling conditions. In the context of insulin resistance, the model was able to reproduce the variations of the senescence level observed in tissues related to T2DMs main morbidity and mortality. Furthermore, by calibrating the pharmacodynamics of mTOR inhibitors, we have been able to reproduce the dose‐dependent effect of rapamycin on liver degeneration and lifespan expansion in wild‐type and HER2–neu mice. Using the model, we have finally performed an in silico prospective screen of the risk–benefit ratio of rapamycin dosage for healthy lifespan expansion strategies. We present here a comprehensive prognostic and predictive systems biology tool for human aging.

A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study

BACKGROUND Because responses of patients with cancer to immunotherapy can vary in success, innovative predictors of response to treatment are urgently needed to improve treatment outcomes. We aimed to develop and independently validate a radiomics-based biomarker of tumour-infiltrating CD8 cells in patients included in phase 1 trials of anti-programmed cell death protein (PD)-1 or anti-programmed cell death ligand 1 (PD-L1) monotherapy. We also aimed to evaluate the association between the biomarker, and tumour immune phenotype and clinical outcomes of these patients. METHODS In this retrospective multicohort study, we used four independent cohorts of patients with advanced solid tumours to develop and validate a radiomic signature predictive of immunotherapy response by combining contrast-enhanced CT images and RNA-seq genomic data from tumour biopsies to assess CD8 cell tumour infiltration. To develop the radiomic signature of CD8 cells, we used the CT images and RNA sequencing data of 135 patients with advanced solid malignant tumours who had been enrolled into the MOSCATO trial between May 1, 2012, and March 31, 2016, in France (training set). The genomic data, which are based on the CD8B gene, were used to estimate the abundance of CD8 cells in the samples and data were then aligned with the images to generate the radiomic signatures. The concordance of the radiomic signature (primary endpoint) was validated in a Cancer Genome Atlas [TGCA] database dataset including 119 patients who had available baseline preoperative imaging data and corresponding transcriptomic data on June 30, 2017. From 84 input variables used for the machine-learning method (78 radiomic features, five location variables, and one technical variable), a radiomics-based predictor of the CD8 cell expression signature was built by use of machine learning (elastic-net regularised regression method). Two other independent cohorts of patients with advanced solid tumours were used to evaluate this predictor. The immune phenotype internal cohort (n=100), were randomly selected from the Gustave Roussy Cancer Campus database of patient medical records based on previously described, extreme tumour-immune phenotypes: immune-inflamed (with dense CD8 cell infiltration) or immune-desert (with low CD8 cell infiltration), irrespective of treatment delivered; these data were used to analyse the correlation of the immune phenotype with this biomarker. Finally, the immunotherapy-treated dataset (n=137) of patients recruited from Dec 1, 2011, to Jan 31, 2014, at the Gustave Roussy Cancer Campus, who had been treated with anti-PD-1 and anti-PD-L1 monotherapy in phase 1 trials, was used to assess the predictive value of this biomarker in terms of clinical outcome. FINDINGS We developed a radiomic signature for CD8 cells that included eight variables, which was validated with the gene expression signature of CD8 cells in the TCGA dataset (area under the curve [AUC]=0·67; 95% CI 0·57-0·77; p=0·0019). In the cohort with assumed immune phenotypes, the signature was also able to discriminate inflamed tumours from immune-desert tumours (0·76; 0·66-0·86; p<0·0001). In patients treated with anti-PD-1 and PD-L1, a high baseline radiomic score (relative to the median) was associated with a higher proportion of patients who achieved an objective response at 3 months (vs those with progressive disease or stable disease; p=0·049) and a higher proportion of patients who had an objective response (vs those with progressive disease or stable disease; p=0·025) or stable disease (vs those with progressive disease; p=0·013) at 6 months. A high baseline radiomic score was also associated with improved overall survival in univariate (median overall survival 24·3 months in the high radiomic score group, 95% CI 18·63-42·1; vs 11·5 months in the low radiomic score group, 7·98-15·6; hazard ratio 0·58, 95% CI 0·39-0·87; p=0·0081) and multivariate analyses (0·52, 0·35-0·79; p=0·0022). INTERPRETATION The radiomic signature of CD8 cells was validated in three independent cohorts. This imaging predictor provided a promising way to predict the immune phenotype of tumours and to infer clinical outcomes for patients with cancer who had been treated with anti-PD-1 and PD-L1. Our imaging biomarker could be useful in estimating CD8 cell count and predicting clinical outcomes of patients treated with immunotherapy, when validated by further prospective randomised trials. FUNDING Fondation pour la Recherche Médicale, and SIRIC-SOCRATE 2.0, French Society of Radiation Oncology.

