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

Heterogeneity 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.

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

BACKGROUND: 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.

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

INTRODUCTION: 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.

Mesenchymal-like immune-altered is the fourth robust triple-negative breast cancer molecular subtype.

BACKGROUND: Robust molecular subtyping of triple-negative breast cancer (TNBC) is a prerequisite for the success of precision medicine. Today, there is a clear consensus on three TNBC molecular subtypes: luminal androgen receptor (LAR), basal-like immune-activated (BLIA), and basal-like immune-suppressed (BLIS). However, the debate about the robustness of other subtypes is still open. METHODS: An unprecedented number (n = 1942) of TNBC patient data was collected. Microarray- and RNAseq-based cohorts were independently investigated. Unsupervised analyses were conducted using k-means consensus clustering. Clusters of patients were then functionally annotated using different approaches. Prediction of response to chemotherapy and targeted therapies, immune checkpoint blockade, and radiotherapy were also screened for each TNBC subtype. RESULTS: Four TNBC subtypes were identified in the cohort: LAR (19.36%); mesenchymal stem-like (MSL/MES) (17.35%); BLIA (31.06%); and BLIS (32.23%). Regarding the MSL/MES subtype, we suggest renaming it to mesenchymal-like immune-altered (MLIA) to emphasize its specific histological background and nature of immune response. Treatment response prediction results show, among other things, that despite immune activation, immune checkpoint blockade is probably less or completely ineffective in MLIA, possibly caused by mesenchymal background and/or an enrichment in dysfunctional cytotoxic T lymphocytes. TNBC subtyping results were included in the bc-GenExMiner v5.0 webtool ( http://bcgenex.ico.unicancer.fr ). CONCLUSION: The mesenchymal TNBC subtype is characterized by an exhausted and altered immune response, and resistance to immune checkpoint inhibitors. Consensus for molecular classification of TNBC subtyping and prediction of cancer treatment responses helps usher in the era of precision medicine for TNBC patients.

bc-GenExMiner: an easy-to-use online platform for gene prognostic analyses in breast cancer.

Gene prognostic meta-analyses should benefit from breast tumour genomic data obtained during the last decade. The aim was to develop a user-friendly, web-based application, based on DNA microarrays results, called "breast cancer Gene-Expression Miner" (bc-GenExMiner) to improve gene prognostic analysis performance by using the same bioinformatics process. bc-GenExMiner was developed as a web-based tool including a MySQL relational database. Survival analyses are performed with R statistical software and packages. Molecular subtyping was performed by means of three single sample predictors (SSPs) and three subtype clustering models (SCMs). Twenty-one public data sets have been included. Among the 3,414 recovered breast cancer patients, 1,209 experienced a pejorative event. Molecular subtyping by means of three SSPs and three SCMs was performed for 3,063 patients. Furthermore, three robust lists of stable subtyped patients were built to maximize reliability of molecular assignment. Gene prognostic analyses are done by means of univariate Cox proportional hazards model and may be conducted on cohorts split by nodal (N), oestrogen receptor (ER), or molecular subtype status. To evaluate independent prognostic impact of genes relative to Nottingham Prognostic Index and Adjuvant! Online, adjusted Cox proportional hazards models are performed. bc-GenExMiner allows researchers without specific computation skills to easily and quickly evaluate the in vivo prognostic role of genes in breast cancer by means of Cox proportional hazards model on large pooled cohorts, which may be split according to different prognostic parameters: N, ER, and molecular subtype. Prognostic analyses by molecular subtype may also be performed in three robust molecular subtype classifications.

Brain Neural Progenitors are New Predictive Biomarkers for Breast Cancer Hormonotherapy.

Heterogeneity of the tumor microenvironment (TME) is one of the major causes of treatment resistance in breast cancer. Among TME components, nervous system role in clinical outcome has been underestimated. Identifying neuronal signatures associated with treatment response will help to characterize neuronal influence on tumor progression and identify new treatment targets. The search for hormonotherapy-predictive biomarkers was implemented by supervised machine learning (ML) analysis on merged transcriptomics datasets from public databases. ML-derived genes were investigated by pathway enrichment analysis, and potential gene signatures were curated by removing the variables that were not strictly nervous system specific. The predictive and prognostic abilities of the generated signatures were examined by Cox models, in the initial cohort and seven external cohorts. Generated signature performances were compared with 14 other published signatures, in both the initial and external cohorts. Underlying biological mechanisms were explored using deconvolution tools (CIBERSORTx and xCell). Our pipeline generated two nervous system-related signatures of 24 genes and 97 genes (NervSign24 and NervSign97). These signatures were prognostic and hormonotherapy-predictive, but not chemotherapy-predictive. When comparing their predictive performance with 14 published risk signatures in six hormonotherapy-treated cohorts, NervSign97 and NervSign24 were the two best performers. Pathway enrichment score and deconvolution analysis identified brain neural progenitor presence and perineural invasion as nervous system-related mechanisms positively associated with NervSign97 and poor clinical prognosis in hormonotherapy-treated patients. Transcriptomic profiling has identified two nervous system-related signatures that were validated in clinical samples as hormonotherapy-predictive signatures, meriting further exploration of neuronal component involvement in tumor progression. Significance: The development of personalized and precision medicine is the future of cancer therapy. With only two gene expression signatures approved by FDA for breast cancer, we are in need of new ones that can reliably stratify patients for optimal treatment. This study provides two hormonotherapy-predictive and prognostic signatures that are related to nervous system in TME. It highlights tumor neuronal components as potential new targets for breast cancer therapy.

c-Myc dependent expression of pro-apoptotic Bim renders HER2-overexpressing breast cancer cells dependent on anti-apoptotic Mcl-1.

BACKGROUND: Anti-apoptotic signals induced downstream of HER2 are known to contribute to the resistance to current treatments of breast cancer cells that overexpress this member of the EGFR family. Whether or not some of these signals are also involved in tumor maintenance by counteracting constitutive death signals is much less understood. To address this, we investigated what role anti- and pro-apoptotic Bcl-2 family members, key regulators of cancer cell survival, might play in the viability of HER2 overexpressing breast cancer cells. METHODS: We used cell lines as an in vitro model of HER2-overexpressing cells in order to evaluate how anti-apoptotic Bcl-2, Bcl-xL and Mcl-1, and pro-apoptotic Puma and Bim impact on their survival, and to investigate how the constitutive expression of these proteins is regulated. Expression of the proteins of interest was confirmed using lysates from HER2-overexpressing tumors and through analysis of publicly available RNA expression data. RESULTS: We show that the depletion of Mcl-1 is sufficient to induce apoptosis in HER2-overexpressing breast cancer cells. This Mcl-1 dependence is due to Bim expression and it directly results from oncogenic signaling, as depletion of the oncoprotein c-Myc, which occupies regions of the Bim promoter as evaluated in ChIP assays, decreases Bim levels and mitigates Mcl-1 dependence. Consistently, a reduction of c-Myc expression by inhibition of mTORC1 activity abrogates occupancy of the Bim promoter by c-Myc, decreases Bim expression and promotes tolerance to Mcl-1 depletion. Western blot analysis confirms that naïve HER2-overexpressing tumors constitutively express detectable levels of Mcl-1 and Bim, while expression data hint on enrichment for Mcl-1 transcripts in these tumors. CONCLUSIONS: This work establishes that, in HER2-overexpressing tumors, it is necessary, and maybe sufficient, to therapeutically impact on the Mcl-1/Bim balance for efficient induction of cancer cell death.

[Interest of the bc-GenExMiner web tool in oncology]. / Intérêt de l'outil web bc-GenExMiner en oncologie.

We 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.

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

'Breast 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. Thirty-nine 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. Database URL: http://bcgenex.ico.unicancer.fr.

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

Triple-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.

Inhibition of mTORC1 activity by REDD1 induction in myeloma cells resistant to bortezomib cytotoxicity.

