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Comprehensive molecular characterization of human colon and rectal cancer

Comprehensive molecular characterization of human colon and rectal cancer ARTICLE doi:10.1038/nature11252 Comprehensivemolecularcharacterization of human colon and rectal cancer The Cancer Genome Atlas Network* To characterize somatic alterations in colorectal carcinoma, we conducted a genome-scale analysis of 276 samples, analysing exome sequence, DNA copy number, promoter methylation and messenger RNA and microRNA expression. A subset of these samples (97) underwent low-depth-of-coverage whole-genome sequencing. In total, 16% of colorectal carcinomas were found to be hypermutated: three-quarters of these had the expected high microsatellite instability, usually with hypermethylation and MLH1 silencing, and one-quarter had somatic mismatch-repair gene and polymerase e (POLE) mutations. Excluding the hypermutated cancers, colon and rectum cancers were found to have considerably similar patterns of genomic alteration. Twenty-four genes were significantly mutated, and in addition to the expected APC, TP53, SMAD4, PIK3CA and KRAS mutations, we found frequent mutations in ARID1A, SOX9 and FAM123B. Recurrent copy-number alterations include potentially drug-targetable amplifications of ERBB2 and newly discovered amplification of IGF2. Recurrent chromosomal translocations include the fusion of NAV2 and WNT pathway member TCF7L1. Integrative analyses suggest new markers for aggressive colorectal carcinoma and an important role for MYC-directed transcriptional activation and repression. 6 6 The Cancer Genome Atlas project plans to profile genomic changes in ,1 per 10 bases, whereas a few had mutations rates of .100 per 10 . 20 different cancer types and has so far published results on two We separated cases (84%) with a mutation rate of ,8.24 per 10 1,2 cancer types . We now present results from multidimensional (median number of non-silent mutations, 58) and those with muta- analyses of human colorectal carcinoma (CRC). tion rates of .12 per 10 (median number of total mutations, 728), CRC is an important contributor to cancer mortality and morbidity. which we designated as hypermutated (Fig. 1). The distinction between the colon and the rectum is largely anatomical, To assess the basis for the considerably different mutation rates, we 7 8–10 but it has both surgical and radiotherapeutic management implications evaluated MSI and mutations in the DNA mismatch-repair pathway and it may have an impact on prognosis. Most investigators divide genes MLH1, MLH3, MSH2, MSH3, MSH6 and PMS2. Among the 30 CRC biologically into those with microsatellite instability (MSI; located hypermutated tumours with a complete data set, 23 (77%) had high primarily in the right colon and frequently associated with the CpG levels of MSI (MSI-H). Included in this group were 19 tumours with island methylator phenotype (CIMP) and hyper-mutation) and those MLH1 methylation, 17 of which had CIMP. By comparison, the that are microsatellite stable but chromosomally unstable. remaining seven hypermutated tumours, including the six with the A rich history of investigations (for a review see ref. 3) has uncovered highest mutation rates, lacked MSI-H, CIMP or MLH1 methylation several critical genes and pathways important in the initiation and but usually had somatic mutations in one or more mismatch-repair progression of CRC (ref. 3). These include the WNT, RAS2MAPK, genes or POLE aberrations seen rarely in the non-hypermutated PI3K, TGF-b, P53 and DNA mismatch-repair pathways. Large-scale tumours (Fig. 1). 4–6 sequencing analyses have identified numerous recurrently mutated genes and a recurrent chromosomal translocation. Despite this back- Gene mutations ground, we have not had a fully integrated view of the genetic and Overall, we identified 32 somatic recurrently mutated genes (defined by MutSig and manual curation) in the hypermutated and non- genomic changes and their significance for colorectal tumorigenesis. Further insight into these changes may enable deeper understanding of hypermutated cancers (Fig. 1b). After removal of non-expressed genes, the pathophysiology of CRC and may identify potential therapeutic there were 15 and 17 in the hypermutated and non-hypermutated cancers, respectively (Fig. 1b; for a complete list see Supplementary targets. Table 3). Among the non-hypermutated tumours, the eight most fre- Results quently mutated genes were APC, TP53, KRAS, PIK3CA, FBXW7, SMAD4, TCF7L2 and NRAS. As expected, the mutated KRAS and Tumour and normal pairs were analysed by different platforms. The NRAS genes usually had oncogenic codon 12 and 13 or codon 61 specific numbers of samples analysed by each platform are shown in mutations, whereas the remaining genes had inactivating mutations. Supplementary Table 1. CTNNB1, SMAD2, FAM123B (also known as WTX)and SOX9 were Exome-sequence analysis also mutated frequently. FAM123B is an X-linked negative regulator of WNT signalling , and virtually all of its mutations were loss of func- To define the mutational spectrum, we performed exome capture DNA sequencing on 224 tumour and normal pairs (all mutations tion. Mutations in SOX9, a gene important for cell differentiation in the 13,14 are listed in Supplementary Table 2). Sequencing achieved .20-fold intestinal stem cell niche , have not been associated previously with coverage of at least 80% of targeted exons. The somatic mutation rates human cancer, but all nine mutated alleles in the non-hypermutated varied considerably among the samples. Some had mutation rates of CRCs were frameshift or nonsense mutations. Tumour-suppressor Lists of participants and their affiliations appear at the end of the paper. 330| NATURE |VOL487 |19JULY2012 ©2012 Macmillan Publishers Limited. All rights reserved ARTICLE RESEARCH a 500 cancers were significantly less frequently mutated in hypermutated tumours: TP53 (60 versus 20%, P, 0.0001) and APC (81% versus MLH1 51%, P5 0.0023; both Fisher’s exact test). Other genes, including MLH3 MSH2 TGFBR2, were mutated recurrently in the hypermutated cancers, MSH3 but not in the non-hypermutated samples. These findings indicate MSH6 PMS2 that hypermutated and non-hypermutated tumours progress through POLE different sequences of genetic events. Epigenetic Frameshift Missense/nonsense silencing mutation mutation As expected, hypermutated tumours with MLH1 silencing and MSI-H showed additional differences in the mutational profile. When we specifically examined 28 genes with long mononucleotide repeats in their coding sequences, we found that the rate of frameshift mutation was 3.6-fold higher than the rate of such mutations in Non-silent hypermutated tumours without MLH1 silencing and 50-fold higher Hypermutated Non-hypermutated Silent 0.1 than that in non-hypermethylated tumours (Supplementary Table 2). Tumour site MSI status CIMP status MLH1 silencing Mutation rate and methylation patterns As mentioned above, patients with colon and rectal tumours are b Hypermutated tumours Non-hypermutated tumours 17 managed differently , and epidemiology also highlights differences between the two . An initial integrative analysis of MSI status, somatic copy-number alterations (SCNAs), CIMP status and gene- expression profiles of 132 colonic and 62 rectal tumours enabled us to examine possible biological differences between tumours in the two locations. Among the non-hypermutated tumours, however, the overall patterns of changes in copy number, CIMP, mRNA and miRNA were indistinguishable between colon and rectal carcinomas (Fig. 2). On the basis of this result, we merged the two for all subsequent analyses. Figure 1 | Mutation frequencies in human CRC. a, Mutation frequencies in Unsupervised clustering of the promoter DNA methylation each of the tumour samples from 224 patients. Note a clear separation of profiles of 236 colorectal tumours identified four subgroups (Sup- hypermutated and non-hypermutated samples. Red, MSI high, CIMP high or plementary Fig. 1 and Supplementary Methods). Two of the clusters MLH1 silenced; light blue, MSI low, or CIMP low; black, rectum; white, colon; grey, no data. Inset, mutations in mismatch-repair genes and POLE among the contained tumours with elevated rates of methylation and were hypermutated samples. The order of the samples is the same as in the main classified as CIMP high and CIMP low, as previously described . graph. b, Significantly mutated genes in hypermutated and non-hypermutated The two non-CIMP clusters were predominantly from tumours that tumours. Blue bars represent genes identified by the MutSig algorithm and were non-hypermutated and derived from different anatomic loca- black bars represent genes identified by manual examination of sequence data. tions. mRNA expression profiles separated the colorectal tumours into three distinct clusters (Supplementary Fig. 2). One significantly genes ATM and ARID1A also had a disproportionately high number of frameshift or nonsense mutations. ARID1A mutations have recently overlapped with CIMP-high tumours (P5 33 10 ) and was 15,16 enriched with hypermutated tumours, and the other two clusters been reported in CRC and many other cancers . did not correspond with any group in the methylation data. In the hypermutated tumours, ACVR2A, APC, TGFBR2, MSH3, MSH6, SLC9A9 and TCF7L2 were frequent targets of mutation Analysis of miRNA expression by unsupervised clustering (Supplemen- (Fig. 1b), along with mostly BRAF(V600E) mutations. However, tary Fig. 3) identified no clear distinctions between rectal cancers and two genes that were frequently mutated in the non-hypermutated non-hypermethylated colon cancers. Chromosome 12 3 4 56 78 9 10 11 12 13 14 15 16 171819202122 Tumour site BRAF(V600E) Methylation cluster mRNA cluster Right colon Transverse Left colon Rectum Yes No CIMP-H CIMP-L Cluster 3 Cluster 4 MSI/CIMP Invasive CIN Figure 2 | Integrative analysis of genomic changes in 195 CRCs. indistinguishable from each other on the basis of their copy-number alteration Hypermutated tumours have near-diploid genomes and are highly enriched for patterns, DNA methylation or gene-expression patterns. Copy-number hypermethylation, CIMP expression phenotype and BRAF(V600E) mutations. changes of the 22 autosomes are shown in shades of red for copy-number gains Non-hypermutated tumours originating from different sites are virtually and shades of blue for copy-number losses. 