Cmero, M;
Yuan, K;
Ong, CS;
Schröder, J;
PCAWG Evolution and Heterogeneity Working Group;
Corcoran, NM;
Papenfuss, T;
... PCAWG Consortium; + view all
(2020)
Inferring structural variant cancer cell fraction.
Nature Communications
, 11
, Article 730. 10.1038/s41467-020-14351-8.
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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.
| Type: | Article |
|---|---|
| Title: | Inferring structural variant cancer cell fraction |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41467-020-14351-8 |
| Publisher version: | https://doi.org/10.1038/s41467-020-14351-8 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Cancer, Computational biology and bioinformatics, Machine learning |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10092193 |
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