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

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

DNA hydroxymethylation is associated with disease severity and persists at enhancers of oncogenic regions in multiple myeloma

International audienceBackground: Multiple myeloma (MM) is a heterogeneous plasma cell malignancy that remains challenging to cure. Global hypomethylation correlates with an aggressive phenotype of the disease, while hypermethylation is observed at particular regions of myeloma such as B cell-specific enhancers. The recently discovered active epigenetic mark 5-hydroxymethylCytosine (5hmC) may also play a role in tumor biology; however, little is known about its level and distribution in myeloma. In this study, we investigated the global level and the genomic localization of 5hmC in myeloma cells from 40 newly diagnosed patients, including paired relapses, and of control individuals. Results: Compared to normal plasma cells, we found global 5hmC levels to be lower in myeloma (P < 0.001). Higher levels of 5hmC were found in lower grades of the International Staging System prognostic index (P < 0.05) and tend to associate with a longer overall survival (P < 0.1). From the hydroxymethylome data, we observed that the remaining 5hmC is organized in large domains overlapping with active chromatin marks and chromatin opening. We discovered that 5hmC strongly persists at key oncogenic genes such as CCND1, CCND2 and MMSET and characterized domains that are specifically hydroxymethylated in myeloma subgroups. Novel 5hmC-enriched domains were found at puta-tive enhancers of CCND2 and MYC in newly diagnosed patients. Conclusions: 5hmC level is associated with clinical aspects of MM. Mapping 5hmC at a genome-wide level provides insights into the disease biology directly from genomic DNA, which makes it a potent mark to study epigenetics on large patient cohorts

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

Logic programming reveals alteration of key transcription factors in multiple myeloma

Innovative approaches combining regulatory networks (RN) and genomic data are needed to extract biological information for a better understanding of diseases, such as cancer, by improving the identification of entities and thereby leading to potential new therapeutic avenues. In this study, we confronted an automatically generated RN with gene expression profiles (GEP) from a cohort of multiple myeloma (MM) patients and normal individuals using global reasoning on the RN causality to identify key-nodes. We modeled each patient by his or her GEP, the RN and the possible automatically detected repairs needed to establish a coherent flow of the information that explains the logic of the GEP. These repairs could represent cancer mutations leading to GEP variability. With this reasoning, unmeasured protein states can be inferred, and we can simulate the impact of a protein perturbation on the RN behavior to identify therapeutic targets. We showed that JUN/FOS and FOXM1 activities are altered in almost all MM patients and identified two survival markers for MM patients. Our results suggest that JUN/FOS-activation has a strong impact on the RN in view of the whole GEP, whereas FOXM1-activation could be an interesting way to perturb an MM subgroup identified by our method

Chromatin accessibility combined with enhancer clusters activation mediates heterogeneous response to dexamethasone in myeloma cells

Glucocorticoids (GC) effects occur through binding to the GC receptor (GR) which, once translocated to the nucleus, binds to GC response elements (GREs) to activate or repress target genes. Among GCs, dexamethasone (Dex) is widely used in treatment of multiple myeloma (MM), mainly in combination regimens. However, despite a definite benefit, all patients relapse. Moreover, while GC efficacy can be largely attributed to lymphocyte-specific apoptosis, its molecular basis remains elusive. To determine the functional role of GR binding in myeloma cells, we generated bulk and single cell multi-omic data and high-resolution contact maps of active enhancers and target genes. We show that a minority (6%) of GR binding sites are associated with enhancer activity gains and increased interaction loops. We find that enhancers contribute to regulate gene activity through combinatorial assembly of large stretches of enhancers and/or enhancer cliques. Furthermore, one enhancer, proximal to GR-responsive genes, is predominantly associated with increased chromatin accessibility and higher H3K27ac occupancy. Finally, we show that Dex exposure leads to co-accessibility changes between predominant enhancer and other regulatory regions of the interaction network. Notably, these epigenomic changes are associated with cell-to-cell transcriptional heterogeneity. As consequences, BIM critical for GR-induced apoptosis and CXCR4 protective from chemotherapy-induced apoptosis are rather upregulated in different cells. In summary, our work provides new insights into the molecular mechanisms involved in Dex escape