Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Moving pan-cancer studies from basic research toward the clinic

Nature Cancer volume 2, pages 879–890 (2021)Cite this article

Subjects

Abstract

Although all cancers share common hallmarks, we have long realized that there is no silver-bullet treatment for the disease. Many clinical oncologists specialize in a single cancer type, based predominantly on the tissue of origin. With advances brought by genetics and cancer genomic research, we now know that cancers are profoundly different, both in origins and in genetic alterations. At the same time, commonalities such as key driver mutations, altered pathways, mutational, immune and microbial signatures and other areas (many revealed by pan-cancer studies) point to the intriguing possibility of targeting common traits across diverse cancer types with the same therapeutic strategies. Studies designed to delineate differences and similarities across cancer types are thus critical in discerning the basic dynamics of oncogenesis, as well as informing diagnoses, prognoses and therapies. We anticipate growing emphases on the development and application of therapies targeting underlying commonalities of different cancer types, while tailoring to the unique tissue environment and intrinsic molecular fingerprints of each cancer type and subtype. Here we summarize the facets of pan-cancer research and how they are pushing progress toward personalized medicine.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of pan-cancer studies.
Fig. 2: Key characteristics of cancers learned from TCGA.
Fig. 3: Landscape summary of ICGC–PCAWG results from 2,583 samples across 38 cancer types.
Fig. 4: Therapeutic targets and treatment responses across cancer types.

Similar content being viewed by others

References

  1. Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

    Article  CAS  PubMed  Google Scholar 

  2. Wheeler, D. A. & Wang, L. From human genome to cancer genome: the first decade. Genome Res. 23, 1054–1062 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Pleasance, E. D. et al. A small-cell lung cancer genome with complex signatures of tobacco exposure. Nature 463, 184–190 (2010).

    Article  CAS  PubMed  Google Scholar 

  4. Shah, S. P. et al. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature 461, 809–813 (2009).

    Article  CAS  PubMed  Google Scholar 

  5. Ley, T. J. et al. DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 456, 66–72 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Barbieri, C. E. et al. Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nat. Genet. 44, 685–689 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Gainor, J. F. et al. ALK rearrangements are mutually exclusive with mutations in EGFR or KRAS: an analysis of 1,683 patients with non-small cell lung cancer. Clin. Cancer Res. 19, 4273–4281 (2013).

    Article  CAS  PubMed  Google Scholar 

  8. Parsons, D. W. et al. An integrated genomic analysis of human glioblastoma multiforme. Science 321, 1807–1812 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Yan, H. et al. IDH1 and IDH2 mutations in gliomas. N. Engl. J. Med. 360, 765–773 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bailey, P. et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 531, 47–52 (2016).

    Article  CAS  PubMed  Google Scholar 

  11. Ding, L. et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481, 506–510 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. The Cancer Genome Atlas Research Network et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

    Article  Google Scholar 

  13. Ding, L. et al. Perspective on oncogenic processes at the end of the beginning of cancer genomics. Cell 173, 305–320 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hutter, C. & Zenklusen, J. C. The Cancer Genome Atlas: creating lasting value beyond its data. Cell 173, 283–285 (2018).

    Article  CAS  PubMed  Google Scholar 

  15. ICGC/TCGA Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).

  16. Singer, D. S., Jacks, T. & Jaffee, E. A US “Cancer Moonshot” to accelerate cancer research. Science 353, 1105–1106 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. Liu, J. et al. An integrated TCGA Pan-Cancer Clinical Data Resource to drive high-quality survival outcome analytics. Cell 173, 400–416 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gendoo, D. M. A. et al. MetaGxData: clinically annotated breast, ovarian and pancreatic cancer datasets and their use in generating a multi-cancer gene signature. Sci. Rep. 9, 8770 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Rozenblatt-Rosen, O. et al. The Human Tumor Atlas Network: charting tumor transitions across space and time at single-cell resolution. Cell 181, 236–249 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Clark, D. J. et al. Integrated proteogenomic characterization of clear cell renal cell carcinoma. Cell 179, 964–983 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Dou, Y. et al. Proteogenomic characterization of endometrial carcinoma. Cell 180, 729–748 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Gillette, M. A. et al. Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell 182, 200–225 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov. 7, 818–831 (2017).

