The consensus molecular subtypes of colorectal cancer

Justin Guinney, Rodrigo Dienstmann, Xin Wang, Aurélien de Reyniès, Andreas Schlicker, Charlotte Soneson, Laetitia Marisa, Paul Roepman, Gift Nyamundanda, Paolo Angelino, Brian M Bot, Jeffrey S Morris, Iris M Simon, Sarah Gerster, Evelyn Fessler, Felipe De Sousa E Melo, Edoardo Missiaglia, Hena Ramay, David Barras, Krisztian Homicsko, Dipen Maru, Ganiraju C Manyam, Bradley Broom, Valerie Boige, Beatriz Perez-Villamil, Ted Laderas, Ramon Salazar, Joe W Gray, Douglas Hanahan, Josep Tabernero, Rene Bernards, Stephen H Friend, Pierre Laurent-Puig, Jan Paul Medema, Anguraj Sadanandam, Lodewyk Wessels, Mauro Delorenzi, Scott Kopetz, Louis Vermeulen, Sabine Tejpar, Justin Guinney, Rodrigo Dienstmann, Xin Wang, Aurélien de Reyniès, Andreas Schlicker, Charlotte Soneson, Laetitia Marisa, Paul Roepman, Gift Nyamundanda, Paolo Angelino, Brian M Bot, Jeffrey S Morris, Iris M Simon, Sarah Gerster, Evelyn Fessler, Felipe De Sousa E Melo, Edoardo Missiaglia, Hena Ramay, David Barras, Krisztian Homicsko, Dipen Maru, Ganiraju C Manyam, Bradley Broom, Valerie Boige, Beatriz Perez-Villamil, Ted Laderas, Ramon Salazar, Joe W Gray, Douglas Hanahan, Josep Tabernero, Rene Bernards, Stephen H Friend, Pierre Laurent-Puig, Jan Paul Medema, Anguraj Sadanandam, Lodewyk Wessels, Mauro Delorenzi, Scott Kopetz, Louis Vermeulen, Sabine Tejpar

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.