1 9 JU LY 20 12 | V O L 48 7 | NATU R E | 3 31 ©2012 Macmillan Publishers Limited. All rights reserved ACVR2A APC TGFBR2 BRAF MSH3 MSH6 MYO1B TCF7L2 CASP8 CDC27 FZD3 MIER3 TCERG1 MAP7 PTPN12 APC TP53 KRAS TTN PIK3CA FBXW7 SMAD4 NRAS TCF7L2 FAM123B SMAD2 CTNNB1 KIAA1804 SOX9 ACVR1B GPC6 EDNRB Tumour site BRAF (V600E) Meth. cluster mRNA cluster Mutation frequency (%) 0 20 40 60 80 63% Mutation rate (mutations per 10 bases) 51% 51% 46% 40% 40% 31% 31% 29% 29% Hyper- Non-hypermutated 29% mutated 29% 29% 26% 26% 81% 60% 43% 31% 18% 11% 10% 9% 9% 7% 6% 5% 4% 4% 4% 4% 3% Y 15 15 RESEARCH ARTICLE Chromosomal and sub-chromosomal changes candidate oncogene CDK8; an adjacent peak at 13q12; a peak contain- ing KLF5 at 13q22.1; and a peak at 20q13.12 adjacent to HNF4A. In total, 257 tumours were profiled for SCNAs with Affymetrix SNP 6.0 arrays. Of these tumours, 97 were also analysed by low-depth-of- Peaks on chromosome 8 included 8p12 (which contains the histone coverage (low-pass) whole-genome sequencing. As expected, the methyl-transferase-coding gene WHSC1L1, adjacent to FGFR1) and hypermutated tumours had far fewer SCNAs (Fig. 2). No difference 8q24 (which contains MYC). An amplicon at 17q21.1, found in 4% of was found between microsatellite-stable and -unstable hypermutated the tumours, contains seven genes, including the tyrosine kinase tumours (Supplementary Fig. 4). We used the GISTIC algorithm to ERBB2. ERBB2 amplifications have been described in colon, breast identify probable gene targets of focal alterations. There were several and gastro–oesophageal tumours, and breast and gastric cancers bear- previously well-defined arm-level changes, including gains of 1q, 7p ing these amplifications have been treated effectively with the anti- 20–22 and q, 8p and q, 12q, 13q, 19q, and 20p and q (ref. 6). (Supplementary ERBB2 antibody trastuzumab . Fig. 4 and Supplementary Table 4). Significantly deleted chromosome One of the most common focal amplifications, found in 7% of the arms were 18p and q (including SMAD4) in 66% of the tumours and tumours, is the gain of a 100–150-kb region of the chromosome arm 17p and q (including TP53) in 56%. Other significantly deleted chro- 11p15.5. It contains genes encoding insulin (INS), insulin-like growth mosome arms were 1p, 4q, 5q, 8p, 14q, 15q, 20p and 22q. factor 2 (IGF2) and tyrosine hydroxylase (TH), as well as miR-483, We identified 28 recurrent deletion peaks (Supplementary Fig. 4 which is embedded within IGF2 (Fig. 3a). We found elevated expres- sion of IGF2 and miR-483 but not of INS and TH (Fig. 3b, c). and Supplementary Table 4), including the genes FHIT, RBFOX1 and WWOX with large genomic footprints located in potentially fragile Immediately adjacent to the amplified region is ASCL2, a transcrip- sites of the genome, in near-diploid hypermutated tumours. Other tion factor active in specifying intestinal stem-cell fate . Although 23–25 focal deletions involved tumour-suppressor genes such as SMAD4, ASCL2 has been implicated as a target of amplification in CRC , APC, PTEN and SMAD3. A significant focal deletion of 10p25.2 it was consistently outside the region of amplification and its expres- spanned four genes, including TCF7L2, which was also frequently sion was not correlated with copy-number changes. These observa- mutated in our data set. A gene fusion between adjacent genes tions suggest that IGF2 and miR-483 are candidate functional targets VTI1A and TCF7L2 through an interstitial deletion was found in of 11p15.5 amplification. IGF2 overexpression through loss of 26, 27 3% of CRCs and is required for survival of CRC cells bearing the imprinting has been implicated in the promotion of CRC . MiR- 4 28 translocation . 483 may also have a role in CRC pathogenesis . There were 17 regions of significant focal amplification (Supplemen- A subset of tumours without IGF2 amplification (15%) also had tary Table 4). Some of these were superimposed on broad gains of considerably higher levels of IGF2 gene expression (as much as a chromosome arms, and included a peak at 13q12.13 near the 100-fold increase), an effect not attributable to methylation changes peptidase-coding gene USP12 and at ,500 kb distal to the CRC at the IGF2 promoter. To assess the context of IGF2 amplification/ IGF2 miR-483 IGF2 TH ASCL2 11p15 miR-483 INS 2.05 Mb 2.30 Mb No Broad Focal No Broad Focal ampl. ampl. ampl. ampl. ampl. ampl. 86 altered samples (of 165 non-hypermutated samples) IGF2 Upregulation miR-483 IRS2 Homozygous deletion PIK3CA PIK3R1 Mutation PTEN d NAV2 TCF7L1 1 192 391 411 2149 2303 1 250 346 414 589 Calponin Actin binding Na-channel 4 AAA+ ATPase core CTNNB1 binding HMG 1/2 box NAV2 TCF7L1 TCGA-AG-A01N TCGA-AA-A00U TCGA-AG-A011 Figure 3 | Copy-number changes and structural aberrations in CRC. exclusive of alterations in PI3K signalling-related genes. d, Recurrent NAV2– a, Focal amplification of 11p15.5. Segmented DNA copy-number data from TCF7L2 fusions. The structure of the two genes, locations of the breakpoints single-nucleotide polymorphism (SNP) arrays and low-pass whole-genome leading to the translocation and circular representations of all rearrangements sequencing (WGS) are shown. Each row represents a patient; amplified regions in tumours with a fusion are shown. Red line lines represent the NAV2–TCF7L2 are shown in red. b, Correlation of expression levels with copy-number changes fusions and black lines represent other rearrangements. The inner ring for IGF2 and miR-483. c, IGF2 amplification and overexpression are mutually represents copy-number changes (blue denotes loss, pink denotes gain). 3 3 2 | NATU R E | V OL 4 8 7 | 1 9 JU LY 20 12 ©2012 Macmillan Publishers Limited. All rights reserved WGS SNP array mRNA expression –4 –2 0 2 4 Expression 048 HM HM HM HM HM ARTICLE RESEARCH overexpression, we systematically searched for mutually exclusive cases the fusions predict inactivation of TTC28, which has been iden- 29 30 genomic events using the MEMo method . We found a pattern of tified as a target of P53 and an inhibitor of tumour cell growth . near exclusivity (corrected P, 0.01) of IGF2 overexpression with Eleven of the 19 (58%) gene–gene translocations were validated by genomic events known to activate the PI3K pathway (mutations of obtaining PCR products or, in some cases, sequencing the junction fragments (Supplementary Fig. 5). PIK3CA and PIK3R1 or deletion/mutation of PTEN; Fig. 3c and Supplementary Table 5). The IRS2 gene, encoding a protein linking Altered pathways in CRC IGF1R (the receptor for IGF2) with PI3K, is on chromosome 13, which is frequently gained in CRC. The cases with the highest IRS2 expression Integrated analysis of mutations, copy number and mRNA expression changes in 195 tumours with complete data enriched our understand- were mutually exclusive of the cases with IGF2 overexpression (P5 0.04) and also lacked mutations in the PI3K pathway ing of how some well-defined pathways are deregulated. We grouped (P5 0.0001; Fig. 3c). These results strongly suggest that the IGF2– samples by hypermutation status and identified recurrent alterations IGF1R–IRS2 axis signals to PI3K in CRC and imply that therapeutic in the WNT, MAPK, PI3K, TGF-b and p53 pathways (Fig. 4, targeting of the pathway could act to block PI3K activity in this subset Supplementary Fig. 6 and Supplementary Table 1). of patients. We found that the WNT signalling pathway was altered in 93% of all tumours, including biallelic inactivation of APC (Supplementary Translocations Table 7) or activating mutations of CTNNB1 in ,80% of cases. There To identify new chromosomal translocations, we performed low-pass, were also mutations in SOX9 and mutations and deletions in TCF7L2, paired-end, whole-genome sequencing on 97 tumours with matched as well as the DKK family members and AXIN2, FBXW7 (Supplemen- normal samples. In each case we achieved sequence coverage of tary Fig. 7), ARID1A and FAM123B (the latter is a negative regulator 12 31 ,3–4-fold and a corresponding physical coverage of 7.5–10-fold. of WNT–b-catenin signalling found mutated in Wilms’ tumour ). Despite the low genome coverage, we detected 250 candidate A few mutations in FAM123B have previously been described in interchromosomal translocation events (range, 0–10 per tumour). CRC . SOX9 has been suggested to have a role in cancer, but no Among these events, 212 had one or both breakpoints in an intergenic mutations have previously been described. The WNT receptor region, whereas the remaining 38 juxtaposed coding regions of two frizzled (FZD10) was overexpressed in ,17% of samples, in some genes in putative fusion events, of which 18 were predicted to code for instances at levels of 1003 normal. Altogether, we found 16 different in-frame events (Supplementary Table 6). We found three separate altered WNT pathway genes, confirming the importance of this cases in which the first two exons of the NAV2 gene on chromosome pathway in CRC. Interestingly, many of these alterations were found 11 are joined with the 39 coding portion of TCF7L1 on chromosome 2 in tumours that harbour APC mutations, suggesting that multiple (Supplementary Fig. 5). TCF7L1 encodes TCF3, a member of the lesions affecting the WNT signalling pathway confer selective advantage. TCF/LEF class of transcription factors that heterodimerize with Genetic alterations in the PI3K and RAS–MAPK pathways are nuclear b-catenin to enable b-catenin-mediated transcriptional regu- common in CRC. In addition to IGF2 and IRS2 overexpression, we lation. Intriguingly, in all three cases, the predicted structure of the found mutually exclusive mutations in PIK3R1 and PIK3CA as well as NAV2–TCF7L1 fusion protein lacks the TCF3 b-catenin-binding deletions in PTEN in 2%, 15% and 4% of non-hypermutated tumours, domain. This translocation is similar to another recurrent transloca- respectively. We found that 55% of non-hypermutated tumours have tion identified in CRC, a fusion in which the amino terminus of alterations in KRAS, NRAS or BRAF, with a significant pattern of VTI1A is joined to TCF4, which is encoded by TCF7L2, a homologue mutual exclusivity (Supplementary Fig. 6 and Supplementary Table 1). of TCF7L1 that is deleted or mutated in 12% of non-hypermutated We also evaluated mutations in the erythroblastic leukemia viral tumours . We also observed 21 cases of translocation involving oncogene homolog (ERBB) family of receptors because of the trans- TTC28 located on chromosome 22 (Supplementary Table 6). In all lational relevance of such mutations. Mutations or amplifications in 92% 97% 27% 87% WNT signalling TGF-β signalling PI3K signalling RTK–RAS signalling Altered IGF2 Altered 50% 53% 59% 80% 22% 0% WNT TGF-β Activin Altered Altered DKK1-4 4% 33% ERBB2 ERBB3 IGF1R LRP5 FZD10 TGFBR1 TGFBR2 ACVR2A ACVR1B 6% 13% 4% 20% <1% 10% 19% 13% <1% 17% 2% 43% 1% 60% 4% 20% IRS2 7% 3% KRAS SMAD3 NRAS SMAD2 FAM123B AXIN2 APC PTEN 10% 10% 43% 30% 6% 13% 2% 17% 7% 37% 4% 23% 81% 53% 4% 20% PIK3CA BRAF 15% 37% 3% 47% PIK3R1 CTNNB1 SMAD4 2% 17% 5% 7% 15% 20% Proliferation, cell survival, translation TCF7L2 FBXW7 ARID1A Nucleus 12% 30% 10% 43% 5% 37% P53 signalling 64% 47% DNA Altered ATM replication 7% 37% CTNNB1 5% 7% stress SOX9 Proliferation, stem/ MYC 4% 7% TP53 Proliferation Oncogenic progenitor phenotype TCF7 59% 17% Cell survival stress Frequency Upregulated Per cent of cases (%) Protein activation Transcriptional activation 50 0 50 Complex Protein inhibition Transcriptional inhibition Inactivated Activated Non-hypermutated Hypermutated Pathway alteration pattern Pathway activated Pathway inactivated Non-hypermutated tumours Hypermutated tumours WNT TGF-β RTK/RAS PI3K TP53 Figure 4 | Diversity and frequency of genetic changes leading to up- or downregulation of gene expression (IGF2, FZD10, SMAD4). Alteration deregulation of signalling pathways in