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.

  • Analysis
  • Published:

The consensus molecular subtypes of colorectal cancer

Nature Medicine volume 21, pages 1350–1356 (2015)Cite this article

Subjects

Abstract

Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression–based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features: CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation; CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation; CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; and CMS4 (mesenchymal, 23%), prominent transforming growth factor–β activation, stromal invasion and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intratumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC—with clear biological interpretability—and the basis for future clinical stratification and subtype-based targeted interventions.

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

Figure 1: Analytical workflow of the Colorectal Cancer Subtyping Consortium.
Figure 2: Identification of the consensus subtypes of colorectal cancer and application of classification framework in non-consensus samples.
Figure 3: Molecular associations of consensus molecular subtype groups.
Figure 4: Clinicopathological and prognostic associations of consensus molecular subtype groups.
Figure 5: Proposed taxonomy of colorectal cancer, reflecting significant biological differences in the gene expression-based molecular subtypes.

Similar content being viewed by others

Accession codes

Accessions

Gene Expression Omnibus

References

  1. 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 

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

  3. Roepman, P. et al. Colorectal cancer intrinsic subtypes predict chemotherapy benefit, deficient mismatch repair and epithelial-to-mesenchymal transition. Int. J. Cancer 134, 552–562 (2014).

    Article  CAS  PubMed  Google Scholar 

  4. Budinska, E. et al. Gene expression patterns unveil a new level of molecular heterogeneity in colorectal cancer. J. Pathol. 231, 63–76 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Schlicker, A. et al. Subtypes of primary colorectal tumors correlate with response to targeted treatment in colorectal cell lines. BMC Med. Genomics 5, 66 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sadanandam, A. et al. A colorectal cancer classification system that associates cellular phenotype and responses to therapy. Nat. Med. 19, 619–625 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. De Sousa E Melo, F. et al. Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions. Nat. Med. 19, 614–618 (2013).

    Article  CAS  PubMed  Google Scholar 

  8. Marisa, L. et al. Gene expression classification of colon cancer into molecular subtypes: characterization, validation and prognostic value. PLoS Med. 10, e1001453 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Perez-Villamil, B. et al. Colon cancer molecular subtypes identified by expression profiling and associated to stroma, mucinous type and different clinical behavior. BMC Cancer 12, 260 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Van Cutsem, E. et al. Randomized phase III trial comparing biweekly infusional fluorouracil/leucovorin alone or with irinotecan in the adjuvant treatment of stage III colon cancer: PETACC-3. J. Clin. Oncol. 27, 3117–3125 (2009).

    Article  CAS  PubMed  Google Scholar 

  11. Van Dongen, S. Graph clustering via a discrete uncoupling process. SIAM J. Matrix Anal. Appl. 30, 121–141 (2008).

    Article  Google Scholar 

  12. Enright, A.J., Van Dongen, S. & Ouzounis, C.A. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 30, 1575–1584 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Llosa, N.J. et al. The vigorous immune microenvironment of microsatellite instable colon cancer is balanced by multiple counter-inhibitory checkpoints. Cancer Discov. 5, 43–51 (2015).

    Article  CAS  PubMed  Google Scholar 

  14. Brunelli, L., Caiola, E., Marabese, M., Broggini, M. & Pastorelli, R. Capturing the metabolomic diversity of KRAS mutants in non-small-cell lung cancer cells. Oncotarget 5, 4722–4731 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Son, J. et al. Glutamine supports pancreatic cancer growth through a KRAS-regulated metabolic pathway. Nature 496, 101–105 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kamphorst, J.J. et al. Hypoxic and Ras-transformed cells support growth by scavenging unsaturated fatty acids from lysophospholipids. Proc. Natl. Acad. Sci. USA 110, 8882–8887 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Ying, H. et al. Oncogenic Kras maintains pancreatic tumors through regulation of anabolic glucose metabolism. Cell 149, 656–670 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Lei, Z. et al. Identification of molecular subtypes of gastric cancer with different responses to PI3-kinase inhibitors and 5-fluorouracil. Gastroenterology 145, 554–565 (2013).

    Article  CAS  PubMed  Google Scholar 

  19. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513, 202–209 (2014).

