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Widespread redundancy in -omics profiles of cancer mutation states

View ORCID ProfileJake Crawford, View ORCID ProfileBrock C. Christensen, View ORCID ProfileMaria Chikina, View ORCID ProfileCasey S. Greene
doi: https://doi.org/10.1101/2021.10.27.466140
Jake Crawford
1Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
2Funded by National Institutes of Health’s National Cancer Institute (R01 CA237170); National Institutes of Health’s National Human Genome Research Institute(R01 CA216265, R01 CA253976)
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Brock C. Christensen
3Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
4Department of Molecular and Systems Biology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
5Funded by National Institutes of Health’s National Cancer Institute (R01 CA216265, R01 CA253976)
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Maria Chikina
6Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
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Casey S. Greene
7Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO, USA
8Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA
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  • For correspondence: casey.s.greene@cuanschutz.edu
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Abstract

In studies of cellular function in cancer, researchers are increasingly able to choose from many -omics assays as functional readouts. Choosing the correct readout for a given study can be difficult, and which layer of cellular function is most suitable to capture the relevant signal may be unclear. In this study, we consider prediction of cancer mutation status (presence or absence) from functional -omics data as a representative problem. Since functional signatures of cancer mutation have been identified across many data types, this problem presents an opportunity to quantify and compare the ability of different -omics readouts to capture signals of dysregulation in cancer. The TCGA Pan-Cancer Atlas contains genetic alteration data including somatic mutations and copy number variants (CNVs), as well as several -omics data types. From TCGA, we focus on RNA sequencing, DNA methylation arrays, reverse phase protein arrays (RPPA), microRNA, and somatic mutational signatures as -omics readouts.

Across a collection of genes recurrently mutated in cancer, RNA sequencing tends to be the most effective predictor of mutation state. Surprisingly, we found that for many of the genes we considered, one or more other data types are approximately equally effective predictors. Performance was more variable between mutations than it was between data types for the same mutation, and there was often little difference between the top data types. We also found that combining data types into a single multi-omics model provided little or no improvement in predictive ability over the best individual data type. Based on our results, for the design of studies focused on the functional outcomes of cancer mutations, there are often multiple -omics types that can serve as effective readouts, although gene expression seems to be a reasonable default option.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • jjc2718 jjc2718

  • mchikina

  • cgreene GreeneScientist

  • Use a broader set of genes to identify downstream changes.

  • https://github.com/greenelab/mpmp

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 April 15, 2022.
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Widespread redundancy in -omics profiles of cancer mutation states
Jake Crawford, Brock C. Christensen, Maria Chikina, Casey S. Greene
bioRxiv 2021.10.27.466140; doi: https://doi.org/10.1101/2021.10.27.466140
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Widespread redundancy in -omics profiles of cancer mutation states
Jake Crawford, Brock C. Christensen, Maria Chikina, Casey S. Greene
bioRxiv 2021.10.27.466140; doi: https://doi.org/10.1101/2021.10.27.466140

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