Comprehensive molecular characterization of human colon and rectal cancer

Cancer Genome Atlas Network

Abstract

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 ε (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.

Conflict of interest statement

The author declare no competing financial interests.

Figures

Figure 1. Mutation frequencies in human CRC.
Figure 1. Mutation frequencies in human CRC.
a, Mutation frequencies in each of the tumour samples from 224 patients. Note a clear separation of hypermutated and non-hypermutated samples. Red, MSI high, CIMP high or 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 hypermutated samples. The order of the samples is the same as in the main graph. b, Significantly mutated genes in hypermutated and non-hypermutated tumours. Blue bars represent genes identified by the MutSig algorithm and black bars represent genes identified by manual examination of sequence data. PowerPoint slide
Figure 2. Integrative analysis of genomic changes…
Figure 2. Integrative analysis of genomic changes in 195 CRCs.
Hypermutated tumours have near-diploid genomes and are highly enriched for hypermethylation, CIMP expression phenotype and BRAF(V600E) mutations. Non-hypermutated tumours originating from different sites are virtually indistinguishable from each other on the basis of their copy-number alteration patterns, DNA methylation or gene-expression patterns. Copy-number changes of the 22 autosomes are shown in shades of red for copy-number gains and shades of blue for copy-number losses. PowerPoint slide
Figure 3. Copy-number changes and structural aberrations…
Figure 3. Copy-number changes and structural aberrations in CRC.
a, Focal amplification of 11p15.5. Segmented DNA copy-number data from single-nucleotide polymorphism (SNP) arrays and low-pass whole-genome sequencing (WGS) are shown. Each row represents a patient; amplified regions are shown in red. b, Correlation of expression levels with copy-number changes for IGF2 and miR-483. c, IGF2 amplification and overexpression are mutually exclusive of alterations in PI3K signalling-related genes. d, Recurrent NAV2TCF7L2 fusions. The structure of the two genes, locations of the breakpoints leading to the translocation and circular representations of all rearrangements in tumours with a fusion are shown. Red line lines represent the NAV2TCF7L2 fusions and black lines represent other rearrangements. The inner ring represents copy-number changes (blue denotes loss, pink denotes gain). PowerPoint slide
Figure 4. Diversity and frequency of genetic…
Figure 4. Diversity and frequency of genetic changes leading to deregulation of signalling pathways in CRC.
Non-hypermutated (nHM; n = 165) and hypermutated (HM; n = 30) samples with complete data were analysed separately. Alterations are defined by somatic mutations, homozygous deletions, high-level focal amplifications, and, in some cases, by significant up- or downregulation of gene expression (IGF2, FZD10, SMAD4). Alteration frequencies are expressed as a percentage of all cases. Red denotes activated genes and blue denotes inactivated genes. Bottom panel shows for each sample if at least one gene in each of the five pathways described in this figure is altered. PowerPoint slide
Figure 5. Integrative analyses of multiple data…
Figure 5. Integrative analyses of multiple data sets.
a, Clustering of genes and pathways affected in colon and rectum tumours deduced by PARADIGM analysis. Blue denotes under-expressed relative to normal and red denotes overexpressed relative to normal. Some of the pathways deduced by this method are shown on the right. NHEJ, non-homologous end joining. b, Gene-expression signatures and SCNAs associated with tumour aggression. Molecular signatures (rows) that show a statistically significant association with tumour aggressiveness according to selected clinical assays (columns) are shown in colour, with red indicating markers of tumour aggressiveness and blue indicating the markers of less-aggressive tumours. Significance is based on the combined P value from the weighted Fisher’s method, corrected for multiple testing. Colour intensity and score is in accordance with the strength of an individual clinical–molecular association, and is proportional to log10(P), where P is the P value for that association. To limit the vertical extent of the figure, gene-expression signatures are restricted to a combined P value of P < 10−9 and SCNAs to P < 10−7, and features are shown only if they are also significant in the subset of non-MSI-H samples (the analysis was performed separately on the full data as well as on the MSI-H and non-MSI-H subgroups). PowerPoint slide

