Inference of Tumor Phylogenies from Genomic Assays on Heterogeneous Samples.
Tumorigenesis can in principle result from many combinations of mutations, but only a few roughly equivalent sequences of mutations, or 'progression pathways,' seem to account for most human tumors. Phylogenetics provides a promising way to identify common progression pathways and markers of those p...
| Published in: | Journal of Biomedicine & Biotechnology Vol. 2012; pp. 1 - 17 |
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| Main Authors: | , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
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Wiley-Blackwell
2012
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104298055&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104298055 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11107243 137K jtl: Journal of Biomedicine & Biotechnology issn: 11107243 maglogo: N pubinfo: dt: 2012 vid: 2012 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104298055 104298055 2011906977 NLM22654484 PMC3359715 104298055 ppf: 1 ppct: 16 formats: fmt: @attributes: type: P tig: atl: Inference of Tumor Phylogenies from Genomic Assays on Heterogeneous Samples. aug: au: Subramanian, Ayshwarya Shackney, Stanley Schwartz, Russell affil: Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA sug: subj: Cytogenetic Analysis Genetic Markers Genomics Neoplasms Familial and Genetic Algorithms Bioinformatics Computer Simulation Disease Progression Factor Analysis Descriptive Statistics Funding Source ab: Tumorigenesis can in principle result from many combinations of mutations, but only a few roughly equivalent sequences of mutations, or 'progression pathways,' seem to account for most human tumors. Phylogenetics provides a promising way to identify common progression pathways and markers of those pathways. This approach, however, can be confounded by the high heterogeneity within and between tumors, which makes it difficult to identify conserved progression stages or organize them into robust progression pathways. To tackle this problem, we previously developed methods for inferring progression stages from heterogeneous tumor profiles through computational unmixing. In this paper, we develop a novel pipeline for building trees of tumor evolution from the unmixed tumor data. The pipeline implements a statistical approach for identifying robust progression markers from unmixed tumor data and calling thosemarkers in inferred cell states. The result is a set of phylogenetic characters and their assignments in progression states to which we apply maximum parsimony phylogenetic inference to infer tumor progression pathways. We demonstrate the full pipeline on simulated and real comparative genomic hybridization (CGH) data, validating its effectiveness and making novel predictions of major progression pathways and ancestral cell states in breast cancers. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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