Medullary breast cancers (MBC) display a basal profile, but a favorable prognosis. We hypothesized that a previously published 368-gene expression signature associated with MBC might serve to define a prognostic classifier in basal cancers. We collected public gene expression and histoclinical data of 2145 invasive early breast adenocarcinomas. We developed a Support Vector Machine (SVM) classifier based on this 368-gene list in a learning set, and tested its predictive performances in an independent validation set. Then, we assessed its prognostic value and that of six prognostic signatures for disease-free survival (DFS) in the remaining 2034 samples. The SVM model accurately classified all MBC samples in the learning and validation sets. A total of 466 cases were basal across other sets. The SVM classifier separated them into two subgroups, subgroup 1 (resembling MBC) and subgroup 2 (not resembling MBC). Subgroup 1 exhibited 71% 5-year DFS, whereas subgroup 2 exhibited 50% (p=9.93E-05). The classifier outperformed the classical prognostic variables in multivariate analysis, conferring lesser risk for relapse in subgroup 1 (HR=0.52, p=3.9E-04). This prognostic value was specific to the basal subtype, in which none of the other prognostic signatures was informative.
A gene expression signature identifies two prognostic subgroups of basal breast cancer.
Age, Specimen part
View SamplesAnalysis of the expression profiles of MCF7 cells transduced with a control shRNA and an TSC2-targeted shRNA (leading to tuberin depletion).
Lymphangioleiomyomatosis Biomarkers Linked to Lung Metastatic Potential and Cell Stemness.
Cell line
View SamplesTo identify novel therapeutic opportunities for patients with acquired resistance to endocrine treatments in breast cancer, we applied a high-throughput drug screen. The IC50 values were determined for MCF7 and MCF7-LTED cells.
VAV3 mediates resistance to breast cancer endocrine therapy.
Cell line
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