In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 9 ( 2022-9-6), p. e1009767-
Abstract:
Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup. We conducted simulation studies that demonstrated the advantages of our approach compared to other clustering methods in the case where the generative model is a mixture of Bayesian networks. We applied bnClustOmics to a hepatocellular carcinoma (HCC) dataset comprising genome (mutation and copy number), transcriptome, proteome, and phosphoproteome data. We identified three main HCC subtypes together with molecular characteristics, some of which are associated with survival even when adjusting for the clinical stage. Cluster-specific networks shed light on the links between genotypes and molecular phenotypes of samples within their respective clusters and suggest targets for personalized treatments.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1009767
DOI:
10.1371/journal.pcbi.1009767.g001
DOI:
10.1371/journal.pcbi.1009767.g002
DOI:
10.1371/journal.pcbi.1009767.g003
DOI:
10.1371/journal.pcbi.1009767.g004
DOI:
10.1371/journal.pcbi.1009767.g005
DOI:
10.1371/journal.pcbi.1009767.g006
DOI:
10.1371/journal.pcbi.1009767.s001
DOI:
10.1371/journal.pcbi.1009767.s002
DOI:
10.1371/journal.pcbi.1009767.s003
DOI:
10.1371/journal.pcbi.1009767.s004
DOI:
10.1371/journal.pcbi.1009767.s005
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10.1371/journal.pcbi.1009767.s006
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10.1371/journal.pcbi.1009767.s007
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10.1371/journal.pcbi.1009767.s008
DOI:
10.1371/journal.pcbi.1009767.s009
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10.1371/journal.pcbi.1009767.s010
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10.1371/journal.pcbi.1009767.s011
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10.1371/journal.pcbi.1009767.s012
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10.1371/journal.pcbi.1009767.s013
DOI:
10.1371/journal.pcbi.1009767.s014
DOI:
10.1371/journal.pcbi.1009767.s015
DOI:
10.1371/journal.pcbi.1009767.s016
DOI:
10.1371/journal.pcbi.1009767.s017
DOI:
10.1371/journal.pcbi.1009767.s018
DOI:
10.1371/journal.pcbi.1009767.s019
DOI:
10.1371/journal.pcbi.1009767.s020
DOI:
10.1371/journal.pcbi.1009767.s021
DOI:
10.1371/journal.pcbi.1009767.s022
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6
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