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Abstract:
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Current cancer therapies succeed only in a subset of patients partly due to the heterogeneity of cells across and within tumors. Recent genomic technologies that measure cell features at the resolution of single cells or in a spatially-resolved manner, present exciting opportunities to study the heterogeneity of cells and characterize complex interactions in the tumor microenvironment. However, analyzing these data types involves significant statistical and computational challenges. I will present a set of statistical machine learning methods developed to address challenges such as distinguishing technical variation from biological heterogeneity and inferring temporal and spatial dynamics of cell states as well as their underlying gene circuitry. I will also present novel biological insights obtained from applying these methods to multiple cancer systems. In particular, I will discuss the application of these methods in detecting key exhausted T cell subsets with divergent temporal dynamics that define response to adoptive cell therapy in leukemia.
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