  24. McLeod, C. et al. St. Jude Cloud: a pediatric cancer genomic data sharing ecosystem. Cancer Discov. 11, 1082–1099 (2021).

  25. Grobner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018).

    Article  PubMed  Google Scholar 

  26. The International Cancer Genome Consortium. International network of cancer genome projects. Nature 464, 993–998 (2010).

  27. Tamborero, D. et al. Comprehensive identification of mutational cancer driver genes across 12 tumor types. Sci. Rep. 3, 2650 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lane, D. P. Cancer. p53, guardian of the genome. Nature 358, 15–16 (1992).

    Article  CAS  PubMed  Google Scholar 

  32. Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Huang, K. L. et al. Pathogenic germline variants in 10,389 adult cancers. Cell 173, 355–370 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Bielski, C. M. et al. Genome doubling shapes the evolution and prognosis of advanced cancers. Nat. Genet. 50, 1189–1195 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hoadley, K. A. et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158, 929–944 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Wilkerson, M. D. & Hayes, D. N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572–1573 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hoadley, K. A. et al. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell 173, 291–304 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Priestley, P. et al. Pan-cancer whole-genome analyses of metastatic solid tumours. Nature 575, 210–216 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Balanis, N. G. et al. Pan-cancer convergence to a small-cell neuroendocrine phenotype that shares susceptibilities with hematological malignancies. Cancer Cell 36, 17–34 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Mendez, P. & Ramirez, J. L. Copy number gains of FGFR1 and 3q chromosome in squamous cell carcinoma of the lung. Transl. Lung Cancer Res. 2, 101–111 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Wright, T. C. et al. Amplification of the 3q chromosomal region as a specific marker in cervical cancer. Am. J. Obstet. Gynecol. 213, 51.e51–51.e58 (2015).

    Article  Google Scholar 

  42. The Cancer Genome Atlas Network. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 517, 576–582 (2015).

    Article  Google Scholar 

  43. Muglia, V. F. & Prando, A. Renal cell carcinoma: histological classification and correlation with imaging findings. Radiol. Bras. 48, 166–174 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Chen, F. et al. Multilevel genomics-based taxonomy of renal cell carcinoma. Cell Rep. 14, 2476–2489 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Ricketts, C. J. et al. The Cancer Genome Atlas comprehensive molecular characterization of renal cell carcinoma. Cell Rep. 23, 313–326 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Hsieh, J. J., Le, V., Cao, D., Cheng, E. H. & Creighton, C. J. Genomic classifications of renal cell carcinoma: a critical step towards the future application of personalized kidney cancer care with pan-omics precision. J. Pathol. 244, 525–537 (2018).

    Article  PubMed  Google Scholar 

  47. Chen, M., Liu, X., Du, J., Wang, X. J. & Xia, L. Differentiated regulation of immune-response related genes between LUAD and LUSC subtypes of lung cancers. Oncotarget 8, 133–144 (2017).

    Article  PubMed  Google Scholar 

  48. Wen, P. et al. Pan-cancer analysis of radiotherapy benefits and immune infiltration in multiple human cancers. Cancers 12, 957 (2020).

  49. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Brash, D. E. UV signature mutations. Photochem. Photobiol. 91, 15–26 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. Alexandrov, L. B. et al. Mutational signatures associated with tobacco smoking in human cancer. Science 354, 618–622 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Cao, S. et al. Divergent viral presentation among human tumors and adjacent normal tissues. Sci. Rep. 6, 28294 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Prakash, R., Zhang, Y., Feng, W. & Jasin, M. Homologous recombination and human health: the roles of BRCA1, BRCA2, and associated proteins. Cold Spring Harb. Perspect. Biol. 7, a016600 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Davies, H. et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat. Med. 23, 517–525 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Nguyen, L., John, W. M. M., Van Hoeck, A. & Cuppen, E. Pan-cancer landscape of homologous recombination deficiency. Nat. Commun. 11, 5584 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Jonsson, P. et al. Tumour lineage shapes BRCA-mediated phenotypes. Nature 571, 576–579 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Mandal, R. et al. Genetic diversity of tumors with mismatch repair deficiency influences anti-PD-1 immunotherapy response. Science 364, 485–491 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wang, L., Ma, Q., Yao, R. & Liu, J. Current status and development of anti-PD-1/PD-L1 immunotherapy for lung cancer. Int. Immunopharmacol. 79, 106088 (2020).

