Triple-negative breast cancer (TNBC) is a deadly disease accounting for 15-20% of the annual new cases of invasive breast cancer in the U.S. Genomic characterization of TNBC has not identified therapeutically targetable, high-frequency “driver” alterations, which has impeded development of molecularly targeted therapies. Most TNBC patients are treated with cytotoxic chemotherapy regimens with only marginal success. Kinases are frequently dysregulated in human cancer and are attractive targets for therapeutic intervention. Recent advancements in mass spectrometry (MS)-based proteomics allow unbiased characterization of proteins and their modifications in clinical tumor specimens, providing ne...
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Triple-negative breast cancer (TNBC) is a deadly disease accounting for 15-20% of the annual new cases of invasive breast cancer in the U.S. Genomic characterization of TNBC has not identified therapeutically targetable, high-frequency “driver” alterations, which has impeded development of molecularly targeted therapies. Most TNBC patients are treated with cytotoxic chemotherapy regimens with only marginal success. Kinases are frequently dysregulated in human cancer and are attractive targets for therapeutic intervention. Recent advancements in mass spectrometry (MS)-based proteomics allow unbiased characterization of proteins and their modifications in clinical tumor specimens, providing new opportunities to comprehensively study kinase signaling. Our overall hypothesis is that TNBC specific signaling confers therapeutic vulnerabilities that can be exploited to treat this devastating disease. We propose two Specific Aims to test this hypothesis. Aim 1 will focus on TNBC intrinsic signaling to identify understudied kinases uniquely activated in TNBC tumors from phosphoproteomics data for therapeutic intervention. Aim 2 will focus on personalized inference of signaling in individual chemoresistant TNBC patients to identify patient-specific chemotherapy-resistant signaling networks and druggable kinases from multi-omics data as targets for overcoming chemoresistance. Computational predictions will be validated using complementary kinase profiling data and perturbation experiments in cell lines and patient-derived xenografts models. Successful completion of the project will identify new therapeutic targets for TNBC and will create computational systems biology methods for signaling network modeling in human cancer.
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