CRC. Non-hypermutated (nHM; frequencies are expressed as a percentage of all cases. Red denotes activated n5 165) and hypermutated (HM; n5 30) samples with complete data were genes and blue denotes inactivated genes. Bottom panel shows for each sample analysed separately. Alterations are defined by somatic mutations, homozygous if at least one gene in each of the five pathways described in this figure is altered. deletions, high-level focal amplifications, and, in some cases, by significant 19 JULY 2 0 1 2 | V OL 487 | NAT URE | 3 3 3 ©2012 Macmillan Publishers Limited. All rights reserved nHM nHM nHM nHM nHM RESEARCH ARTICLE one of the four ERBB family genes are present in 22 out of 165 of tumour stage, lymph node status, distant metastasis and vascular (13%) non-hypermutated and 16 out of 30 (53%) hypermutated invasion at the time of surgery. We found numerous molecular cases. Some of the mutations are listed in the COSMIC database , signatures associated with tumour aggressiveness, a subset of which suggesting a functional role. Intriguingly, recurrent ERBB2(V842I) is shown in Fig. 5b. They include specific focal amplifications and and ERBB3(V104M) mutations were found in four and two non- deletions, and altered gene-expression levels, including those of hypermutated cases, respectively. Mutations and focal amplifications SCN5A (ref. 36), a reported regulator of colon cancer invasion (see of ERBB2 (Supplementary Fig. 6) should be evaluated as predictors Supplementary Tables 10 and 11 for a full list). Association with tumour aggressiveness is also observed in altered expression of of response to agents that target those receptors. We observed co-occurrence of alterations involving the RAS and PI3K pathways miRNAs and specific somatic mutations (APC, TP53, PIK3CA, in one-third of tumours (Fig. 4; P5 0.039, Fisher’s exact test). These BRAF and FBXW7; Supplementary Fig. 8b). Mutations in FBXW7 results indicate that simultaneous inhibition of the RAS and PI3K (38 cases) and distant metastasis (32 cases) never co-occurred pathways may be required to achieve therapeutic benefit. (P5 0.0019). Interestingly, a number of genomic regions have multiple molecular associations with tumour aggressiveness that manifest as The TGF-b signalling pathway is known to be deregulated in CRC and other cancers . We found genomic alterations in TGFBR1, clinically related genomic hotspots. Examples of this are the region 20q13.12, which includes a focal amplification and multiple genes TGFBR2, ACVR2A, ACVR1B, SMAD2, SMAD3 and SMAD4 in 27% of the non-hypermutated and 87% of the hypermutated tumours. We correlating with tumour aggression, and the region 22q12.3, contain- ing APOL6 (ref. 37) (Supplementary Figures 8 and 9). also evaluated the p53 pathway, finding alterations in TP53 in 59% of non-hypermutated cases (mostly biallelic; Supplementary Table 8) Discussion and alterations in ATM, a kinase that phosphorylates and activates P53 after DNA damage, in 7%. Alterations in these two genes showed This comprehensive integrative analysis of 224 colorectal tumour and a trend towards mutual exclusivity (P5 0.016) (Fig. 4, Supplementary normal pairs provides a number of insights into the biology of CRC Fig. 6 and Supplementary Table 1). and identifies potential therapeutic targets. To identify possible bio- We integrated copy number, gene expression, methylation and logical differences in colon and rectum tumours, we found, in the pathway data using the PARADIGM software platform . The non-hypermutated tumours irrespective of their anatomical origin, analysis showed a number of new characteristics of CRC (Fig. 5a). the same type of copy number, expression profile, DNA methylation For example, despite the diversity in anatomical origin or mutation and miRNA changes. Over 94% had a mutation in one or more levels, nearly 100% of these tumours have changes in MYC transcrip- members of the WNT signalling pathway, predominantly in APC. tional targets, both those promoted by and those inhibited by MYC. However, there were some differences between tumours from the These findings are consistent with patterns deduced from genetic right colon and all other sites. Hypermethylation was more common alterations (Fig. 4) and suggest an important role for MYC in CRC. in the right colon, and three-quarters of hypermutated samples came The analysis also identified several gene networks altered across all from the same site, although not all of them had MSI (Fig. 2). Why tumour samples and those with differential alterations in hypermutated most of the hypermutated samples came from the right colon and why versus non-hypermutated samples (Supplementary Table 7, Supplemen- there are two classes of tumours at this site is not known. The origins tary Data on the Cancer Genome Atlas publication webpage). of the colon from embryonic midgut and hindgut may provide an Because most of the tumours used in this study were derived from a explanation. As the survival rate of patients with high MSI-related prospective collection, survival data are not available. However, the cancers is better and these cancers are hypermutated, mutation rate tumours can be classified as aggressive or non-aggressive on the basis may be a better prognostic indicator. Hyper- FOXA1 network ab Non-hypermutated mutated Repressed by MYC HIF2-α network –10 1p36.11 deletion 1.4 10 5q11.2 deletion –9 1.4 10 –8 1.00 6p21.1 amplification 6.3 10 18q21.2 deletion –7 Markers for 0.67 1.3 10 13q12.2 amplification –7 0.33 2.0 10 more- –7 5q22.2 deletion 0.00 2.0 10 aggressive –0.33 –12 MOSPD3 3.2 10 disease –0.67 TH –11 1.1 10 –1.00 –11 TSC22D4 1.4 10 SCN5A –11 4.9 10 –10 ARL8A 2.6 10 Inflammatory –10 CUX1 3.0 10 response –10 CDIPT 4.3 10 –10 Score POLR2J 6.7 10 Angiogenesis –10 PPP1R35 9.0 10 –9 C6orf48 1.5 10 Integrins 4 –9 EFNA1 2.4 10 –9 CLEC6A 4.6 10 –9 TP53 effectors CASP5 2.4 10 –9 CATSPERB 2.4 10 G2/M damage 0 –9 CIITA 2.3 10 –1 –9 checkpoint GBP4 2.3 10 –9 –2 KIR2DL4 2.3 10 –9 –3 IL18R1 2.0 10 MYB –10 –4 SLC39A8 4.3 10 –10 3.0 10 FOXM1 CD274 –10 ENOSF1 3.0 10 –10 Markers for 1.3 10 FFAR2 Induced by MYC –11 TNFRSF11A 8.2 10 less- –11 RB1, E2F 6.0 10 BIRC3 aggressive –11 GBP1 4.3 10 TP63 targets –11 disease 2.8 10 CXCL11 –12 NHEJ repair 2.8 10 CASP1 –12 1.5 10 GSR Chromatin remodelling, –16 1.0 10 APOL6 –16 1.0 10 excision repair GFI1 Figure 5 | Integrative analyses of multiple data sets. a, Clustering of genes blue indicating the markers of less-aggressive tumours. Significance is based on and pathways affected in colon and rectum tumours deduced by PARADIGM the combined P value from the weighted Fisher’s method, corrected for analysis. Blue denotes under-expressed relative to normal and red denotes multiple testing. Colour intensity and score is in accordance with the strength of overexpressed relative to normal. Some of the pathways deduced by this an individual clinical–molecular association, and is proportional to log (P), method are shown on the right. NHEJ, non-homologous end joining. b, Gene- where P is the P value for that association. To limit the vertical extent of the expression signatures and SCNAs associated with tumour aggression. figure, gene-expression signatures are restricted to a combined P value of 29 27 Molecular signatures (rows) that show a statistically significant association with P, 10 and SCNAs to P, 10 , and features are shown only if they are also tumour aggressiveness according to selected clinical assays (columns) are significant in the subset of non-MSI-H samples (the analysis was performed shown in colour, with red indicating markers of tumour aggressiveness and separately on the full data as well as on the MSI-H and non-MSI-H subgroups). 3 3 4 | NA TU RE | V O L 48 7 | 19 JULY 201 2 ©2012 Macmillan Publishers Limited. 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Taylor , Timothy 37 38 34 Supplementary Information is linked to the online version of the paper at A. Chan , Marc Ladanyi , Chris Sander ; Genome Data Analysis Center University 39 39 www.nature.com/nature. of Texas MD Anderson Cancer Center Rehan Akbani , Nianxiang Zhang , Bradley 39 39 39 39 33 M. Broom , Tod Casasent , Anna Unruh , Chris Wakefield , Stanley R. Hamilton , Acknowledgements This work was supported by the following grants from the 33 39 39,40 R. Craig Cason , Keith A. Baggerly , John N. Weinstein ; Genome Data Analysis National Institutes of Health: U24CA143799, U24CA143835, U24CA143840, Centers, University of California, Santa Cruz and the Buck Institute David U24CA143843, U24CA143845, U24CA143848, U24CA143858, U24CA143866, 41,42 43 41 41 Haussler , Christopher C. Benz , Joshua M. Stuart , Stephen C. Benz ,J. U24CA143867, U24CA143882, U24CA143883, U24CA144025, U54HG003067, 41 41 41 41 Zachary Sanborn , Charles J. Vaske , Jingchun Zhu , Christopher Szeto , Gary K. U54HG003079 and U54HG003273. 43 43 41 41 44 Scott , Christina Yau , Sam Ng , Ted Goldstein , Kyle Ellrott41, Eric Collisson , 41 41 41 41 Aaron E. Cozen , Daniel Zerbino , Christopher Wilks , Brian Craft , Paul Author Contributions The Cancer Genome Atlas research network contributed Spellman ; Biospecimen Core Resource International Genomics Consortium collectively to this study. Biospecimens were provided by the tissue source sites and 46 46 46 46 46 Robert Penny , Troy Shelton , Martha Hatfield , Scott Morris , Peggy Yena , processed by the Biospecimen Core Resource. Data generation and analyses were 46 46 46 Candace Shelton , Mark Sherman , Joseph Paulauskis ; Nationwide Children’s performed by the genome-sequencing centers, cancer genome-characterization 47–49 47 Hospital Biospecimen Core Resource Julie M. Gastier-Foster , Jay Bowen , Nilsa centers and genome data analysis centers. All data were released through the Data 47,48 47 47,48 47 47,49 C. Ramirez , Aaron Black , Robert Pyatt , Lisa Wise , Peter White ; Tissue Coordinating Center. Project activities were coordinated by the National Cancer 50 51 source sites and disease working group Monica Bertagnolli , Jen Brown , Timothy Institute and National Human Genome Research Institute project teams. Project 52 53 51 54 55 A. Chan , Gerald C. Chu , Christine Czerwinski , Fred Denstman , Rajiv Dhir , leaders were R.K. and D.A.W. Writing team, T.A., A.J.B., T.A.C., L.D., A.H., S.R.H., R.K., 56 57,58 59 51 Arnulf Do¨rner , Charles S. Fuchs , Jose G. Guillem , Mary Iacocca , Hartmut P.W.L., M.M., N.S., I.S., J.M.S., J.T., V.T. and D.A.W.; mutations, M.S.L., L.R.T., D.A.W. and 60 52 61 61 62 Juhl , Andrew Kaufman , Bernard Kohl III , Xuan Van Le , Maria C. Mariano , G.G.; copy-number and structural aberrations, A.H.R., A.J.B., A.H. and P.