    Article  CAS  PubMed  Google Scholar 

  59. Hause, R. J., Pritchard, C. C., Shendure, J. & Salipante, S. J. Classification and characterization of microsatellite instability across 18 cancer types. Nat. Med. 22, 1342–1350 (2016).

    Article  CAS  PubMed  Google Scholar 

  60. Niu, B. et al. MSIsensor: microsatellite instability detection using paired tumor–normal sequence data. Bioinformatics 30, 1015–1016 (2014).

    Article  CAS  PubMed  Google Scholar 

  61. Heydt, C. et al. Analysis of tumor mutational burden: correlation of five large gene panels with whole exome sequencing. Sci. Rep. 10, 11387 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Ratti, M., Lampis, A., Hahne, J. C., Passalacqua, R. & Valeri, N. Microsatellite instability in gastric cancer: molecular bases, clinical perspectives, and new treatment approaches. Cell. Mol. Life Sci. 75, 4151–4162 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Warth, A. et al. Microsatellite instability in pulmonary adenocarcinomas: a comprehensive study of 480 cases. Virchows Arch. 468, 313–319 (2016).

    Article  CAS  PubMed  Google Scholar 

  64. Gray, S. E., Kay, E. W., Leader, M. & Mabruk, M. J. Enhanced detection of microsatellite instability and mismatch repair gene expression in cutaneous squamous cell carcinomas. Mol. Diagn. Ther. 10, 327–334 (2006).

    Article  CAS  PubMed  Google Scholar 

  65. Feig, C. et al. The pancreas cancer microenvironment. Clin. Cancer Res. 18, 4266–4276 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Ho, W. J., Jaffee, E. M. & Zheng, L. The tumour microenvironment in pancreatic cancer—clinical challenges and opportunities. Nat. Rev. Clin. Oncol. 17, 527–540 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Kinker, G. S. et al. Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity. Nat. Genet. 52, 1208–1218 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Bhandari, V., Li, C. H., Bristow, R. G., Boutros, P. C. & PCAWG Consortium. Divergent mutational processes distinguish hypoxic and normoxic tumours. Nat. Commun. 11, 737 (2020).

  69. Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Mizuno, S. et al. Immuno-genomic pan-cancer landscape reveals diverse immune escape mechanisms and immuno-editing histories. Sci. Rep. 11, 15713 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Navio, P., Hernandez Madrid, A. & de Farges, V. [Iatrogenic massive pleural effusion and cardiac tamponade]. Arch. Bronconeumol. 34, 318 (1998).

    CAS  PubMed  Google Scholar 

  72. Danaher, P. et al. Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from the Cancer Genome Atlas (TCGA). J. Immunother. Cancer 6, 63 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Berger, A. C. et al. A comprehensive pan-cancer molecular study of gynecologic and breast cancers. Cancer Cell 33, 690–705 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Chen, H. et al. A pan-cancer analysis of enhancer expression in nearly 9000 patient samples. Cell 173, 386–399 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Wang, Y. et al. Comprehensive molecular characterization of the Hippo signaling pathway in cancer. Cell Rep. 25, 1304–1317 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Reyna, M. A. et al. Pathway and network analysis of more than 2500 whole cancer genomes. Nat. Commun. 11, 729 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Griffith, M. et al. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat. Genet. 49, 170–174 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. 2017, PO.17.00011 (2017).

