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Outcomes & Impact of The Cancer Genome Atlas

The Cancer Genome Atlas Legacy: Pushing the Boundaries of Research

Dr. Francis Collins and other key figures reflect on the creation of The Cancer Genome Atlas and how the program has made major impacts in cancer research and beyond.

The Cancer Genome Atlas (TCGA) has helped establish the importance of cancer genomics, transformed our understanding of cancer, and even begun to change how the disease is treated in the clinic. The impact goes even further, reaching health and science technologies, computational biology, and other research fields.

After 12 years, contributions from over 11,000 patients, and incredible effort from thousands of researchers, TCGA has produced a rich data set of immeasurable value. This data remains available to the public as a trusted reference that will be mined for many years.

TCGA Outcomes and Impact

Below are just some examples of how TCGA has made an impact, either directly through the program and the program’s researchers, or indirectly through independent researchers who have creatively utilized the data.

Deepened our understanding of cancer through molecular characterizations

  • In addition to canonical substitutions and indels,1 DNA alterations can occur as various other types, such as fusions,2,3 copy number alterations,4,5 and other complex structural variations.6
  • Researchers have detected aberrations in DNA sequence,1 gene expression,7,8,9 epigenetics (e.g., miRNAs,10,11,12 ncRNAs,13,14 and methylation15), and protein expression and structure,3,16,17 each implicating different functional consequences.
  • While thousands of alterations have been identified, they can be better understood in the context of functional pathways18,19 or assembled together as distinct mutation signatures.20
  • Cancers of different tissues can share the same alterations and be biologically more similar to each other than to other tumors of the same tissue of origin.21
  • Tumors are diverse populations of cells composed of cancer clones22 and immune cells23 of varying heterogeneity.

Established a rich genomics data resource for the broad research community

  • Scientists studying metagenomics,24 immunology,25 and other diseases26 and topics continue to mine and learn from TCGA data.
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Bolstered the computational biology field

  • The vast amount of data and many data types produced by TCGA have spurred tremendous growth in the computational biology field.
  • Researchers developing tools for a wide range of purposes, such as calling somatic and germline mutations,27 predicting genes of prognostic significance,28 constructing regulatory networks,29 batch analysis and correction,30 and automated analysis of cancer images31 routinely make use of TCGA data.

Helped advance health and science technologies

  • The pursuit of TCGA's mission helped lead to substantial improvements in data quality and reductions in cost for DNA and RNA sequencing.
  • Reverse phase protein arrays, formalin-fixed paraffin-embedded sample analyte extraction, and other molecular technologies also saw substantial growth and development.

Changed the way cancer patients are treated in the clinic

  • More accurate stratification and prognosis of disease can now be provided through molecular and clinical data, particularly in the case of low grade gliomas32,33 and gastric cancer.34
  • Many molecular subtypes of cancer may be treated by available drugs35,36 or have potential targets to investigate.37

 

Selected References

  1. Bailey MH, Tokheim C, Porta-Pardo E, et al. Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell. 2018;173(2):371-385.e18. doi:10.1016/j.cell.2018.02.060

  2. Bolton KL. Association Between BRCA1 and BRCA2 Mutations and Survival in Women With Invasive Epithelial Ovarian Cancer. Jama. 2012;307(4):382. doi:10.1001/jama.2012.20

  3. Gao Q, Liang W-W, Foltz SM, et al. Driver Fusions and Their Implications in the Development and Treatment of Human Cancers. Cell Rep. 2018;23(1):227-238.e3. doi:10.1016/j.celrep.2018.03.050

  4. Davoli T, Uno H, Wooten EC, Elledge SJ. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science. 2017;355(6322):eaaf8399. doi:10.1126/science.aaf8399

  5. Zack TI, Schumacher SE, Carter SL, et al. Pan-cancer patterns of somatic copy number alteration. Nat Genet. 2013;45(10):1134-1140. doi:10.1038/ng.2760

  6. Lee E, Iskow R, Yang L, et al. Landscape of somatic retrotransposition in human cancers. Science. 2012;337(6097):967-971. doi:10.1126/science.1222077

  7. Fumagalli D, Gacquer D, Rothé F, et al. Principles Governing A-to-I RNA Editing in the Breast Cancer Transcriptome. Cell Rep. 2015;13(2):277-289. doi:10.1016/j.celrep.2015.09.032

  8. Han L, Diao L, Yu S, et al. The Genomic Landscape and Clinical Relevance of A-to-I RNA Editing in Human Cancers. Cancer Cell. 2015;28(4):515-528. doi:10.1016/j.ccell.2015.08.013

  9. Paz-Yaacov N, Bazak L, Buchumenski I, et al. Elevated RNA Editing Activity Is a Major Contributor to Transcriptomic Diversity in Tumors. Cell Rep. 2015;13(2):267-276. doi:10.1016/j.celrep.2015.08.080

  10. Sumazin P, Yang X, Chiu H-S, et al. An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma. Cell. 2011;147(2):370-381. doi:10.1016/J.CELL.2011.09.041

  11. Kim H, Huang W, Jiang X, Pennicooke B, Park PJ, Johnson MD. Integrative genome analysis reveals an oncomir/oncogene cluster regulating glioblastoma survivorship. Proc Natl Acad Sci U S A. 2010;107(5):2183-2188. doi:10.1073/pnas.0909896107

