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To characterize somatic alterations in colorectal carcinoma (CRC), we conducted genome-scale analysis of 276 samples, analyzing exome sequence, DNA copy number, promoter methylation, mRNA and microRNA expression. A subset (97) underwent low-depth-of-coverage whole-genome sequencing. 16% of CRC have hypermutation, three quarters of which have the expected high microsatellite instability (MSI), usually with hypermethylation and MLH1 silencing, but one quarter has somatic mismatch repair gene mutations. Excluding hypermutated cancers, colon and rectum cancers have remarkably similar patterns of genomic alteration. Twenty-four genes are significantly mutated. In addition to the expected APC, TP53, SMAD4, PIK3CA and KRAS mutations, we found frequent mutations in ARID1A, SOX9, and FAM123B/WTX. Recurrent copy number alterations include potentially drug-targetable amplifications of ERBB2 and newly discovered amplification of IGF2. Recurrent chromosomal translocations include fusion of NAV2 and WNT pathway member TCF7L1. Integrative analyses suggest new markers for aggressive CRC and important role for MYC-directed transcriptional activation and repression.
The Cancer Genome Atlas (TCGA) project plans to profile genomic changes in 20 different cancer types and has published results on two cancer types1,2. We now present results from multidimensional analyses of human colorectal cancer (CRC).
CRC is an important contributor to cancer mortality and morbidity. The distinction between colon and rectum is largely anatomical, but it impacts both surgical and radiotherapeutic management and it may impact prognosis. Most investigators divide CRC biologically into those with microsatellite instability (MSI) (located primarily in the right colon and frequently associated with the CpG island methylator phenotype (CIMP) and hyper-mutation) and those that are microsatellite-stable (MSS) but chromosomally unstable (CIN).
A rich history of investigations (for a review see3) has revealed several critical genes and pathways important to the initiation and progression of CRC3. These include the WNT, RAS-MAPK, PI3K, TGF-β, P53 and DNA mismatch repair pathways. Large-scale sequencing analyses4–6 have identified numerous recurrently mutated genes and a recurrent chromosomal translocation. Despite that background, we have not had a fully integrated view of the genetic and genomic changes and their significance for colorectal tumorigenesis. Further insight into those changes may enable deeper understanding of the pathophysiology of CRC and may identify potential therapeutic targets.
Tumor/normal pairs were analyzed by different platforms. The specific numbers of samples analyzed by each platform are shown in Supplementary Table 1.
To define the mutational spectrum, we performed exome capture DNA sequencing on 224 tumor/normal pairs (Supplementary Table 2 lists all mutations). Sequencing achieved >20X coverage of at least 80% of targeted exons. The somatic mutation rates varied considerably among the samples. Some had mutation rates <1/106 bases, whereas a few had mutations rates >100/106. We separated those cases (84%) with a mutation rate <8.24/106 (median number of non-synonymous mutations: 58) and those with mutations rates >12/106 (median number of mutations: 728), which we designated as hypermutated (Figure 1).
To assess the basis for the strikingly different mutation rates, we evaluated microsatellite instability (MSI)7 and mutations in the DNA mismatch repair pathway8–10 genes MLH1, MLH3, MSH2, MSH3, MSH6 and PMS2. Among the 30 hypermutated tumors with a complete data set, 23 (77%) had high levels of MSI (MSI-H). Included were 19 with MLH1 methylation, 17 of which had high CpG island methylation phenotype (CIMP). By comparison, the remaining seven hypermutated tumors, including the six with the highest mutation rates, lacked MSI-H, CIMP or MLH1 methylation but usually had somatic mutations in one or more mismatch repair genes or Polε aberrations rarely seen in the non-hypermutated tumors (Figure 1).
Overall, we identified 32 somatic recurrently mutated genes (defined by MutSig11 and manual curation) in the hypermutated and non-hypermutated cancers (Figure 1B). After removal of non-expressed genes, there were 15 and 17, respectively, in the hypermutated and non-hypermutated cancers (Figure 1B, see Supplementary Table 3 for complete list). Among the non-hypermutated tumors, the eight most frequently mutated genes were APC, TP53, KRAS, PIK3CA, FBXW7, SMAD4, TCF7L2 and NRAS. As expected, the mutated KRAS and NRAS genes usually had oncogenic codon 12/13 or 61 mutations, whereas the remaining genes had inactivating mutations. CTNNB1, SMAD2, FAM123B and SOX9 were also mutated frequently. FAM123B (WTX) is an X-linked negative regulator of WNT signaling12, and virtually all its mutations were loss-of-function. Mutations in SOX9, a gene important in cell differentiation in the intestinal stem cell niche13,14, have not been associated previously with human cancer, but all nine mutated alleles in the non-hypermutated CRCs were frameshift or nonsense mutations. Tumor suppressors ATM and ARID1A also had a disproportionately high number of frameshift or nonsense mutations. ARID1A mutations have recently been reported in CRC and many other cancers15,16.
