Cargando…

Characterization of tumor heterogeneity by latent haplotypes: a sequential Monte Carlo approach

Tumor samples obtained from a single cancer patient spatially or temporally often consist of varying cell populations, each harboring distinct mutations that uniquely characterize its genome. Thus, in any given samples of a tumor having more than two haplotypes, defined as a scaffold of single nucle...

Descripción completa

Detalles Bibliográficos
Autores principales: Ogundijo, Oyetunji E., Wang, Xiaodong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5984585/
https://www.ncbi.nlm.nih.gov/pubmed/29868266
http://dx.doi.org/10.7717/peerj.4838
_version_ 1783328636700459008
author Ogundijo, Oyetunji E.
Wang, Xiaodong
author_facet Ogundijo, Oyetunji E.
Wang, Xiaodong
author_sort Ogundijo, Oyetunji E.
collection PubMed
description Tumor samples obtained from a single cancer patient spatially or temporally often consist of varying cell populations, each harboring distinct mutations that uniquely characterize its genome. Thus, in any given samples of a tumor having more than two haplotypes, defined as a scaffold of single nucleotide variants (SNVs) on the same homologous genome, is evidence of heterogeneity because humans are diploid and we would therefore only observe up to two haplotypes if all cells in a tumor sample were genetically homogeneous. We characterize tumor heterogeneity by latent haplotypes and present state-space formulation of the feature allocation model for estimating the haplotypes and their proportions in the tumor samples. We develop an efficient sequential Monte Carlo (SMC) algorithm that estimates the states and the parameters of our proposed state-space model, which are equivalently the haplotypes and their proportions in the tumor samples. The sequential algorithm produces more accurate estimates of the model parameters when compared with existing methods. Also, because our algorithm processes the variant allele frequency (VAF) of a locus as the observation at a single time-step, VAF from newly sequenced candidate SNVs from next-generation sequencing (NGS) can be analyzed to improve existing estimates without re-analyzing the previous datasets, a feature that existing solutions do not possess.
format Online
Article
Text
id pubmed-5984585
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher PeerJ Inc.
record_format MEDLINE/PubMed
spelling pubmed-59845852018-06-04 Characterization of tumor heterogeneity by latent haplotypes: a sequential Monte Carlo approach Ogundijo, Oyetunji E. Wang, Xiaodong PeerJ Bioinformatics Tumor samples obtained from a single cancer patient spatially or temporally often consist of varying cell populations, each harboring distinct mutations that uniquely characterize its genome. Thus, in any given samples of a tumor having more than two haplotypes, defined as a scaffold of single nucleotide variants (SNVs) on the same homologous genome, is evidence of heterogeneity because humans are diploid and we would therefore only observe up to two haplotypes if all cells in a tumor sample were genetically homogeneous. We characterize tumor heterogeneity by latent haplotypes and present state-space formulation of the feature allocation model for estimating the haplotypes and their proportions in the tumor samples. We develop an efficient sequential Monte Carlo (SMC) algorithm that estimates the states and the parameters of our proposed state-space model, which are equivalently the haplotypes and their proportions in the tumor samples. The sequential algorithm produces more accurate estimates of the model parameters when compared with existing methods. Also, because our algorithm processes the variant allele frequency (VAF) of a locus as the observation at a single time-step, VAF from newly sequenced candidate SNVs from next-generation sequencing (NGS) can be analyzed to improve existing estimates without re-analyzing the previous datasets, a feature that existing solutions do not possess. PeerJ Inc. 2018-05-30 /pmc/articles/PMC5984585/ /pubmed/29868266 http://dx.doi.org/10.7717/peerj.4838 Text en ©2018 Ogundijo and Wang http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Ogundijo, Oyetunji E.
Wang, Xiaodong
Characterization of tumor heterogeneity by latent haplotypes: a sequential Monte Carlo approach
title Characterization of tumor heterogeneity by latent haplotypes: a sequential Monte Carlo approach
title_full Characterization of tumor heterogeneity by latent haplotypes: a sequential Monte Carlo approach
title_fullStr Characterization of tumor heterogeneity by latent haplotypes: a sequential Monte Carlo approach
title_full_unstemmed Characterization of tumor heterogeneity by latent haplotypes: a sequential Monte Carlo approach
title_short Characterization of tumor heterogeneity by latent haplotypes: a sequential Monte Carlo approach
title_sort characterization of tumor heterogeneity by latent haplotypes: a sequential monte carlo approach
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5984585/
https://www.ncbi.nlm.nih.gov/pubmed/29868266
http://dx.doi.org/10.7717/peerj.4838
work_keys_str_mv AT ogundijooyetunjie characterizationoftumorheterogeneitybylatenthaplotypesasequentialmontecarloapproach
AT wangxiaodong characterizationoftumorheterogeneitybylatenthaplotypesasequentialmontecarloapproach