Chemotherapy and targeted drugs are effective treatments against cancer, but commonly lead to treatment-resistant cancer cells. Cancer heterogeneity is known to play a significant role in this resistance. In order to overcome treatment resistance in cancer, our goal is to develop new computational methods and algorithms to quantify and visualize cancer heterogeneity to better understand the underlying mechanisms of resistance. To accomplish this goal, we are creating integrative tools at different scales (such as between cancer subtypes or between individual cells within a single cancer population) and data modalities (such as the transcriptome and the epigenome) to guide therapy.
Dr. Schwartz became interested in heterogeneous systems by studying the diversity and selection of the immune receptor repertoire during his Ph.D research at Drexel University (2011-2016). He subsequently joined the Perelman School of Medicine at the University of Pennsylvania as a postdoctoral researcher, where he studied heterogeneous responses to strong selective pressure in cancer by developing and applying new methods and algorithms to elucidate drug response (2016-2021). He has designed methods including the integration of different data modalities such as transcriptomes and proteomes to discover pan-cancer biomarkers as well as techniques to characterize and quantify new classes of diverse mutations at the nucleotide level in acute myeloid leukemia. More recently, he has leveraged single-cell technologies to deconvolve resistance to targeted therapy in T-cell acute lymphoblastic leukemia.