Research

 

A diagnosis of triple negative breast cancer (TNBC) carries with it a poor clinical prognosis and limited treatment options. Currently there are no targeted therapies available for TNBC, and treatment relies on the administration of conventional chemotherapeutics and radiation therapy. These treatments are only significantly effective in a subset of patients so there is strong clinical demand for novel targeted therapies for patients with TNBC. Coupled with the need for new treatment options, is the need for better ways to identify which patients will benefit the most from these therapies. The main goals of my research are to identify vulnerabilities for targeted therapies in subsets of TNBCs, and to bridge the gap between cell lines and patients so that the wealth of data collected in established cell lines can be leveraged for meaningful clinical gain. Since joining the LSP, I have undertaken an extensive profiling effort of TNBC cell lines and patient derived xenograft (PDX) models. This has involved measuring the phenotypic response (cell death and growth arrest) of 36 cell lines and PDX cultures to a panel of clinically relevant compounds mostly comprised of kinase inhibitors. To complement these data, baseline transcript and protein expression profiles were collected across the same models with the goal of using them to identify signatures predictive of the measured phenotypic responses. Predictions will be validated on additional PDX models, and on clinical data and samples providing insight into potential targeted therapies for patients with TNBC.

In addition to identifying targeted therapy options for patients with TNBC, I am interested in understanding differences between drugs of the same class. Often, compounds with the same nominal target(s) induce variable phenotypic responses. To this effect, we are working to uncover the polypharmacology of kinase inhibitors of interest to better understand how it contributes to drug-induced phenotypes. These data will be integrated with the baseline expression profiles with the goal of improving drug response predictions ultimately leading to better and more personalized patient-treatment pairings.

Although the focus of my research to date has been on breast cancer, the approaches that we are using will be broadly applicable to other disease settings and drug classes.