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Activity Number: 40 - Statistical Methods for Microbiome and Tumor Data
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #306557 Presentation
Title: A Hidden Markov Modeling Approach for Identifying Tumor Subclones in Next-Generation Sequencing Studies
Author(s): BIN ZHU* and HYOYOUNG CHOO-WOSOBA and Paul Albert
Companies: NIH/NCI and NCI and National Cancer Insititute
Keywords: somatic copy number alteration; tumor heterogeneity; E-M algorithm

Allele-specific copy number alteration (ASCNA) analysis is for identifying copy number abnormalities in tumor cells. Unlike normal cells, tumor cells are heterogeneous with a combination of dominant and minor subclones with distinct copy number profiles. Several ASCNA tools have recently been developed, but they have been limited to the identification of subclone regions, and not the genotype of the subclones. In this paper, we propose subHMM, a hidden Markov model-based approach that estimates both subclone region as well as region-specific subclone genotype and clonal proportion. We specify a hidden state variable representing the conglomeration of clonal genotypes and subclone status. We propose a two-step algorithm for parameter estimation, where in the first step, a standard hidden Markov model with this conglomerated state space is fit. Then, in the second step, region-specific estimates of the clonal proportions are obtained by maximizing region-specific pseudo-likelihoods. We apply subHMM to a renal cell carcinoma dataset from The Cancer Genome Atlas. In addition, we conduct simulation studies that show the good performance of the proposed approach.

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

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