HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data

作     者：Myers, Matthew A. Arnold, Brian J. Bansal, Vineet Balaban, Metin Mullen, Katelyn M. Zaccaria, Simone Raphael, Benjamin J. 

作者机构：Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Princeton Univ Ctr Stat & Machine Learning Princeton NJ USA Princeton Univ Princeton Res Comp Princeton NJ USA UCL Canc Inst Computat Canc Genom Res Grp London England Mem Sloan Kettering Canc Ctr Human Oncol & Pathogenesis Program New York NY USA 

出 版 物：《GENOME BIOLOGY》 (Genome Biol.)

年 卷 期：2024年第25卷第1期

页      面：1-28页

核心收录：

基　　金：National Cancer Institute 

主　　题：Copy-number aberrations Cancer Genomics Tumor heterogeneity Clone Allele-specific Haplotype DNA sequencing 

摘      要：Bulk DNA sequencing of multiple samples from the same tumor is becoming common, yet most methods to infer copy-number aberrations (CNAs) from this data analyze individual samples independently. We introduce HATCHet2, an algorithm to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 extends the earlier HATCHet method by improving identification of focal CNAs and introducing a novel statistic, the minor haplotype B-allele frequency (mhBAF), that enables identification of mirrored-subclonal CNAs. We demonstrate HATCHet2 s improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 10 prostate cancer patients reveals previously unreported mirrored-subclonal CNAs affecting cancer genes.