The combination of bortezomib and dexamethasone is becoming the reference induction treatment for multiple myeloma patients younger than 65 years. Despite its advantage over vincristin adryamicin dexamethasone induction treatment, bortezomib does not benefit all patients. We hypothesize that heterogeneity of the response experienced by myeloma patients is, at least in part, due to genomic variations in the malignant plasma cells. To test this hypothesis we used gene expression profiling to identify early responsive genes induced by bortezomib in resistant myeloma cells. Our study revealed: (i) a dramatic induction of REDD1, a negative regulator of mammalian target of rapamycin kinase complex 1 (mTORC1) activity, in these cells; (ii) a transient cell size decrease associated with REDD1 overexpression; and (iii) partial restoration of bortezomib sensitivity in REDD1 knockdown bortezomib-resistant myeloma cells. Together, these results identify a possible novel mechanism of bortezomib resistance in myeloma patients mediated by REDD1 overexpression involving inhibition of mTORC1 activity and suggest that the use of mammalian target of rapamycin inhibitors in myeloma patients could be deleterious.

Molecular characterization of the response to chemotherapy in conventional osteosarcomas: predictive value of HSD17B10 and IFITM2.

The therapy regimen of high-grade osteosarcoma includes chemotherapy followed by surgical resection and postoperative chemotherapy. The degree of necrosis following definitive surgery remains the only reliable prognostic factor and is used to guide the choice of postoperative chemotherapy. The aim of this study was to find molecular markers able to classify patients with an osteosarcoma as good or poor responders to chemotherapy before beginning treatment. Gene expression screening of 20 nonmetastatic high-grade osteosarcoma patients was performed using cDNA microarray. Expression of selected relevant genes was validated using QRT-PCR. Immunohistochemistry on tissue microarrays sections of 73 biopsies was performed to investigate protein expression. Fluorescent in situ hybridization was performed for RPL8 gene. We have found that HSD17B10 gene expression was up-regulated in poor responders and that immunohistochemistry expression of HSD17B10 on biopsy before treatment was correlated to response to chemotherapy. Other results include correlation of IFITM2, IFITM3, and RPL8 gene expression to chemotherapy response. A statistical correlation was found between polysomy 8 or gain of RPL8 and good response to chemotherapy. These data suggest that HSD17B10, RPL8, IFITM2, and IFITM3 genes are involved in the response to the chemotherapy and that HSD17B10 may be a therapeutic target. RPL8 and IFITM2 may be useful in the assessment at diagnosis and for stratifying patients taking part in randomized trials.

A 38-gene expression signature to predict metastasis risk in node-positive breast cancer after systemic adjuvant chemotherapy: a genomic substudy of PACS01 clinical trial.

Currently, no prognostic gene-expression signature (GES) established from node-positive breast cancer cohorts, able to predict evolution after systemic adjuvant chemotherapy, exists. Gene-expression profiles of 252 node-positive breast cancer patients (median follow-up: 7.7 years), mostly included in a randomized clinical trial (PACS01), receiving systemic adjuvant regimen, were determined by means of cDNA custom array. In the training cohort, we established a GES composed of 38 genes (38-GES) for the purpose of predicting metastasis-free survival. The 38-GES yielded unadjusted hazard ratio (HR) of 4.86 (95% confidence interval = 2.76-8.56). Even when adjusted with the best two clinicopathological prognostic indexes: Nottingham prognostic index (NPI) and Adjuvant!, 38-GES HRs were 3.30 (1.81-5.99) and 3.40 (1.85-6.24), respectively. Furthermore, 38-GES improved NPI and Adjuvant! classification. In particular, NPI intermediate-risk patients were divided into 2/3 close to low-risk group and 1/3 close to high-risk group (HR = 6.97 [2.51-19.36]). Similarly, Adjuvant! intermediate-risk patients were divided into 2/3 close to low-risk group and 1/3 close to high-risk group (HR = 4.34 [1.64-11.48]). The 38-GES was validated on gene-expression datasets from three external node-positive breast cancer subcohorts (n = 224) generated from different microarray platforms, with HR = 2.95 (1.74-5.01). Moreover, 38-GES showed prognostic performance in supplementary cohorts with different lymph-node status and endpoints (1,040 new patients). The 38-GES represents a robust tool able to type systemic adjuvant treated node-positive patients at high risk of metastatic relapse, and is especially powerful to refine NPI and Adjuvant! classification for those patients.

Molecular screening of interleukin-6 gene promoter and influence of -174G/C polymorphism on breast cancer.