Challenges for the implementation of high-throughput testing and liquid biopsies in personalized medicine cancer trials

During recent decades, major advances in the comprehension of biology and in biotechnologies have paved the way for what is commonly named personalized medicine. For cancer therapy, personalized medicine represents a paradigm shift in which patient treatment is based on biology in addition to histology and tumor location. Here, we report the major personalized medicine trials in oncology that are either based on molecular alterations from tumor tissue or from circulating blood markers. We next review important challenges facing the implementation of personalized medicine in daily clinical practice, including tumor heterogeneity, reliability of high-throughput technologies, the key role of bioinformatics and the assessment of biomarkers and synthetic models, in order to use big data in actual cancer biology.

Abstract 1509: In silico prediction of the clinical response to the mTOR inhibitor everolimus using a Boolean model: validation from a cohort of the SHIVA trial

Introduction: Around half of currently marketed molecularly targeted agents (MTAs) in oncology have a companion diagnostic predictive biomarker that is usually a single molecular alteration (e.g. V600E BRAF mutation for the BRAF inhibitor vemurafenib), whereas around half of MTAs lack a predictive biomarker of efficacy (e.g. mTOR inhibitors). We aimed at modeling mathematically the response to the mTOR inhibitor everolimus taking into account coexisting molecular alterations and validating the model on clinical data from the everolimus cohort of the SHIVA trial (Lancet Oncol. 2015). Material and methods: We constructed the molecular signaling network corresponding to the most frequent altered genes found in different cancer types. We translated this network in a Boolean model and simulated it stochastically. We also developed a method to simulate the pharmacodynamics of everolimus. A function comprising the probabilities of the phenotypes of the model (cell cycling, apoptosis, senescence and quiescence) has been used to predict the tumor growth kinetics from the molecular profiles. We evaluated whether the in silico model could be validated on clinical data from 45 patients with various tumor types treated with everolimus in the SHIVA trial following the identification of a molecular alteration involving the PI3K/AKT/mTOR pathway. The correlation between the reported progression-free survival (PFS) in the SHIVA trial and the PFS predicted by the model was evaluated. Results: There were 34 unique associations of alterations detected by target sequencing, comparative genomic hybridization array and immunohistochemistry in the cohort of 45 patients. We found a non-linear relation between the predicted PFS from the model and the reported PFS from the trial (Pearson correlation = 0.91, p value = 1e-17). The two patients with the best PFS, one harboring a metastatic germinal tumor and the other a metastatic breast cancer, are precisely predicted by the model. Similarly, the model was able to predict efficiently the resistant tumors to everolimus. Conclusion: We were able to predict the response to everolimus and confirmed these predictions in data reported in the SHIVA trial using an in silico model of the molecular determinant of tumor growth. By this mean, we provide a novel approach to predict treatment response from the association of molecular alterations. Citation Format: Loic Verlingue, Laurence Calzone, Maud Kamal, Nicolas Servant, Lisa Belin, Emmanuel Barillot, Christophe Le Tourneau. In silico prediction of the clinical response to the mTOR inhibitor everolimus using a Boolean model: validation from a cohort of the SHIVA trial. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1509.

Efficacy of histology-agnostic and molecularly-driven HER2 inhibitors for refractory cancers

A targeted therapy is recommended in case of ERBB2 alteration for breast and gastric carcinomas, but miscellaneous other tumor types are ERBB2-altered at low prevalence. Broadening the administration of HER2 inhibitors across tumor types and genomic alterations could benefit to patients with refractory metastatic tumors. Targeted next-generation-sequencing (tNGS) and comparative genomic hybridization array (CGH) have been performed on fresh tumor biopsies of patients included in the MOSCATO-01 and ongoing MOSCATO-02 trials to administrate HER2 inhibitors in case of ERBB2 pathogenic mutation of amplification. Between December 2011 and January 2017 a molecular analysis was performed for 934 patients (759 CGH and 912 tNGS). A novel ERBB2 alteration has been found in 4.7% (n = 44/934), including 1.5% (n = 14/912) ERBB2 mutations, and 4% (n = 30/759) ERBB2 amplifications. A matched HER2 inhibitor was administrated to 70% (31/44) of patients and consisted in trastuzumab plus chemotherapy for 90% of them (28/31). On the 31 evaluable patients, 1 complete response (CR), 10 partial response (PR) and 2 stable disease (SD) >24 weeks were observed accounting for a clinical benefit rate (CBR) of 42% (n = 13/31, 95% CI 25–61%). Besides breast and oesogastric carcinomas, 19 patients affected by 8 different tumor types had a CBR of 25% for ERBB2 mutations (n = 2/8, 95% CI 3%–65%, with 2 PR) and 64% for ERBB2 amplifications (n = 7/11, 95% CI 31%–89%; with 1 CR, 4 PR, 2 SD). ERBB2 genomic alterations were diffuse across metastatic tumor types and signs of efficacy emerged for HER2 targeted treatments, especially in case of ERBB2 amplifications or a p.S310Y ERBB2 mutation.

Abstract A051: Prediction of clinical outcomes of cancer patients treated with anti-PD-1/PD-L1 using a radiomics-based imaging score of immune infiltrate

Background: The discovery of biomarkers identifying responders to immunotherapy is a major challenge. Tumor and peritumoral immune infiltration has been shown to be associated with response to anti-PD-1/PD-L1. The aim of this study was to develop a radiomics-based imaging tool of tumor immune infiltrate and to assess whether such a tool could predict clinical outcomes of patients treated with anti-PD1/PDL1. Methods: A predictive radiomics-based model of tumor-infiltrating CD8+ T cells was trained using data from the head and neck cohort of The Cancer Imaging Archive (HNSC-TCIA). Two cohorts from our institute were used for validation. Contrast-enhanced CTs of 57 patients from the HNSC-TCIA were manually segmented (tumor and surrounding tissue) and 76 radiomics features extracted. A radiomics-based score was build using radiomics features to predict tumor-infiltrating CD8+ T-cells9 abundance, which was estimated using RNA-sequencing data from The Cancer Genome Atlas, and the Microenvironment Cell Populations-counter signature. As a first validation, this signature was applied to an independent cohort of 100 patients for whom the pathologic tumor immune infiltrate was postulated as either favorable (lymphoma, melanoma, lung, bladder, renal, MSI+ cancers, and adenopathy; 70 patients) or unfavorable (adenoid cystic carcinoma, low-grade neuroendocrine tumors, uterine leiomyoma; 30 patients). The signature was then applied on baseline-CTs of a second external cohort of 139 patients prospectively enrolled in anti PD-1/PD-L1 phase 1 trials. The median of the radiomics-based CD8+ score was used to separate patients into two groups (high and low score). Survival was estimated using Cox-proportional hazards model. Results: We developed a radiomics-based CD8+ signature using the six radiomics features that had highest performance on random forest. In the first external cohort, the radiomics-based CD8 T-cells score was associated with the postulated tumor immune infiltrate (Wilcoxon test, P Conclusions: The radiomics-based signature of CD8+ T cells appears as a promising tool to estimate tumor immune infiltrate and to infer the outcome of patients treated with anti-PD-1/PD-L1. Citation Format: Roger Sun, Elaine Johanna Limkin, Laurent Dercle, Sylvain Reuze, Stephane Champiat, David Brandao, Loic Verlingue, Samy Ammari, Sandrine Aspeslagh, Antoine Hollebecque, Christophe Massard, Aurelien Marabelle, Jean-Yves Scoazec, Charlotte Robert, Jean-Charles Soria, Eric Deutsch, Charles Ferte. Prediction of clinical outcomes of cancer patients treated with anti-PD-1/PD-L1 using a radiomics-based imaging score of immune infiltrate [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A051.

