Research Description

Our research focus is clinical computational oncology – the application of computational approaches to leverage the immense available and potential data from tumor genomic profiling to improve outcomes for patients with cancer.

The explosion of high dimensional data rapidly advanced our understanding of cancer but requires new approaches to answer clinical questions. Specifically, we study resistance to therapy in breast and other cancers by integrating high dimensional data – including digital pathology, circulating tumor DNA, and genomics – with patient features and outcomes. By studying patient samples, including on clinical trials, we seek to develop innovative approaches to understand tumor biology, detect and track cancer, and guide treatment choices.

The ultimate goal of this work is to identify predictive markers for treatment response while identifying novel susceptibilities that allow us to overcome drug resistance.