The current histoclinical breast cancer classification is simple but imprecise. Several molecular classifications of breast cancers based on expression profiling have been proposed as alternatives. However, their reliability and clinical utility have been repeatedly questioned, notably because most of them were derived from relatively small initial patient populations. We analyzed the transcriptomes of 537 breast tumors using three unsupervised classification methods. A core subset of 355 tumors was assigned to six clusters by all three methods. These six subgroups overlapped with previously defined molecular classes of breast cancer, but also showed important differences, notably the absence of an ERBB2 subgroup and the division of the large luminal ER+ group into four subgroups, two of them being highly proliferative. Of the six subgroups, four were ER+/PR+/AR+, one was ER−/PR−/AR+ and one was triple negative (AR−/ER−/PR−). ERBB2-amplified tumors were split between the ER−/PR−/AR+ subgroup and the highly proliferative ER+ LumC subgroup. Importantly, each of these six molecular subgroups showed specific copy-number alterations. Gene expression changes were correlated to specific signaling pathways. Each of these six subgroups showed very significant differences in tumor grade, metastatic sites, relapse-free survival or response to chemotherapy. All these findings were validated on large external datasets including more than 3000 tumors. Our data thus indicate that these six molecular subgroups represent well-defined clinico-biological entities of breast cancer. Their identification should facilitate the detection of novel prognostic factors or therapeutical targets in breast cancer.

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Purpose: Inflammatory breast cancer (IBC) is a rare but particularly aggressive form of primary breast cancer. The molecular mechanisms responsible for IBC are largely unknown.Experimental Design: To obtain further insight into the molecular pathogenesis of IBC, we used real-time quantitative reverse transcription (RT)-PCR to quantify the mRNA expression of 538 selected genes in IBC relative to non-IBC.Results: Twenty-seven (5.0%) of the 538 genes were significantly up-regulated in IBC compared with non-IBC. None were down-regulated. The 27 up-regulated genes mainly encoded transcription factors (JUN, EGR1, JUNB, FOS, FOSB, MYCN, and SNAIL1), growth factors (VEGF, DTR/HB-EGF, IGFBP7, IL6, ANGPT2, EREG, CCL3/ MIP1A, and CCL5/RANTES) and growth factor receptors (TBXA2R, TNFRSF10A/TRAILR1, and ROBO2). We also identified a gene expression profile, based on MYCN, EREG, and SHH, which discriminated subgroups of IBC patients with good, intermediate, and poor outcome.Conclusion: Our study has identified a limited number of signaling pathways that require inappropriate activation for IBC development. Some of the up-regulated genes identified here could offer useful diagnostic or prognostic markers and could form the basis of novel therapeutic strategies.