Skip to main content

Thank you for visiting 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.

A colorectal cancer classification system that associates cellular phenotype and responses to therapy



Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise, individualized treatment strategies are needed. To that end, we analyzed gene expression profiles from 1,290 CRC tumors using consensus-based unsupervised clustering. The resultant clusters were then associated with therapeutic response data to the epidermal growth factor receptor–targeted drug cetuximab in 80 patients. The results of these studies define six clinically relevant CRC subtypes. Each subtype shares similarities to distinct cell types within the normal colon crypt and shows differing degrees of 'stemness' and Wnt signaling. Subtype-specific gene signatures are proposed to identify these subtypes. Three subtypes have markedly better disease-free survival (DFS) after surgical resection, suggesting these patients might be spared from the adverse effects of chemotherapy when they have localized disease. One of these three subtypes, identified by filamin A expression, does not respond to cetuximab but may respond to cMET receptor tyrosine kinase inhibitors in the metastatic setting. Two other subtypes, with poor and intermediate DFS, associate with improved response to the chemotherapy regimen FOLFIRI1 in adjuvant or metastatic settings. Development of clinically deployable assays for these subtypes and of subtype-specific therapies may contribute to more effective management of this challenging disease.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



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

Figure 1: Classification of colorectal tumors and cell lines into subtypes.
Figure 2: Cellular phenotype and Wnt signaling in the CRC subtypes.
Figure 3: Differential sensitivity among CRC subtypes to cetuximab.
Figure 4: Specific response to chemotherapy in CRC subtypes.
Figure 5: Summary, including clinically deployable markers and potential subtype-guided therapies for CRC.

Accession codes


Gene Expression Omnibus


  1. Andre, T. et al. CPT-11 (irinotecan) addition to bimonthly, high-dose leucovorin and bolus and continuous-infusion 5-fluorouracil (FOLFIRI) for pretreated metastatic colorectal cancer. GERCOR. Eur. J. Cancer 35, 1343–1347 (1999).

    Article  CAS  Google Scholar 

  2. Verhaak, R.G. et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98–110 (2010).

    Article  CAS  Google Scholar 

  3. Markert, E.K., Mizuno, H., Vazquez, A. & Levine, A.J. Molecular classification of prostate cancer using curated expression signatures. Proc. Natl. Acad. Sci. USA 108, 21276–21281 (2011).

    Article  CAS  Google Scholar 

  4. Perou, C.M. et al. Molecular portraits of human breast tumours. Nature 406, 747–752 (2000).

    Article  CAS  Google Scholar 

  5. Collisson, E.A. et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat. Med. 17, 500–503 (2011).

    Article  CAS  Google Scholar 

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

  7. Alizadeh, A.A. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000).

    Article  CAS  Google Scholar 

  8. Tothill, R.W. et al. Novel molecular subtypes of serous and endometrioid cancer linked to clinical outcome. Clin. Cancer Res. 14, 5198–5208 (2008).

    Article  CAS  Google Scholar 

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

  10. 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  Google Scholar 

  11. 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  Google Scholar 

  12. Jorissen, R.N. et al. DNA copy-number alterations underlie gene expression differences between microsatellite stable and unstable colorectal cancers. Clin. Cancer Res. 14, 8061–8069 (2008).

    Article  CAS  Google Scholar 

  13. Jorissen, R.N. et al. Metastasis-associated gene expression changes predict poor outcomes in patients with Dukes stage B and C colorectal cancer. Clin. Cancer Res. 15, 7642–7651 (2009).

    Article  CAS  Google Scholar 

  14. Benito, M. et al. Adjustment of systematic microarray data biases. Bioinformatics 20, 105–114 (2004).

    Article  CAS  Google Scholar 

  15. Rousseeuw, P.J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).

    Article  Google Scholar 

  16. 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  Google Scholar 

  17. 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  Google Scholar 

  18. Dalerba, P. et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat. Biotechnol. 29, 1120–1127 (2011).

    Article  CAS  Google Scholar 

  19. Greshock, J. et al. Molecular target class is predictive of in vitro response profile. Cancer Res. 70, 3677–3686 (2010).

    Article  CAS  Google Scholar 

  20. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    Article  CAS  Google Scholar 

  21. Van Cutsem, E. & Oliveira, J. Primary colon cancer: ESMO clinical recommendations for diagnosis, adjuvant treatment and follow-up. Ann. Oncol. 20 (suppl. 4), 49–50 (2009).

    PubMed  Google Scholar 

  22. Banerjea, A. et al. Colorectal cancers with microsatellite instability display mRNA expression signatures characteristic of increased immunogenicity. Mol. Cancer 3, 21 (2004).

    Article  Google Scholar 

  23. Hoshida, Y. Nearest template prediction: a single-sample-based flexible class prediction with confidence assessment. PLoS ONE 5, e15543 (2010).

    Article  Google Scholar 

  24. Humphries, A. & Wright, N.A. Colonic crypt organization and tumorigenesis. Nat. Rev. Cancer 8, 415–424 (2008).

    Article  CAS  Google Scholar 

  25. Shih, I.M. et al. Top-down morphogenesis of colorectal tumors. Proc. Natl. Acad. Sci. USA 98, 2640–2645 (2001).

    Article  CAS  Google Scholar 

  26. Schwitalla, S. et al. Intestinal tumorigenesis initiated by dedifferentiation and acquisition of stem-cell-like properties. Cell 152, 25–38 (2013).

    Article  CAS  Google Scholar 

  27. 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  CAS  Google Scholar 

  28. Vermeulen, L. et al. Wnt activity defines colon cancer stem cells and is regulated by the microenvironment. Nat. Cell Biol. 12, 468–476 (2010).

