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

View ORCID ProfilePolina Suter, Eva Dazert, View ORCID ProfileJack Kuipers, Charlotte K.Y. Ng, Tuyana Boldanova, Michael N. Hall, View ORCID ProfileMarkus H. Heim, View ORCID ProfileNiko Beerenwinkel
doi: https://doi.org/10.1101/2021.12.16.473083
Polina Suter
1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
2SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
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Eva Dazert
3Biozentrum, University of Basel, Basel, Switzerland
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Jack Kuipers
1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
2SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
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Charlotte K.Y. Ng
2SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
4Department for BioMedical Research (DBMR), University of Bern, Bern, Switzerland
5Department of Biomedicine, University Hospital Basel, University of Basel, Basel, Switzerland
6Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, Switzerland
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Tuyana Boldanova
5Department of Biomedicine, University Hospital Basel, University of Basel, Basel, Switzerland
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Michael N. Hall
3Biozentrum, University of Basel, Basel, Switzerland
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Markus H. Heim
5Department of Biomedicine, University Hospital Basel, University of Basel, Basel, Switzerland
7Department of Gastroenterology and Hepatology, Clarunis, University Center for Gastrointestinal and Liver Diseases, Basel, Switzerland
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Niko Beerenwinkel
1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
2SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
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  • For correspondence: niko.beerenwinkel@bsse.ethz.ch
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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.

Author summary Multi-omics approaches to cancer subtyping can provide more insights into molecular changes in tumors compared to single-omics approaches. However, most multi-omics clustering methods do not take into account that gene products interact, for example, as parts of protein complexes or signaling networks. Here we present bnClustOmics, a Bayesian network mixture model for unsupervised clustering of multi-omics data, which can represent dependencies among molecular changes of various omics types explicitly. Unlike other approaches that use data from public interaction databases as ground truth, bnClustOmics learns the dependencies between genes from the analyzed multi-omics dataset. At the same time, our approach can also account for prior knowledge from public interaction databases and use it to guide network learning without losing the ability to learn new dependencies. We applied bnClustOmics to a multi-omics HCC dataset and identified three subtypes similar to those identified in other HCC studies. The cluster-specific networks learned by bnClustOmics revealed additional insights into the molecular characterization of the discovered subgroups and highlighted the changes in signaling networks leading to distinct HCC phenotypes.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Legend to Figure 3 is updated, Table numbering in supporting information changed.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 21, 2021.
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Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model
Polina Suter, Eva Dazert, Jack Kuipers, Charlotte K.Y. Ng, Tuyana Boldanova, Michael N. Hall, Markus H. Heim, Niko Beerenwinkel
bioRxiv 2021.12.16.473083; doi: https://doi.org/10.1101/2021.12.16.473083
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Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model
Polina Suter, Eva Dazert, Jack Kuipers, Charlotte K.Y. Ng, Tuyana Boldanova, Michael N. Hall, Markus H. Heim, Niko Beerenwinkel
bioRxiv 2021.12.16.473083; doi: https://doi.org/10.1101/2021.12.16.473083

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