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.
Therapies elicit short-term responses to cancer, but they are commonly followed by the emergence of cells resistant to the treatment. This resistance is facilitated by a diverse population of cells. While we are beginning to understand the extent of heterogeneity in cancer, there are existing knowledge gaps in the strategies to overcome resistance to therapy. We are leveraging new technologies such as single-cell -omics to deconvolve this complex system by outputting large volumes of data per individual cell. However, new methods and algorithms are required to interpret this data. As such, our research program focuses on developing new analytical tools to understand heterogeneous responses to therapy.
In order to accomplish this goal, we are currently focusing on designing algorithms to:
• Integrate multi-omic data to better understand heterogeneity at different data modalities such as the transcriptome, epigenome, and proteome
• Infer cell-cell communication networks between and within cancer cells and the surrounding microenvironment
• Understand clonal shifts in response to therapies over time
• Better visualize complex, multi-omic data in data science
• Help improve patient treatment
Assistant Professor, Department of Medical Biophysics, University of Toronto