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Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model

Suter, P. and Dazert, E. and Kuipers, J. and Ng, C. K. Y. and Boldanova, T. and Hall , M. N. and Heim, M. H. and Beerenwinkel, N.. (2021) Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model.

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Official URL: https://edoc.unibas.ch/86374/

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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.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Growth & Development > Biochemistry (Hall)
UniBasel Contributors:Hall, Michael N.
Item Type:Preprint
Publisher:bioRxiv
Note:Publication type according to Uni Basel Research Database: Discussion paper / Internet publication
Language:English
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Last Modified:10 Mar 2022 14:38
Deposited On:03 Mar 2022 11:07

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