Gene integrated set profile analysis: a context-based approach for inferring biological endpoints.

• Type of Material: Article
• Authors: Jeanne Kowalski, Emory University; Bhakti Dwivedi, Emory University; Scott Newman, Emory University; Jeffrey Switchenko, Emory University; Rini Pauly, Emory University; David Gutman, Emory University; Jyoti Arora, Emory University; Khanjan Gandhi, Emory University; Kylie Ainslie, Emory University; Gregory Doho, Centers for Disease Control and Prevention; Zhaohui Qin, Emory University; Carlos Moreno, Emory University; Michael Rossi, Emory University; Paula Vertino, Emory University; Sagar Lonial, Emory University; Leon Bernal-Mizrachi, Emory University; Lawrence Boise, Emory University
• Language: English
• Date: 2016-01-29
• Publisher: Oxford University Press
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1093/nar/gkv1503
• Title of Journal or Parent Work: Nucleic Acids Research
• ISSN: 0305-1048
• Volume: 44
• Issue: 7
• Grant/Funding Information: Funding for open access charge: Georgia Research Alliance Scientist Award.; Georgia Research Alliance Scientist Award (J.K.); Biostatistics and Bioinformatics Shared Resource of Winship Cancer Institute of Emory University and NIH/NCI [Award number P30CA138292, in part]; Leukemia and Lymphoma Society Translational Research Program Award (to J.K.); a Team Science Seed Funding from the Winship Cancer Institute of Emory University (L.H.B., S.L., M.R.R.)
• Research Categories: Biology, Bioinformatics; Biology, Genetics; Health Sciences, Pathology

Abstract

The identification of genes with specific patterns of change (e.g. down-regulated and methylated) as phenotype drivers or samples with similar profiles for a given gene set as drivers of clinical outcome, requires the integration of several genomic data types for which an 'integrate by intersection' (IBI) approach is often applied. In this approach, results from separate analyses of each data type are intersected, which has the limitation of a smaller intersection with more data types. We introduce a new method, GISPA (Gene Integrated Set Profile Analysis) for integrated genomic analysis and its variation, SISPA (Sample Integrated Set Profile Analysis) for defining respective genes and samples with the context of similar, a priori specified molecular profiles. With GISPA, the user defines a molecular profile that is compared among several classes and obtains ranked gene sets that satisfy the profile as drivers of each class. With SISPA, the user defines a gene set that satisfies a profile and obtains sample groups of profile activity. Our results from applying GISPA to human multiple myeloma (MM) cell lines contained genes of known profiles and importance, along with several novel targets, and their further SISPA application to MM coMMpass trial data showed clinical relevance.