Interleukin-6 (IL-6) is a cytokine involved in different physiologic and pathophysiologic processes including carcinogenesis. In 2003, a single nucleotide polymorphism (-174G/C) of the IL-6 gene promoter has been linked to breast cancer prognosis in node-positive (N+) breast cancer patients. Since, different studies have led to conflicting conclusions about its role as a prognostic and/or diagnostic marker. The primary aim of our study was to investigate the link between -174G/C polymorphism and breast cancer risk on the one hand, and -174G/C polymorphism and prognosis in different groups of patients: sporadic N+breast cancers (n=138), sporadic N- breast cancers (n=95) and familial breast cancer (n=60) on the other hand. The variables of interest were disease-free survival and overall survival. The secondary aim of the study was to screen IL-6 gene promoter using direct sequencing to identify new polymorphisms in our French Caucasian breast cancer population. No association or trend of association between -174G/C polymorphism of IL-6 gene promoter gene and breast cancer diagnosis or prognosis was shown, even in meta-analyses. Furthermore, we have identified four novel polymorphic sites in the IL-6 gene promoter region: -764G-->A, -757C-->T, -233T-->A, 15C-->A.

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.

A Genome-Wide Association Study Identifies a Novel Locus for Bortezomib-Induced Peripheral Neuropathy in European Patients with Multiple Myeloma.

PURPOSE: Painful peripheral neuropathy is a frequent toxicity associated with bortezomib therapy. This study aimed to identify loci that affect susceptibility to this toxicity. EXPERIMENTAL DESIGN: A genome-wide association study (GWAS) of 370,605 SNPs was performed to identify risk variants for developing severe bortezomib-induced peripheral neuropathy (BiPN) in 469 patients with multiple myeloma who received bortezomib-dexamethasone therapy prior to autologous stem cell in randomized clinical trials of the Intergroupe Francophone du Myelome (IFM) and findings were replicated in 114 patients with multiple myeloma of the HOVON-65/GMMG-HD4 clinical trial. RESULTS: An SNP in the PKNOX1 gene was associated with BiPN in the exploratory cohort [rs2839629; OR, 1.89, 95% confidence interval (CI), 1.45-2.44; P = 7.6 × 10(-6)] and in the replication cohort (OR, 2.04; 95% CI, = 1.11-3.33; P = 8.3 × 10(-3)). In addition, rs2839629 is in strong linkage disequilibrium (r(2) = 0.87) with rs915854, located in the intergenic region between PKNOX1 and cystathionine-ß-synthetase (CBS) Expression quantitative trait loci mapping showed that both rs2839629 and rs915854 genotypes have an impact on PKNOX1 expression in nerve tissue, whereas rs2839629 affects CBS expression in skin and blood. CONCLUSIONS: The use of GWAS in multiple myeloma pharmacogenomics has identified a novel candidate genetic locus mapping to PKNOX1 and in the immediate vicinity of CBS at 21q22.3 associated with the severe bortezomib-induced toxicity. The proximity of these two genes involved in neurologic pain whose tissue-specific expression is modified by the two variants provides new targets for neuroprotective strategies. Clin Cancer Res; 22(17); 4350-5. ©2016 AACR.

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

BACKGROUND: 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.

bc-GenExMiner 3.0: new mining module computes breast cancer gene expression correlation analyses.

We recently developed a user-friendly web-based application called bc-GenExMiner (http://bcgenex.centregauducheau.fr), which offered the possibility to evaluate prognostic informativity of genes in breast cancer by means of a 'prognostic module'. In this study, we develop a new module called 'correlation module', which includes three kinds of gene expression correlation analyses. The first one computes correlation coefficient between 2 or more (up to 10) chosen genes. The second one produces two lists of genes that are most correlated (positively and negatively) to a 'tested' gene. A gene ontology (GO) mining function is also proposed to explore GO 'biological process', 'molecular function' and 'cellular component' terms enrichment for the output lists of most correlated genes. The third one explores gene expression correlation between the 15 telomeric and 15 centromeric genes surrounding a 'tested' gene. These correlation analyses can be performed in different groups of patients: all patients (without any subtyping), in molecular subtypes (basal-like, HER2+, luminal A and luminal B) and according to oestrogen receptor status. Validation tests based on published data showed that these automatized analyses lead to results consistent with studies' conclusions. In brief, this new module has been developed to help basic researchers explore molecular mechanisms of breast cancer. DATABASE URL: http://bcgenex.centregauducheau.fr

Early dynamic transcriptomic changes during preoperative radiotherapy in patients with rectal cancer: a feasibility study.

