Inferring structural variant cancer cell fraction

Cmero, M. et al. (2020) Inferring structural variant cancer cell fraction. Nature Communications, 11, 730. (doi: 10.1038/s41467-020-14351-8) (PMID:32024845) (PMCID:PMC7002525)

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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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Yuan, Dr Ke
Authors: Cmero, M., Yuan, K., Ong, C. S., Schröder, J., PCAWG Evolution and Heterogeneity Working Group, , Corcoran, N. M., Papenfuss, T., Hovens, C. M., Markowetz, F., Macintyre, G., and PCAWG Consortium,
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Nature Communications
Publisher:Nature Research
ISSN (Online):2041-1723
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in Nature Communications 11: 730
Publisher Policy:Reproduced under a Creative Commons License

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