Noncanonical splicing junctions between exons and transposable elements represent a source of immunogenic recurrent neo-antigens in patients with lung cancer

Although most characterized tumor antigens are encoded by canonical transcripts (such as differentiation or tumor-testis antigens) or mutations (both driver and passenger mutations), recent results have shown that noncanonical transcripts including long noncoding RNAs and transposable elements (TEs) can also encode tumor-specific neo-antigens. Here, we investigate the presentation and immunogenicity of tumor antigens derived from noncanonical mRNA splicing events between coding exons and TEs. Comparing human non-small cell lung cancer (NSCLC) and diverse healthy tissues, we identified a subset of splicing junctions that is both tumor specific and shared across patients. We used HLA-I peptidomics to identify peptides encoded by tumor-specific junctions in primary NSCLC samples and lung tumor cell lines. Recurrent junction-encoded peptides were immunogenic in vitro, and CD8+ T cells specific for junction-encoded epitopes were present in tumors and tumor-draining lymph nodes from patients with NSCLC. We conclude that noncanonical splicing junctions between exons and TEs represent a source of recurrent, immunogenic tumor-specific antigens in patients with NSCLC

Epigenetically controlled tumor antigens derived from splice junctions between exons and transposable elements

Oncogenesis often implicates epigenetic alterations, including derepression of transposable elements (TEs) and defects in alternative splicing. Here, we explore the possibility that noncanonical splice junctions between exons and TEs represent a source of tumor-specific antigens. We show that mouse normal tissues and tumor cell lines express wide but distinct ranges of mRNA junctions between exons and TEs, some of which are tumor specific. Immunopeptidome analyses in tumor cell lines identified peptides derived from exon-TE splicing junctions associated to MHC-I molecules. Exon-TE junction-derived peptides were immunogenic in tumor-bearing mice. Both prophylactic and therapeutic vaccinations with junction-derived peptides delayed tumor growth in vivo. Inactivation of the TE-silencing histone 3-lysine 9 methyltransferase Setdb1 caused overexpression of new immunogenic junctions in tumor cells. Our results identify exon-TE splicing junctions as epigenetically controlled, immunogenic, and protective tumor antigens in mice, opening possibilities for tumor targeting and vaccination in patients with cancer

Analyse de données pharmacogénomiques et moléculaires pour comprendre la résistance aux traitements des cancers du sein triple négatif

Given the large number of treatment-resistant triple-negative breast cancers, it is essential to understand the mechanisms of resistance and to find new effective molecules. First, we analyze two large-scale pharmacogenomic datasets. We propose a novel classification based on transcriptomic profiles of cell lines, according to a biological network-driven gene selection process. Our molecular classification shows greater homogeneity in drug response than when cell lines are grouped according to their original tissue. It also helps identify similar patterns of treatment response. In a second analysis, we study a cohort of patients with triple-negative breast cancer who have resisted to neoadjuvant chemotherapy. We perform complete molecular analyzes based on RNAseq and WES. We observe a high molecular heterogeneity of tumors before and after treatment. Although we highlighted clonal evolution under treatment, no recurrent mechanism of resistance could be identified Our results strongly suggest that each tumor has a unique molecular profile and that that it is increasingly important to have large series of tumors. Finally, we are improving a method for testing the overrepresentation of known RNA binding protein motifs in a given set of regulated sequences. This tool uses an innovative approach to control the proportion of false positives that is not realized by the existing algorithm. We show the effectiveness of our approach using two different datasets.Devant le grand nombre de tumeurs du sein triple négatif résistant aux traitements, il est essentiel de comprendre les mécanismes de résistance et de trouver de nouvelles molécules efficaces. En premier lieu, nous analysons deux ensembles de données pharmacogénomiques à grande échelle. Nous proposons une nouvelle classification basée sur des profils transcriptomiques de lignées cellulaires, selon un processus de sélection de gènes basé sur des réseaux biologiques. Notre classification moléculaire montre une plus grande homogénéité dans la réponse aux médicaments que lorsque l’on regroupe les lignées cellulaires en fonction de leur tissu d'origine. Elle permet également d’identifier des profils similaires de réponse aux traitements. Dans un second travail, nous étudions une cohorte de patients atteints d’un cancer du sein triple négatif ayant résisté à la chimiothérapie néoadjuvante. Nous effectuons des analyses moléculaires complètes basées sur du RNAseq et WES. Nous constatons une forte hétérogénéité moléculaire des tumeurs avant et après traitement. Bien que nous observons une évolution clonale sous traitement, aucun mécanisme récurrent de résistance n’a pu être identifié. Nos résultats suggèrent fortement que chaque tumeur a un profil moléculaire unique et qu'il est important d'étudier de grandes séries de tumeurs. Enfin, nous améliorons une méthode pour tester la surreprésentation de motifs connus de protéines de liaison à l'ARN, dans un ensemble donné de séquences régulées. Cet outil utilise une approche innovante pour contrôler la proportion de faux positifs qui n'est pas réalisé par l'algorithme existant. Nous montrons l'efficacité de notre approche en utilisant deux séries de données différentes

New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels (vol 7, 15126, 2016)

Author Correction: New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panelsInternational audienc

No evidence for TSLP pathway activity in human breast cancer

Thymic stromal lymphopoietin (TSLP) is an epithelial cell-derived cytokine that primes dendritic cells for Th2 induction. It has been implicated in different types of allergic diseases. Recent work suggested that TSLP could play an important role in the tumor microenvironment and influence tumor progression, in particular in breast cancer. In this study we systematically assessed the production of TSLP at the mRNA and protein levels in several human breast cancer cell lines, large-scale public transcriptomics data sets, and primary human breast tumors. We found that TSLP production was marginal, and concerned less than 10% of the tumors, with very low mRNA and protein levels. In most cases TSLP was undetectable and found to be expressed at lower levels in breast cancer as compared to normal breast tissue. Last, we could not detect any functional TSLP receptor (TSLPR) expression neither on hematopoietic cells nor on stromal cells within the primary tumor microenvironment. We conclude that TSLP-TSLPR pathway activity is not significantly detected within human breast cancer. Taken together, these observations do not support TSLP targeting in breast cancer

A Stromal Immune Module Correlated with the Response to Neoadjuvant Chemotherapy, Prognosis and Lymphocyte Infiltration in <i>HER2</i>-Positive Breast Carcinoma Is Inversely Correlated with Hormonal Pathways

<div><p>Introduction</p><p><i>HER2</i>-positive breast cancer (BC) is a heterogeneous group of aggressive breast cancers, the prognosis of which has greatly improved since the introduction of treatments targeting <i>HER2</i>. However, these tumors may display intrinsic or acquired resistance to treatment, and classifiers of <i>HER2</i>-positive tumors are required to improve the prediction of prognosis and to develop novel therapeutic interventions.</p><p>Methods</p><p>We analyzed 2893 primary human breast cancer samples from 21 publicly available datasets and developed a six-metagene signature on a training set of 448 <i>HER2</i>-positive BC. We then used external public datasets to assess the ability of these metagenes to predict the response to chemotherapy (Ignatiadis dataset), and prognosis (METABRIC dataset).</p><p>Results</p><p>We identified a six-metagene signature (138 genes) containing metagenes enriched in different gene ontologies. The gene clusters were named as follows: Immunity, Tumor suppressors/proliferation, Interferon, Signal transduction, Hormone/survival and Matrix clusters. In all datasets, the Immunity metagene was less strongly expressed in ER-positive than in ER-negative tumors, and was inversely correlated with the Hormonal/survival metagene. Within the signature, multivariate analyses showed that strong expression of the “Immunity” metagene was associated with higher pCR rates after NAC (OR = 3.71[1.28–11.91], <i>p</i> = 0.019) than weak expression, and with a better prognosis in <i>HER2</i>-positive/ER-negative breast cancers (HR = 0.58 [0.36–0.94], <i>p</i> = 0.026). Immunity metagene expression was associated with the presence of tumor-infiltrating lymphocytes (TILs).</p><p>Conclusion</p><p>The identification of a predictive and prognostic immune module in <i>HER2</i>-positive BC confirms the need for clinical testing for immune checkpoint modulators and vaccines for this specific subtype. The inverse correlation between Immunity and hormone pathways opens research perspectives and deserves further investigation.</p></div

Association between tumor-infiltrating lymphocyte levels and Immunity metagene expression in the REMAGUS dataset.

<p><b>A:</b> Percentage of intratumoral TILs according to Immunity metagene status (low <i>versus</i> high). <b>B</b> Percentage of stromal TILs according to Immunity metagene status (low <i>versus</i> high). <b>C</b>: Correlation between metagene expression and the percentages of intratumoral TILs. <b>D</b>: Correlation between metagene expression and the percentage of stromal TILs.</p

Gene selection process.

<p><b>A</b> Heatmap showing the 616 most variable genes in the 448 <i>HER2</i>-positive samples (training set). <b>B</b> String database software confidence view of the Matrix genes cluster. Stronger associations between genes are represented by thicker lines. <b>C</b> Cytoscape View for the Immunity gene cluster. GE correlations between genes are indicated by edges (edge color varies from green to red and edge size increases with increasing correlation) and gene expression variance is represented by node color (node color varies from green to red and node size increases with increasing variance). <b>D</b> Heatmap showing the relative expression of 138 selected genes in 448 <i>HER2</i>-positive samples from the training set. <b>E</b> Table of Pearson’s correlation coefficient values for the correlations between the 6 metagenes. <b>F</b> Heatmap showing the anticorrelation between the Immunity and the Hormone/Survival metagene.</p

Lymphocytic infiltration in breast tumors.

<p><b>A</b> and <b>B:</b> Tumor specimen with weak lymphocytic infiltration (A: zoom x10 B: zoom x 40). Abbreviations: S = stroma, T = tumor, L = lymphocytes. Intratumoral TILs are indicated by a black star. <b>C</b> and <b>D</b>: Tumor specimen with prominent lymphocytic infiltration. (C: zoom x10 D: zoom x 40). Abbreviations: S = stroma, T = tumor, L = lymphocytes. Intratumoral TILs are indicated by a black star; stromal TILs are indicated by a blue star.</p

pCR and DSS outcomes in the Ignatiadis and the METABRIC dataset.

<p><b>A:</b> pCR rates by ER and Immunity metagene status (low <i>versus</i> high in the Ignatiadis dataset). <b>B:</b> Kaplan-Meier plots. Disease-specific survival of the ER-negative population (<i>n</i> = 138) according to Immunity metagene expression (low/high) and nodal status in the METABRIC dataset.</p