Publication Date

2018

Journal Title

J Stat Phys

Abstract

© 2017, Springer Science+Business Media, LLC, part of Springer Nature. One cause of cancer mortality is tumor evolution to therapy-resistant disease. First line therapy often targets the dominant clone, and drug resistance can emerge from preexisting clones that gain fitness through therapy-induced natural selection. Such mutations may be identified using targeted sequencing assays by analysis of noise in high-depth data. Here, we develop a comprehensive, unbiased model for sequencing error background. We find that noise in sufficiently deep DNA sequencing data can be approximated by aggregating negative binomial distributions. Mutations with frequencies above noise may have prognostic value. We evaluate our model with simulated exponentially expanded populations as well as data from cell line and patient sample dilution experiments, demonstrating its utility in prognosticating tumor progression. Our results may have the potential to identify significant mutations that can cause recurrence. These results are relevant in the pretreatment clinical setting to determine appropriate therapy and prepare for potential recurrence pretreatment.

Volume Number

172

Issue Number

1

Pages

143 - 155

Document Type

Article

Status

Faculty

Facility

School of Medicine

Primary Department

Molecular Medicine

Additional Departments

General Internal Medicine

PMID

30034030

DOI

10.1007/s10955-017-1945-1


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