    Article  CAS  Google Scholar 

  29. Cunningham, D. et al. Cetuximab monotherapy and cetuximab plus irinotecan in irinotecan-refractory metastatic colorectal cancer. N. Engl. J. Med. 351, 337–345 (2004).

    Article  CAS  Google Scholar 

  30. De Roock, W. et al. KRAS wild-type state predicts survival and is associated to early radiological response in metastatic colorectal cancer treated with cetuximab. Ann. Oncol. 19, 508–515 (2008).

    Article  CAS  Google Scholar 

  31. Ogino, S. et al. KRAS mutation in stage III colon cancer and clinical outcome following intergroup trial CALGB 89803. Clin. Cancer Res. 15, 7322–7329 (2009).

    Article  CAS  Google Scholar 

  32. Khambata-Ford, S. et al. Expression of epiregulin and amphiregulin and K-ras mutation status predict disease control in metastatic colorectal cancer patients treated with cetuximab. J. Clin. Oncol. 25, 3230–3237 (2007).

    Article  CAS  Google Scholar 

  33. Zhou, A.X. et al. Filamin a mediates HGF/c-MET signaling in tumor cell migration. Int. J. Cancer 128, 839–846 (2011).

    Article  CAS  Google Scholar 

  34. Del Rio, M. et al. Gene expression signature in advanced colorectal cancer patients select drugs and response for the use of leucovorin, fluorouracil, and irinotecan. J. Clin. Oncol. 25, 773–780 (2007).

    Article  CAS  Google Scholar 

  35. Graudens, E. et al. Deciphering cellular states of innate tumor drug responses. Genome Biol. 7, R19 (2006).

    Article  Google Scholar 

  36. Heiser, L.M. et al. Subtype and pathway specific responses to anticancer compounds in breast cancer. Proc. Natl. Acad. Sci. USA 109, 2724–2729 (2012).

    Article  CAS  Google Scholar 

  37. 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  Google Scholar 

  38. Pao, W. & Girard, N. New driver mutations in non-small-cell lung cancer. Lancet Oncol. 12, 175–180 (2011).

    Article  CAS  Google Scholar 

Download references


We thank P. Schulz (Charité, Universitätsmedizin) for providing RNA from xenograft tumors and for comments of the manuscript. We thank R.A. Du Pasquier (CHUV) for providing the HT29 cell line, P. Depeille (University of California–San Francisco) for the SW480, SW48, HCT8, LS174T and SW948 cell lines, and H. Ying (MD Anderson Medical Center) for the NCI-H508, LS1034, SW620, COLO320, SW1417, HCT116, RKO and DLD1 cell lines. The TOP/FOP-flash and Renilla constructs were a generous gift from S. Kobayashi (Beth Israel Deaconess Medical Center). We particularly acknowledge G. Poulogiannis for insightful feedback and assistance with statistical analysis of survival data. We also thank C. Fuerer, S.S. Sidhu, J. Yun and N. Divorne-Formenton for advice on the experimental design, C.R. Thomas for help with editing the manuscript, the Histology Core Facility of EPFL for help with immunohistochemistry. A.S. was partially supported by a US Department of Defense Postdoctoral Fellowship (BC087768). C.A.L. is the Amgen Fellow of the Damon Runyon Cancer Research Foundation (DRG-2056-10). J.W.G. is supported by the US National Institutes of Health grant U54 CA 112970 and by the Stand Up To Cancer–AACR Dream Team Translational Cancer Research Grant SU2C-AACR-DT0409. This work was supported by a Swiss National Science Foundation project grant awarded to D.H.

Author information

Authors and Affiliations



A.S. conceived of the hypothesis, designed and performed experiments, interpreted results and co-wrote the manuscript. C.A.L., K.H., S.W., L.C.G.O., W.A.L. and C.G. performed experiments. M.D.R. provided CRC microarray data with FOLFIRI response data. B.L. provided pathology expertise, and A.B.O. provided statistical expertise. C.A.L., K.H., E.A.C., W.J.G., L.C.C. and B.W. participated in critical discussions and helped edit the manuscript. J.W.G. interpreted results, helped edit the manuscript, and co-supervised the project. D.H. co-supervised the project, interpreted results and co-wrote the manuscript.

Corresponding authors

Correspondence to Joe W Gray or Douglas Hanahan.

Ethics declarations

Competing interests

A.S., C.A.L., J.W.G. and D.H. have filed a priority patent application (PCT/IB2012/056728) entitled "Colorectal cancer classification with differential prognosis and personalized therapeutic responses" at the EPFL Technology Transfer Office, and the priority patent is on the CRCassigner signatures and their use for decision making for colorectal cancer therapy. W.J.G. is an employee of Genomic Health. L.C.C. owns equity in, receives compensation from Agios Pharmaceuticals, and serves on the Board of Directors and Scientific Advisory Board of Agios Pharmaceuticals. Agios Pharmaceuticals is identifying metabolic pathways of cancer cells and developing drugs to inhibit such enzymes in order to disrupt tumor cell growth and survival.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8, Supplementary Discussion and Supplementary Methods (PDF 26895 kb)

Supplementary Table 1

Results from SAM and PAM analysis, the list of genes associated with each subtype and qRT-PCR and IHC assays (XLS 163 kb)

Supplementary Table 2

Summary of gene expression profile data sets used (XLS 274 kb)

Supplementary Table 3

Clinical/histopathological, subtype and statistical information for GSE14333 samples (XLS 88 kb)

Supplementary Table 4

Khambata-Ford data set liver genes and PCR primers (XLSX 54 kb)

Supplementary Data

R scripts (PDF 32 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing