Abstract P4-06-11: High immune response identified as a good prognostic factor by proteomic SWATH-MS approach in 157 ER+/HER2- early breast cancer

International audienceBackground: Estrogen positive/HER2 negative (ER+/HER2-) early (e) breast carcinoma (BC) is an heterogeneous entity, on the prognostic and predictive plan. Several prognostic multigene tests have been developed to identify patients in whom chemotherapy could be safely avoided. Proteomic is a complementary approach as variations in mRNA expression only account for ≈40% of the tumor-encoded protein range and it is taking into account, as other OMICs the tumoral microenvironment Sequential Windowed Acquisition of All Theoretical fragment (SWATH-MS) proteomic approach let an accurate and reproducible label-free quantification of large proteome. To our knowledge, no study has been conducted in a large cohort of luminal BC by SWATH-MS. The aim of this study was to establish a proteomic cartography of ER+/HER2- eBC and identify prognostic biomarkers. Methods: Frozen primary tumors were collected from 157 ER+/HER2- eBC treated in the ICO cancer center between 2006 and 2009. Patients were included if they fulfilled the following criteria: 1) ductal carcinoma; 2) unilateral; 3) first occurrence; 4) have received adjuvant chemotherapy. Clinicopathologic characteristics as outcomes were collected. Each sample was analyzed using SWATH-MS acquisition method as previously described (Aebersold et al, 2012). Peak extraction of the SWATH data was performed using either the Spectronaut software (ver 8.0, Biognosys, Switzerland). Peptide identification results were filtered with a q-value of < 1%. We performed clustering analysis (fuzzy clustering method) based on the 15% of most variant proteins. Functional annotation of clusters based on GO biological process terms enrichment (GOEA) was performed by means of ToppGene and GORILLA web tools. Results: The median of follow-up was 8.34 years. Respectively 32, 4 and 7 patients presented a metastatic, locoregional and controlateral recurrence. 684 among 4555 proteins represented the 15% of most variants proteins.Two ER+/HER2- eBC clusters were identified (C1 [23%] and C2 [77%]) by means of fuzzy clustering and GOEA. Two significant clinicopathological differences were observed between the two subgroups: more unifocal tumors in C1 (P = 0.0415) and mostly a clear better outcome in term of Disease Free Survival (DFS), Distant DFS (DDFS) and Overall survival (OS) in patients belonging to C2 (cf table 1). Functional annotation found that C1 was characterized by mRNA processing and protein synthesis (GO:0006396: RNA processing; GO:0008380: RNA splicing; GO:0016071: mRNA metabolic process; GO:0022613: ribonucleoprotein complex biogenesis), and C2 by a high immune response (GO:0002757: immune response-activating signal transduction; GO:0050778: positive regulation of immune response; GO:0002253: activation of immune response; GO:0050776: regulation of immune response). Differential protein expression according to the C1-C2 clusters will be presented at the meeting. Conclusion: Proteomic cartography by SWATH-MS can clearly distinguish two ER+/HER2- eBC subgroups with clear different prognosis with a better outcome for C2 patients compared to C1 patients. High immune response observed in C2 could underlie this difference with results that must be confirmed on external cohort. Nevertheless this approach could be considered as a complementary approach, helpful for clinical decision for administration of adjuvant treatment

iTRAQ‐Based Quantitative Proteomic Analysis Strengthens Transcriptomic Subtyping of Triple‐Negative Breast Cancer Tumors

International audienceHeterogeneity and lack of targeted therapies represent the two main impediments to precision treatment of triple-negative breast cancer (TNBC). Therefore, molecular subtyping and identification of therapeutic pathways are required to optimize medical care. The aim of the present study is to define robust TNBC subtypes with clinical relevance by means of proteomics and transcriptomics. As a first step, unsupervised analyses are conducted in parallel on proteomics and transcriptomics data of 83 TNBC tumors. Proteomics data unsupervised analysis did not permit separation of TNBC into different subtypes, whereas transcriptomics data are able to clearly and robustly identify three subtypes: molecular apocrine (C1), basal-like immune-suppressed (C2), and basal-like immune response (C3). Supervised analysis of proteomics data are then conducted based on transcriptomics subtyping. Thirty out of 62 proteins differentially expressed between C1, C2, and C3 belonged to biological categories which characterized these TNBC clusters: luminal and androgen-regulated proteins (C1), basal, invasion, and extracellular matrix (C2), and basal and immune response (interferon pathway and immunoglobulins) (C3). Although proteomics unsupervised analysis of TNBC tumors is unsuccessful at identifying clusters, the integrated approach is promising. Identification and measurement of 30 proteins strengthen subtyping of TNBC based on robust transcriptomics unsupervised analysis

bc-GenExMiner 4.5: new mining module computes breast cancer differential gene expression analyses

International audienceBreast cancer gene-expression miner' (bc-GenExMiner) is a breast cancer-associated web portal (http://bcgenex.ico.unicancer.fr). Here, we describe the development of a new statistical mining module, which permits several differential gene expression analyses, i.e. 'Expression' module. Sixty-two breast cancer cohorts and one healthy breast cohort with their corresponding clinicopathological information are included in bc-GenExMiner v4.5 version. Analyses are based on microarray or RNAseq transcriptomic data. Thirtynine differential gene expression analyses, grouped into 13 categories, according to clinicopathological and molecular characteristics ('Targeted' and 'Exhaustive') and gene expression ('Customized'), have been developed. Output results are visualized in four forms of plots. This new statistical mining module offers, among other things, the possibility to compare gene expression in healthy (cancer-free), tumour-adjacent and tumour tissues at once and in three triple-negative breast cancer subtypes (i.e. C1: molecular apocrine tumours; C2: basal-like tumours infiltrated by immune suppressive cells and C3: basal-like tumours triggering an ineffective immune response). Several validation tests showed that bioinformatics process did not alter the pathobiological information contained in the source data. In this work, we developed and demonstrated that bc-GenExMiner 'Expression' module can be used for exploratory and validation purposes

Intérêt de l’outil web bc-GenExMiner en oncologie

International audienceWe are taking advantage of the launch of the latest version (v4.6) of our web-based data mining tool "breast cancer gene-expression miner" (bc-GenExMiner) to take stock of its position within the oncology research landscape and to present an activity report ten years after its establishment (http://bcgenex.ico.unicancer.fr). bc-GenExMiner is an open-access, user-friendly tool for statistical mining on breast tumor transcriptomes, annotated with more than 20 clinicopathologic and molecular characteristics. The database comprises more than 16,000 patients from 64 cohorts - including TCGA, METABRIC and SCAN-B - for whom several thousands of genes have been quantified by microarrays or RNA-seq. Correlation, expression and prognostic analyses are available for targeted, exhaustive or customized explorations of queried genes. bc-GenExMiner facilitates the validation, investigation, and prioritization of discoveries and hypotheses on genes of interest. It allows users to analyse large databases, create data visualizations, and obtain robust statistical analysis, thereby accelerating biomarker discovery. Ten years after its launch, judging by the number of visits, analyses, and scientific citations of bc-GenExMiner, we conclude that this web resource serves its purpose in the international scientific community working in breast cancer research, with a never-ending rise in its use.Nous profitons de l’occasion de la mise en ligne de la dernière version (v.4.6) de l’outil web breast cancer gene-expression miner (bc-GenExMiner) pour rappeler sa place dans le paysage de la recherche en oncologie et réaliser un bilan d’activité, dix ans après sa création (http://bcgenex.ico.unicancer.fr). bc-GenExMiner est un outil web de fouille statistique de données d’expressions géniques issues du criblage du transcriptome de tumeurs du sein annotées par une vingtaine de critères clinicopathologiques et moléculaires. Sa base de données inclut plus de 16 000 patientes issues de 64 cohortes, dont les cohortes METABRIC, SCAN-B et TCGA, pour lesquelles l’expression de plusieurs milliers de gènes a été mesurée par des puces à ADN ou par RNAseq. Il est composé de trois modules : « Corrélation », « Expression » et « Pronostic », qui permettent différentes sortes d’analyses. bc-GenExMiner offre aux chercheurs des possibilités de validation, d’exploration, de hiérarchisation d’hypothèses et de découverte concernant leurs gènes d’intérêt. Il permet de s’autonomiser par rapport à l’analyse de grandes bases de données, la production de figures, d’obtenir des résultats robustes et ainsi de gagner du temps pour la découverte de biomarqueurs. Dix ans après sa mise en ligne, à en juger par le nombre de visites, d’analyses et de citations de bc-GenExMiner dans des articles scientifiques, nous pouvons conclure que cet outil web sert la communauté scientifique internationale engagée dans la recherche sur le cancer du sein, et qu’il est de plus en plus utilisé

Gene-expression signature functional annotation of breast cancer tumours in function of age

International audienceBACKGROUND: Breast cancer biological characteristics change as age advances. Today, there is a lack of knowledge regarding age-specific molecular alterations that characterize breast tumours, notably in elderly patients. The vast majority of studies that aimed at exploring breast cancer in function of age are based on clinico-pathological data. Gene-expression signatures (GES), which in some ways capture biological information in a non-reductionist manner, represent powerful tools able to explore tumour heterogeneity.METHODS: Twenty-five GES were used for functional annotation of breast tumours in function of age: five for molecular subtyping, seven for immune response, three for metabolism, seven for critical pathways in cancer and three for prognosis. Affymetrix® genomics datasets were exclusively used to avoid cross-platform normalization issues. Available corresponding clinico-pathological data were also retrieved and analysed.RESULTS: Fifteen publicly available datasets were pooled for a total of 2378 breast cancer patients (whole cohort), out of whom 1413 were of Caucasian origin. Three age groups were defined: ≤ 40 years (AG1), > 40 to < 70 years (AG2) and ≥ 70 years (AG3). We confirmed that age influenced the incidence of molecular subtypes. We found a significant growing incidence of luminal B and a decreasing kinetics for basal-like in function of age. We showed that AG3 luminal B tumours were less aggressive than AG1 luminal B tumours based on different GES (iron metabolism, mitochondrial oxidative phosphorylation and reactive stroma), recurrence score prognostic GES and histological grade (SBR). Contrary to tumours of young patients, tumours of elderly patients concentrated favourable GES scores: high oestrogen receptor and mitochondrial oxidative phosphorylation, low proliferation, basal-like, glycolysis, chromosomal instability and iron metabolism, and low GES prognostic scores (van't Veer 70-GES, genomic grade index and recurrence score).CONCLUSIONS: Functional annotation of breast tumours by means of 25 GES demonstrated a decreasing aggressiveness of breast tumours in function of age. This strategy, which can be strengthened by increasing the number of representative GES to gain more insight into biological systems involved in this disease, provides a framework to develop rational therapeutic strategies in function of age

Gene-expression molecular subtyping of triple-negative breast cancer tumours: importance of immune response

International audienceINTRODUCTION: Triple-negative breast cancers need to be refined in order to identify therapeutic subgroups of patients.METHODS: We conducted an unsupervised analysis of microarray gene-expression profiles of 107 triple-negative breast cancer patients and undertook robust functional annotation of the molecular entities found by means of numerous approaches including immunohistochemistry and gene-expression signatures. A triple-negative external cohort (n=87) was used for validation.RESULTS: Fuzzy clustering separated triple-negative tumours into three clusters: C1 (22.4%), C2 (44.9%) and C3 (32.7%). C1 patients were older (mean=64.6 years) than C2 (mean=56.8 years; P=0.03) and C3 patients (mean=51.9 years; P=0.0004). Histological grade and Nottingham prognostic index were higher in C2 and C3 than in C1 (P<0.0001 for both comparisons). Significant event-free survival (P=0.03) was found according to cluster membership: patients belonging to C3 had a better outcome than patients in C1 (P=0.01) and C2 (P=0.02). Event-free survival analysis results were confirmed when our cohort was pooled with the external cohort (n=194; P=0.01). Functional annotation showed that 22% of triple-negative patients were not basal-like (C1). C1 was enriched in luminal subtypes and positive androgen receptor (luminal androgen receptor). C2 could be considered as an almost pure basal-like cluster. C3, enriched in basal-like subtypes but to a lesser extent, included 26% of claudin-low subtypes. Dissection of immune response showed that high immune response and low M2-like macrophages were a hallmark of C3, and that these patients had a better event-free survival than C2 patients, characterized by low immune response and high M2-like macrophages: P=0.02 for our cohort, and P=0.03 for pooled cohorts.CONCLUSIONS: We identified three subtypes of triple-negative patients: luminal androgen receptor (22%), basal-like with low immune response and high M2-like macrophages (45%), and basal-enriched with high immune response and low M2-like macrophages (33%). We noted out that macrophages and other immune effectors offer a variety of therapeutic targets in breast cancer, and particularly in triple-negative basal-like tumours. Furthermore, we showed that CK5 antibody was better suited than CK5/6 antibody to subtype triple-negative patients

Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches

International audienceTriple-negative breast cancer (TNBC) heterogeneity represents one of the main obstacles to precision medicine for this disease. Recent concordant transcriptomics studies have shown that TNBC could be divided into at least three subtypes with potential therapeutic implications. Although a few studies have been conducted to predict TNBC subtype using transcriptomics data, the subtyping was partially sensitive and limited by batch effect and dependence on a given dataset, which may penalize the switch to routine diagnostic testing. Therefore, we sought to build an absolute predictor (i.e., intra-patient diagnosis) based on machine learning algorithms with a limited number of probes. To that end, we started by introducing probe binary comparison for each patient (indicators). We based the predictive analysis on this transformed data. Probe selection was first involved combining both filter and wrapper methods for variable selection using cross-validation. We tested three prediction models (random forest, gradient boosting [GB], and extreme gradient boosting) using this optimal subset of indicators as inputs. Nested cross-validation consistently allowed us to choose the best model. The results showed that the fifty selected indicators highlighted the biological characteristics associated with each TNBC subtype. The GB based on this subset of indicators performs better than other models

Prognostic Value of Metabolic, Volumetric and Textural Parameters of Baseline [18F]FDG PET/CT in Early Triple-Negative Breast Cancer

International audience(1) Background: triple-negative breast cancer (TNBC) remains a clinical and therapeutic challenge primarily affecting young women with poor prognosis. TNBC is currently treated as a single entity but presents a very diverse profile in terms of prognosis and response to treatment. Positron emission tomography/computed tomography (PET/CT) with 18F-fluorodeoxyglucose ([18F]FDG) is gaining importance for the staging of breast cancers. TNBCs often show high [18F]FDG uptake and some studies have suggested a prognostic value for metabolic and volumetric parameters, but no study to our knowledge has examined textural features in TNBC. The objective of this study was to evaluate the association between metabolic, volumetric and textural parameters measured at the initial [18F]FDG PET/CT and disease-free survival (DFS) and overall survival (OS) in patients with nonmetastatic TBNC. (2) Methods: all consecutive nonmetastatic TNBC patients who underwent a [18F]FDG PET/CT examination upon diagnosis between 2012 and 2018 were retrospectively included. The metabolic and volumetric parameters (SUVmax, SUVmean, SUVpeak, MTV, and TLG) and the textural features (entropy, homogeneity, SRE, LRE, LGZE, and HGZE) of the primary tumor were collected. (3) Results: 111 patients were enrolled (median follow-up: 53.6 months). In the univariate analysis, high TLG, MTV and entropy values of the primary tumor were associated with lower DFS (p = 0.008, p = 0.006 and p = 0.025, respectively) and lower OS (p = 0.002, p = 0.001 and p = 0.046, respectively). The discriminating thresholds for two-year DFS were calculated as 7.5 for MTV, 55.8 for TLG and 2.6 for entropy. The discriminating thresholds for two-year OS were calculated as 9.3 for MTV, 57.4 for TLG and 2.67 for entropy. In the multivariate analysis, lymph node involvement in PET/CT was associated with lower DFS (p = 0.036), and the high MTV of the primary tumor was correlated with lower OS (p = 0.014). (4) Conclusions: textural features associated with metabolic and volumetric parameters of baseline [18F]FDG PET/CT have a prognostic value for identifying high-relapse-risk groups in early TNBC patients

Identification of three subtypes of triple-negative breast cancer with potential therapeutic implications

International audienceBACKGROUND:Heterogeneity and lack of targeted therapies represent the two main impediments to precision treatment of triple-negative breast cancer (TNBC), and therefore, molecular subtyping and identification of therapeutic pathways are required to optimize medical care. The aim of the present study was to define robust TNBC subtypes with clinical relevance.METHODS:Gene expression profiling by means of DNA chips was conducted in an internal TNBC cohort composed of 238 patients. In addition, external data (n = 257), obtained by using the same DNA chip, were used for validation. Fuzzy clustering was followed by functional annotation of the clusters. Immunohistochemistry was used to confirm transcriptomics results: CD138 and CD20 were used to test for plasma cell and B lymphocyte infiltrations, respectively; MECA79 and CD31 for tertiary lymphoid structures; and UCHL1/PGP9.5 and S100 for neurogenesis.RESULTS:We identified three molecular clusters within TNBC: one molecular apocrine (C1) and two basal-like-enriched (C2 and C3). C2 presented pro-tumorigenic immune response (immune suppressive), high neurogenesis (nerve infiltration), and high biological aggressiveness. In contrast, C3 exhibited adaptive immune response associated with complete B cell differentiation that occurs in tertiary lymphoid structures, and immune checkpoint upregulation. External cohort subtyping by means of the same approach proved the robustness of these results. Furthermore, plasma cell and B lymphocyte infiltrates, tertiary lymphoid structures, and neurogenesis were validated at the protein levels by means of histological evaluation and immunohistochemistry.CONCLUSION:Our work showed that TNBC can be subcategorized in three different subtypes characterized by marked biological features, some of which could be targeted by specific therapies