References

    1. The Cancer Genome Atlas Research Network Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008;455:1061–1068.
    1. The Cancer Genome Atlas Research Network Integrated genomic analyses of ovarian carcinoma. Nature. 2011;474:609–615.
    1. Fearon ER. Molecular genetics of colorectal cancer. Annu. Rev. Pathol. 2011;6:479–507.
    1. Bass AJ, et al. Genomic sequencing of colorectal adenocarcinomas identifies a recurrent VTI1A–TCF7L2 fusion. Nature Genet. 2011;43:964–968.
    1. Sjoblom T, et al. The consensus coding sequences of human breast and colorectal cancers. Science. 2006;314:268–274.
    1. Wood LD, et al. The genomic landscapes of human breast and colorectal cancers. Science. 2007;318:1108–1113.
    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. Aaltonen LA, et al. Clues to the pathogenesis of familial colorectal cancer. Science. 1993;260:812–816.
    1. Ionov Y, Peinado MA, Malkhosyan S, Shibata D, Perucho M. Ubiquitous somatic mutations in simple repeated sequences reveal a new mechanism for colonic carcinogenesis. Nature. 1993;363:558–561.
    1. Parsons R, et al. Hypermutability and mismatch repair deficiency in RER+ tumor cells. Cell. 1993;75:1227–1236.
    1. Dooley AL, et al. Nuclear factor I/B is an oncogene in small cell lung cancer. Genes Dev. 2011;25:1470–1475.
    1. Major MB, et al. Wilms tumor suppressor WTX negatively regulates WNT/β-catenin signaling. Science. 2007;316:1043–1046.
    1. Mori-Akiyama Y, et al. SOX9 is required for the differentiation of paneth cells in the intestinal epithelium. Gastroenterology. 2007;133:539–546.
    1. Bastide P, et al. Sox9 regulates cell proliferation and is required for Paneth cell differentiation in the intestinal epithelium. J. Cell Biol. 2007;178:635–648.
    1. Jones S, et al. Somatic mutations in the chromatin remodeling gene ARID1A occur in several tumor types. Hum. Mutat. 2012;33:100–103.
    1. Wilson BG, Roberts CW. SWI/SNF nucleosome remodellers and cancer. Nat. Rev. Cancer. 2011;11:481–492.
    1. Minsky BD. Unique considerations in the patient with rectal cancer. Semin. Oncol. 2011;38:542–551.
    1. Hinoue T, et al. Genome-scale analysis of aberrant DNA methylation in colorectal cancer. Genome Res. 2012;22:271–282.
    1. Beroukhim R, et al. Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc. Natl Acad. Sci. USA. 2007;104:20007–20012.
    1. Camps J, et al. Integrative genomics reveals mechanisms of copy number alterations responsible for transcriptional deregulation in colorectal cancer. Genes Chromosom. Cancer. 2009;48:1002–1017.
    1. Varley JM, Swallow JE, Brammar WJ, Whittaker JL, Walker RA. Alterations to either c-erbB-2(neu) or c-myc proto-oncogenes in breast carcinomas correlate with poor short-term prognosis. Oncogene. 1987;1:423–430.
    1. Yokota J, et al. Amplification of c-erbB-2 oncogene in human adenocarcinomas in vivo. Lancet. 1986;327:765–767.
    1. van der Flier LG, et al. Transcription factor achaete scute-like 2 controls intestinal stem cell fate. Cell. 2009;136:903–912.
    1. Jubb AM, Hoeflich KP, Haverty PM, Wang J, Koeppen H. Ascl2 and 11p15.5 amplification in colorectal cancer. Gut. 2011;60:1606–1607.
    1. Stange DE, et al. Expression of an ASCL2 related stem cell signature and IGF2 in colorectal cancer liver metastases with 11p15.5 gain. Gut. 2010;59:1236–1244.
    1. Cui H, et al. Loss of IGF2 imprinting: a potential marker of colorectal cancer risk. Science. 2003;299:1753–1755.
    1. Nakagawa H, et al. Loss of imprinting of the insulin-like growth factor II gene occurs by biallelic methylation in a core region of H19-associated CTCF-binding sites in colorectal cancer. Proc. Natl Acad. Sci. USA. 2001;98:591–596.
    1. Veronese A, et al. Oncogenic role of miR-483-3p at the IGF2/483 locus. Cancer Res. 2010;70:3140–3149.
    1. Ciriello G, Cerami E, Sander C, Schultz N. Mutual exclusivity analysis identifies oncogenic network modules. Genome Res. 2012;22:398–406.
    1. Brady CA, et al. Distinct p53 transcriptional programs dictate acute DNA-damage responses and tumor suppression. Cell. 2011;145:571–583.
    1. Rivera MN, et al. An X chromosome gene, WTX, is commonly inactivated in Wilms tumor. Science. 2007;315:642–645.
    1. Scheel SK, et al. Mutations in the WTX-gene are found in some high-grade microsatellite instable (MSI-H) colorectal cancers. BMC Cancer. 2010;10:413.
    1. Forbes, S. A. et al. The catalogue of somatic mutations in cancer (COSMIC). Curr. Protoc. Hum. Genet. Ch. 10, Unit 10.11. (2008)
    1. Massagué J, Blain SW, Lo RS. TGFβ signaling in growth control, cancer, and heritable disorders. Cell. 2000;103:295–309.
    1. Vaske CJ, et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics. 2010;26:i237–i245.
    1. House CD, et al. Voltage-gated Na+ channel SCN5A is a key regulator of a gene transcriptional network that controls colon cancer invasion. Cancer Res. 2010;70:6957–6967.
    1. Liu Z, Lu H, Jiang Z, Pastuszyn A, Hu CA. Apolipoprotein l6, a novel proapoptotic Bcl-2 homology 3-only protein, induces mitochondria-mediated apoptosis in cancer cells. Mol. Cancer Res. 2005;3:21–31.
    1. Topol L, Chen W, Song H, Day TF, Yang Y. Sox9 inhibits Wnt signaling by promoting β-catenin phosphorylation in the nucleus. J. Biol. Chem. 2009;284:3323–3333.
    1. Nagl NG, Jr, Zweitzig DR, Thimmapaya B, Beck GR, Jr, Moran E. The c-myc gene is a direct target of mammalian SWI/SNF-related complexes during differentiation-associated cell cycle arrest. Cancer Res. 2006;66:1289–1293.
    1. Chen B, et al. Small molecule-mediated disruption of Wnt-dependent signaling in tissue regeneration and cancer. Nat. Chem. Biol. 2009;5:100–107.
    1. Ewan K, et al. A useful approach to identify novel small-molecule inhibitors of Wnt-dependent transcription. Cancer Res. 2010;70:5963–5973.
    1. Sack U, et al. S100A4-induced cell motility and metastasis is restricted by the Wnt/β-catenin pathway inhibitor calcimycin in colon cancer cells. Mol. Biol. Cell. 2011;22:3344–3354.
    1. Chen K, et al. BreakDancer: an algorithm for high-resolution mapping of genomic structural variation. Nature Methods. 2009;6:677–681.
    1. Xi R, et al. Copy number variation detection in whole-genome sequencing data using the Bayesian information criterion. Proc. Natl Acad. Sci. USA. 2011;108:E1128–E1136.

Source: PubMed

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