Figures

Figure 1. Analytical workflow of the Colorectal…
Figure 1. Analytical workflow of the Colorectal Cancer Subtyping Consortium
(a) Subtype classification on 18 shared data sets across six groups. (b) Concordance analysis of the six subtyping platforms, and application of a network analytical method to identify consensus subtype cluster. (c) Development of a consensus subtype classifier from an aggregated gene expression data set and the consensus subtype labels. (d) Biological and clinical characterization of the consensus subtypes.
Figure 2. Identification of the consensus subtypes…
Figure 2. Identification of the consensus subtypes of colorectal cancer and application of classification framework in non-consensus samples
(a) Network of CRC subtypes across six classification systems: each node corresponds to a single subtype (colored according to group) and edge width corresponds to Jaccard similarity coefficient. The four primary clusters – identified from the Markov cluster algorithm – are highlighted and correspond to the four CMS groups. (b) Per sample distribution of each of the six CRC subtyping systems (A–F), grouped by the four consensus subtyping clusters (n = 3,104), and the unlabeled non-consensus set of samples (n = 858). Colors within each row represent a different subtype. (c) Patient network: each node represents a single patient sample (n = 3,962). Network edges correspond to highly concordant (5/6 of 6) subtyping calls between samples. Nodes are colored according to their CMS, with non-consensus samples gray. (d) Final distribution of the CMS1–4 groups (solid colors), ‘mixed’ samples (gradient colors) or indeterminate samples (gray color) as per classification framework.
Figure 3. Molecular associations of consensus molecular…
Figure 3. Molecular associations of consensus molecular subtype groups
(a) Distribution of non–synonymous somatic mutation events; and (b) somatic copy-number alterations (SCNAs), defined as non-zero GISTIC scores in TCGA data set, across consensus subtype samples (median, lower [Q1] and upper [Q3] quartiles, horizontal lines define minimum and maximum, dots define outliers). (c) Key genomic and epigenomic markers, with darker brown representing positivity for SCNA high (≥Q1 for non–zero GISTIC score events), hypermutation (≥180 events in exome sequencing), microsatellite instability (MSI) high or CpG Island Methylator Phenotype (CIMP) cluster high. (d) Mutation profile, with darker gray representing positivity for KRAS, BRAF, APC and TP53 mutations. (e) Heatmap of copy number changes of the 22 autosomes, with shades of red for gains and blue for losses. CMS1 samples have fewer SCNAs and an intermediate pattern is seen in CMS3. (f) Heatmap representation of DNA methylation beta-values of most variable probes with yellow denoting high DNA methylation and blue low methylation. CMS1 samples show a distinguished hypermethylation profile and an intermediate pattern is seen in CMS3. (g) Heatmap of top differentially expressed proteins in TCGA colored with a gradient from blue (low expression) to yellow (high expression). (h) Heatmap of top differentially expressed microRNAs in TCGA with shades of blue for downregulation and orange for upregulation. (i) Gene set mRNA enrichment analysis: signatures of special interest in CRC, ESTIMATE algorithm to infer immune and stromal cell admixture in tumor samples, canonical pathways, immune signatures and metabolic pathways. (j) Gene set enrichment analysis of proteomic TCGA data. Detailed statistics in Supplementary Tables 5, 8, 9 and 11.
Figure 4. Clinicopathological and prognostic associations of…
Figure 4. Clinicopathological and prognostic associations of consensus molecular subtype groups
(a) Distribution of gender; (b) Tumor site location; (c) Stage at diagnosis; and (d) Histopathological grade across consensus subtype samples. Prognostic value of CMS groups with Kaplan-Meier survival analysis in the aggregated cohort for (e) overall survival, (f) relapse-free survival and (g) survival after relapse with hazard ratios (HR) and 95% Confidence Interval (CI) for significant pairwise comparisons in univariate analyses (log-rank test). Numbers below the x axes represent patients at risk at selected time points. Detailed statistics in Supplementary Tables 5 and 13.
Figure 5. Proposed taxonomy of colorectal cancer…
Figure 5. Proposed taxonomy of colorectal cancer reflecting significant biological differences in the gene expression-based molecular subtypes
CIMP, CpG Island Methylator Phenotype; MSI, microsatellite instability; SCNA, somatic copy number alterations; TGF, transforming growth factor.

References

    1. Hoadley KA, et al. Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin. Cell. 2014;158:929–944.
    1. Muzny DM, et al. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012;487:330–337.
    1. Roepman P, et al. Colorectal cancer intrinsic subtypes predict chemotherapy benefit, deficient mismatch repair and epithelial-to-mesenchymal transition. Int. J. Cancer. 2014;134:552–562.
    1. Budinska E, et al. Gene expression patterns unveil a new level of molecular heterogeneity in colorectal cancer. J. Pathol. 2013;231:63–76.
    1. Schlicker A, et al. Subtypes of primary colorectal tumors correlate with response to targeted treatment in colorectal cell lines. BMC Med. Genomics. 2012;5:66.
    1. Sadanandam A, et al. A colorectal cancer classification system that associates cellular phenotype and responses to therapy. Nat. Med. 2013;19:619–625.
    1. 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. 2013;19:614–618.
    1. Marisa L, et al. Gene expression classification of colon cancer into molecular subtypes: characterization, validation, and prognostic value. PLoS Med. 2013;10:e1001453.
    1. 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. 2012;12:260.
    1. 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. 2009;27:3117–3125.
    1. Van Dongen S. Graph Clustering Via a Discrete Uncoupling Process. SIAM J. Matrix Anal. Appl. 2008;30:121–141.
    1. Enright AJ, Van Dongen S, Ouzounis CA. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 2002;30:1575–1584.
    1. Llosa NJ, et al. The vigorous immune microenvironment of microsatellite instable colon cancer is balanced by multiple counter-inhibitory checkpoints. Cancer Discov. 2015;5:43–51.
    1. 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. 2014;5:4722–4731.
    1. Son J, et al. Glutamine supports pancreatic cancer growth through a KRAS-regulated metabolic pathway. Nature. 2013;496:101–105.
    1. Kamphorst JJ, et al. Hypoxic and Ras-transformed cells support growth by scavenging unsaturated fatty acids from lysophospholipids. Proc. Natl. Acad. Sci. U. S. A. 2013;110:8882–8887.
    1. Ying H, et al. Oncogenic Kras maintains pancreatic tumors through regulation of anabolic glucose metabolism. Cell. 2012;149:656–670.
    1. Lei Z, et al. Identification of molecular subtypes of gastric cancer with different responses to PI3-kinase inhibitors and 5-fluorouracil. Gastroenterology. 2013;145:554–565.
    1. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature. 2014;513:202–209.
    1. Carter SL, et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 2012;30:413–421.
    1. Zhang B, et al. Proteogenomic characterization of human colon and rectal cancer. Nature. 2014 at < >.
    1. Li Y, Choi PS, Casey SC, Dill DL, Felsher DW. MYC through miR-17-92 suppresses specific target genes to maintain survival, autonomous proliferation, and a neoplastic state. Cancer Cell. 2014;26:262–272.
    1. Park S-M, Gaur AB, Lengyel E, Peter ME. The miR-200 family determines the epithelial phenotype of cancer cells by targeting the E-cadherin repressors ZEB1 and ZEB2. Genes Dev. 2008;22:894–907.
    1. Carmona FJ, et al. A Comprehensive DNA Methylation Profile of Epithelial-to-Mesenchymal Transition. Cancer Res. 2014;74:5608–5619.
    1. Tran B, et al. Impact of BRAF mutation and microsatellite instability on the pattern of metastatic spread and prognosis in metastatic colorectal cancer. Cancer. 2011;117:4623–4632.
    1. Gavin PG, 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. 2012;18:6531–6541.
    1. Popovici V, et al. Context-dependent interpretation of the prognostic value of BRAF and KRAS mutations in colorectal cancer. BMC Cancer. 2013;13:439.
    1. Sinicrope FA, et al. Molecular markers identify subtypes of stage III colon cancer associated with patient outcomes. Gastroenterology. 2015;148:88–99.
    1. Le DT, et al. PD-1 Blockade in Tumors with Mismatch-Repair Deficiency. N. Engl. J. Med. 2015;372:2509–2520.
    1. Yoshihara K, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 2013;4:2612.
    1. Derry JMJ, et al. Developing predictive molecular maps of human disease through community-based modeling. Nat. Genet. 2012;44:127–130.
    1. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–127.
    1. McCall MN, Bolstad BM, Irizarry RA. Frozen robust multiarray analysis (fRMA) Biostatistics. 2010;11:242–253.
    1. Zilliox MJ, Irizarry RA. A gene expression bar code for microarray data. Nat. Methods. 2007;4:911–913.
    1. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U. S. A. 2001;98:5116–5121.