In the hypermutated tumors, ACVR2A, APC, TGFBR2, MSH3, MSH6, SLC9A9 and TCF7L2 were frequent targets of mutation (Figure 1B), along with mostly BRAF V600E mutations. However, two genes that were frequently mutated in the non-hypermutated cancers were significantly less frequently mutated in hypermutated tumors: TP53 (60 vs 20%, p < 0.0001), and APC (81% vs 51%, p = 0.0023, both Fisher’s exact test). Other genes, including TGFBR2, were recurrently mutated in the hypermutated cancers, but not in the non-hypermutated samples. These findings suggest that hypermutated and non-hypermutated tumors progress through different sequences of genetic events.
As expected, hypermutated tumors with MLH1 silencing and MSI-H exhibited additional differences in 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 mutation in hypermutated tumors without MLH1 silencing, and 50-fold higher than in non-hypermethylated tumors (Supplementary Table 2).
As mentioned above, patients with colon and rectal tumors are managed differently17, and epidemiology also shows differences between the two17. An initial integrative analysis of MSI status, somatic copy number alterations (SCNAs), CIMP status and gene expression profiles of 132 colonic and 62 rectal tumors enabled us to examine possible biological differences between tumors in the two locations. Among the non-hypermutated tumors, however, the overall patterns of changes in copy number, CIMP, mRNA and miRNA were indistinguishable between colon and rectal carcinomas (Figure 2). Based on that result, we merged the two for all subsequent analyses.
Unsupervised clustering of the promoter DNA methylation profiles of 236 colorectal tumors revealed four subgroups (Supplementary Methods; Supplementary Figure 1). Two of the clusters contained tumors with elevated rates of methylation and were classified as CIMP-high (CIMP-H) and CIMP-low (CIMP-L), as previously described18. The two non-CIMP clusters were predominantly from tumors that were non-hypermutated and derived from different anatomic locations. mRNA expression profiles separated the colorectal tumors into three distinct clusters (Supplementary Figure 2). One significantly overlapped with CIMP-H tumors (p=3×10−12) and was enriched with hypermutated tumors; the other two clusters did not correspond with any group in the methylation data. Analysis of miRNA expression by unsupervised clustering (Supplementary Figure 3) identified no clear distinctions between rectal cancers and non-hypermethylated colon cancers.
257 tumors were profiled for somatic copy-number alterations (SCNAs) with Affymetrix SNP 6.0 arrays. Of those tumors, 97 were also analyzed by low depth-of-coverage (low-pass) whole-genome sequencing (WGS). As expected, the hypermutated tumors had far fewer SCNAs (Figure 2). No difference was found between MSI and MSS hypermutated tumors (Supplementary Figure 4). We used the GISTIC algorithm19 to identify likely gene targets of focal alterations. There were several previously well-defined arm-level changes, including gains of 1q, 7p/q, 8p/q, 12q, 13q, 19q, and 20p/q6 (Supplementary Figure 4; Supplementary Table 4). Significantly deleted chromosome arms were 18p/q (including SMAD4) in 66% of the tumors and 17p/q (including TP53) in 56%. Also significantly deleted genes were 1p, 4q, 5q, 8p, 14q, 15q, 20p, and 22q.
We identified 28 recurrent deletion peaks (Supplementary Table 4; Supplementary Figure 4), including genes like FHIT, A2BP1 and WWOX with large genomic footprints located in potentially fragile sites of the genome, in near-diploid hypermutated tumors. Other focal deletions involved tumor suppressor genes such as SMAD4, APC, PTEN and SMAD3. A significant focal deletion of 10p25.2 spanned four genes, including TCF7L2, which was also frequently mutated in our dataset. A gene fusion between adjacent genes VTI1A and TCF7L2 through an interstitial deletion was found in 3% of CRCs and is required for survival of CRC cells bearing the translocation4.
