| Literature DB >> 28837809 |
Jong Wook Kim1, Omar O Abudayyeh2, Huwate Yeerna3, Chen-Hsiang Yeang4, Michelle Stewart2, Russell W Jenkins5, Shunsuke Kitajima5, David J Konieczkowski6, Kate Medetgul-Ernar3, Taylor Cavazos3, Clarence Mah7, Stephanie Ting3, Eliezer M Van Allen8, Ofir Cohen8, John Mcdermott2, Emily Damato2, Andrew J Aguirre1, Jonathan Liang2, Arthur Liberzon2, Gabriella Alexe9, John Doench2, Mahmoud Ghandi2, Francisca Vazquez8, Barbara A Weir2, Aviad Tsherniak2, Aravind Subramanian2, Karina Meneses-Cime3, Jason Park3, Paul Clemons10, Levi A Garraway1, David Thomas2, Jesse S Boehm2, David A Barbie8, William C Hahn11, Jill P Mesirov12, Pablo Tamayo13.
Abstract
The systematic sequencing of the cancer genome has led to the identification of numerous genetic alterations in cancer. However, a deeper understanding of the functional consequences of these alterations is necessary to guide appropriate therapeutic strategies. Here, we describe Onco-GPS (OncoGenic Positioning System), a data-driven analysis framework to organize individual tumor samples with shared oncogenic alterations onto a reference map defined by their underlying cellular states. We applied the methodology to the RAS pathway and identified nine distinct components that reflect transcriptional activities downstream of RAS and defined several functional states associated with patterns of transcriptional component activation that associates with genomic hallmarks and response to genetic and pharmacological perturbations. These results show that the Onco-GPS is an effective approach to explore the complex landscape of oncogenic cellular states across cancers, and an analytic framework to summarize knowledge, establish relationships, and generate more effective disease models for research or as part of individualized precision medicine paradigms.Entities:
Keywords: Bayesian nomogram; cellular states; drug sensitivity; genetic dependency; inferential model; matrix factorization; oncoGPS; oncogenic pathway; precision medicine; transcriptional signatures
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Year: 2017 PMID: 28837809 PMCID: PMC5639711 DOI: 10.1016/j.cels.2017.08.002
Source DB: PubMed Journal: Cell Syst ISSN: 2405-4712 Impact factor: 10.304