Search

'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 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Dogs spontaneously develop invasive mammary carcinoma with a high prevalence of the triple-negative (TN) subtype (lack of ER-Estrogen Receptor and PR-Progesterone Receptor expression, lack of HER2-Human Epidermal Growth Factor Receptor 2 overexpression), making this animal model relevant for investigating new therapeutic pathways. Insulin-like growth factor Type-1 receptor (IGF1R) is frequently overexpressed in primary human breast cancers, with a growing role in the TN phenotype. The purpose of this study was to investigate the Dog as a candidate model for IGF1R-overexpressing mammary carcinoma.Methods: 150 bitches with canine mammary carcinoma (CMC) and a known 2-year follow-up were retrospectively included. IGF1R expression was assessed by immunohistochemistry (IHC) using a similar scoring system as for HER2 in breast cancer. The prognostic value of the IGF1R expression was assessed in terms of overall and specific survival as well as disease-free interval (DFI).Results: 47 CMC (31 %) were classified as luminal and 103 (69 %) as triple-negative (TN-CMC). 41 % of CMC overexpressed IGF1R (IHC score 3+) of which 76 % were TN-CMC and 62 % grade III. IGF1R overexpression was associated with aggressive features including lymphovascular invasion, histological grade III, low ER expression and the TN phenotype. Univariate and multivariate analyses revealed that IGF1R overexpression was associated with shorter overall and specific survivals and shorter DFI in TN-CMC.Conclusions: IGF1R overexpression is common and related to a poor outcome in canine invasive mammary carcinoma, particularly in the triple negative subtype, as in human breast cancer. Preclinical studies using the Dog as a spontaneous animal model could be considered to investigate new therapies targeting IGF1R in triple-negative breast cancer. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: Twenty years were passed between the discovery of oncogene HER2, the description of its implication in mammary carcinogenesis, and the development of specific targeted therapies. To date, trastuzumab and lapatinib are the two anti-HER2 targeted therapies commonly used, demonstrating therapeutic effects. Although their clinical efficacy seems to be exclusively related to the amplification of the HER2 gene or to the overexpression of the protein, these factors are not sufficient since tumors can develop resistance. Because of a better knowledge in those mechanisms of resistance, novel therapeutic agents could help to bypass them. How should these be used with respect to current anti-HER2 targeted therapies? Recent notions such as oncogene addiction, tumor cell dormancy and residual disease led us to propose a new entity that we named the “sedimentation strategy”, in which distinct targeted approaches are summed during the treatment of metastatic breast cancer patients. [Copyright &y& Elsevier]

To better understand the relationship between tumor-host interactions and the efficacy of chemotherapy, we have developed an analytical approach to quantify several biological processes observed in gene expression data sets. We tested the approach on tumor biopsies from individuals with estrogen receptor–negative breast cancer treated with chemotherapy. We report that increased stromal gene expression predicts resistance to preoperative chemotherapy with 5-fluorouracil, epirubicin and cyclophosphamide (FEC) in subjects in the EORTC 10994/BIG 00-01 trial. The predictive value of the stromal signature was successfully validated in two independent cohorts of subjects who received chemotherapy but not in an untreated control group, indicating that the signature is predictive rather than prognostic. The genes in the signature are expressed in reactive stroma, according to reanalysis of data from microdissected breast tumor samples. These findings identify a previously undescribed resistance mechanism to FEC treatment and suggest that antistromal agents may offer new ways to overcome resistance to chemotherapy. [ABSTRACT FROM AUTHOR]