-C.C.; DNA 62 63 59 59 Elizabeth N. Medina , Michael Meyers , Garrett M. Nash , Phillip B. Paty , Nicholas methylation, T.H.; expression, J.T.A.; miRNA, G.R., A.C.; pathways, C.J.C., L.D., T.G., S.N., 54 51 64 66 51 Petrelli , Brenda Rabeno , William G. Richards , David Solit , Pat Swanson , J.D.R., C.S., N.S., J.M.S. and V.T. 52 65 61 62 Larissa Temple , Joel E. Tepper , Richard Thorp , Efsevia Vakiani , Martin R. 59 67 51 59 63 Author Information dbGaP accession numbers have been provided in Supplementary Weiser , Joseph E. Willis , Gary Witkin , Zhaoshi Zeng , Michael J. Zinner , 68 69 69 Table 1. The authors declare no competing financial interests. Reprints and Carsten Zornig ; Data-Coordination Center Mark A. Jensen , Robert Sfeir , Ari B. 69 69 69 69 69 permissions information is available at www.nature.com/reprints. Readers are Kahn , Anna L. Chu , Prachi Kothiyal , Zhining Wang , Eric E. Snyder , Joan 69 69 69 69 69 welcome to comment on the online version of this article at www.nature.com/nature. Pontius , Todd D. Pihl , Brenda Ayala , Mark Backus , Jessica Walton , Jon 69 69 69 69 This paper is distributed under the terms of the Creative Commons Whitmore , Julien Baboud , Dominique L. Berton , Matthew C. Nicholls , Deepak 69 69 69 69 69 Attribution-Non-Commercial-Share Alike licence, and is freely available to all readers at Srinivasan , Rohini Raman , Stanley Girshik , Peter A. Kigonya , Shelley Alonso , 69 69 69 69 www.nature.com/nature. Correspondence and requests for materials should be Rashmi N. Sanbhadti , Sean P. Barletta , John M. Greene , David A. Pot ; Project 70 70 addressed to R.K. ([email protected]). Team National Cancer Institute Kenna R. Mills Shaw , Laura A. L. Dillon , Ken 71 71 70 72 73 Buetow , Tanja Davidsen , John A. Demchok , Greg Eley , Martin Ferguson , 70 71 70 70 Peter Fielding , Carl Schaefer , Margi Sheth and Liming Yang ; Project Team National Human Genome Research Institute Mark S. Guyer , Bradley A. 74 74 74 74 Ozenberger , Jacqueline D. Palchik , Jane Peterson , Heidi J. Sofia & Elizabeth Genome Sequencing Center Baylor College of Medicine Donna M. Muzny , Matthew Thomson . 1 1 1 1 1 N. Bainbridge , Kyle Chang , Huyen H. Dinh , Jennifer A. Drummond , Gerald Fowler , 1 1 1 1 Christie L. Kovar , Lora R. Lewis , Margaret B. Morgan , Irene F. Newsham , Jeffrey G. 1 1 1 1 1 1 Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, Reid , Jireh Santibanez , Eve Shinbrot , Lisa R. Trevino , Yuan-Qing Wu , Min Wang , 1,2 1,3 1,3 USA. Department of Biology and Biochemistry, University of Houston, Houston, Texas Preethi Gunaratne , Lawrence A. Donehower , Chad J. Creighton , David A. 1 1 77204, USA. Dan L. Duncan Cancer Center, Human Genome Sequencing Center, Baylor Wheeler , Richard A. Gibbs ; Genome Sequencing Center Broad Institute Michael S. 4 4 4 5 3 4 Lawrence , Douglas Voet , Rui Jing , Kristian Cibulskis , Andrey Sivachenko , Petar College of Medicine, Houston, Texas 77030, USA. The Eli and Edythe L. Broad Institute of 4 4 4,6,7 8 4 Stojanov , Aaron McKenna , Eric S. Lander , Stacey Gabriel , Gad Getz ; Genome Massachusetts Institute of Technology and Harvard University, Cambridge, 9,10 9 5 Sequencing Center Washington University in St Louis Li Ding , Robert S. Fulton , Massachusetts 02142, USA. Medical Sequencing Analysis and Informatics, The Eli and 9 9 9 9,10 9 Daniel C. Koboldt , Todd Wylie , Jason Walker , David J. Dooling , Lucinda Fulton , Edythe L. Broad Institute of Massachusetts Institute of Technology and Harvard 9 9 9 9–11 6 Kim D. Delehaunty , Catrina C. Fronick , Ryan Demeter , Elaine R. Mardis , Richard University, Cambridge, Massachusetts 02142, USA. Department of Biology, 9–11 12 K. Wilson ; Genome Characterization Center BC Cancer Agency Andy Chu , Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA. 12 12 12 12 7 Hye-Jung E. Chun , Andrew J. Mungall , Erin Pleasance , A. Gordon Robertson , Department of Systems Biology, Harvard University, Boston, Massachusetts 02115, 12 12 12 12 8 Dominik Stoll , Miruna Balasundaram , Inanc Birol , Yaron S. N. Butterfield , Eric USA. Genetic Analysis Platform, The Eli and Edythe L. Broad Institute of Massachusetts 12 12 12 12 12 Chuah , Robin J. N. Coope , Noreen Dhalla , Ranabir Guin , Carrie Hirst , Martin Institute of Technology and Harvard University, Cambridge, Massachusetts 02142, USA. 12 12 12 12 12 9 Hirst , Robert A. Holt , Darlene Lee , Haiyan I. Li , Michael Mayo , Richard A. The Genome Institute, Washington University School of Medicine, St Louis, Missouri 12 12 12 12 12 10 Moore , Jacqueline E. Schein , Jared R. Slobodan , Angela Tam , Nina Thiessen , 63108 USA. Department of Genetics, Washington University School of Medicine, St 12 12 12 12 11 Richard Varhol , Thomas Zeng , Yongjun Zhao , Steven J. M. Jones , Marco A. Louis, Missouri 63108, USA. Siteman Cancer Center, Washington University School of 12 4,13 12 Marra ; Genome-Characterization Center Broad Institute Adam J. Bass , Alex H. Medicine, St Louis, Missouri 63108, USA. Canada’s Michael Smith Genome Sciences 4,13 4 4 4,13 13 Centre, BC Cancer Agency, Vancouver, British Columbia V5Z 1L3, Canada. Department Ramos , Gordon Saksena , Andrew D. Cherniack , Stephen E. Schumacher , 4,13 4,13 4 4 4 of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. Barbara Tabak , Scott L. Carter , Nam H. Pho , Huy Nguyen , Robert C. Onofrio , 4 4 4,13 4 14 Departmentof Pathology, Harvard Medical School, Boston, Massachusetts02115, USA. Andrew Crenshaw , Kristin Ardlie , Rameen Beroukhim , Wendy Winckler , Gad 4 4,13,14 15 Belfer Institute for Applied Cancer Science, Department of Medical Oncology, Getz , Matthew Meyerson ; Genome-Characterization Center Brigham and 15 16 Women’s Hospital and Harvard Medical School Alexei Protopopov , Juinhua Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. Department of 15 16,17 17,18 18 18 17 Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA. Division of Zhang , Angela Hadjipanayis , Eunjung Lee , Ruibin Xi , Lixing Yang , 15 15 19 15 18 Genetics, Brigham and Women’s Hospital, Boston, Massachusetts 02115, USA. The Xiaojia Ren , Hailei Zhang , Narayanan Sathiamoorthy , Sachet Shukla , 16,17 17,18 15 18 Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts Peng-Chieh Chen , Psalm Haseley , Yonghong Xiao , Semin Lee , Jonathan 16 4,15,20 17–19 16,17 19 02115, USA. Informatics Program, Children’s Hospital, Boston, Massachusetts 02115, Seidman , Lynda Chin , Peter J. Park , Raju Kucherlapati ; USA. Department of Dermatology, Harvard Medical School, Boston, Massachusetts Genome-Characterization Center University of North Carolina, Chapel Hill J. Todd 21,22 23–25 25 25 25 21 02115, USA. Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Auman , Katherine A. Hoadley , Ying Du , Matthew D. Wilkerson , Yan Shi , 25 25 25 25 24,25 22 Chapel Hill, North Carolina 27599, USA. Institute for Pharmacogenetics and Christina Liquori , Shaowu Meng , Ling Li , Yidi J. Turman , Michael D. Topal , 26 25 25 25 27 Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, North Donghui Tan , Scot Waring , Elizabeth Buda , Jesse Walsh , Corbin D. Jones , 23 28 25 25 23 Carolina 27599, USA. Department of Genetics, University of North Carolina at Chapel Piotr A. Mieczkowski , Darshan Singh , Junyuan Wu , Anisha Gulabani , Peter 25 25 25 25 Dolina , Tom Bodenheimer , Alan P. Hoyle , Janae V. Simons , Matthew Hill, Chapel Hill, North Carolina 27599, USA. Department of Pathology and Laboratory 25 24 24 25 25 Soloway , Lisle E. Mose , Stuart R. Jefferys , Saianand Balu , Brian D. O’Connor , Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, 3 3 6 | NA TU RE | V O L 48 7 | 19 JULY 201 2 ©2012 Macmillan Publishers Limited. All rights reserved ARTICLE RESEARCH 25 48 USA. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Children’s Hospital, Columbus, Ohio 43205, USA. The Ohio State University College of 26 49 Hill, Chapel Hill, North Carolina 27599, USA. Carolina Center for Genome Sciences, Medicine, Department of Pathology, Columbus, Ohio 43205, USA. The Ohio State University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. University College of Medicine, Department of Pediatrics, Columbus, Ohio 43205, USA. 27 50 Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Department of Surgery, Brigham and Women’s Hospital, Harvard Medical School, 28 51 Carolina 27599, USA. Department of Computer Science, University of North Carolina at Brookline, Massachusetts 02115, USA. Department of Pathology, Christiana Care 29 52 Chapel Hill, Chapel Hill, North Carolina 27599, USA. Department of Internal Medicine, Health Services, Newark, Delaware 19718, USA. Human Oncology and Pathogenesis Division of Medical Oncology, University of North Carolina at Chapel Hill, Chapel Hill, North Program, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. 30 53 Carolina 27599, USA. University of Southern California Epigenome Center, University of Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, 31 54 Southern California, Los Angeles, California 90089 USA. Cancer Biology Division, The Brookline, Massachusetts 02115, USA. Department of Surgery, Helen F. Graham Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, Cancer Center at Christiana Care, Newark, Delaware 19718, USA. Department of 32 56 Maryland 21231, USA. Institute for Systems Biology, Seattle, Washington 98109, USA. Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA. Klinik fu¨r 33 57 Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Chirurgie, Krankenhaus Alten Eichen, 22527 Hamburg, Germany. Department of Cancer Center, Houston, Texas 77030, USA. 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MLF Consulting, Arlington, Massachusetts 02474, USA. 46 47 74 International Genomics Consortium, Phoenix, Arizona 85004, USA. Nationwide National Human Genome Research Institute, National Institutes of Health, Bethesda, Children’s Hospital Biospecimen Core Resource, The Research Institute at Nationwide Maryland 20892, USA. 1 9 JU LY 2012 | V O L 4 8 7 | NA TUR E | 3 37 ©2012 Macmillan Publishers Limited. All rights reserved http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nature Springer Journals

Comprehensive molecular characterization of human colon and rectal cancer

Nature , Volume 487 (7407) – Jul 18, 2012
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Springer Journals
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Copyright © 2012 by The Author(s)
Subject
Science, Humanities and Social Sciences, multidisciplinary; Science, Humanities and Social Sciences, multidisciplinary; Science, multidisciplinary
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0028-0836
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1476-4687
DOI
10.1038/nature11252
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Abstract

