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

PLoS Comput Biol. 2022 Sep 6;18(9):e1009767. doi: 10.1371/journal.pcbi.1009767. eCollection 2022 Sep.

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.

MeSH terms

  • Bayes Theorem
  • Carcinoma, Hepatocellular* / genetics
  • Cluster Analysis
  • Humans
  • Liver Neoplasms* / genetics
  • Proteome
  • Transcriptome

Substances

  • Proteome

Grant support

Part of this research was supported by the European Research Council (ERC) Synergy Grant 609883 (awarded to NB, MNH, MHH). Part of this research was supported by the SystemsX.ch Research, Technology and Development (RTD) Grant 2013/150 (awarded to NB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.