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Inferring structural variant cancer cell fraction.

Accepted version
Peer-reviewed

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Authors

Schröder, Jan 
PCAWG Evolution and Heterogeneity Working Group 

Abstract

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.

Description

Keywords

Algorithms, Computational 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

Journal Title

Nat Commun

Conference Name

Journal ISSN

2041-1723
2041-1723

Volume Title

11

Publisher

Springer Science and Business Media LLC

Rights

All rights reserved
Sponsorship
Cancer Research Uk (None)
Cancer Research UK (C14303/A17197)
Cancer Research UK (15973)
Cancer Research UK (19274)
Cancer Research UK (28290)
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