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

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Citações na Scopus
1358
Tipo de produção
article
Data de publicação
2018
Título da Revista
ISSN da Revista
Título do Volume
Editora
CELL PRESS
Autores
HOADLEY, Katherine A.
YAU, Christina
HINOUE, Toshinori
WOLF, Denise M.
LAZAR, Alexander J.
DRILL, Esther
SHEN, Ronglai
TAYLOR, Alison M.
CHERNIACK, Andrew D.
THORSSON, Vesteinn
Citação
CELL, v.173, n.2, p.291-304.e6, 2018
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
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.
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Referências
  1. Abeshouse A, 2015, CELL, V163, P1011, DOI 10.1016/j.cell.2015.10.025
  2. Agrawal N, 2014, CELL, V159, P676, DOI 10.1016/j.cell.2014.09.050
  3. Akbani R, 2015, CELL, V161, P1681, DOI 10.1016/j.cell.2015.05.044
  4. Alencar A., 2011, DRL VXORD
  5. Ally A, 2017, CELL, V169, P1327, DOI 10.1016/j.cell.2017.05.046
  6. Banerjee S, 2017, DRUGS, V77, P521, DOI 10.1007/s40265-017-0701-9
  7. Beck AH, 2009, CLIN CANCER RES, V15, P778, DOI 10.1158/1078-0432.CCR-08-1283
  8. Bell D, 2011, NATURE, V474, P609, DOI 10.1038/nature10166
  9. Berger A. C., 2018, CANC CELL, V33
  10. Brat DJ, 2015, NEW ENGL J MED, V372, P2481, DOI 10.1056/NEJMoa1402121
  11. Calabro A, 2009, BREAST CANCER RES TR, V116, P69, DOI 10.1007/s10549-008-0105-3
  12. Campbell J. D., 2018, CELL REP, V23
  13. Carter SL, 2012, NAT BIOTECHNOL, V30, P413, DOI 10.1038/nbt.2203
  14. Chang HY, 2004, PLOS BIOL, V2, P206, DOI 10.1371/journal.pbio.0020007
  15. Cherniack AD, 2017, CANCER CELL, V31, P411, DOI 10.1016/j.ccell.2017.02.010
  16. Chu J, 2014, BIOINFORMATICS, V30, P3402, DOI 10.1093/bioinformatics/btu558
  17. Collisson EA, 2014, NATURE, V511, P543, DOI 10.1038/nature13385
  18. Covington K., 2016, MUTATION SIGNATURES
  19. Davidson G. S., 2001, IEEE INFORM VISUALIZ
  20. Davis CF, 2014, CANCER CELL, V26, P319, DOI 10.1016/j.ccr.2014.07.014
  21. Getz G, 2013, NATURE, V497, P67, DOI 10.1038/nature12113
  22. Hainsworth JD, 2013, J CLIN ONCOL, V31, P217, DOI 10.1200/JCO.2012.43.3755
  23. Hoadley KA, 2014, CELL, V158, P929, DOI 10.1016/j.cell.2014.06.049
  24. Jin X, 2017, TUMOR BIOL, V39, DOI 10.1177/1010428317729933
  25. Knijnenburg T., 2018, CELL REP, V23
  26. Koboldt DC, 2012, NATURE, V490, P61, DOI 10.1038/nature11412
  27. Korn JM, 2008, NAT GENET, V40, P1253, DOI 10.1038/ng.237
  28. Langfelder P, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-559
  29. Lawrence MS, 2013, NATURE, V499, P214, DOI 10.1038/nature12213
  30. Ley TJ, 2013, NEW ENGL J MED, V368, P2059, DOI 10.1056/NEJMoa1301689
  31. Liu Y., 2018, CANC CELL, V33
  32. Malta T. M., 2018, CELL, V173
  33. McCarroll SA, 2008, NAT GENET, V40, P1166, DOI 10.1038/ng.238
  34. Mermel CH, 2011, GENOME BIOL, V12, DOI 10.1186/gb-2011-12-4-r41
  35. Mo QX, 2013, P NATL ACAD SCI USA, V110, P4245, DOI 10.1073/pnas.1208949110
  36. Moran S, 2016, LANCET ONCOL, V17, P1386, DOI 10.1016/S1470-2045(16)30297-2
  37. Newton Y, 2017, CANCER RES, V77, pE111, DOI 10.1158/0008-5472.CAN-17-0580
  38. Olshen AB, 2004, BIOSTATISTICS, V5, P557, DOI 10.1093/biostatistics/kxh008
  39. Ramos AH, 2015, HUM MUTAT, V36, pE2423, DOI 10.1002/humu.22771
  40. Ricketts C. J., 2018, CELL REP, V23
  41. Robertson AG, 2017, CANCER CELL, V32, P204, DOI 10.1016/j.ccell.2017.07.003
  42. Saunders CT, 2012, BIOINFORMATICS, V28, P1811, DOI 10.1093/bioinformatics/bts271
  43. Scrucca L, 2016, R J, V8, P289
  44. Shen R, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0035236
  45. Shen RL, 2009, BIOINFORMATICS, V25, P2906, DOI 10.1093/bioinformatics/btp543
  46. Taylor A. M., 2018, CANC CELL, V33
  47. Teschendorff AE, 2010, BMC CANCER, V10, DOI 10.1186/1471-2407-10-604
  48. Thorsson V., 2018, IMMUNITY, V48
  49. Vaske CJ, 2010, BIOINFORMATICS, V26, pi237, DOI 10.1093/bioinformatics/btq182
  50. Wilkerson MD, 2010, BIOINFORMATICS, V26, P1572, DOI 10.1093/bioinformatics/btq170
  51. Wolf DM, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0088309
  52. Zheng SY, 2016, CANCER CELL, V29, P723, DOI 10.1016/j.ccell.2016.04.002