High-Definition Reconstruction of Clonal Composition in Cancer

The extensive genetic heterogeneity of cancers can greatly affect therapy success due to the existence of subclonal mutations conferring resistance. However, the characterization of subclones in mixed-cell populations is computationally challenging due to the short length of sequence reads that are...

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Main Authors: Andrej Fischer, Ignacio Vázquez-García, Christopher J.R. Illingworth, Ville Mustonen
Format: Article
Language:English
Published: Elsevier 2014-06-01
Series:Cell Reports
Online Access:http://www.sciencedirect.com/science/article/pii/S2211124714003738
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spelling doaj-d976cc5fb0104049af074b016efe680b2020-11-25T01:17:03ZengElsevierCell Reports2211-12472014-06-01751740175210.1016/j.celrep.2014.04.055High-Definition Reconstruction of Clonal Composition in CancerAndrej Fischer0Ignacio Vázquez-García1Christopher J.R. Illingworth2Ville Mustonen3Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UKWellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UKDepartment of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UKWellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UKThe extensive genetic heterogeneity of cancers can greatly affect therapy success due to the existence of subclonal mutations conferring resistance. However, the characterization of subclones in mixed-cell populations is computationally challenging due to the short length of sequence reads that are generated by current sequencing technologies. Here, we report cloneHD, a probabilistic algorithm for the performance of subclone reconstruction from data generated by high-throughput DNA sequencing: read depth, B-allele counts at germline heterozygous loci, and somatic mutation counts. The algorithm can exploit the added information present in correlated longitudinal or multiregion samples and takes into account correlations along genomes caused by events such as copy-number changes. We apply cloneHD to two case studies: a breast cancer sample and time-resolved samples of chronic lymphocytic leukemia, where we demonstrate that monitoring the response of a patient to therapy regimens is feasible. Our work provides new opportunities for tracking cancer development.http://www.sciencedirect.com/science/article/pii/S2211124714003738
collection DOAJ
language English
format Article
sources DOAJ
author Andrej Fischer
Ignacio Vázquez-García
Christopher J.R. Illingworth
Ville Mustonen
spellingShingle Andrej Fischer
Ignacio Vázquez-García
Christopher J.R. Illingworth
Ville Mustonen
High-Definition Reconstruction of Clonal Composition in Cancer
Cell Reports
author_facet Andrej Fischer
Ignacio Vázquez-García
Christopher J.R. Illingworth
Ville Mustonen
author_sort Andrej Fischer
title High-Definition Reconstruction of Clonal Composition in Cancer
title_short High-Definition Reconstruction of Clonal Composition in Cancer
title_full High-Definition Reconstruction of Clonal Composition in Cancer
title_fullStr High-Definition Reconstruction of Clonal Composition in Cancer
title_full_unstemmed High-Definition Reconstruction of Clonal Composition in Cancer
title_sort high-definition reconstruction of clonal composition in cancer
publisher Elsevier
series Cell Reports
issn 2211-1247
publishDate 2014-06-01
description The extensive genetic heterogeneity of cancers can greatly affect therapy success due to the existence of subclonal mutations conferring resistance. However, the characterization of subclones in mixed-cell populations is computationally challenging due to the short length of sequence reads that are generated by current sequencing technologies. Here, we report cloneHD, a probabilistic algorithm for the performance of subclone reconstruction from data generated by high-throughput DNA sequencing: read depth, B-allele counts at germline heterozygous loci, and somatic mutation counts. The algorithm can exploit the added information present in correlated longitudinal or multiregion samples and takes into account correlations along genomes caused by events such as copy-number changes. We apply cloneHD to two case studies: a breast cancer sample and time-resolved samples of chronic lymphocytic leukemia, where we demonstrate that monitoring the response of a patient to therapy regimens is feasible. Our work provides new opportunities for tracking cancer development.
url http://www.sciencedirect.com/science/article/pii/S2211124714003738
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AT ignaciovazquezgarcia highdefinitionreconstructionofclonalcompositionincancer
AT christopherjrillingworth highdefinitionreconstructionofclonalcompositionincancer
AT villemustonen highdefinitionreconstructionofclonalcompositionincancer
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