ARTICLE doi:10.1038/nature11252 Comprehensivemolecularcharacterization of human colon and rectal cancer The Cancer Genome Atlas Network* To characterize somatic alterations in colorectal carcinoma, we conducted a genome-scale analysis of 276 samples, analysing exome sequence, DNA copy number, promoter methylation and messenger RNA and microRNA expression. A subset of these samples (97) underwent low-depth-of-coverage whole-genome sequencing. In total, 16% of colorectal carcinomas were found to be hypermutated: three-quarters of these had the expected high microsatellite instability, usually with hypermethylation and MLH1 silencing, and one-quarter had somatic mismatch-repair gene and polymerase e (POLE) mutations. Excluding the hypermutated cancers, colon and rectum cancers were found to have considerably similar patterns of genomic alteration. Twenty-four genes were significantly mutated, and in addition to the expected APC, TP53, SMAD4, PIK3CA and KRAS mutations, we found frequent mutations in ARID1A, SOX9 and FAM123B. Recurrent copy-number alterations include potentially drug-targetable amplifications of ERBB2 and newly discovered amplification of IGF2. Recurrent chromosomal translocations include the fusion of NAV2 and WNT pathway member TCF7L1. Integrative analyses suggest new markers for aggressive colorectal carcinoma and an important role for MYC-directed transcriptional activation and repression. 6 6 The Cancer Genome Atlas project plans to profile genomic changes in ,1 per 10 bases, whereas a few had mutations rates of .100 per 10 . 20 different cancer types and has so far published results on two We separated cases (84%) with a mutation rate of ,8.24 per 10 1,2 cancer types . We now present results from multidimensional (median number of non-silent mutations, 58) and those with muta- analyses of human colorectal carcinoma (CRC). tion rates of .12 per 10 (median number of total mutations, 728), CRC is an important contributor to cancer mortality and morbidity. which we designated as hypermutated (Fig. 1). The distinction between the colon and the rectum is largely anatomical, To assess the basis for the considerably different mutation rates, we 7 8–10 but it has both surgical and radiotherapeutic management implications evaluated MSI and mutations in the DNA mismatch-repair pathway and it may have an impact on prognosis. Most investigators divide genes MLH1, MLH3, MSH2, MSH3, MSH6 and PMS2. Among the 30 CRC biologically into those with microsatellite instability (MSI; located hypermutated tumours with a complete data set, 23 (77%) had high primarily in the right colon and frequently associated with the CpG levels of MSI (MSI-H). Included in this group were 19 tumours with island methylator phenotype (CIMP) and hyper-mutation) and those MLH1 methylation, 17 of which had CIMP. By comparison, the that are microsatellite stable but chromosomally unstable. remaining seven hypermutated tumours, including the six with the A rich history of investigations (for a review see ref. 3) has uncovered highest mutation rates, lacked MSI-H, CIMP or MLH1 methylation several critical genes and pathways important in the initiation and but usually had somatic mutations in one or more mismatch-repair progression of CRC (ref. 3). These include the WNT, RAS2MAPK, genes or POLE aberrations seen rarely in the non-hypermutated PI3K, TGF-b, P53 and DNA mismatch-repair pathways. Large-scale tumours (Fig. 1). 4–6 sequencing analyses have identified numerous recurrently mutated genes and a recurrent chromosomal translocation. Despite this back- Gene mutations ground, we have not had a fully integrated view of the genetic and Overall, we identified 32 somatic recurrently mutated genes (defined by MutSig and manual curation) in the hypermutated and non- genomic changes and their significance for colorectal tumorigenesis. Further insight into these changes may enable deeper understanding of hypermutated cancers (Fig. 1b). After removal of non-expressed genes, the pathophysiology of CRC and may identify potential therapeutic there were 15 and 17 in the hypermutated and non-hypermutated cancers, respectively (Fig. 1b; for a complete list see Supplementary targets. Table 3). Among the non-hypermutated tumours, the eight most fre- Results quently mutated genes were APC, TP53, KRAS, PIK3CA, FBXW7, SMAD4, TCF7L2 and NRAS. As expected, the mutated KRAS and Tumour and normal pairs were analysed by different platforms. The NRAS genes usually had oncogenic codon 12 and 13 or codon 61 specific numbers of samples analysed by each platform are shown in mutations, whereas the remaining genes had inactivating mutations. Supplementary Table 1. CTNNB1, SMAD2, FAM123B (also known as WTX)and SOX9 were Exome-sequence analysis also mutated frequently. FAM123B is an X-linked negative regulator of WNT signalling , and virtually all of its mutations were loss of func- To define the mutational spectrum, we performed exome capture DNA sequencing on 224 tumour and normal pairs (all mutations tion. Mutations in SOX9, a gene important for cell differentiation in the 13,14 are listed in Supplementary Table 2). Sequencing achieved .20-fold intestinal stem cell niche , have not been associated previously with coverage of at least 80% of targeted exons. The somatic mutation rates human cancer, but all nine mutated alleles in the non-hypermutated varied considerably among the samples. Some had mutation rates of CRCs were frameshift or nonsense mutations. Tumour-suppressor Lists of participants and their affiliations appear at the end of the paper. 330| NATURE |VOL487 |19JULY2012 ©2012 Macmillan Publishers Limited. All rights reserved ARTICLE RESEARCH a 500 cancers were significantly less frequently mutated in hypermutated tumours: TP53 (60 versus 20%, P, 0.0001) and APC (81% versus MLH1 51%, P5 0.0023; both Fisher’s exact test). Other genes, including MLH3 MSH2 TGFBR2, were mutated recurrently in the hypermutated cancers, MSH3 but not in the non-hypermutated samples. These findings indicate MSH6 PMS2 that hypermutated and non-hypermutated tumours progress through POLE different sequences of genetic events. Epigenetic Frameshift Missense/nonsense silencing mutation mutation As expected, hypermutated tumours with MLH1 silencing and MSI-H showed additional differences in the mutational profile. When we specifically examined 28 genes with long mononucleotide repeats in their coding sequences, we found that the rate of frameshift mutation was 3.6-fold higher than the rate of such mutations in Non-silent hypermutated tumours without MLH1 silencing and 50-fold higher Hypermutated Non-hypermutated Silent 0.1 than that in non-hypermethylated tumours (Supplementary Table 2). Tumour site MSI status CIMP status MLH1 silencing Mutation rate and methylation patterns As mentioned above, patients with colon and rectal tumours are b Hypermutated tumours Non-hypermutated tumours 17 managed differently , and epidemiology also highlights differences between the two . An initial integrative analysis of MSI status, somatic copy-number alterations (SCNAs), CIMP status and gene- expression profiles of 132 colonic and 62 rectal tumours enabled us to examine possible biological differences between tumours in the two locations. Among the non-hypermutated tumours, however, the overall patterns of changes in copy number, CIMP, mRNA and miRNA were indistinguishable between colon and rectal carcinomas (Fig. 2). On the basis of this result, we merged the two for all subsequent analyses. Figure 1 | Mutation frequencies in human CRC. a, Mutation frequencies in Unsupervised clustering of the promoter DNA methylation each of the tumour samples from 224 patients. Note a clear separation of profiles of 236 colorectal tumours identified four subgroups (Sup- hypermutated and non-hypermutated samples. Red, MSI high, CIMP high or plementary Fig. 1 and Supplementary Methods). Two of the clusters MLH1 silenced; light blue, MSI low, or CIMP low; black, rectum; white, colon; grey, no data. Inset, mutations in mismatch-repair genes and POLE among the contained tumours with elevated rates of methylation and were hypermutated samples. The order of the samples is the same as in the main classified as CIMP high and CIMP low, as previously described . graph. b, Significantly mutated genes in hypermutated and non-hypermutated The two non-CIMP clusters were predominantly from tumours that tumours. Blue bars represent genes identified by the MutSig algorithm and were non-hypermutated and derived from different anatomic loca- black bars represent genes identified by manual examination of sequence data. tions. mRNA expression profiles separated the colorectal tumours into three distinct clusters (Supplementary Fig. 2). One significantly genes ATM and ARID1A also had a disproportionately high number of frameshift or nonsense mutations. ARID1A mutations have recently overlapped with CIMP-high tumours (P5 33 10 ) and was 15,16 enriched with hypermutated tumours, and the other two clusters been reported in CRC and many other cancers . did not correspond with any group in the methylation data. In the hypermutated tumours, ACVR2A, APC, TGFBR2, MSH3, MSH6, SLC9A9 and TCF7L2 were frequent targets of mutation Analysis of miRNA expression by unsupervised clustering (Supplemen- (Fig. 1b), along with mostly BRAF(V600E) mutations. However, tary Fig. 3) identified no clear distinctions between rectal cancers and two genes that were frequently mutated in the non-hypermutated non-hypermethylated colon cancers. Chromosome 12 3 4 56 78 9 10 11 12 13 14 15 16 171819202122 Tumour site BRAF(V600E) Methylation cluster mRNA cluster Right colon Transverse Left colon Rectum Yes No CIMP-H CIMP-L Cluster 3 Cluster 4 MSI/CIMP Invasive CIN Figure 2 | Integrative analysis of genomic changes in 195 CRCs. indistinguishable from each other on the basis of their copy-number alteration Hypermutated tumours have near-diploid genomes and are highly enriched for patterns, DNA methylation or gene-expression patterns. Copy-number hypermethylation, CIMP expression phenotype and BRAF(V600E) mutations. changes of the 22 autosomes are shown in shades of red for copy-number gains Non-hypermutated tumours originating from different sites are virtually and shades of blue for copy-number losses. 1 9 JU LY 20 12 | V O L 48 7 | NATU R E | 3 31 ©2012 Macmillan Publishers Limited. All rights reserved ACVR2A APC TGFBR2 BRAF MSH3 MSH6 MYO1B TCF7L2 CASP8 CDC27 FZD3 MIER3 TCERG1 MAP7 PTPN12 APC TP53 KRAS TTN PIK3CA FBXW7 SMAD4 NRAS TCF7L2 FAM123B SMAD2 CTNNB1 KIAA1804 SOX9 ACVR1B GPC6 EDNRB Tumour site BRAF (V600E) Meth. cluster mRNA cluster Mutation frequency (%) 0 20 40 60 80 63% Mutation rate (mutations per 10 bases) 51% 51% 46% 40% 40% 31% 31% 29% 29% Hyper- Non-hypermutated 29% mutated 29% 29% 26% 26% 81% 60% 43% 31% 18% 11% 10% 9% 9% 7% 6% 5% 4% 4% 4% 4% 3% Y 15 15 RESEARCH ARTICLE Chromosomal and sub-chromosomal changes candidate oncogene CDK8; an adjacent peak at 13q12; a peak contain- ing KLF5 at 13q22.1; and a peak at 20q13.12 adjacent to HNF4A. In total, 257 tumours were profiled for SCNAs with Affymetrix SNP 6.0 arrays. Of these tumours, 97 were also analysed by low-depth-of- Peaks on chromosome 8 included 8p12 (which contains the histone coverage (low-pass) whole-genome sequencing. As expected, the methyl-transferase-coding gene WHSC1L1, adjacent to FGFR1) and hypermutated tumours had far fewer SCNAs (Fig. 2). No difference 8q24 (which contains MYC). An amplicon at 17q21.1, found in 4% of was found between microsatellite-stable and -unstable hypermutated the tumours, contains seven genes, including the tyrosine kinase tumours (Supplementary Fig. 4). We used the GISTIC algorithm to ERBB2. ERBB2 amplifications have been described in colon, breast identify probable gene targets of focal alterations. There were several and gastro–oesophageal tumours, and breast and gastric cancers bear- previously well-defined arm-level changes, including gains of 1q, 7p ing these amplifications have been treated effectively with the anti- 20–22 and q, 8p