  12. Yang D, Sun Y, Hu L, et al. Integrated analyses identify a master microRNA regulatory network for the mesenchymal subtype in serous ovarian cancer. Cancer Cell. 2013;23(2):186-199. doi:10.1016/j.ccr.2012.12.020

  13. Chiu H-S, Somvanshi S, Patel E, et al. Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context. Cell Rep. 2018;23(1):297-312.e12. doi:10.1016/j.celrep.2018.03.064

  14. Wang Z, Yang B, Zhang M, et al. lncRNA Epigenetic Landscape Analysis Identifies EPIC1 as an Oncogenic lncRNA that Interacts with MYC and Promotes Cell-Cycle Progression in Cancer. Cancer Cell. 2018;33(4):706-720.e9. doi:10.1016/j.ccell.2018.03.006

  15. Chiappinelli KB, Strissel PL, Desrichard A, et al. Inhibiting DNA Methylation Causes an Interferon Response in Cancer via dsRNA Including Endogenous Retroviruses. Cell. 2015;162(5):974-986. doi:10.1016/j.cell.2015.07.011

  16. Knijnenburg TA, Wang L, Zimmermann MT, et al. Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas. Cell Rep. 2018;23(1):239-254.e6. doi:10.1016/j.celrep.2018.03.076

  17. Shen H, Shih J, Hollern DP, et al. Integrated Molecular Characterization of Testicular Germ Cell Tumors. Cell Rep. 2018;23(11):3392-3406. doi:10.1016/j.celrep.2018.05.039

  18. Stegh AH, Brennan C, Mahoney JA, et al. Glioma oncoprotein Bcl2L12 inhibits the p53 tumor suppressor. Genes Dev. 2010;24(19):2194-2204. doi:10.1101/gad.1924710

  19. Sanchez-Vega F, Mina M, Armenia J, et al. Oncogenic Signaling Pathways in The Cancer Genome Atlas. Cell. 2018;173(2):321-337.e10. doi:10.1016/j.cell.2018.03.035

  20. Alexandrov LB, Nik-Zainal S, Wedge DC, et al. Signatures of mutational processes in human cancer. Nature. 2013;500(7463):415-421. doi:10.1038/nature12477

  21. Hoadley KA, Yau C, Hinoue T, et al. Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer. Cell. 2018;173(2):291-304.e6. doi:10.1016/j.cell.2018.03.022

  22. Wang J, Cazzato E, Ladewig E, et al. Clonal evolution of glioblastoma under therapy. Nat Genet. 2016;48(7):768-776. doi:10.1038/ng.3590

  23. McGranahan N, Furness AJS, Rosenthal R, et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science. 2016;351(6280):1463-1469. doi:10.1126/science.aaf1490

  24. Zhang C, Cleveland K, Schnoll-Sussman F, et al. Identification of low abundance microbiome in clinical samples using whole genome sequencing. Genome Biol. 2015;16(1):265. doi:10.1186/s13059-015-0821-z

  25. Brown SD, Raeburn LA, Holt RA. Profiling tissue-resident T cell repertoires by RNA sequencing. Genome Med. 2015;7(1):125. doi:10.1186/s13073-015-0248-x

  26. Yaspan BL, Williams DF, Holz FG, et al. Targeting factor D of the alternative complement pathway reduces geographic atrophy progression secondary to age-related macular degeneration. Sci Transl Med. 2017;9(395):eaaf1443. doi:10.1126/scitranslmed.aaf1443

  27. Ellrott K, Bailey MH, Saksena G, et al. Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines. Cell Syst. 2018;6(3):271-281.e7. doi:10.1016/j.cels.2018.03.002

  28. Masica DL, Karchin R. Correlation of somatic mutation and expression identifies genes important in human glioblastoma progression and survival. Cancer Res. 2011;71(13):4550-4561. doi:10.1158/0008-5472.CAN-11-0180

  29. Regulome Explorer. http://explorer.cancerregulome.org/. Accessed July 3, 2018.

  30. TCGA Batch Effects. http://bioinformatics.mdanderson.org/tcgambatch/. Accessed July 3, 2018.

  31. Yu K-H, Zhang C, Berry GJ, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun. 2016;7:12474. doi:10.1038/ncomms12474

  32. Killock D. Molecular classification of glioma. Nat Rev Clin Oncol. 2015;12(9):502-502. doi:10.1038/nrclinonc.2015.111

  33. Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas. N Engl J Med. 2015;372(26):2481-2498. doi:10.1056/NEJMoa1402121

  34. Setia N, Agoston AT, Han HS, et al. A protein and mRNA expression-based classification of gastric cancer. Mod Pathol. 2016;29(7):772-784. doi:10.1038/modpathol.2016.55

  35. Wagle N, Grabiner BC, Van Allen EM, et al. Activating mTOR mutations in a patient with an extraordinary response on a phase I trial of everolimus and pazopanib. Cancer Discov. 2014;4(5):546-553. doi:10.1158/2159-8290.CD-13-0353

  36. Grabiner BC, Nardi V, Birsoy K, et al. A diverse array of cancer-associated MTOR mutations are hyperactivating and can predict rapamycin sensitivity. Cancer Discov. 2014;4(5):554-563. doi:10.1158/2159-8290.CD-13-0929

  37. Grieb BC, Chen X, Eischen CM. MTBP is overexpressed in triple-negative breast cancer and contributes to its growth and survival. Mol Cancer Res. 2014;12(9):1216-1224. doi:10.1158/1541-7786.MCR-14-0069

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