  20. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Li, Y., Choi, P.S., Casey, S.C., Dill, D.L. & Felsher, D.W. MYC through miR-17–92 suppresses specific target genes to maintain survival, autonomous proliferation and a neoplastic state. Cancer Cell 26, 262–272 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Park, S.-M., Gaur, A.B., Lengyel, E. & Peter, M.E. The miR-200 family determines the epithelial phenotype of cancer cells by targeting the E-cadherin repressors ZEB1 and ZEB2. Genes Dev. 22, 894–907 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Carmona, F.J. et al. A comprehensive DNA methylation profile of epithelial-to-mesenchymal transition. Cancer Res. 74, 5608–5619 (2014).

    Article  CAS  PubMed  Google Scholar 

  25. Tran, B. et al. Impact of BRAF mutation and microsatellite instability on the pattern of metastatic spread and prognosis in metastatic colorectal cancer. Cancer 117, 4623–4632 (2011).

    Article  CAS  PubMed  Google Scholar 

  26. Gavin, P.G. et al. Mutation profiling and microsatellite instability in stage II and III colon cancer: an assessment of their prognostic and oxaliplatin predictive value. Clin. Cancer Res. 18, 6531–6541 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Popovici, V. et al. Context-dependent interpretation of the prognostic value of BRAF and KRAS mutations in colorectal cancer. BMC Cancer 13, 439 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Sinicrope, F.A. et al. Molecular markers identify subtypes of stage III colon cancer associated with patient outcomes. Gastroenterology 148, 88–99 (2015).

    Article  CAS  PubMed  Google Scholar 

  29. Le, D.T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Yoshihara, K. et al. Inferring tumor purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).

    Article  CAS  PubMed  Google Scholar 

  31. Derry, J.M.J. et al. Developing predictive molecular maps of human disease through community-based modeling. Nat. Genet. 44, 127–130 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Johnson, W.E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    Article  PubMed  Google Scholar 

  33. McCall, M.N., Bolstad, B.M. & Irizarry, R.A. Frozen robust multiarray analysis (fRMA). Biostatistics 11, 242–253 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Zilliox, M.J. & Irizarry, R.A. A gene expression bar code for microarray data. Nat. Methods 4, 911–913 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Tusher, V.G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98, 5116–5121 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Tibshirani, R., Hastie, T., Narasimhan, B. & Chu, G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc. Natl. Acad. Sci. USA 99, 6567–6572 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Brunet, J.-P., Tamayo, P., Golub, T.R. & Mesirov, J.P. Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl. Acad. Sci. USA 101, 4164–4169 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Marron, J.S., Todd, M.J. & Ahn, J. Distance-weighted discrimination. J. Am. Stat. Assoc. 102, 1267–1271 (2007).

    Article  CAS  Google Scholar 

  39. Gautier, L., Cope, L., Bolstad, B.M. & Irizarry, R.A. affy–analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004).

    Article  CAS  PubMed  Google Scholar 

  40. Li, B. & Dewey, C.N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kauffmann, A., Gentleman, R. & Huber, W. arrayQualityMetrics–a bioconductor package for quality assessment of microarray data. Bioinformatics 25, 415–416 (2009).

    Article  CAS  PubMed  Google Scholar 

  42. Tian, S. et al. A robust genomic signature for the detection of colorectal cancer patients with microsatellite instability phenotype and high mutation frequency. J. Pathol. 228, 586–595 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Breiman, L. Random forest. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  44. Chen, X. & Ishwaran, H. Random forests for genomic data analysis. Genomics 99, 323–329 (2012).

    Article  CAS  PubMed  Google Scholar 

  45. Murray, J.S., Dunson, D.B., Carin, L. & Lucas, J.E. Bayesian Gaussian copula factor models for mixed data. J. Am. Stat. Assoc. 108, 656–665 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ghosh, J. & Dunson, D.B. Default prior distributions and efficient posterior computation in Bayesian factor analysis. J. Comput. Graph. Stat. 18, 306–320 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  47. 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 

  48. Umar, A. et al. Revised Bethesda guidelines for hereditary nonpolyposis colorectal cancer (Lynch syndrome) and microsatellite instability. J. Natl. Cancer Inst. 96, 261–268 (2004).

    Article  CAS  PubMed  Google Scholar 

  49. Lindor, N.M. et al. Immunohistochemistry versus microsatellite instability testing in phenotyping colorectal tumors. J. Clin. Oncol. 20, 1043–1048 (2002).

    Article  CAS  PubMed  Google Scholar 

  50. Weisenberger, D.J. et al. CpG island methylator phenotype underlies sporadic microsatellite instability and is tightly associated with BRAF mutation in colorectal cancer. Nat. Genet. 38, 787–793 (2006).

    Article  CAS  PubMed  Google Scholar 

  51. Kosinski, C. et al. Gene expression patterns of human colon tops and basal crypts and BMP antagonists as intestinal stem cell niche factors. Proc. Natl. Acad. Sci. USA 104, 15418–15423 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Van der Flier, L.G. et al. The intestinal Wnt/TCF signature. Gastroenterology 132, 628–632 (2007).

    Article  CAS  PubMed  Google Scholar 

  53. Zeller, K.I., Jegga, A.G., Aronow, B.J., O'Donnell, K.A. & Dang, C.V. An integrated database of genes responsive to the Myc oncogenic transcription factor: identification of direct genomic targets. Genome Biol. 4, R69 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Loboda, A. et al. EMT is the dominant program in human colon cancer. BMC Med. Genomics 4, 9 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Merlos-Suárez, A. et al. The intestinal stem cell signature identifies colorectal cancer stem cells and predicts disease relapse. Cell Stem Cell 8, 511–524 (2011).

    Article  CAS  PubMed  Google Scholar 

  56. Mlecnik, B. et al. Biomolecular network reconstruction identifies T cell homing factors associated with survival in colorectal cancer. Gastroenterology 138, 1429–1440 (2010).

    Article  CAS  PubMed  Google Scholar 

  57. Tosolini, M. et al. Clinical impact of different classes of infiltrating T cytotoxic and helper cells (TH1, TH2, Treg, TH17) in patients with colorectal cancer. Cancer Res. 71, 1263–1271 (2011).

    Article  CAS  PubMed  Google Scholar 

  58. Galon, J. et al. Type, density and location of immune cells within human colorectal tumors predict clinical outcome. Science 313, 1960–1964 (2006).

    Article  CAS  PubMed  Google Scholar 

  59. Ascierto, M.L. et al. Molecular signatures mostly associated with NK cells are predictive of relapse-free survival in breast cancer patients. J. Transl. Med. 11, 145 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Gu-Trantien, C. et al. CD4+ follicular helper T cell infiltration predicts breast cancer survival. J. Clin. Invest. 123, 2873–2892 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Keerthivasan, S. et al. β-Catenin promotes colitis and colon cancer through imprinting of proinflammatory properties in T cells. Sci. Transl. Med. 6, 225ra28 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Stockis, J. et al. Comparison of stable human Treg and TH clones by transcriptional profiling. Eur. J. Immunol. 39, 869–882 (2009).

    Article  CAS  PubMed  Google Scholar 

  63. Fridlender, Z.G. et al. Transcriptomic analysis comparing tumor-associated neutrophils with granulocytic myeloid-derived suppressor cells and normal neutrophils. PLoS ONE 7, e31524 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Efron, B. & Tibshirani, R. On testing the significance of sets of genes. Ann. Appl. Stat. 1, 107–129 (2007).

    Article  Google Scholar 

  65. Wang, L. et al. miR-143 acts as a tumor suppressor by targeting N-RAS and enhances temozolomide-induced apoptosis in glioma. Oncotarget 5, 5416–5427 (2014).

    PubMed  PubMed Central  Google Scholar 

  66. Johnson, S.M. et al. RAS is regulated by the let-7 microRNA family. Cell 120, 635–647 (2005).

    Article  CAS  PubMed  Google Scholar 

  67. Kim, T. et al. p53 regulates epithelial-mesenchymal transition through microRNAs targeting ZEB1 and ZEB2. J. Exp. Med. 208, 875–883 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Lewis, B.P., Burge, C.B. & Bartel, D.P. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15–20 (2005).

    Article  CAS  PubMed  Google Scholar 

  69. Gentleman, R.C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the goodwill and generosity of the colorectal research community who made this study possible. J.G. and S.H.F. are supported by the Integrative Cancer Biology Program of the National Cancer Institute (grant U54CA149237). R.D. is supported by La Caixa International Program for Cancer Research & Education. L.V. is supported by grants from the Dutch Cancer Society (UVA2011-4969 and UVA2014-7245), Worldwide Cancer Research (14-1164), the Maag Lever Darm Stichting (MLDS) (MLDS-CDG 14-03) and the European Research Council (ERG-StG 638193). J.P.M. is supported by grants from the Dutch Cancer Society (UVA2012-573, UVA2013-6331 and UVA2015-7587) and the MLDS (FP012). S.K. is supported by the US National Institutes of Health (grants R01CA172670, R01CA184843, R01 CA187238 and P30CA016672 (Biostatistic and Bioinformatic Core)). A. Sadanandam and G.N. acknowledge support from the National Health Service. S.T. is supported by the Katholieke Universiteit Leuven GOA/12/2106 grant, the EU FP7 Coltheres grant, the Research Foundation Flanders and the Belgian National Cancer Plan.

Author information

Author notes

  1. Justin Guinney, Rodrigo Dienstmann, Xin Wang, Aurélien de Reyniès, Andreas Schlicker, Charlotte Soneson, Laetitia Marisa, Paul Roepman and Gift Nyamundanda: These authors contributed equally to this work.

  2. Pierre Laurent-Puig, Jan Paul Medema, Anguraj Sadanandam, Lodewyk Wessels, Mauro Delorenzi, Scott Kopetz, Louis Vermeulen and Sabine Tejpar: These authors jointly directed this work.

Authors and Affiliations

  1. Sage Bionetworks, Seattle, Washington, USA

    Justin Guinney, Rodrigo Dienstmann, Brian M Bot, Ted Laderas & Stephen H Friend

  2. Vall d'Hebron Institute of Oncology (VHIO), Universitat Autònoma de Barcelona, Barcelona, Spain

    Rodrigo Dienstmann & Josep Tabernero

  3. Laboratory for Experimental Oncology and Radiobiology (LEXOR), Center for Experimental Molecular Medicine (CEMM), Academic Medical Center (AMC), University of Amsterdam, Amsterdam, the Netherlands

    Xin Wang, Evelyn Fessler, Felipe De Sousa E Melo, Jan Paul Medema & Louis Vermeulen

  4. Department of Biomedical Sciences, City University of Hong Kong, Hong Kong

    Xin Wang

  5. Ligue Nationale Contre le Cancer, Paris, France

    Aurélien de Reyniès & Laetitia Marisa

  6. Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands

    Andreas Schlicker, Rene Bernards & Lodewyk Wessels

  7. Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland

    Charlotte Soneson, Paolo Angelino, Sarah Gerster, Edoardo Missiaglia, Hena Ramay, David Barras & Mauro Delorenzi

  8. Agendia NV, Amsterdam, the Netherlands

    Paul Roepman & Iris M Simon

  9. Institute of Cancer Research, London, UK

    Gift Nyamundanda & Anguraj Sadanandam

  10. The University of Texas, M.D. Anderson Cancer Center, Houston, Texas, USA

    Jeffrey S Morris, Dipen Maru, Ganiraju C Manyam, Bradley Broom & Scott Kopetz

  11. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Krisztian Homicsko & Douglas Hanahan

  12. Gustave Roussy, Villejuif, France

    Valerie Boige

  13. Laboratorio de Genomica y Microarrays, Instituto de Investigación Sanitaria San Carlos, Hospital Clinico San Carlos, Madrid, Spain