and q, 12q, 13q, 19q, and 20p and q (ref. 6). (Supplementary ERBB2 antibody trastuzumab . Fig. 4 and Supplementary Table 4). Significantly deleted chromosome One of the most common focal amplifications, found in 7% of the arms were 18p and q (including SMAD4) in 66% of the tumours and tumours, is the gain of a 100–150-kb region of the chromosome arm 17p and q (including TP53) in 56%. Other significantly deleted chro- 11p15.5. It contains genes encoding insulin (INS), insulin-like growth mosome arms were 1p, 4q, 5q, 8p, 14q, 15q, 20p and 22q. factor 2 (IGF2) and tyrosine hydroxylase (TH), as well as miR-483, We identified 28 recurrent deletion peaks (Supplementary Fig. 4 which is embedded within IGF2 (Fig. 3a). We found elevated expres- sion of IGF2 and miR-483 but not of INS and TH (Fig. 3b, c). and Supplementary Table 4), including the genes FHIT, RBFOX1 and WWOX with large genomic footprints located in potentially fragile Immediately adjacent to the amplified region is ASCL2, a transcrip- sites of the genome, in near-diploid hypermutated tumours. Other tion factor active in specifying intestinal stem-cell fate . Although 23–25 focal deletions involved tumour-suppressor genes such as SMAD4, ASCL2 has been implicated as a target of amplification in CRC , APC, PTEN and SMAD3. A significant focal deletion of 10p25.2 it was consistently outside the region of amplification and its expres- spanned four genes, including TCF7L2, which was also frequently sion was not correlated with copy-number changes. These observa- mutated in our data set. A gene fusion between adjacent genes tions suggest that IGF2 and miR-483 are candidate functional targets VTI1A and TCF7L2 through an interstitial deletion was found in of 11p15.5 amplification. IGF2 overexpression through loss of 26, 27 3% of CRCs and is required for survival of CRC cells bearing the imprinting has been implicated in the promotion of CRC . MiR- 4 28 translocation . 483 may also have a role in CRC pathogenesis . There were 17 regions of significant focal amplification (Supplemen- A subset of tumours without IGF2 amplification (15%) also had tary Table 4). Some of these were superimposed on broad gains of considerably higher levels of IGF2 gene expression (as much as a chromosome arms, and included a peak at 13q12.13 near the 100-fold increase), an effect not attributable to methylation changes peptidase-coding gene USP12 and at ,500 kb distal to the CRC at the IGF2 promoter. To assess the context of IGF2 amplification/ IGF2 miR-483 IGF2 TH ASCL2 11p15 miR-483 INS 2.05 Mb 2.30 Mb No Broad Focal No Broad Focal ampl. ampl. ampl. ampl. ampl. ampl. 86 altered samples (of 165 non-hypermutated samples) IGF2 Upregulation miR-483 IRS2 Homozygous deletion PIK3CA PIK3R1 Mutation PTEN d NAV2 TCF7L1 1 192 391 411 2149 2303 1 250 346 414 589 Calponin Actin binding Na-channel 4 AAA+ ATPase core CTNNB1 binding HMG 1/2 box NAV2 TCF7L1 TCGA-AG-A01N TCGA-AA-A00U TCGA-AG-A011 Figure 3 | Copy-number changes and structural aberrations in CRC. exclusive of alterations in PI3K signalling-related genes. d, Recurrent NAV2– a, Focal amplification of 11p15.5. Segmented DNA copy-number data from TCF7L2 fusions. The structure of the two genes, locations of the breakpoints single-nucleotide polymorphism (SNP) arrays and low-pass whole-genome leading to the translocation and circular representations of all rearrangements sequencing (WGS) are shown. Each row represents a patient; amplified regions in tumours with a fusion are shown. Red line lines represent the NAV2–TCF7L2 are shown in red. b, Correlation of expression levels with copy-number changes fusions and black lines represent other rearrangements. The inner ring for IGF2 and miR-483. c, IGF2 amplification and overexpression are mutually represents copy-number changes (blue denotes loss, pink denotes gain). 3 3 2 | NATU R E | V OL 4 8 7 | 1 9 JU LY 20 12 ©2012 Macmillan Publishers Limited. All rights reserved WGS SNP array mRNA expression –4 –2 0 2 4 Expression 048 HM HM HM HM HM ARTICLE RESEARCH overexpression, we systematically searched for mutually exclusive cases the fusions predict inactivation of TTC28, which has been iden- 29 30 genomic events using the MEMo method . We found a pattern of tified as a target of P53 and an inhibitor of tumour cell growth . near exclusivity (corrected P, 0.01) of IGF2 overexpression with Eleven of the 19 (58%) gene–gene translocations were validated by genomic events known to activate the PI3K pathway (mutations of obtaining PCR products or, in some cases, sequencing the junction fragments (Supplementary Fig. 5). PIK3CA and PIK3R1 or deletion/mutation of PTEN; Fig. 3c and Supplementary Table 5). The IRS2 gene, encoding a protein linking Altered pathways in CRC IGF1R (the receptor for IGF2) with PI3K, is on chromosome 13, which is frequently gained in CRC. The cases with the highest IRS2 expression Integrated analysis of mutations, copy number and mRNA expression changes in 195 tumours with complete data enriched our understand- were mutually exclusive of the cases with IGF2 overexpression (P5 0.04) and also lacked mutations in the PI3K pathway ing of how some well-defined pathways are deregulated. We grouped (P5 0.0001; Fig. 3c). These results strongly suggest that the IGF2– samples by hypermutation status and identified recurrent alterations IGF1R–IRS2 axis signals to PI3K in CRC and imply that therapeutic in the WNT, MAPK, PI3K, TGF-b and p53 pathways (Fig. 4, targeting of the pathway could act to block PI3K activity in this subset Supplementary Fig. 6 and Supplementary Table 1). of patients. We found that the WNT signalling pathway was altered in 93% of all tumours, including biallelic inactivation of APC (Supplementary Translocations Table 7) or activating mutations of CTNNB1 in ,80% of cases. There To identify new chromosomal translocations, we performed low-pass, were also mutations in SOX9 and mutations and deletions in TCF7L2, paired-end, whole-genome sequencing on 97 tumours with matched as well as the DKK family members and AXIN2, FBXW7 (Supplemen- normal samples. In each case we achieved sequence coverage of tary Fig. 7), ARID1A and FAM123B (the latter is a negative regulator 12 31 ,3–4-fold and a corresponding physical coverage of 7.5–10-fold. of WNT–b-catenin signalling found mutated in Wilms’ tumour ). Despite the low genome coverage, we detected 250 candidate A few mutations in FAM123B have previously been described in interchromosomal translocation events (range, 0–10 per tumour). CRC . SOX9 has been suggested to have a role in cancer, but no Among these events, 212 had one or both breakpoints in an intergenic mutations have previously been described. The WNT receptor region, whereas the remaining 38 juxtaposed coding regions of two frizzled (FZD10) was overexpressed in ,17% of samples, in some genes in putative fusion events, of which 18 were predicted to code for instances at levels of 1003 normal. Altogether, we found 16 different in-frame events (Supplementary Table 6). We found three separate altered WNT pathway genes, confirming the importance of this cases in which the first two exons of the NAV2 gene on chromosome pathway in CRC. Interestingly, many of these alterations were found 11 are joined with the 39 coding portion of TCF7L1 on chromosome 2 in tumours that harbour APC mutations, suggesting that multiple (Supplementary Fig. 5). TCF7L1 encodes TCF3, a member of the lesions affecting the WNT signalling pathway confer selective advantage. TCF/LEF class of transcription factors that heterodimerize with Genetic alterations in the PI3K and RAS–MAPK pathways are nuclear b-catenin to enable b-catenin-mediated transcriptional regu- common in CRC. In addition to IGF2 and IRS2 overexpression, we lation. Intriguingly, in all three cases, the predicted structure of the found mutually exclusive mutations in PIK3R1 and PIK3CA as well as NAV2–TCF7L1 fusion protein lacks the TCF3 b-catenin-binding deletions in PTEN in 2%, 15% and 4% of non-hypermutated tumours, domain. This translocation is similar to another recurrent transloca- respectively. We found that 55% of non-hypermutated tumours have tion identified in CRC, a fusion in which the amino terminus of alterations in KRAS, NRAS or BRAF, with a significant pattern of VTI1A is joined to TCF4, which is encoded by TCF7L2, a homologue mutual exclusivity (Supplementary Fig. 6 and Supplementary Table 1). of TCF7L1 that is deleted or mutated in 12% of non-hypermutated We also evaluated mutations in the erythroblastic leukemia viral tumours . We also observed 21 cases of translocation involving oncogene homolog (ERBB) family of receptors because of the trans- TTC28 located on chromosome 22 (Supplementary Table 6). In all lational relevance of such mutations. Mutations or amplifications in 92% 97% 27% 87% WNT signalling TGF-β signalling PI3K signalling RTK–RAS signalling Altered IGF2 Altered 50% 53% 59% 80% 22% 0% WNT TGF-β Activin Altered Altered DKK1-4 4% 33% ERBB2 ERBB3 IGF1R LRP5 FZD10 TGFBR1 TGFBR2 ACVR2A ACVR1B 6% 13% 4% 20% <1% 10% 19% 13% <1% 17% 2% 43% 1% 60% 4% 20% IRS2 7% 3% KRAS SMAD3 NRAS SMAD2 FAM123B AXIN2 APC PTEN 10% 10% 43% 30% 6% 13% 2% 17% 7% 37% 4% 23% 81% 53% 4% 20% PIK3CA BRAF 15% 37% 3% 47% PIK3R1 CTNNB1 SMAD4 2% 17% 5% 7% 15% 20% Proliferation, cell survival, translation TCF7L2 FBXW7 ARID1A Nucleus 12% 30% 10% 43% 5% 37% P53 signalling 64% 47% DNA Altered ATM replication 7% 37% CTNNB1 5% 7% stress SOX9 Proliferation, stem/ MYC 4% 7% TP53 Proliferation Oncogenic progenitor phenotype TCF7 59% 17% Cell survival stress Frequency Upregulated Per cent of cases (%) Protein activation Transcriptional activation 50 0 50 Complex Protein inhibition Transcriptional inhibition Inactivated Activated Non-hypermutated Hypermutated Pathway alteration pattern Pathway activated Pathway inactivated Non-hypermutated tumours Hypermutated tumours WNT TGF-β RTK/RAS PI3K TP53 Figure 4 | Diversity and frequency of genetic changes leading to up- or downregulation of gene expression (IGF2, FZD10, SMAD4). Alteration deregulation of signalling pathways in CRC. Non-hypermutated (nHM; frequencies are expressed as a percentage of all cases. Red denotes activated n5 165) and hypermutated (HM; n5 30) samples with complete data were genes and blue denotes inactivated genes. Bottom panel shows for each sample analysed separately. Alterations are defined by somatic mutations, homozygous if at least one gene in each of the five pathways described in this figure is altered. deletions, high-level focal amplifications, and, in some cases, by significant 19 JULY 2 0 1 2 | V OL 487 | NAT URE | 3 3 3 ©2012 Macmillan Publishers Limited. All rights reserved nHM nHM nHM nHM nHM RESEARCH ARTICLE one of the four ERBB family genes are present in 22 out of 165 of tumour stage, lymph node status, distant metastasis and vascular (13%) non-hypermutated and 16 out of 30 (53%) hypermutated invasion at the time of surgery. We found numerous molecular cases. Some of the mutations are listed in the COSMIC database , signatures associated with tumour aggressiveness, a subset of which suggesting a functional role. Intriguingly, recurrent ERBB2(V842I) is shown in Fig. 