  79. Tao, J. J., Schram, A. M. & Hyman, D. M. Basket studies: redefining clinical trials in the era of genome-driven oncology. Annu. Rev. Med. 69, 319–331 (2018).

    Article  CAS  PubMed  Google Scholar 

  80. Leonetti, A. et al. BRAF in non-small cell lung cancer (NSCLC): pickaxing another brick in the wall. Cancer Treat. Rev. 66, 82–94 (2018).

    Article  CAS  PubMed  Google Scholar 

  81. Raje, N. et al. Vemurafenib in patients with relapsed refractory multiple myeloma harboring BRAFV600 mutations: a cohort of the histology-independent VE-BASKET study. JCO Precis. Oncol. 2, PO.18.00070 (2018).

  82. Kopetz, S. et al. Randomized trial of irinotecan and cetuximab with or without vemurafenib in BRAF-mutant metastatic colorectal cancer (SWOG S1406). J. Clin. Oncol. 39, 285–294 (2020).

  83. Hyman, D. M. et al. Vemurafenib in multiple nonmelanoma cancers with BRAFV600 mutations. N. Engl. J. Med. 373, 726–736 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Corcoran, R. B. et al. Combined BRAF and MEK inhibition with dabrafenib and trametinib in BRAFV600-mutant colorectal cancer. J. Clin. Oncol. 33, 4023–4031 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Qin, B. D. et al. Basket trials for intractable cancer. Front. Oncol. 9, 229 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  86. McNeil, C. NCI-MATCH launch highlights new trial design in precision-medicine era. J. Natl Cancer Inst. 107, djv193 (2015).

  87. Mullard, A. NCI-MATCH trial pushes cancer umbrella trial paradigm. Nat. Rev. Drug Discov. 14, 513–515 (2015).

    Article  CAS  PubMed  Google Scholar 

  88. Barroilhet, L. & Matulonis, U. The NCI-MATCH trial and precision medicine in gynecologic cancers. Gynecol. Oncol. 148, 585–590 (2018).

    Article  PubMed  Google Scholar 

  89. Patel, S. P. et al. A phase II basket trial of dual anti-CTLA-4 and anti-PD-1 blockade in rare tumors (DART SWOG 1609) in patients with nonpancreatic neuroendocrine tumors. Clin. Cancer Res. 26, 2290–2296 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Li, B. T. et al. Ado-trastuzumab emtansine for patients with HER2-mutant lung cancers: results from a phase II basket trial. J. Clin. Oncol. 36, 2532–2537 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Looney, A. M., Nawaz, K. & Webster, R. M. Tumour-agnostic therapies. Nat. Rev. Drug Discov. 19, 383–384 (2020).

    Article  CAS  PubMed  Google Scholar 

  92. The Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

    Article  Google Scholar 

  93. The Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).

    Article  PubMed Central  Google Scholar 

  94. The Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).

    Article  Google Scholar 

  95. The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

    Article  PubMed Central  Google Scholar 

  96. Levine, D. A. & the Cancer Genome Atlas Research Network. Integrated genomic characterization of endometrial carcinoma. Nature 497, 67–73 (2013).

  97. The Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).

    Article  PubMed Central  Google Scholar 

  98. The Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).

    Article  PubMed Central  Google Scholar 

  99. Rheinbay, E. et al. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. Nature 578, 102–111 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Yuan, X., Larsson, C. & Xu, D. Mechanisms underlying the activation of TERT transcription and telomerase activity in human cancer: old actors and new players. Oncogene 38, 6172–6183 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Eberwine, J., Sul, J. Y., Bartfai, T. & Kim, J. The promise of single-cell sequencing. Nat. Methods 11, 25–27 (2014).

    Article  CAS  PubMed  Google Scholar 

  102. Wu, H., Kirita, Y., Donnelly, E. L. & Humphreys, B. D. Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: rare cell types and novel cell states revealed in fibrosis. J. Am. Soc. Nephrol. 30, 23–32 (2019).

