Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Inferring structural variant cancer cell fraction

Cmero, Marek ; Yuan, Ke ; Ong, Cheng Soon ; Schröder, Jan ; Corcoran, Niall M ; Papenfuss, Tony ; Hovens, Christopher M ; Markowetz, Florian and Macintyre, Geoff (2020) In Nature Communications 11.
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... (More)

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.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; and
contributor
LU ; LU orcid and LU orcid
author collaboration
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Algorithms, Computational Biology/methods, Computer Simulation, DNA Copy Number Variations, Female, Gene Frequency, Genome, Human, Humans, Liver Neoplasms/genetics, Male, Neoplasms/genetics, Ovarian Neoplasms/genetics, Pancreatic Neoplasms/genetics, Prostatic Neoplasms/genetics, Sensitivity and Specificity, Whole Genome Sequencing
in
Nature Communications
volume
11
article number
730
pages
15 pages
publisher
Nature Publishing Group
external identifiers
  • pmid:32024845
  • scopus:85079039901
ISSN
2041-1723
DOI
10.1038/s41467-020-14351-8
language
English
LU publication?
yes
id
7c5783db-5a1a-434f-a158-33a012d75dfe
date added to LUP
2023-01-05 14:33:20
date last changed
2024-06-13 13:22:53
@article{7c5783db-5a1a-434f-a158-33a012d75dfe,
  abstract     = {{<p>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.</p>}},
  author       = {{Cmero, Marek and Yuan, Ke and Ong, Cheng Soon and Schröder, Jan and Corcoran, Niall M and Papenfuss, Tony and Hovens, Christopher M and Markowetz, Florian and Macintyre, Geoff}},
  issn         = {{2041-1723}},
  keywords     = {{Algorithms; Computational Biology/methods; Computer Simulation; DNA Copy Number Variations; Female; Gene Frequency; Genome, Human; Humans; Liver Neoplasms/genetics; Male; Neoplasms/genetics; Ovarian Neoplasms/genetics; Pancreatic Neoplasms/genetics; Prostatic Neoplasms/genetics; Sensitivity and Specificity; Whole Genome Sequencing}},
  language     = {{eng}},
  month        = {{02}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Communications}},
  title        = {{Inferring structural variant cancer cell fraction}},
  url          = {{http://dx.doi.org/10.1038/s41467-020-14351-8}},
  doi          = {{10.1038/s41467-020-14351-8}},
  volume       = {{11}},
  year         = {{2020}},
}