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Explore advanced computational approaches to understanding cancer evolution in this 36-minute conference talk from Princeton University's Ben Raphael at IPAM's Mathematics of Cancer workshop. Delve into sophisticated models and algorithms designed to tackle key challenges in inferring cancer evolutionary processes from high-throughput DNA and RNA sequencing data. Learn how to deconvolve complex mixtures of mutations found in bulk tumor samples, address the significant error rates and missing data inherent in single-cell sequencing technologies, and model copy number aberrations that affect large genomic regions. Discover methods for leveraging valuable information from regional, spatial, and longitudinal tumor sampling to build more accurate evolutionary models. Gain insights into the mathematical frameworks used to reconstruct how cancer develops through the accumulation of genetic and epigenetic alterations in tissue cells, with practical applications for cancer research and treatment development.
