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Activity Number: 156 - Modern Statistical Methods for Biological Discovery
Type: Topic Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: International Indian Statistical Association
Abstract #323185
Title: Comparing the Mixing Fractions in a Latent Mixture Model to Investigate Change in Mutational Processes During Cancer Growth and Development
Author(s): Kimberly Siegmund* and Zhi Yang and Priyatama Pandey and Paul Marjoram and Darryl Shibata
Companies: Univ of Southern California-School of Medicine and University of Southern California and University of Southern California and University of Southern California and University of Southern California
Keywords: mutation signatures ; latent Dirichlet allocation models ; mixed membership models
Abstract:

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.


Authors who are presenting talks have a * after their name.

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