Unknown

Dataset Information

Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data.


ABSTRACT: Copy-number aberrations (CNAs) and whole-genome duplications (WGDs) are frequent somatic mutations in cancer but their quantification from DNA sequencing of bulk tumor samples is challenging. Standard methods for CNA inference analyze tumor samples individually; however, DNA sequencing of multiple samples from a cancer patient has recently become more common. We introduce HATCHet (Holistic Allele-specific Tumor Copy-number Heterogeneity), an algorithm that infers allele- and clone-specific CNAs and WGDs jointly across multiple tumor samples from the same patient. We show that HATCHet outperforms current state-of-the-art methods on multi-sample DNA sequencing data that we simulate using MASCoTE (Multiple Allele-specific Simulation of Copy-number Tumor Evolution). Applying HATCHet to 84 tumo

SUBMITTER: Zaccaria S 

PROVIDER: S-EPMC7468132 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature