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

Authors :
Hoadley, Katherine A.
Yau, Christina
Hinoue, Toshinori
Wolf, Denise M.
Lazar, Alexander J.
Drill, Esther
Shen, Ronglai
Taylor, Alison M.
Cherniack, Andrew D.
Thorsson, Vésteinn
Akbani, Rehan
Bowlby, Reanne
Wong, Christopher K.
Wiznerowicz, Maciej
Sanchez-Vega, Francisco
Robertson, A. Gordon
Schneider, Barbara G.
Lawrence, Michael S.
Noushmehr, Houtan
Malta, Tathiane M.
Caesar-Johnson, Samantha J.
Demchok, John A.
Felau, Ina
Kasapi, Melpomeni
Ferguson, Martin L.
Hutter, Carolyn M.
Sofia, Heidi J.
Tarnuzzer, Roy
Wang, Zhining
Yang, Liming
Zenklusen, Jean C.
Zhang, Jiashan (Julia)
Chudamani, Sudha
Liu, Jia
Lolla, Laxmi
Naresh, Rashi
Pihl, Todd
Sun, Qiang
Wan, Yunhu
Wu, Ye
Cho, Juok
DeFreitas, Timothy
Frazer, Scott
Gehlenborg, Nils
Getz, Gad
Heiman, David I.
Kim, Jaegil
Lin, Pei
de Krijger, Ronald
The Cancer Genome Atlas Network
Publication Year :
2018

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.

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.od.....10691..2d9274ad7f3d11f894890827519692d1