    Beatriz Perez-Villamil

  14. Institut Catala d'Oncologia, L'Institut d'Investigació Biomèdica de Bellvitge, Barcelona, Spain

    Ramon Salazar

  15. Biomedical Engineering, Oregon Health Sciences University, Portland, Oregon, USA

    Joe W Gray

  16. Université Paris Descartes, Paris, France

    Pierre Laurent-Puig

  17. Department of Biology, Hôpital Européen Georges-Pompidou, Assistance Publique - Hôpitaux de Paris, Paris, France

    Pierre Laurent-Puig

  18. Ludwig Center for Cancer Research, University of Lausanne, Lausanne, Switzerland

    Mauro Delorenzi

  19. Department of Oncology, University of Lausanne, Lausanne, Switzerland

    Mauro Delorenzi

  20. Universitair ziekenhuis Leuven, Leuven, Belgium

    Sabine Tejpar

Authors
  1. Justin Guinney
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rodrigo Dienstmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aurélien de Reyniès
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andreas Schlicker
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Charlotte Soneson
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Laetitia Marisa
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Paul Roepman
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Gift Nyamundanda
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Paolo Angelino
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Brian M Bot
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jeffrey S Morris
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Iris M Simon
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Sarah Gerster
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Evelyn Fessler
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Felipe De Sousa E Melo
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Edoardo Missiaglia
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Hena Ramay
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. David Barras
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Krisztian Homicsko
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Dipen Maru
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Ganiraju C Manyam
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Bradley Broom
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Valerie Boige
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Beatriz Perez-Villamil
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Ted Laderas
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Ramon Salazar
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Joe W Gray
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Douglas Hanahan
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Josep Tabernero
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Rene Bernards
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Stephen H Friend
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Pierre Laurent-Puig
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Jan Paul Medema
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Anguraj Sadanandam
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Lodewyk Wessels
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Mauro Delorenzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. Scott Kopetz
    View author publications

    You can also search for this author in PubMed Google Scholar

  39. Louis Vermeulen
    View author publications

    You can also search for this author in PubMed Google Scholar

  40. Sabine Tejpar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.G., R.D., J.P.M., A. Sadanandam, L.W., M.D., S.K., L.M., L.V., S.T. and S.H.F. conceived and designed the study. A.d.R., P.R., P.L.-P., I.M.S., E.F., F.D.S.E.M., E.M., D.B., K.H., J.W.G., B.B., D.H., J.T., R.B., J.P.M., A. Sadanandam, L.W., M.D., S.K., L.V., V.B. and S.T. provided study materials. J.G., R.D., P.A., B.B., S.G., E.F., D.B., K.H., D.M., G.C.M. and B.M.B. collected and assembled the data. J.G., R.D., X.W., A.d.R., A. Schlicker, C.S., L.M., G.N., P.A., B.M.B., J.M., T.L., L.V., A. Schlicker, J.S.M., B.P.-V., R.S. and M.D. analysed and interpreted the data. J.G., R.D., X.W., A.d.R., A. Sadanandam, C.S., L.M., J.T., R.S., J.P.M., A. Schlicker, M.D., S.K., L.V. and S.T. wrote the manuscript. All authors contributed to the final approval of the manuscript.

Corresponding authors

Correspondence to Justin Guinney, Louis Vermeulen or Sabine Tejpar.

Ethics declarations

Competing interests

I.M.S. and P.R. are employees of Agendia. R.B. is a shareholder of Agendia.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13 (PDF 5271 kb)

Supplementary Table 1

Summary of individual groups subtyping strategy (XLSX 17 kb)

Supplementary Table 2

Summary of clinical, pathological and molecular associations of individual groups' subtypes (XLSX 17 kb)

Supplementary Table 3

Data sets and variables used for correlative analyses (XLSX 19 kb)

Supplementary Table 4

Report of Random Forest CMS classifier during training and validation steps (XLSX 11 kb)

Supplementary Table 5

Clinicopathological and molecular associations of CMS groups (XLSX 24 kb)

Supplementary Table 6

Adjusted P values for enrichment in selected copy number counts across CMS groups (XLSX 15 kb)

Supplementary Table 7

Adjusted P values for enrichment in reverse-phase protein array measurements across CMS groups (XLSX 21 kb)

Supplementary Table 8

Adjusted P values for enrichment in cancer drivers mutations across CMS groups (XLSX 22 kb)

Supplementary Table 9

Adjusted P values for gene set mRNA enrichment analysis (XLSX 20 kb)

Supplementary Table 10

Comparison of TCGA proteomic subtypes and CMS groups (XLSX 11 kb)

Supplementary Table 11

Adjusted P values for gene set protein enrichment analysis (XLSX 18 kb)

Supplementary Table 12

Differential microRNA expression levels across CMS groups (XLSX 53 kb)

Supplementary Table 13

Univariate and multivariate survival models (XLSX 19 kb)

Supplementary Table 14

Major clinicopathological and molecular features of classified and undeterminate samples (XLSX 15 kb)

Supplementary Table 15

Major clinicopathological and molecular features of samples with network labels (consensus samples) versus samples with classifier labels (non-consensus classified samples) for each CMS group (XLSX 18 kb)

Supplementary Table 16

Final performance metrics of CMS classifiers (Random Forest and Single Sample Predictor) applied to consensus samples (XLSX 13 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guinney, J., Dienstmann, R., Wang, X. et al. The consensus molecular subtypes of colorectal cancer. Nat Med 21, 1350–1356 (2015). https://doi.org/10.1038/nm.3967

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nm.3967

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