5b. They include specific focal amplifications and and ERBB3(V104M) mutations were found in four and two non- deletions, and altered gene-expression levels, including those of hypermutated cases, respectively. Mutations and focal amplifications SCN5A (ref. 36), a reported regulator of colon cancer invasion (see of ERBB2 (Supplementary Fig. 6) should be evaluated as predictors Supplementary Tables 10 and 11 for a full list). Association with tumour aggressiveness is also observed in altered expression of of response to agents that target those receptors. We observed co-occurrence of alterations involving the RAS and PI3K pathways miRNAs and specific somatic mutations (APC, TP53, PIK3CA, in one-third of tumours (Fig. 4; P5 0.039, Fisher’s exact test). These BRAF and FBXW7; Supplementary Fig. 8b). Mutations in FBXW7 results indicate that simultaneous inhibition of the RAS and PI3K (38 cases) and distant metastasis (32 cases) never co-occurred pathways may be required to achieve therapeutic benefit. (P5 0.0019). Interestingly, a number of genomic regions have multiple molecular associations with tumour aggressiveness that manifest as The TGF-b signalling pathway is known to be deregulated in CRC and other cancers . We found genomic alterations in TGFBR1, clinically related genomic hotspots. Examples of this are the region 20q13.12, which includes a focal amplification and multiple genes TGFBR2, ACVR2A, ACVR1B, SMAD2, SMAD3 and SMAD4 in 27% of the non-hypermutated and 87% of the hypermutated tumours. We correlating with tumour aggression, and the region 22q12.3, contain- ing APOL6 (ref. 37) (Supplementary Figures 8 and 9). also evaluated the p53 pathway, finding alterations in TP53 in 59% of non-hypermutated cases (mostly biallelic; Supplementary Table 8) Discussion and alterations in ATM, a kinase that phosphorylates and activates P53 after DNA damage, in 7%. Alterations in these two genes showed This comprehensive integrative analysis of 224 colorectal tumour and a trend towards mutual exclusivity (P5 0.016) (Fig. 4, Supplementary normal pairs provides a number of insights into the biology of CRC Fig. 6 and Supplementary Table 1). and identifies potential therapeutic targets. To identify possible bio- We integrated copy number, gene expression, methylation and logical differences in colon and rectum tumours, we found, in the pathway data using the PARADIGM software platform . The non-hypermutated tumours irrespective of their anatomical origin, analysis showed a number of new characteristics of CRC (Fig. 5a). the same type of copy number, expression profile, DNA methylation For example, despite the diversity in anatomical origin or mutation and miRNA changes. Over 94% had a mutation in one or more levels, nearly 100% of these tumours have changes in MYC transcrip- members of the WNT signalling pathway, predominantly in APC. tional targets, both those promoted by and those inhibited by MYC. However, there were some differences between tumours from the These findings are consistent with patterns deduced from genetic right colon and all other sites. Hypermethylation was more common alterations (Fig. 4) and suggest an important role for MYC in CRC. in the right colon, and three-quarters of hypermutated samples came The analysis also identified several gene networks altered across all from the same site, although not all of them had MSI (Fig. 2). Why tumour samples and those with differential alterations in hypermutated most of the hypermutated samples came from the right colon and why versus non-hypermutated samples (Supplementary Table 7, Supplemen- there are two classes of tumours at this site is not known. The origins tary Data on the Cancer Genome Atlas publication webpage). of the colon from embryonic midgut and hindgut may provide an Because most of the tumours used in this study were derived from a explanation. As the survival rate of patients with high MSI-related prospective collection, survival data are not available. However, the cancers is better and these cancers are hypermutated, mutation rate tumours can be classified as aggressive or non-aggressive on the basis may be a better prognostic indicator. Hyper- FOXA1 network ab Non-hypermutated mutated Repressed by MYC HIF2-α network –10 1p36.11 deletion 1.4 10 5q11.2 deletion –9 1.4 10 –8 1.00 6p21.1 amplification 6.3 10 18q21.2 deletion –7 Markers for 0.67 1.3 10 13q12.2 amplification –7 0.33 2.0 10 more- –7 5q22.2 deletion 0.00 2.0 10 aggressive –0.33 –12 MOSPD3 3.2 10 disease –0.67 TH –11 1.1 10 –1.00 –11 TSC22D4 1.4 10 SCN5A –11 4.9 10 –10 ARL8A 2.6 10 Inflammatory –10 CUX1 3.0 10 response –10 CDIPT 4.3 10 –10 Score POLR2J 6.7 10 Angiogenesis –10 PPP1R35 9.0 10 –9 C6orf48 1.5 10 Integrins 4 –9 EFNA1 2.4 10 –9 CLEC6A 4.6 10 –9 TP53 effectors CASP5 2.4 10 –9 CATSPERB 2.4 10 G2/M damage 0 –9 CIITA 2.3 10 –1 –9 checkpoint GBP4 2.3 10 –9 –2 KIR2DL4 2.3 10 –9 –3 IL18R1 2.0 10 MYB –10 –4 SLC39A8 4.3 10 –10 3.0 10 FOXM1 CD274 –10 ENOSF1 3.0 10 –10 Markers for 1.3 10 FFAR2 Induced by MYC –11 TNFRSF11A 8.2 10 less- –11 RB1, E2F 6.0 10 BIRC3 aggressive –11 GBP1 4.3 10 TP63 targets –11 disease 2.8 10 CXCL11 –12 NHEJ repair 2.8 10 CASP1 –12 1.5 10 GSR Chromatin remodelling, –16 1.0 10 APOL6 –16 1.0 10 excision repair GFI1 Figure 5 | Integrative analyses of multiple data sets. a, Clustering of genes blue indicating the markers of less-aggressive tumours. Significance is based on and pathways affected in colon and rectum tumours deduced by PARADIGM the combined P value from the weighted Fisher’s method, corrected for analysis. Blue denotes under-expressed relative to normal and red denotes multiple testing. Colour intensity and score is in accordance with the strength of overexpressed relative to normal. Some of the pathways deduced by this an individual clinical–molecular association, and is proportional to log (P), method are shown on the right. NHEJ, non-homologous end joining. b, Gene- where P is the P value for that association. To limit the vertical extent of the expression signatures and SCNAs associated with tumour aggression. figure, gene-expression signatures are restricted to a combined P value of 29 27 Molecular signatures (rows) that show a statistically significant association with P, 10 and SCNAs to P, 10 , and features are shown only if they are also tumour aggressiveness according to selected clinical assays (columns) are significant in the subset of non-MSI-H samples (the analysis was performed shown in colour, with red indicating markers of tumour aggressiveness and separately on the full data as well as on the MSI-H and non-MSI-H subgroups). 3 3 4 | NA TU RE | V O L 48 7 | 19 JULY 201 2 ©2012 Macmillan Publishers Limited. 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Taylor , Timothy 37 38 34 Supplementary Information is linked to the online version of the paper at A. Chan , Marc Ladanyi , Chris Sander ; Genome Data Analysis Center University 39 39 www.nature.com/nature. of Texas MD Anderson Cancer Center Rehan Akbani , Nianxiang Zhang , Bradley 39 39 39 39 33 M. Broom , Tod Casasent , Anna Unruh , Chris Wakefield , Stanley R. Hamilton , Acknowledgements This work was supported by the following grants from the 33 39 39,40 R. Craig Cason , Keith A. Baggerly , John N. Weinstein ; Genome Data Analysis National Institutes of Health: U24CA143799, U24CA143835, U24CA143840, Centers, University of California, Santa Cruz and the Buck Institute David U24CA143843, U24CA143845, U24CA143848, U24CA143858, U24CA143866, 41,42 43 41 41 Haussler , Christopher C. Benz , Joshua M. Stuart , Stephen C. Benz ,J. U24CA143867, U24CA143882, U24CA143883, U24CA144025, U54HG003067, 41 41 41 41 Zachary Sanborn , Charles J. Vaske , Jingchun Zhu , Christopher Szeto , Gary K. U54HG003079 and U54HG003273. 43 43 41 41 44 Scott , Christina Yau , Sam Ng , Ted Goldstein , Kyle Ellrott41, Eric Collisson , 41 41 41 41 Aaron E. Cozen , Daniel Zerbino , Christopher Wilks , Brian Craft , Paul Author Contributions The Cancer Genome Atlas research network contributed Spellman ; Biospecimen Core Resource International Genomics Consortium collectively to this study. Biospecimens were provided by the tissue source sites and 46 46 46 46 46 Robert Penny , Troy Shelton , Martha Hatfield , Scott Morris , Peggy Yena , processed by the Biospecimen Core Resource. Data generation and analyses were 46 46 46 Candace Shelton , Mark Sherman , Joseph Paulauskis ; Nationwide Children’s performed by the genome-sequencing centers, cancer genome-characterization 47–49 47 Hospital Biospecimen Core Resource Julie M. Gastier-Foster , Jay Bowen , Nilsa centers and genome data analysis centers. All data were released through the Data 47,48 47 47,48 47 47,49 C. Ramirez , Aaron Black , Robert Pyatt , Lisa Wise , Peter White ; Tissue Coordinating Center. Project activities were coordinated by the National Cancer 50 51 source sites and disease working group Monica Bertagnolli , Jen Brown , Timothy Institute and National Human Genome Research Institute project teams. Project 52 53 51 54 55 A. Chan , Gerald C. Chu , Christine Czerwinski , Fred Denstman , Rajiv Dhir , leaders were R.K. and D.A.W. Writing team, T.A., A.J.B., T.A.C., L.D., A.H., S.R.H., R.K., 56 57,58 59 51 Arnulf Do¨rner , Charles S. Fuchs , Jose G. Guillem , Mary Iacocca , Hartmut P.W.L., M.M., N.S., I.S., J.M.S., J.T., V.T. and D.A.W.; mutations, M.S.L., L.R.T., D.A.W. and 60 52 61 61 62 Juhl , Andrew Kaufman , Bernard Kohl III , Xuan Van Le , Maria C. Mariano , G.G.; copy-number and structural aberrations, A.H.R., A.J.B., A.H. and P.-C.C.; DNA 62 63 59 59 Elizabeth N. Medina , Michael Meyers , Garrett M. Nash , Phillip B. Paty , Nicholas methylation, T.H.; expression, J.T.A.; miRNA, G.R., A.C.; pathways, C.J.C., L.D., T.G., S.N., 54 51 64 66 51 Petrelli , Brenda Rabeno , William G. Richards , David Solit , Pat Swanson , J.D.R., C.S., N.S., J.M.S. and V.T. 52 65 61 62 Larissa Temple , Joel E. Tepper , Richard Thorp , Efsevia Vakiani , Martin R. 59 67 51 59 63 Author Information dbGaP accession numbers have been provided in Supplementary Weiser , Joseph E. Willis , Gary Witkin , Zhaoshi Zeng , Michael J. Zinner , 68 69 69 Table 1. The authors declare no competing financial interests. Reprints and Carsten Zornig ; Data-Coordination Center Mark A. Jensen , Robert Sfeir , Ari B. 69 69 69 69 69 permissions information is available at www.nature.com/reprints. Readers are Kahn , Anna L. Chu , Prachi Kothiyal , Zhining Wang , Eric E. Snyder , Joan 69 69 69 69 69 welcome to comment on the online version of this article at www.nature.com/nature. Pontius , Todd D. Pihl , Brenda Ayala , Mark Backus , Jessica Walton , Jon 69 69 69 69 This paper is distributed under the terms of the Creative Commons Whitmore , Julien Baboud , Dominique L. Berton , Matthew C. Nicholls , Deepak 69 69 69 69 69 Attribution-Non-Commercial-Share Alike licence, and is freely available to all readers at Srinivasan , Rohini Raman , Stanley Girshik , Peter A. Kigonya , Shelley Alonso , 69 69 69 69 www.nature.com/nature. Correspondence and requests for materials should be Rashmi N. Sanbhadti , Sean P. Barletta , John M. Greene , David A. Pot ; Project 70 70 addressed to R.K. ([email protected]). Team National Cancer Institute Kenna R. Mills Shaw , Laura A. L. Dillon , Ken 71 71 70 72 73 Buetow , Tanja Davidsen , John A. Demchok , Greg Eley , Martin Ferguson , 70 71 70 70 Peter Fielding , Carl Schaefer , Margi Sheth and Liming Yang ; Project Team National Human Genome Research Institute Mark S. Guyer , Bradley A. 74 74 74 74 Ozenberger , Jacqueline D. Palchik , Jane Peterson , Heidi J. Sofia & Elizabeth Genome Sequencing Center Baylor College of Medicine Donna M. Muzny , Matthew Thomson . 