A hidden Markov modeling approach for identifying tumor subclones in next-generation sequencing studies.
Allele-specific copy number alteration (ASCNA) analysis is for identifying copy number abnormalities in tumor cells. Unlike normal cells, tumor cells are heterogeneous as a combination of dominant and minor subclones with distinct copy number profiles. Estimating the clonal proportion and identifyin...
| Publicado en: | Biostatistics Vol. 23; no. 1; pp. 69 - 83 |
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| Autores principales: | , , |
| Formato: | equations & formulas review tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Jan2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156110875&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156110875 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14654644 N58 jtl: Biostatistics issn: 14654644 maglogo: N pubinfo: dt: Jan2022 vid: 23 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 156110875 156110875 NLM32282873 156110875 10.1093/biostatistics/kxaa013 NLM32282873 156110875 ppf: 69 ppct: 14 formats: tig: atl: A hidden Markov modeling approach for identifying tumor subclones in next-generation sequencing studies. aug: au: Choo-Wosoba, Hyoyoung Albert, Paul S Zhu, Bin affil: Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute , 9609 Medical Center Dr, Rockville MD 20850 USA sug: subj: Sequence Analysis Neoplasms Hidden Markov Models Algorithms Software Genetics Funding Source ab: Allele-specific copy number alteration (ASCNA) analysis is for identifying copy number abnormalities in tumor cells. Unlike normal cells, tumor cells are heterogeneous as a combination of dominant and minor subclones with distinct copy number profiles. Estimating the clonal proportion and identifying mainclone and subclone genotypes across the genome are important for understanding tumor progression. Several ASCNA tools have recently been developed, but they have been limited to the identification of subclone regions, and not the genotype of subclones. In this article, we propose subHMM, a hidden Markov model-based approach that estimates both subclone region and region-specific subclone genotype and clonal proportion. We specify a hidden state variable representing the conglomeration of clonal genotype and subclone status. We propose a two-step algorithm for parameter estimation, where in the first step, a standard hidden Markov model with this conglomerated state variable is fit. Then, in the second step, region-specific estimates of the clonal proportions are obtained by maximizing region-specific pseudo-likelihoods. We apply subHMM to study renal cell carcinoma datasets in The Cancer Genome Atlas. In addition, we conduct simulation studies that show the good performance of the proposed approach. The R source code is available online at https://dceg.cancer.gov/tools/analysis/subhmm. Expectation-Maximization algorithm; Forward-backward algorithm; Somatic copy number alteration; Tumor subclones. pubtype: Academic Journal doctype: equations & formulas review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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