    1. Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc. Natl. Acad. Sci. U. S. A. 2002;99:6567–6572.
    1. Brunet J-P, Tamayo P, Golub TR, Mesirov JP. Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl. Acad. Sci. U. S. A. 2004;101:4164–4169.
    1. Marron JS, Todd MJ, Ahn J. Distance-Weighted Discrimination. J. Am. Stat. Assoc. 2007;102:1267–1271.
    1. Gautier L, Cope L, Bolstad BM, Irizarry RA. affy--analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004;20:307–315.
    1. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323.
    1. Kauffmann A, Gentleman R, Huber W. arrayQualityMetrics--a bioconductor package for quality assessment of microarray data. Bioinformatics. 2009;25:415–416.
    1. 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. 2012
    1. Breiman L. Random forest. Mach. Learn. 2001;45:5–32.
    1. Chen X, Ishwaran H. Random forests for genomic data analysis. Genomics. 2012;99:323–329.
    1. Murray JS, Dunson DB, Carin L, Lucas JE. Bayesian Gaussian Copula Factor Models for Mixed Data. J. Am. Stat. Assoc. 2013;108:656–665.
    1. Ghosh J, Dunson DB. Default Prior Distributions and Efficient Posterior Computation in Bayesian Factor Analysis. J. Comput. Graph. Stat. 2009;18:306–320.
    1. Tamborero D, et al. Comprehensive identification of mutational cancer driver genes across 12 tumor types. Sci. Rep. 2013;3:2650.
    1. Umar A, et al. Revised Bethesda Guidelines for hereditary nonpolyposis colorectal cancer (Lynch syndrome) and microsatellite instability. J. Natl. Cancer Inst. 2004;96:261–268.
    1. Lindor NM, et al. Immunohistochemistry versus microsatellite instability testing in phenotyping colorectal tumors. J. Clin. Oncol. 2002;20:1043–1048.
    1. Weisenberger DJ, et al. CpG island methylator phenotype underlies sporadic microsatellite instability and is tightly associated with BRAF mutation in colorectal cancer. Nat. Genet. 2006;38:787–793.
    1. 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. U. S. A. 2007;104:15418–15423.
    1. Van der Flier LG, et al. The Intestinal Wnt/TCF Signature. Gastroenterology. 2007;132:628–632.
    1. Zeller KI, Jegga AG, Aronow BJ, O’Donnell KA, Dang CV. An integrated database of genes responsive to the Myc oncogenic transcription factor: identification of direct genomic targets. Genome Biol. 2003;4:R69.
    1. Loboda A, et al. EMT is the dominant program in human colon cancer. BMC Med. Genomics. 2011;4:9.
    1. Merlos-Suárez A, et al. The intestinal stem cell signature identifies colorectal cancer stem cells and predicts disease relapse. Cell Stem Cell. 2011;8:511–524.
    1. Mlecnik B, et al. Biomolecular network reconstruction identifies T-cell homing factors associated with survival in colorectal cancer. Gastroenterology. 2010;138:1429–1440.
    1. 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. 2011;71:1263–1271.
    1. Galon J, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 2006;313:1960–1964.
    1. Ascierto ML, et al. Molecular signatures mostly associated with NK cells are predictive of relapse free survival in breast cancer patients. J. Transl. Med. 2013;11:145.
    1. Gu-Trantien C, et al. CD4+ follicular helper T cell infiltration predicts breast cancer survival. J. Clin. Invest. 2013;123:2873–2892.
    1. Keerthivasan S, et al. β-Catenin promotes colitis and colon cancer through imprinting of proinflammatory properties in T cells. Sci. Transl. Med. 2014;6:225ra28.
    1. Stockis J, et al. Comparison of stable human Treg and Th clones by transcriptional profiling. Eur. J. Immunol. 2009;39:869–882.
    1. Fridlender ZG, et al. Transcriptomic analysis comparing tumor-associated neutrophils with granulocytic myeloid-derived suppressor cells and normal neutrophils. PLoS One. 2012;7:e31524.
    1. Efron B, Tibshirani R. On testing the significance of sets of genes. Ann. Appl. Stat. 2007;1:107–129.
    1. Wang L, et al. MiR-143 acts as a tumor suppressor by targeting N-RAS and enhances temozolomide-induced apoptosis in glioma. Oncotarget. 2014;5:5416–5427.
    1. Johnson SM, et al. RAS is regulated by the let-7 microRNA family. Cell. 2005;120:635–647.
    1. Kim T, et al. p53 regulates epithelial-mesenchymal transition through microRNAs targeting ZEB1 and ZEB2. J. Exp. Med. 2011;208:875–883.
    1. Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005;120:15–20.
    1. Gentleman RC, et al. Bioconductor: open software development for computational biology and bioinformatics. Bioconductor. 2004;5:R80.

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe