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Statistical approach for investigating change in mutational process during cancer growth and development.

Banff International Research Station for Mathematical Innovation and Discovery

Human cancer somatic mutations arise from a variety of biological processes. Different processes produce different patterns of somatic mutations called mutation signatures. Tumor growth, just like phylogeny and human development, requires genome replication, which generates intratumor heterogeneity from replication errors. Early somatic mutations accumulated between the zygote and the first initiating tumor cell should appear in all descendant cells, and those that appear later in growth, in progressively smaller subsets. These are called trunk and branch mutations, respectively. By multi-regional tumor sampling, we can distinguish trunk from branch mutations and ask whether the mutational signatures in the first tumor cell differ from the signatures of tumor growth. Presently, investigators use latent mixture models to infer the mutation signatures and the relative frequency each signature contributes to the overall tumor catalog. We develop a hierarchical mixed-membership model for testing whether the contributions of the signatures differ before and after tumor initiation. Our methods are applied to mutations identified using whole exome sequencing from a set of colon tumors.

Mathematics; Statistics; Computer science

video/mp4

Author affiliation: University of Southern California

2019-05-08

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/70108

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/