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SeqClone: sequential Monte Carlo based inference of tumor subclones

BACKGROUND: Tumor samples are heterogeneous. They consist of varying cell populations or subclones and each subclone is characterized with a distinct single nucleotide variant (SNV) profile. This explains the source of genetic heterogeneity observed in tumor sequencing data. To make precise prognosi...

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Autores principales: Ogundijo, Oyetunji E., Wang, Xiaodong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320595/
https://www.ncbi.nlm.nih.gov/pubmed/30611189
http://dx.doi.org/10.1186/s12859-018-2562-y
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author Ogundijo, Oyetunji E.
Wang, Xiaodong
author_facet Ogundijo, Oyetunji E.
Wang, Xiaodong
author_sort Ogundijo, Oyetunji E.
collection PubMed
description BACKGROUND: Tumor samples are heterogeneous. They consist of varying cell populations or subclones and each subclone is characterized with a distinct single nucleotide variant (SNV) profile. This explains the source of genetic heterogeneity observed in tumor sequencing data. To make precise prognosis and design effective therapy for cancer, ascertaining the subclonal composition of a tumor is of great importance. RESULTS: In this paper, we propose a state-space formulation of the feature allocation model. This model is interpreted as the blind deconvolution of the expected variant allele fractions (VAFs). VAFs are deconvolved into a binary matrix of genotypes and a matrix of genotype proportions in the samples. Specifically, we consider a sequential construction of the genotype matrix which we model by Indian buffet process (IBP). We describe an efficient sequential Monte Carlo (SMC) algorithm, SeqClone, that jointly estimates the genotypes of subclones and their proportions in the samples. When compared to other methods for resolving tumor heterogeneity, SeqClone provides comparable and sometimes, better estimates of model parameters. By design, SeqClone conveniently handles any number of probed SNVs in the samples. In particular, we can analyze VAFs from newly probed SNVs to improve existing estimates, an attribute not present in existing solutions. CONCLUSIONS: We show that the SMC algorithm for deconvolving VAFs from tumor sequencing data is a robust and promising alternative for explaining the observed genetic heterogeneity in tumor samples. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2562-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-63205952019-01-08 SeqClone: sequential Monte Carlo based inference of tumor subclones Ogundijo, Oyetunji E. Wang, Xiaodong BMC Bioinformatics Research Article BACKGROUND: Tumor samples are heterogeneous. They consist of varying cell populations or subclones and each subclone is characterized with a distinct single nucleotide variant (SNV) profile. This explains the source of genetic heterogeneity observed in tumor sequencing data. To make precise prognosis and design effective therapy for cancer, ascertaining the subclonal composition of a tumor is of great importance. RESULTS: In this paper, we propose a state-space formulation of the feature allocation model. This model is interpreted as the blind deconvolution of the expected variant allele fractions (VAFs). VAFs are deconvolved into a binary matrix of genotypes and a matrix of genotype proportions in the samples. Specifically, we consider a sequential construction of the genotype matrix which we model by Indian buffet process (IBP). We describe an efficient sequential Monte Carlo (SMC) algorithm, SeqClone, that jointly estimates the genotypes of subclones and their proportions in the samples. When compared to other methods for resolving tumor heterogeneity, SeqClone provides comparable and sometimes, better estimates of model parameters. By design, SeqClone conveniently handles any number of probed SNVs in the samples. In particular, we can analyze VAFs from newly probed SNVs to improve existing estimates, an attribute not present in existing solutions. CONCLUSIONS: We show that the SMC algorithm for deconvolving VAFs from tumor sequencing data is a robust and promising alternative for explaining the observed genetic heterogeneity in tumor samples. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2562-y) contains supplementary material, which is available to authorized users. BioMed Central 2019-01-05 /pmc/articles/PMC6320595/ /pubmed/30611189 http://dx.doi.org/10.1186/s12859-018-2562-y Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Ogundijo, Oyetunji E.
Wang, Xiaodong
SeqClone: sequential Monte Carlo based inference of tumor subclones
title SeqClone: sequential Monte Carlo based inference of tumor subclones
title_full SeqClone: sequential Monte Carlo based inference of tumor subclones
title_fullStr SeqClone: sequential Monte Carlo based inference of tumor subclones
title_full_unstemmed SeqClone: sequential Monte Carlo based inference of tumor subclones
title_short SeqClone: sequential Monte Carlo based inference of tumor subclones
title_sort seqclone: sequential monte carlo based inference of tumor subclones
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320595/
https://www.ncbi.nlm.nih.gov/pubmed/30611189
http://dx.doi.org/10.1186/s12859-018-2562-y
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