1 1 1 1 1 N. Bainbridge , Kyle Chang , Huyen H. Dinh , Jennifer A. Drummond , Gerald Fowler , 1 1 1 1 Christie L. Kovar , Lora R. Lewis , Margaret B. Morgan , Irene F. Newsham , Jeffrey G. 1 1 1 1 1 1 Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, Reid , Jireh Santibanez , Eve Shinbrot , Lisa R. Trevino , Yuan-Qing Wu , Min Wang , 1,2 1,3 1,3 USA. Department of Biology and Biochemistry, University of Houston, Houston, Texas Preethi Gunaratne , Lawrence A. Donehower , Chad J. Creighton , David A. 1 1 77204, USA. Dan L. Duncan Cancer Center, Human Genome Sequencing Center, Baylor Wheeler , Richard A. Gibbs ; Genome Sequencing Center Broad Institute Michael S. 4 4 4 5 3 4 Lawrence , Douglas Voet , Rui Jing , Kristian Cibulskis , Andrey Sivachenko , Petar College of Medicine, Houston, Texas 77030, USA. The Eli and Edythe L. Broad Institute of 4 4 4,6,7 8 4 Stojanov , Aaron McKenna , Eric S. Lander , Stacey Gabriel , Gad Getz ; Genome Massachusetts Institute of Technology and Harvard University, Cambridge, 9,10 9 5 Sequencing Center Washington University in St Louis Li Ding , Robert S. Fulton , Massachusetts 02142, USA. Medical Sequencing Analysis and Informatics, The Eli and 9 9 9 9,10 9 Daniel C. Koboldt , Todd Wylie , Jason Walker , David J. Dooling , Lucinda Fulton , Edythe L. Broad Institute of Massachusetts Institute of Technology and Harvard 9 9 9 9–11 6 Kim D. Delehaunty , Catrina C. Fronick , Ryan Demeter , Elaine R. Mardis , Richard University, Cambridge, Massachusetts 02142, USA. Department of Biology, 9–11 12 K. Wilson ; Genome Characterization Center BC Cancer Agency Andy Chu , Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA. 12 12 12 12 7 Hye-Jung E. Chun , Andrew J. Mungall , Erin Pleasance , A. Gordon Robertson , Department of Systems Biology, Harvard University, Boston, Massachusetts 02115, 12 12 12 12 8 Dominik Stoll , Miruna Balasundaram , Inanc Birol , Yaron S. N. Butterfield , Eric USA. Genetic Analysis Platform, The Eli and Edythe L. Broad Institute of Massachusetts 12 12 12 12 12 Chuah , Robin J. N. Coope , Noreen Dhalla , Ranabir Guin , Carrie Hirst , Martin Institute of Technology and Harvard University, Cambridge, Massachusetts 02142, USA. 12 12 12 12 12 9 Hirst , Robert A. Holt , Darlene Lee , Haiyan I. Li , Michael Mayo , Richard A. The Genome Institute, Washington University School of Medicine, St Louis, Missouri 12 12 12 12 12 10 Moore , Jacqueline E. Schein , Jared R. Slobodan , Angela Tam , Nina Thiessen , 63108 USA. Department of Genetics, Washington University School of Medicine, St 12 12 12 12 11 Richard Varhol , Thomas Zeng , Yongjun Zhao , Steven J. M. Jones , Marco A. Louis, Missouri 63108, USA. Siteman Cancer Center, Washington University School of 12 4,13 12 Marra ; Genome-Characterization Center Broad Institute Adam J. Bass , Alex H. Medicine, St Louis, Missouri 63108, USA. Canada’s Michael Smith Genome Sciences 4,13 4 4 4,13 13 Centre, BC Cancer Agency, Vancouver, British Columbia V5Z 1L3, Canada. Department Ramos , Gordon Saksena , Andrew D. Cherniack , Stephen E. Schumacher , 4,13 4,13 4 4 4 of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. Barbara Tabak , Scott L. Carter , Nam H. Pho , Huy Nguyen , Robert C. Onofrio , 4 4 4,13 4 14 Departmentof Pathology, Harvard Medical School, Boston, Massachusetts02115, USA. Andrew Crenshaw , Kristin Ardlie , Rameen Beroukhim , Wendy Winckler , Gad 4 4,13,14 15 Belfer Institute for Applied Cancer Science, Department of Medical Oncology, Getz , Matthew Meyerson ; Genome-Characterization Center Brigham and 15 16 Women’s Hospital and Harvard Medical School Alexei Protopopov , Juinhua Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. Department of 15 16,17 17,18 18 18 17 Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA. Division of Zhang , Angela Hadjipanayis , Eunjung Lee , Ruibin Xi , Lixing Yang , 15 15 19 15 18 Genetics, Brigham and Women’s Hospital, Boston, Massachusetts 02115, USA. The Xiaojia Ren , Hailei Zhang , Narayanan Sathiamoorthy , Sachet Shukla , 16,17 17,18 15 18 Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts Peng-Chieh Chen , Psalm Haseley , Yonghong Xiao , Semin Lee , Jonathan 16 4,15,20 17–19 16,17 19 02115, USA. Informatics Program, Children’s Hospital, Boston, Massachusetts 02115, Seidman , Lynda Chin , Peter J. Park , Raju Kucherlapati ; USA. Department of Dermatology, Harvard Medical School, Boston, Massachusetts Genome-Characterization Center University of North Carolina, Chapel Hill J. Todd 21,22 23–25 25 25 25 21 02115, USA. Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Auman , Katherine A. Hoadley , Ying Du , Matthew D. Wilkerson , Yan Shi , 25 25 25 25 24,25 22 Chapel Hill, North Carolina 27599, USA. Institute for Pharmacogenetics and Christina Liquori , Shaowu Meng , Ling Li , Yidi J. Turman , Michael D. Topal , 26 25 25 25 27 Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, North Donghui Tan , Scot Waring , Elizabeth Buda , Jesse Walsh , Corbin D. Jones , 23 28 25 25 23 Carolina 27599, USA. Department of Genetics, University of North Carolina at Chapel Piotr A. Mieczkowski , Darshan Singh , Junyuan Wu , Anisha Gulabani , Peter 25 25 25 25 Dolina , Tom Bodenheimer , Alan P. Hoyle , Janae V. Simons , Matthew Hill, Chapel Hill, North Carolina 27599, USA. Department of Pathology and Laboratory 25 24 24 25 25 Soloway , Lisle E. Mose , Stuart R. Jefferys , Saianand Balu , Brian D. O’Connor , Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, 3 3 6 | NA TU RE | V O L 48 7 | 19 JULY 201 2 ©2012 Macmillan Publishers Limited. All rights reserved ARTICLE RESEARCH 25 48 USA. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Children’s Hospital, Columbus, Ohio 43205, USA. The Ohio State University College of 26 49 Hill, Chapel Hill, North Carolina 27599, USA. Carolina Center for Genome Sciences, Medicine, Department of Pathology, Columbus, Ohio 43205, USA. The Ohio State University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. University College of Medicine, Department of Pediatrics, Columbus, Ohio 43205, USA. 27 50 Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Department of Surgery, Brigham and Women’s Hospital, Harvard Medical School, 28 51 Carolina 27599, USA. Department of Computer Science, University of North Carolina at Brookline, Massachusetts 02115, USA. Department of Pathology, Christiana Care 29 52 Chapel Hill, Chapel Hill, North Carolina 27599, USA. Department of Internal Medicine, Health Services, Newark, Delaware 19718, USA. Human Oncology and Pathogenesis Division of Medical Oncology, University of North Carolina at Chapel Hill, Chapel Hill, North Program, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. 30 53 Carolina 27599, USA. University of Southern California Epigenome Center, University of Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, 31 54 Southern California, Los Angeles, California 90089 USA. Cancer Biology Division, The Brookline, Massachusetts 02115, USA. Department of Surgery, Helen F. Graham Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, Cancer Center at Christiana Care, Newark, Delaware 19718, USA. Department of 32 56 Maryland 21231, USA. Institute for Systems Biology, Seattle, Washington 98109, USA. Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA. Klinik fu¨r 33 57 Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Chirurgie, Krankenhaus Alten Eichen, 22527 Hamburg, Germany. Department of Cancer Center, Houston, Texas 77030, USA. Computational Biology Center, Memorial Medical Oncology, Dana-Farber Cancer Institute, Brookline, Massachusetts 02115, USA. 35 58 Sloan-Kettering Cancer Center, New York, New York 10065, USA. Divisions of Department of Medicine, Brigham and Women’s Hospital, Brookline, Massachusetts Experimental Therapy, Molecular Biology, Surgical Oncology, The Netherlands Cancer 02115, USA. Department of Surgery, Memorial Sloan-Kettering Cancer Center, New 36 60 Department of Epidemiology and Indivumed Inc., Kensington, Maryland 20895, USA. Institute, 1066 CX Amsterdam, The Netherlands. York, New York 10065, USA. 61 62 Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. ILSbio, LLC, Chestertown, Maryland 21620, USA. Department of Pathology, Memorial 37 63 Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Sloan-Kettering Cancer Center, New York, New York 10065, USA. Department of Center, New York, New York 10065, USA. Department of Pathology, Human Oncology Surgery, Brigham and Women’s Hospital, Brookline, Massachusetts 02115, USA. and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, New Tissue and Blood Repository, Brigham and Women’s Hospital, Brookline, 39 65 York 10065, USA. Department of Bioinformatics and Computational Biology, The Massachusetts 02115, USA. Dept of Radiation Oncology, University of North Carolina University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA. School of Medicine. Chapel Hill, North Carolina 27599, USA. Department of Medicine, 40 67 Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. Department 41 68 Houston, Texas 77030, USA. Department of Biomolecular Engineering and Center for of Pathology, Case Medical Center, Cleveland, Ohio 44106, USA. Chirugische Klinik, Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, Israelitisches Krankenhaus, 22297 Hamburg, Germany. SRA International, Fairfax, 42 70 California 95064, USA. Howard Hughes Medical Institute, University of California Santa Virginia 22033, USA. The Cancer Genome Atlas Program Office, National Cancer 43 71 Cruz, Santa Cruz, California 95064, USA. Buck Institute for Age Research, Novato, Institute, National Institutes of Health, Bethesda, Maryland 20892, USA. Center for California 94945, USA. Division of Hematology/Oncology, University of California San Biomedical Informatics and Information Technology (CBIIT), National Cancer Institute, 45 72 Francisco, San Francisco, California 94143, USA. Oregon Health and Science University, National Institutes of Health, Rockville, Maryland 20852, USA. Scimentis, LLC, Statham, Department of Molecular and Medical Genetics, Portland, Oregon 97239, USA. Georgia 30666, USA. MLF Consulting, Arlington, Massachusetts 02474, USA. 46 47 74 International Genomics Consortium, Phoenix, Arizona 85004, USA. Nationwide National Human Genome Research Institute, National Institutes of Health, Bethesda, Children’s Hospital Biospecimen Core Resource, The Research Institute at Nationwide Maryland 20892, USA. 1 9 JU LY 2012 | V O L 4 8 7 | NA TUR E | 3 37 ©2012 Macmillan Publishers Limited. All rights reserved

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Published: Jul 18, 2012

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