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Talk Title:

Probabilistic Modeling of Dynamics of the Tumor Microenvironment

Abstract:

Cancer therapies succeed only in a subset of patients partly due to the heterogeneity of cells across and within tumors. Single-cell and spatial genomic technologies present exciting opportunities to characterize unknown cell types in complex tissues such as tumor microenvironments and elucidate their interactions, circuitry, and role in driving response to therapies. However, analyzing and integrating single-cell data across conditions, patients, time points, and data modalities involve significant statistical and computational challenges. I will present a set of probabilistic and deep generative models developed for addressing these problems and modeling temporal and spatial dynamics of key immune subsets defining cancer progression and response to immunotherapy. I will also present Starfysh for spatial mapping of heterogeneous cell states and crosstalk in complex tissues, from the integration of spatial transcriptomics and histology images.