AIM: To develop novel biomarkers of rectal radiotherapy, we measured gene expression profiles on biopsies taken before and during preoperative radiotherapy. METHODS: Six patients presenting with a locally advanced rectal cancer (T>T2, N0/Nx, M0) eligible for preoperative radiotherapy (45 Gy in 25 fractions) were selected in a pilot study. Six tumor and 3 normal tissues biopsies were taken before and during radiotherapy, after a dose of 7.2 Gy at a median time of 1 h following irradiation (0:27-2:12). Tumor or normal tissue purity was assessed by a pathologist prior to RNA extraction. Mean RNA content was 23 µg/biopsy (14-37) before radiotherapy and 22.7 µg/biopsy (12-35) during radiotherapy. After RNA amplification, biopsies were analysed with 54K HG-U133A Plus 2.0 Affymetrix expression micro-arrays. Data were normalized according to MAS5 algorithm. A gene expression ratio was calculated as: (gene expression during radiotherapy - gene expression before radiotherapy)/gene expression before radiotherapy. Were selected genes that showed a ratio higher than ± 0.5 in all 6 patients. RESULTS: Microarray analysis showed that preoperative radiotherapy significantly up-regulated 31 genes and down-regulated 6 genes. According to the Gene Ontology project classification, these genes are involved in protein metabolism (ADAMDEC1; AKAP7; CAPN5; CLIC5; CPE; CREB3L1; NEDD4L; RAB27A), ion transport (AKAP7; ATP2A3; CCL28; CLIC5; F2RL2; NEDD4L; SLC6A8), transcription (AKAP7; CREB3L1; ISX; PABPC1L; TXNIP), signal transduction (CAPN5; F2RL2; RAB27A; TNFRSF11A), cell adhesion (ADAMDEC1; PXDN; SPON1; S100A2), immune response (CCL28; PXDN; TNFRSF11A) and apoptosis (ITM2C; PDCD4; PVT1). Up-regulation of 3 genes (CCL28; CLIC5; PDCD4) was detected by 2 different probes and up-regulation of 2 genes (RAB27A; TXNIP) by 3 probes. CONCLUSION: Micro-arrays can efficiently assess early transcriptomic changes during preoperative radiotherapy for rectal cancer, and may help better understand tumor radioresistance.

Prognostic significance of copy-number alterations in multiple myeloma.

PURPOSE: Chromosomal aberrations are a hallmark of multiple myeloma but their global prognostic impact is largely unknown. PATIENTS AND METHODS: We performed a genome-wide analysis of malignant plasma cells from 192 newly diagnosed patients with myeloma using high-density, single-nucleotide polymorphism (SNP) arrays to identify genetic lesions associated with prognosis. RESULTS: Our analyses revealed deletions and amplifications in 98% of patients. Amplifications in 1q and deletions in 1p, 12p, 14q, 16q, and 22q were the most frequent lesions associated with adverse prognosis, whereas recurrent amplifications of chromosomes 5, 9, 11, 15, and 19 conferred a favorable prognosis. Multivariate analysis retained three independent lesions: amp(1q23.3), amp(5q31.3), and del(12p13.31). When adjusted to the established prognostic variables (ie, t(4;14), del(17p), and serum beta(2)-microglobulin [Sbeta(2)M]), del(12p13.31) remained the most powerful independent adverse marker (P < .0001; hazard ratio [HR], 3.17) followed by Sbeta(2)M (P < .0001; HR, 2.78) and the favorable marker amp(5q31.3) (P = .0005; HR, 0.37). Patients with amp(5q31.3) alone and low Sbeta(2)M had an excellent prognosis (5-year overall survival, 87%); conversely, patients with del(12p13.31) alone or amp(5q31.3) and del(12p13.31) and high Sbeta(2)M had a very poor outcome (5-year overall survival, 20%). This prognostic model was validated in an independent validation cohort of 273 patients with myeloma. CONCLUSION: These findings demonstrate the power and accessibility of molecular karyotyping to predict outcome in myeloma. In addition, integration of expression of genes residing in the lesions of interest revealed putative features of the disease driving short survival.