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PLoS Comput Biol. 2019 Jul 29;15(7):e1007245. doi: 10.1371/journal.pcbi.1007245. eCollection 2019 Jul.

The impact of DNA methylation on the cancer proteome.

Author information

1
Biomedical Informatics Training Program, Department of Biomedical Data Science, Stanford University, Palo Alto, California, United States of America.
2
Department of Bioengineering, Stanford University, Palo Alto, California, United States of America.
3
Department of Statistics, College of Pharmaceutical and Biochemical Sciences, National University of Rosario, Rosario, Argentina.
4
Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Palo Alto, California, United States of America.
5
Department of Biomedical Data Science, Stanford University, Palo Alto, California, United States of America.

Abstract

Aberrant DNA methylation disrupts normal gene expression in cancer and broadly contributes to oncogenesis. We previously developed MethylMix, a model-based algorithmic approach to identify epigenetically regulated driver genes. MethylMix identifies genes where methylation likely executes a functional role by using transcriptomic data to select only methylation events that can be linked to changes in gene expression. However, given that proteins more closely link genotype to phenotype recent high-throughput proteomic data provides an opportunity to more accurately identify functionally relevant abnormal methylation events. Here we present a MethylMix analysis that refines nominations for epigenetic driver genes by leveraging quantitative high-throughput proteomic data to select only genes where DNA methylation is predictive of protein abundance. Applying our algorithm across three cancer cohorts we find that using protein abundance data narrows candidate nominations, where the effect of DNA methylation is often buffered at the protein level. Next, we find that MethylMix genes predictive of protein abundance are enriched for biological processes involved in cancer including functions involved in epithelial and mesenchymal transition. Moreover, our results are also enriched for tumor markers which are predictive of clinical features like tumor stage and we find clustering using MethylMix genes predictive of protein abundance captures cancer subtypes.

PMID:
31356589
PMCID:
PMC6695193
DOI:
10.1371/journal.pcbi.1007245
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Conflict of interest statement

The authors have declared that no competing interests exist.

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