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

K. A. Hoadley, C. Yau, T. Hinoue, D. M. Wolf, A. J. Lazar, E. Drill, R. Shen, A. M. Taylor, A. D. Cherniack, V. Thorsson, R. Akbani, R. Bowlby, C. K. Wong, M. Wiznerowicz, F. Sanchez-Vega, A. G. Robertson, B. G. Schneider, M. S. Lawrence, H. Noushmehr, T. M. MaltaCancer Genome Atlas Network, J. M. Stuart, C. C. Benz, P. W. Laird, M. (come contributors) Marino

Research output: Contribution to journalArticle

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.
Original languageEnglish
Pages (from-to)291-304.e6
JournalCell
Volume173
Issue number2
DOIs
Publication statusPublished - Apr 5 2018
Externally publishedYes

Fingerprint

Tumors
Tissue
Histology
Methylation
Cluster Analysis
Chromosomes
MicroRNAs
Neoplasms
Genes
Aneuploidy
Messenger RNA
DNA
Protein Array Analysis
Proteins
Atlases
DNA Methylation
Genome
Kidney
Therapeutics

Keywords

  • TCGA
  • cancer
  • cell-of-origin
  • genome
  • methylome
  • organs
  • proteome
  • subtypes
  • tissues
  • transcriptome

Cite this

Hoadley, K. A., Yau, C., Hinoue, T., Wolf, D. M., Lazar, A. J., Drill, E., ... Marino, M. . C. (2018). Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer. Cell, 173(2), 291-304.e6. https://doi.org/10.1016/j.cell.2018.03.022

Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer. / Hoadley, K. A.; Yau, C.; Hinoue, T.; Wolf, D. M.; Lazar, A. J.; Drill, E.; Shen, R.; Taylor, A. M.; Cherniack, A. D.; Thorsson, V.; Akbani, R.; Bowlby, R.; Wong, C. K.; Wiznerowicz, M.; Sanchez-Vega, F.; Robertson, A. G.; Schneider, B. G.; Lawrence, M. S.; Noushmehr, H.; Malta, T. M.; Network, Cancer Genome Atlas; Stuart, J. M.; Benz, C. C.; Laird, P. W.; Marino, M. (come contributors).

In: Cell, Vol. 173, No. 2, 05.04.2018, p. 291-304.e6.

Research output: Contribution to journalArticle

Hoadley, KA, Yau, C, Hinoue, T, Wolf, DM, Lazar, AJ, Drill, E, Shen, R, Taylor, AM, Cherniack, AD, Thorsson, V, Akbani, R, Bowlby, R, Wong, CK, Wiznerowicz, M, Sanchez-Vega, F, Robertson, AG, Schneider, BG, Lawrence, MS, Noushmehr, H, Malta, TM, Network, CGA, Stuart, JM, Benz, CC, Laird, PW & Marino, MC 2018, 'Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer', Cell, vol. 173, no. 2, pp. 291-304.e6. https://doi.org/10.1016/j.cell.2018.03.022
Hoadley, K. A. ; Yau, C. ; Hinoue, T. ; Wolf, D. M. ; Lazar, A. J. ; Drill, E. ; Shen, R. ; Taylor, A. M. ; Cherniack, A. D. ; Thorsson, V. ; Akbani, R. ; Bowlby, R. ; Wong, C. K. ; Wiznerowicz, M. ; Sanchez-Vega, F. ; Robertson, A. G. ; Schneider, B. G. ; Lawrence, M. S. ; Noushmehr, H. ; Malta, T. M. ; Network, Cancer Genome Atlas ; Stuart, J. M. ; Benz, C. C. ; Laird, P. W. ; Marino, M. (come contributors). / Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer. In: Cell. 2018 ; Vol. 173, No. 2. pp. 291-304.e6.
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AU - Hinoue, T.

AU - Wolf, D. M.

AU - Lazar, A. J.

AU - Drill, E.

AU - Shen, R.

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AU - Cherniack, A. D.

AU - Thorsson, V.

AU - Akbani, R.

AU - Bowlby, R.

AU - Wong, C. K.

AU - Wiznerowicz, M.

AU - Sanchez-Vega, F.

AU - Robertson, A. G.

AU - Schneider, B. G.

AU - Lawrence, M. S.

AU - Noushmehr, H.

AU - Malta, T. M.

AU - Network, Cancer Genome Atlas

AU - Stuart, J. M.

AU - Benz, C. C.

AU - Laird, P. W.

AU - Marino, M. (come contributors)

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PY - 2018/4/5

Y1 - 2018/4/5

N2 - 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.

AB - 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.

KW - TCGA

KW - cancer

KW - cell-of-origin

KW - genome

KW - methylome

KW - organs

KW - proteome

KW - subtypes

KW - tissues

KW - transcriptome

U2 - 10.1016/j.cell.2018.03.022

DO - 10.1016/j.cell.2018.03.022

M3 - Article

VL - 173

SP - 291-304.e6

JO - Cell

JF - Cell

SN - 0092-8674

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ER -