    Article  CAS  PubMed  Google Scholar 

  103. Petti, A. A. et al. A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing. Nat. Commun. 10, 3660 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Sharma, A. et al. Longitudinal single-cell RNA sequencing of patient-derived primary cells reveals drug-induced infidelity in stem cell hierarchy. Nat. Commun. 9, 4931 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Lawson, D. A., Kessenbrock, K., Davis, R. T., Pervolarakis, N. & Werb, Z. Tumour heterogeneity and metastasis at single-cell resolution. Nat. Cell Biol. 20, 1349–1360 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Zhang, L. & Zhang, Z. Recharacterizing tumor-infiltrating lymphocytes by single-cell RNA sequencing. Cancer Immunol. Res. 7, 1040–1046 (2019).

    Article  CAS  PubMed  Google Scholar 

  107. Gan, Y., Li, N., Zou, G., Xin, Y. & Guan, J. Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method. BMC Med. Genomics 11, 117 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Zhang, H. et al. Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell 166, 755–765 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Zhang, B. et al. Proteogenomic characterization of human colon and rectal cancer. Nature 513, 382–387 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Vasaikar, S. et al. Proteogenomic analysis of human colon cancer reveals new therapeutic opportunities. Cell 177, 1035–1049 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Mertins, P. et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 534, 55–62 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).

    Article  CAS  PubMed  Google Scholar 

  115. Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Nakamura, K. et al. Sequence-specific error profile of Illumina sequencers. Nucleic Acids Res. 39, e90 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Wendl, M. C. et al. PathScan: a tool for discerning mutational significance in groups of putative cancer genes. Bioinformatics 27, 1595–1602 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Dees, N. D. et al. MuSiC: identifying mutational significance in cancer genomes. Genome Res. 22, 1589–1598 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Tamborero, D., Gonzalez-Perez, A. & Lopez-Bigas, N. OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes. Bioinformatics 29, 2238–2244 (2013).

    Article  CAS  PubMed  Google Scholar 

  121. Reimand, J., Wagih, O. & Bader, G. D. The mutational landscape of phosphorylation signaling in cancer. Sci. Rep. 3, 2651 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  122. Mularoni, L., Sabarinathan, R., Deu-Pons, J., Gonzalez-Perez, A. & Lopez-Bigas, N. OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations. Genome Biol. 17, 128 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  123. Tokheim, C. J., Papadopoulos, N., Kinzler, K. W., Vogelstein, B. & Karchin, R. Evaluating the evaluation of cancer driver genes. Proc. Natl Acad. Sci. USA 113, 14330–14335 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Ng, P. C. & Henikoff, S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Carter, H. et al. Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations. Cancer Res. 69, 6660–6667 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Reva, B., Antipin, Y. & Sander, C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 39, e118 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Adzhubei, I., Jordan, D. M. & Sunyaev, S. R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. 76, 7.20.1–7.20.41 (2013).

  128. Mao, Y. et al. CanDrA: cancer-specific driver missense mutation annotation with optimized features. PLoS ONE 8, e77945 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Porta-Pardo, E. & Godzik, A. e-Driver: a novel method to identify protein regions driving cancer. Bioinformatics 30, 3109–3114 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Tokheim, C. et al. Exome-scale discovery of hotspot mutation regions in human cancer using 3D protein structure. Cancer Res. 76, 3719–3731 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Niu, B. et al. Protein-structure-guided discovery of functional mutations across 19 cancer types. Nat. Genet. 48, 827–837 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Van Allen, E. M. et al. Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine. Nat. Med. 20, 682–688 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  133. Conte, N. et al. PDX Finder: a portal for patient-derived tumor xenograft model discovery. Nucleic Acids Res. 47, D1073–D1079 (2019).

    Article  CAS  PubMed  Google Scholar 

  134. Yao, L. C. et al. Creation of PDX-bearing humanized mice to study immuno-oncology. Methods Mol. Biol. 1953, 241–252 (2019).

