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Tracing the evolution of aneuploid cancers by multiregional sequencing with CRUST

Chattopadhyay, Subhayan LU orcid ; Karlsson, Jenny LU ; Valind, Anders LU orcid ; Andersson, Natalie LU orcid and Gisselsson, David LU (2021) In Briefings in Bioinformatics 22(6).
Abstract

Clonal deconvolution of mutational landscapes is crucial to understand the evolutionary dynamics of cancer. Two limiting factors for clonal deconvolution that have remained unresolved are variation in purity and chromosomal copy number across different samples of the same tumor. We developed a semi-supervised algorithm that tracks variant calls through multi-sample spatiotemporal tumor data. While normalizing allele frequencies based on purity, it also adjusts for copy number changes at clonal deconvolution. Absent à priori copy number data, it renders in silico copy number estimations from bulk sequences. Using published and simulated tumor sequences, we reliably segregated clonal/subclonal variants even at a low sequencing depth... (More)

Clonal deconvolution of mutational landscapes is crucial to understand the evolutionary dynamics of cancer. Two limiting factors for clonal deconvolution that have remained unresolved are variation in purity and chromosomal copy number across different samples of the same tumor. We developed a semi-supervised algorithm that tracks variant calls through multi-sample spatiotemporal tumor data. While normalizing allele frequencies based on purity, it also adjusts for copy number changes at clonal deconvolution. Absent à priori copy number data, it renders in silico copy number estimations from bulk sequences. Using published and simulated tumor sequences, we reliably segregated clonal/subclonal variants even at a low sequencing depth (~50×). Given at least one pure tumor sample (>70% purity), we could normalize and deconvolve paired samples down to a purity of 40%. This renders a reliable clonal reconstruction well adapted to multi-regionally sampled solid tumors, which are often aneuploid and contaminated by non-cancer cells.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Briefings in Bioinformatics
volume
22
issue
6
publisher
Oxford University Press
external identifiers
  • pmid:34343239
  • scopus:85121949131
ISSN
1477-4054
DOI
10.1093/bib/bbab292
language
English
LU publication?
yes
id
bac74b02-ab1d-49c8-b751-e52a07fb1e28
date added to LUP
2021-09-08 11:46:34
date last changed
2024-05-30 08:50:57
@article{bac74b02-ab1d-49c8-b751-e52a07fb1e28,
  abstract     = {{<p>Clonal deconvolution of mutational landscapes is crucial to understand the evolutionary dynamics of cancer. Two limiting factors for clonal deconvolution that have remained unresolved are variation in purity and chromosomal copy number across different samples of the same tumor. We developed a semi-supervised algorithm that tracks variant calls through multi-sample spatiotemporal tumor data. While normalizing allele frequencies based on purity, it also adjusts for copy number changes at clonal deconvolution. Absent à priori copy number data, it renders in silico copy number estimations from bulk sequences. Using published and simulated tumor sequences, we reliably segregated clonal/subclonal variants even at a low sequencing depth (~50×). Given at least one pure tumor sample (&gt;70% purity), we could normalize and deconvolve paired samples down to a purity of 40%. This renders a reliable clonal reconstruction well adapted to multi-regionally sampled solid tumors, which are often aneuploid and contaminated by non-cancer cells.</p>}},
  author       = {{Chattopadhyay, Subhayan and Karlsson, Jenny and Valind, Anders and Andersson, Natalie and Gisselsson, David}},
  issn         = {{1477-4054}},
  language     = {{eng}},
  month        = {{08}},
  number       = {{6}},
  publisher    = {{Oxford University Press}},
  series       = {{Briefings in Bioinformatics}},
  title        = {{Tracing the evolution of aneuploid cancers by multiregional sequencing with CRUST}},
  url          = {{http://dx.doi.org/10.1093/bib/bbab292}},
  doi          = {{10.1093/bib/bbab292}},
  volume       = {{22}},
  year         = {{2021}},
}