Inferring structural variant cancer cell fraction. Nat Commun 2020 Feb 05;11(1):730
Date
02/07/2020Pubmed ID
32024845Pubmed Central ID
PMC7002525DOI
10.1038/s41467-020-14351-8Scopus ID
2-s2.0-85079039901 (requires institutional sign-in at Scopus site) 27 CitationsAbstract
We present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone's performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity.
Author List
Cmero M, Yuan K, Ong CS, Schröder J, PCAWG Evolution and Heterogeneity Working Group, Corcoran NM, Papenfuss T, Hovens CM, Markowetz F, Macintyre G, PCAWG ConsortiumAuthors
Akinyemi Ojesina MD, PhD Assistant Professor in the Obstetrics and Gynecology department at Medical College of WisconsinJanet Sue Rader MD Chair, Professor in the Obstetrics and Gynecology department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
AlgorithmsComputational Biology
Computer Simulation
DNA Copy Number Variations
Female
Gene Frequency
Genome, Human
Humans
Liver Neoplasms
Male
Neoplasms
Ovarian Neoplasms
Pancreatic Neoplasms
Prostatic Neoplasms
Sensitivity and Specificity
Whole Genome Sequencing
