Fast and scalable inference of multi-sample cancer lineages.

Handle URI:
http://hdl.handle.net/10754/596784
Title:
Fast and scalable inference of multi-sample cancer lineages.
Authors:
Popic, Victoria; Salari, Raheleh; Hajirasouliha, Iman; Kashef-Haghighi, Dorna; West, Robert B; Batzoglou, Serafim
Abstract:
Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee .
Citation:
Popic V, Salari R, Hajirasouliha I, Kashef-Haghighi D, West RB, et al. (2015) Fast and scalable inference of multi-sample cancer lineages. Genome Biology 16. Available: http://dx.doi.org/10.1186/s13059-015-0647-8.
Publisher:
Springer Science + Business Media
Journal:
Genome Biology
Issue Date:
6-May-2015
DOI:
10.1186/s13059-015-0647-8
PubMed ID:
25944252
PubMed Central ID:
PMC4501097
Type:
Article
ISSN:
1465-6906
Sponsors:
Authors would like to thank Arend Sidow for valuable discussions and Aaron C. Abajian for contributing to the simulation studies. VP was supported by the Stanford-KAUST grant. RS and IH were also supported by Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowships. DK was supported by an STMicroelectronics Stanford Graduate Fellowship. This work was funded by a grant from KAUST to SB.
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Full metadata record

DC FieldValue Language
dc.contributor.authorPopic, Victoriaen
dc.contributor.authorSalari, Rahelehen
dc.contributor.authorHajirasouliha, Imanen
dc.contributor.authorKashef-Haghighi, Dornaen
dc.contributor.authorWest, Robert Ben
dc.contributor.authorBatzoglou, Serafimen
dc.date.accessioned2016-02-21T08:50:37Zen
dc.date.available2016-02-21T08:50:37Zen
dc.date.issued2015-05-06en
dc.identifier.citationPopic V, Salari R, Hajirasouliha I, Kashef-Haghighi D, West RB, et al. (2015) Fast and scalable inference of multi-sample cancer lineages. Genome Biology 16. Available: http://dx.doi.org/10.1186/s13059-015-0647-8.en
dc.identifier.issn1465-6906en
dc.identifier.pmid25944252en
dc.identifier.doi10.1186/s13059-015-0647-8en
dc.identifier.urihttp://hdl.handle.net/10754/596784en
dc.description.abstractSomatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee .en
dc.description.sponsorshipAuthors would like to thank Arend Sidow for valuable discussions and Aaron C. Abajian for contributing to the simulation studies. VP was supported by the Stanford-KAUST grant. RS and IH were also supported by Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowships. DK was supported by an STMicroelectronics Stanford Graduate Fellowship. This work was funded by a grant from KAUST to SB.en
dc.publisherSpringer Science + Business Mediaen
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License(), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver () applies to the data made available in this article, unless otherwise stated.en
dc.titleFast and scalable inference of multi-sample cancer lineages.en
dc.typeArticleen
dc.identifier.journalGenome Biologyen
dc.identifier.pmcidPMC4501097en
dc.contributor.institutionDepartment of Computer Science, Stanford University, Stanford, CA, USA. viq@stanford.edu.en
dc.contributor.institutionDepartment of Computer Science, Stanford University, Stanford, CA, USA. rahelehs@cs.stanford.edu.en
dc.contributor.institutionDepartment of Computer Science, Stanford University, Stanford, CA, USA. imanh@stanford.edu.en
dc.contributor.institutionDepartment of Computer Science, Stanford University, Stanford, CA, USA. dkashef@stanford.edu.en
dc.contributor.institutionDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA. rbwest@stanford.edu.en
dc.contributor.institutionDepartment of Computer Science, Stanford University, Stanford, CA, USA. serafim@cs.stanford.edu.en

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