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Medoidshift clustering applied to genomic bulk tumor data.

Roman, Theodore and Xie, Lu and Schwartz, Russell (2016) Medoidshift clustering applied to genomic bulk tumor data. BMC Genomics, 17 Sup. 6 - ?.

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Abstract

Despite the enormous medical impact of cancers and intensive study of their biology, detailed characterization of tumor growth and development remains elusive. This difficulty occurs in large part because of enormous heterogeneity in the molecular mechanisms of cancer progression, both tumor-to-tumor and cell-to-cell in single tumors. Advances in genomic technologies, especially at the single-cell level, are improving the situation, but these approaches are held back by limitations of the biotechnologies for gathering genomic data from heterogeneous cell populations and the computational methods for making sense of those data. One popular way to gain the advantages of whole-genome methods without the cost of single-cell genomics has been the use of computational deconvolution (unmixing) methods to reconstruct clonal heterogeneity from bulk genomic data. These methods, too, are limited by the difficulty of inferring genomic profiles of rare or subtly varying clonal subpopulations from bulk data, a problem that can be computationally reduced to that of reconstructing the geometry of point clouds of tumor samples in a genome space. Here, we present a new method to improve that reconstruction by better identifying subspaces corresponding to tumors produced from mixtures of distinct combinations of clonal subpopulations. We develop a nonparametric clustering method based on medoidshift clustering for identifying subgroups of tumors expected to correspond to distinct trajectories of evolutionary progression. We show on synthetic and real tumor copy-number data that this new method substantially improves our ability to resolve discrete tumor subgroups, a key step in the process of accurately deconvolving tumor genomic data and inferring clonal heterogeneity from bulk data.

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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Roman, Theodore
Xie, Lu
Schwartz, Russell
Date: 11 January 2016
Date Type: Publication
Journal or Publication Title: BMC Genomics
Volume: 17 Sup
Page Range: 6 - ?
DOI or Unique Handle: 10.1186/s12864-015-2302-x
Schools and Programs: Dietrich School of Arts and Sciences > Computational Biology
School of Medicine > Computational Biology
Refereed: Yes
Uncontrolled Keywords: Algorithms, Cluster Analysis, Comparative Genomic Hybridization, Gene Dosage, Humans, Neoplasms
Funders: NIBIB NIH HHS (T32EB009403), NIAID NIH HHS (R01 AI076318), NIBIB NIH HHS (T32 EB009403), NIAID NIH HHS (1R01AI076318), NCI NIH HHS (R01 CA140214), NCI NIH HHS (1R01CA140214)
Date Deposited: 25 Jul 2016 17:43
Last Modified: 20 Dec 2018 00:55
URI: http://d-scholarship.pitt.edu/id/eprint/28917

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