Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer

• Type of Material: Article
• Authors: Katherine A. Hoadley, University of North Carolina; Christina Yau, Buck Institute for Research on Aging; Toshinori Hinoue, Van Andel Research Institute; Denise M. Wolf, University of California San Francisco; Alexander J. Lazar, University of Texas MD Anderson Cancer Center; Esther Drill, Memorial Sloan Kettering Cancer Center; Ronglai Shen, Memorial Sloan Kettering Cancer Center; Alison M. Taylor, Broad Institute of MIT and Harvard; Andrew D. Cherniack, Broad Institute of MIT and Harvard; Vesteinn Thorsson, Institute for Systems Biology; Rehan Akbani, University of Texas MD Anderson Cancer Center; Reanne Bowlby, BC Cancer Agency; Christopher K. Wong, Dana Farber Cancer Institute; Maciej Wiznerowicz, Dana Farber Cancer Institute; Francisco Sanchez-Vega, Memorial Sloan Kettering Cancer Center; A. Gordon Robertson, BC Cancer Agency; Barbara G. Schneider, Vanderbilt University; Michael S. Lawrence, Broad Institute of MIT and Harvard; Houtan Noushmehr, Henry Ford Health System; Tathiane M. Malta, Henry Ford Health System; Joshua M. Stuart, University of California Santa Cruz; Christopher C. Benz, Buck Institute for Research on Aging; Peter W. Laird, Van Andel Research Institute; Daniel Brat, Emory University; Amy Chen, Emory University; Keith Delman, Emory University; Fadlo Khuri, Emory University; Shishir Maithel, Emory University; Jeffrey Olson, Emory University; Taofeek Owonikoko, Emory University; Suresh Ramalingam, Emory University; Dong Shin, Emory University; Gabriel Sica, Emory University; Erwin Van Meir, Emory University
• Language: English
• Date: 2018-04-05
• Publisher: IOS Press
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2018 Elsevier Inc.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.cell.2018.03.022
• Title of Journal or Parent Work: Analytical Cellular Pathology / Cellular Oncology
• ISSN: 1570-5870
• Volume: 173
• Issue: 2
• Start Page: 291
• End Page: +
• Grant/Funding Information: This work was supported by NIH grants (U54 HG003273, U54 HG003067, U54 HG003079, U24 CA143799, U24 CA143835, U24 CA143840, U24 CA143843, U24 CA143845, U24 CA143848, U24 CA143858, U24 CA143866, U24 CA143867, U24 CA143882, U24 CA143883, U24 CA144025, and P30 CA016672).
• Keywords: Life Sciences & Biomedicine; INTEGRATED GENOMIC CHARACTERIZATION; Biochemistry & Molecular Biology; UNKNOWN PRIMARY; Science & Technology; DISCOVERY; ACCURATE; Cell Biology; SIGNATURE; SIMILARITIES; BREAST
• Research Categories: Biology, Cell; Chemistry, Biochemistry

Abstract

We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development. Comprehensive, integrated molecular analysis identifies molecular relationships across a large diverse set of human cancers, suggesting future directions for exploring clinical actionability in cancer treatment.