Abstract 1011: RNAseq analysis obtained from on-purpose tumor biopsies of patients in the MATCH-R trial allows the identification of potential mechanisms of acquired resistance to PD(L)1 therapies

Background: MATCH-R is a prospective molecular characterization trial (NCT02517892) aiming at defining the molecular basis of acquired resistance to targeted agents and immune checkpoint blockers. RNA sequencing (RNAseq) has been used to identify mechanisms of secondary resistance to immunotherapy. Patients and methods: Patients’ metastatic tumors were multi-site biopsied at relapse under immunotherapies after a period of clinical benefit, defined by a partial response or a stable disease of more than 6 months. Genome-wide RNAseq counts were intra-patient normalized and a score of each gene’s expression was computed in comparison to a cohort of 450 metastatic cancer patients with RNAseq available at the time of analysis. Results: To date, 10 patients treated by immunotherapies have had a successful RNAseq in the MATCH-R trial. Five patients were treated with PD-1 inhibitors and 5 with PD-L1 inhibitors. Three patients had NSCLC, 2 MSI high endometrial carcinoma, 2 anal carcinoma, 2 urothelial carcinoma and 1 TNBC. Eight out off ten patients had an expression of IDO1 higher than the median expression of IDO1 in our 450 controls (p value = 0.005). A patient with endometrial carcinoma had one of the highest expressions of IDO1 in the cohort. Consistently, IDO1 activation has previously been reported as a mechanism of secondary resistance to immunotherapies. A 40 year old smoker NSCLC patient with a TP53 mutation has been treated during 11 months with anti-PD1. RNAseq analysis on the biopsy of a progressive lesion showed decreased expression of different actors of the JAK-STAT pathway (biopsy composed of 40% tumor cells and 60% microenvironment). Of the 78 genes signatures used (including 52 immunogenes signatures), the interferon gamma signature had the lowest expression (p value = 0.004), consistent with a previous report of JAK-STAT-induced resistance to immunotherapy. Two more patients had an altered immune profile that could be involved in resistance to immunotherapies, but was not yet reported in the litterature. Confirmation of the RNAseq analysis with immunohistochemistry is currently ongoing. The gene signatures of the 10 patients, composed of immunogenes, DNA repair genes and epigenes, were compared to the whole cohort in order to deduce corresponding false discovery rates. As such we could identify 2 gene clusters, one enriched in T cells, dendritic cells and macrophages, and the other enriched in epigenes and DNA repair genes. Analysis of more patients is currently ongoing in order to cluster the results with clinical characteristics. Conclusion: Gene expression in the biopsy of patients that relapsed after initial benefit to immunotherapy is informative and helps to identify the mechanism of acquired resistance. Citation Format: Loic Verlingue, Linda Mahjoubi, Sandrine Aspeslagh, Marion Pedrero, Giulia Buzzatti, David Brandao, Zsofia Balogh, Etienne Rouleau, Ludovic Lacroix, Rastislav Bahleda, Christophe Massard, Antoine Hollebecque, Anas Gazzah, Celine Lefebvre, Serge Koscielny, Jean Yves Scoazec, Eric Angevin, Fabrice Andre, Aurelien Marabelle, Jean Charles Soria. RNAseq analysis obtained from on-purpose tumor biopsies of patients in the MATCH-R trial allows the identification of potential mechanisms of acquired resistance to PD(L)1 therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1011. doi:10.1158/1538-7445.AM2017-1011

Essai OLIVIA : quadrithérapie avec bévacizumab

Il existe depuis 20 ans une escalade progressive dans l’intensite des traitements de premiere ligne du cancer colorectal metastatique (CCRm). En 2007, le groupe italien GONO a demontre pour la premiere fois dans un essai de phase 3 dans cette indication la superiorite, malgre une toxicite plus importante, d’une tri-chimiotherapie combinant 5-fluoro-uracile, oxaliplatine et irinotecan (schema FOLFOXIRI) comparativement a une bi-chimiotherapie standard (5-fluoro-uracile plus irinotecan, schema FOLFIRI) en termes de taux de reponse objective tumorale (66 % versus 41 %, p = 0,0002), de survie sans progression et de survie globale [1].

1379PCLINICO-BIOLOGICAL CHARACTERISTICS OF PATIENTS SURVIVING MORE THAN TWO YEARS WITH RECURRENT AND/OR METASTATIC (R/M) CANCER: RESULTS OF A TRANSVERSAL NATIONAL MULTICENTRIC SURVEY

ABSTRACT 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.