    Article  CAS  PubMed  Google Scholar 

  135. Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Li, H. et al. The landscape of cancer cell line metabolism. Nat. Med. 25, 850–860 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Wallace, S. E., Kirby, E. & Knoppers, B. M. How can we not waste legacy genomic research data? Front. Genet. 11, 446 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  139. Ostrom, Q. T. et al. CBTRUS Statistical Report: primary brain and other central nervous system tumors diagnosed in the United States in 2012–2016. Neuro Oncol. 21, v1–v100 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  140. Ng, C. S. et al. Renal cell carcinoma: diagnosis, staging, and surveillance. AJR Am. J. Roentgenol. 191, 1220–1232 (2008).

    Article  PubMed  Google Scholar 

  141. Patard, J. J. et al. Prognostic value of histologic subtypes in renal cell carcinoma: a multicenter experience. J. Clin. Oncol. 23, 2763–2771 (2005).

    Article  PubMed  Google Scholar 

  142. Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2019. CA Cancer J. Clin. 69, 7–34 (2019).

    Article  PubMed  Google Scholar 

  143. Denisenko, T. V., Budkevich, I. N. & Zhivotovsky, B. Cell death-based treatment of lung adenocarcinoma. Cell Death Dis. 9, 117 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  144. Shi, J. et al. Somatic genomics and clinical features of lung adenocarcinoma: a retrospective study. PLoS Med. 13, e1002162 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  145. Noguchi, M. et al. Small adenocarcinoma of the lung. Histologic characteristics and prognosis. Cancer 75, 2844–2852 (1995).

    Article  CAS  PubMed  Google Scholar 

  146. Znaor, A., Lortet-Tieulent, J., Jemal, A. & Bray, F. International variations and trends in testicular cancer incidence and mortality. Eur. Urol. 65, 1095–1106 (2014).

    Article  PubMed  Google Scholar 

  147. Engels, E. A. Epidemiology of thymoma and associated malignancies. J. Thorac. Oncol. 5, S260–S265 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  148. Wilkins, K. B. et al. Clinical and pathologic predictors of survival in patients with thymoma. Ann. Surg. 230, 562–572 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Nama, N. et al. Carcinosarcoma of the uterus: a study from the Surveillance Epidemiology and End Result (SEER) database. Cureus 12, e10283 (2020).

    PubMed  PubMed Central  Google Scholar 

  150. Steinweber, P. & Koller A., Similar Diversity (Photograph). Hanger-7, Salzburg, Austria. In Visual Complexity Mapping Patterns of Information (ed., Lima, M.) 124–125 (Princeton Architectural Press, 2013).

  151. Sanchez, G. Arcdiagram: plot pretty arc diagrams. R package version 0.1.11 (2014).

  152. Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We are grateful for comments from A. Karpova and D.C. Zhou, both of the Washington University School of Medicine. Additionally, M.H.B. is supported by a T32 training fellowship from the NIH Ruth L. Kirschstein National Research Service Award (HG008962).

Author information

Authors and Affiliations

  1. Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA

    Feng Chen, Matthew A. Wyczalkowski, Yize Li & Li Ding

  2. Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA

    Feng Chen & Li Ding

  3. Department of Cell Biology and Physiology, Washington University in St. Louis, St. Louis, MO, USA

    Feng Chen

  4. Department of Genetics, Washington University in St. Louis, St. Louis, MO, USA

    Michael C. Wendl & Li Ding

  5. Department of Mathematics, Washington University in St. Louis, St. Louis, MO, USA

    Michael C. Wendl

  6. McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA

    Michael C. Wendl, Matthew A. Wyczalkowski, Yize Li & Li Ding

  7. Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA

    Matthew H. Bailey

  8. Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT, USA

    Matthew H. Bailey

Authors
  1. Feng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael C. Wendl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthew A. Wyczalkowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matthew H. Bailey
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yize Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Li Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Ding.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Cancer thanks Lincoln Stein and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, F., Wendl, M.C., Wyczalkowski, M.A. et al. Moving pan-cancer studies from basic research toward the clinic. Nat Cancer 2, 879–890 (2021). https://doi.org/10.1038/s43018-021-00250-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43018-021-00250-4

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer