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Talk Title:
Quantifying tumor heterogeneity in space and
time

Abstract:

Tumors are heterogeneous mixtures of cancerous and non-cancerous cells that interact in distinct spatial niches and that evolve over time and in response to treatment. Recent spatial transcriptomics technologies measure RNA expression at thousands of locations in a 2D tumor slice quantifying important features of tumor heterogeneity such as the spatial distribution of cell types and spatial variation in gene expression. Due to limitations in technology and cost, these measurements are typically sparse with high rates of missing data. I will present algorithms that overcome these technical limitations by modeling the geometry of individual tumor slices and integrating measurements from multiple slices. We use these algorithms to analyze spatial transcriptomics data from multiple cancer types and identify copy-number aberrations, reconstruct spatial tumor evolution, derive gene expression gradients in the tumor microenvironment, and construct 3D tumor atlases.