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From data to bench to bedside - prognostic markers and therapeutic targets in breast cancer

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If you have a question about this talk, please contact Florian Markowetz .

I will describe two case studies in breast cancer to identify and validate prognostic markers and therapeutic targets in breast cancer from high throughput data In the first study, I will show how “supervised outlier analysis” applied to gene expression data identified novel amplicons on Chr 17 and Chr 8 which predict failure of tamoxifen therapy in ER+/HER2- breast cancer. This finding was validated in a CGH dataset and is currently being validated further in a large retrospective study (with survival information) using FISH on TMA samples. Next I will describe the motivation, definition and use of a “gene centrality” score which identified YES1 (and other kinases) as potential drug targets in an identifiable subset of triple negative breast cancers. Knock down of YES1 in “basal like” cell lines showed significant growth inhibition, suggesting that drugs like Dasatinib, which target the SRC pathway, may be an effective treatment for breast cancer patients with triple negative tumors.

Hosted by Nitzan Rosenfeld.

This talk is part of the Seminars on Quantitative Biology @ CRUK Cambridge Institute series.

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