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About the Authors:
Simon Koplev
Roles Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft
Affiliation: Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, United States of America
Katie Lin
Roles Data curation, Formal analysis, Software
Affiliation: Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, United States of America
Anders B. Dohlman
Roles Data curation, Formal analysis
Affiliation: Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, United States of America
Avi Ma’ayan
Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing - original draft, Writing - review & editing
* E-mail: [email protected]
Affiliation: Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, United States of America
ORCID http://orcid.org/0000-0002-6904-1017Abstract
Integrating data from multiple regulatory layers across cancer types could elucidate additional mechanisms of oncogenesis. Using antibody-based protein profiling of 736 cancer cell lines, along with matching transcriptomic data, we show that pan-cancer bimodality in the amounts of mRNA, protein, and protein phosphorylation reveals mechanisms related to the epithelial-mesenchymal transition (EMT). Based on the bimodal expression of E-cadherin, we define an EMT signature consisting of 239 genes, many of which were not previously associated with EMT. By querying gene expression signatures collected from cancer cell lines after small-molecule perturbations, we identify enrichment for histone deacetylase (HDAC) inhibitors as inducers of EMT, and kinase inhibitors as mesenchymal-to-epithelial transition (MET) promoters. Causal modeling of protein-based signaling identifies putative drivers of EMT. In conclusion, integrative analysis of pan-cancer proteomic and transcriptomic data reveals key regulatory mechanisms of oncogenic transformation.
Author summary
Profiling molecular and phenotypic characteristics of large collections of cancer cell lines can be used to identify distinct and common oncogenic pathways across cancer types. So far, most large-scale data obtained from cancer cell lines have been at the genomic, transcriptomic, and phenotypic levels. Recently, high-quality data at the level of cell signaling through protein abundances and phosphorylation sites...