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The consensus molecular subtypes of colorectal cancer

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

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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.

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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.

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Gene Expression Omnibus

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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
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  2. Rodrigo Dienstmann
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  3. Xin Wang
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  4. Aurélien de Reyniès
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  5. Andreas Schlicker
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  6. Charlotte Soneson
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  7. Laetitia Marisa
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  12. Jeffrey S Morris
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  40. Sabine Tejpar
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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)

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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

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