There were 17 regions of significant focal amplification (Supplementary Table 4). Some of them were superimposed on broad gains of chromosome arms. Included were a peak at 13q12.13 near the peptidase gene USP12 and ~500kB distal to the CRC candidate oncogene CDK8; an adjacent peak at 13q12; a peak containing KLF5 at 13q22.1; and a peak at 20q13.12 adjacent to HNF4A. Peaks on chromosome 8 included 8p12 (which contains the histone methyl-transferase WHSC1L1, adjacent to FGFR1) and 8q24 (which contains MYC). An amplicon at 17q21.1, found in 4% of the tumors, contains seven genes, including the tyrosine kinase ERBB2. ERBB2 amplifications have been described in colon, breast and gastric/esophageal tumors, and breast and gastric cancers bearing these amplifications have been treated effectively with the anti-ERBB2 antibody trastuzumab20–22.
One of the most common focal amplifications, found in 7% of the tumors, is gain of a 100–150 kb region of chromosome arm 11p15.5. It contains the genes encoding insulin (INS), insulin-like growth factor 2 (IGF2), and tyrosine hydroxylase (TH), as well as miR-483, which is embedded within IGF2 (Figure 3a). We found elevated expression of IGF2 and miR-483 but not of INS and TH (Figure 3b–c). Immediately adjacent to the amplified region is ASCL2, a transcription factor active in specifying intestinal stem cell fate23. Although ASCL2 has been implicated as a target of amplification in CRC23–25, it was consistently outside the region of amplification and its expression was not correlated with copy-number changes. These observations suggest that IGF2 and miR-483 are candidate functional targets of 11p15.5 amplification. IGF2 overexpression through loss of imprinting has been implicated in the promotion of CRC26,27. MiR-483 may also play a role in CRC pathogenesis28.
A subset of tumors (15%) without IGF2 amplification also had dramatically higher levels (as much as 100X) of IGF2 gene expression, an effect not attributable to methylation changes at the IGF2 promoter. To assess the context of IGF2 amplification/overexpression, we systematically looked for mutually exclusive genomic events using the MEMo method29. We found a pattern of near exclusivity (corrected p < 0.01) of IGF2 overexpression with genomic events known to activate the PI3-K pathway (mutations of PIK3CA/PIK3R1 or deletion/mutation of PTEN, Figure 3c, and Supplementary Table 5). The IRS2 gene, whose product links IGF1R, the receptor for IGF2, with PI3-K, is on chromosome 13, which is frequently gained in colorectal cancer. Those cases with the highest IRS2 expression were mutually exclusive of the cases with IGF2 overexpression (p= 0.04) and also lacked mutations in the PI3-K pathway (p= 0.0001)(Figure 3c). Those results strongly suggest that the IGF2/IGF1R/IRS2 axis signals to PI3-K in CRC and imply that therapeutic targeting of the pathway could act to block PI3-K activity in this subset of patients.
To identify novel chromosomal translocations, we performed low-pass, paired-end, whole-genome sequencing on 97 tumors with matched normals. In each case we achieved sequence coverage of ~3–4X and a corresponding physical coverage of 7.5–10X. Despite the low genome coverage, we detected 250 candidate inter-chromosomal translocation events (range 0–10/tumor). Among those events, 212 had one or both breakpoints in an intergenic region, whereas the remaining 38 juxtaposed coding regions of two genes in putative fusion events, of which 18 were predicted to code for in-frame events (Supplementary Table 6). We found three separate cases in which the first two exons of the NAV2 gene on chromosome 11 are joined with the 3’ coding portion of TCF7L1 on chromosome 2 (Supplementary Figure 5). TCF7L1 encodes TCF3, a member of the TCF/LEF class of transcription factors that heterodimerize with nuclear β-catenin to enable β-catenin-mediated transcriptional regulation. Intriguingly, in all three cases, the predicted structure of the NAV2-TCF7L1 fusion protein lacks the TCF3 β-catenin binding domain. This translocation is similar to another recurrent translocation identified in CRC, a fusion in which the amino terminus of VTI1A is joined to TCF4 that is encoded by TCF7L2, deleted or mutated in 12% of non-hypermutated tumors and a homolog of TCF7L14. We also observed 21 cases of translocation involving TTC28 located on Chromosome 22 (Supplementary Table 6). In all cases the fusions predict inactivation of TTC28, which has been identified as a target of p53 and an inhibitor of tumor cell growth30. Eleven of the 19 (58%) gene-gene translocations are validated by either obtaining PCR products and in some case sequencing the junction fragments (Supplementary Figure 5).
Integrated analysis of mutations, copy-number, and mRNA expression changes in 195 tumors with complete data enriched our understanding of how some well-defined pathways are deregulated. We grouped samples by hypermutation status and identified recurrent alterations in the WNT, MAPK, PI3K, TGF-β and p53 pathways (Figure 4, Supplementary Figure 6, Supplementary Table 1).
We found that the WNT signaling pathway was altered in 93% of all tumors, including biallelic inactivation of APC (Supplementary Table 7) or activating mutations of CTNNB1 in ~80% of cases. There were also mutations in SOX9 and mutations and deletions in TCF7L2, as well as the DKK family members and AXIN2, FBXW7 (Supplementary Figure 7), ARID1A and FAM123B/WTX (the latter a negative regulator of WNT/β-catenin signaling12 found mutated in Wilm’s tumor31). A few mutations in FAM123B/WTX have been described in colorectal cancer32. SOX9 has been suggested to play a role in cancer, but no mutations have previously been described. The WNT receptor Frizzled (FZD10) was overexpressed in ~17% of samples, in some instances at levels 100X normal. Altogether, we found 16 different altered WNT pathway genes, confirming the importance of that pathway in CRC. Interestingly, many of those alterations were found in tumors that harbor APC mutations, suggesting that multiple lesions affecting the WNT signaling pathway confer selective advantage.
Genetic alterations in the PI3K and RAS-MAPK pathways are common in CRC. In addition to IGF2 and IRS2 overexpression, we found mutually exclusive mutations in PIK3R1 and PIK3CA as well as deletions in PTEN in 2%, 15% and 4% of non-hypermutated tumors, respectively. We found that 55% of non-hypermutated tumors have alterations in KRAS, NRAS or BRAF, with a significant pattern of mutual exclusivity (Supplementary Figure 6, Supplementary Table 1). We also evaluated mutations in the ERBB family of receptors because of the translational relevance of such mutations. Mutations or amplifications in one of the four genes are present in 22/165 (13%) non-hypermutated and 16/30 (53%) hypermutated cases. Some of the mutations are listed in the COSMIC database33, suggesting a functional role. Intriguingly, recurrent V842I ERBB2 and V104M ERBB3 mutations were found in four and two non-hypermutated cases, respectively. Mutations and focal amplifications of ERBB2 (Supplementary Figure 6) should be evaluated as predictors of response to agents that target those receptors. We observed co-occurrence of alterations involving the RAS and PI3K pathways in a third of tumors (Figure 4; Fisher’s exact test p = 0.039). These results suggest that simultaneous inhibition of the RAS and PI3K pathways may be required to achieve therapeutic benefit.
The TGF-β signaling pathway is known to be deregulated in colorectal and other cancers34. We found genomic alterations in TGFBR1, TGFBR2, ACVR2A, ACVR1B, SMAD2, SMAD3 and SMAD4 in 27% of the non-hypermutated and 87% of the hypermutated tumors. We also evaluated the p53 pathway, finding alterations in TP53 in 59% of non-hypermutated cases (mostly biallelic, Supplementary Table 8) and alterations in ATM, a kinase that phosphorylates and activates p53 following DNA damage, in 7%. Alterations in those two genes showed a trend towards mutual exclusivity (p = 0.016) (Figure 4, Supplementary Figure 6, Supplementary Table 1).
We integrated copy number, gene expression, methylation and pathway data using the PARADIGM software platform35. The analysis revealed a number of novel characteristics of CRC (Figure 5A). For example, despite the diversity in anatomical origin or mutation levels, nearly 100% of these tumors have changes in MYC transcriptional targets, both those promoted by and those inhibited by MYC. These findings are consistent with patterns deduced from genetic alterations (Figure 4) and suggest an important role for MYC in the CRC. The analysis also identified several gene networks altered across all tumor samples and those with differential alterations in hypermutated vs. non-hypermutated samples (Supplementary Table 7, Supplemental Data on the TCGA publication webpage).
Since most of the tumors used in this study were derived from prospective collection, survival data are not available. However, the tumors can be classified as aggressive or non-aggressive on the basis of tumor stage, lymph node status, distant metastasis and vascular invasion at the time of surgery. We found numerous molecular signatures associated with tumor aggressiveness, a subset of which is shown in Figure 5B. They include specific focal amplifications and deletions, and altered gene expression levels, including those of SCN5A36, a reported regulator of colon cancer invasion (full list: Supplementary Tables 10–11). Association with tumor aggressiveness is also observed in altered expression of miRNAs and specific somatic mutations (APC, TP53, PIK3CA, BRAF, and FBXW7; Supplementary Figure 8B). Mutations in FBXW7 (38 cases) and distant metastasis (32 cases) never co-occurred (p = 0.0019). Interestingly, a number of genomic regions have multiple molecular associations with tumor aggressiveness that manifest as “clinically-related genomic hotspots”. Examples of this are the region 20q13.12, which includes a focal amplification and multiple genes correlating with tumor aggression, and the region 22q12.3, containing APOL637 (Supplementary Figures 8–9).
This comprehensive integrative analysis of 224 colorectal tumor/normal pairs provides a number of insights into the biology of CRC and identifies potential therapeutic targets. To identify possible biological differences in colon and rectum tumors we found, in the non-hypermutated tumors, irrespective of their anatomical origin, the same type of copy number, expression profile, DNA methylation and miRNA changes. Over 94% of them had a mutation in one or more members of the WNT signaling pathway, predominantly in APC. However, there were some differences between tumors from the right colon and the remaining sites. Hypermethylation was more common in the right colon, and three quarters of hypermutated samples came from the same site, although not all of them had MSI (Figure 2). Why most of the hypermutated samples come from the right colon and why there are two classes of tumors at this site is not known. The origins of the colon from embryonic midgut and hindgut may provide an explanation. Since the survival of patients with high MSI cancers are better and these cancers have hypermutation, mutation rate may be a better prognostic indicator.
Whole exome sequencing and integrative analysis of genomic data provided further insights into the pathways that are dysregulated in CRC. We found that 93% of non-hypermutated and 97% of hypermutated cases had deregulated WNT signaling pathway. Novel findings included recurrent mutations in FAM123B, ARID1A and SOX9 and very high levels of overexpression of the WNT ligand receptor Frizzled 10. To our knowledge, SOX9 has not previously been described as frequently mutated in any human cancer. SOX9 is transcriptionally repressed by WNT signaling, and the SOX9 protein has been shown to facilitate β-catenin degradation38. ARID1A is frequently mutated in gynecological cancers and has been shown to suppress Myc transcription39. Activation of WNT signaling and inactivation of the TGF-β signaling pathway are known to result in activation of MYC. Our mutational and integrative analyses emphasize the critical role of MYC in CRC. We also compared our results with other large-scale analyses6 and find many similarities and few differences in mutated genes (Supplementary Table 3).
Our integrated analysis revealed a diverse set of changes in TCF/LEF encoding genes suggesting additional roles for TCF/LEF factors in CRC beyond being passive partners for β-catenin.
Our data suggest a number of therapeutic approaches to CRC. Included are WNT signaling inhibitors and small-molecule β-catenin inhibitors that are showing initial promise40–42. We find that several proteins in the RTK/RAS and PI3K pathways including IGF2, IGFR, ERBB2, ERBB3, MEK, AKT and mTOR could be targets for inhibition.
Our analyses show that non-hypermutated adenocarcinomas of the colon and rectum are not distinguishable at the genomic level. However, tumors from the right/ascending colon were more likely to be hypermethylated and to have elevated mutation rates than were other CRCs. As has been recognized previously, activation of the WNT signaling pathway and inactivation of the TGF-β signaling pathway, resulting in increased activity of MYC, are nearly ubiquitous events in CRC. Genomic aberrations frequently target the MAPK and PI3-K pathways but less frequently target receptor tyrosine kinases. In conclusion, the data presented here provide an unprecedented resource for understanding this deadly disease and identifying possibilities for treating it in a targeted way.
Tumor and normal samples were processed by either of two Biospecimen Core Resources (BCRs), and aliquots of purified nucleic acids were shipped to the genome characterization and sequencing centers (Supplementary Methods). The BCRs provided sample sets in several different batches. To assess any batch effects we examined the mRNA expression, miRNA expression and DNA methylation data sets using a combination of cluster analysis, enhanced principal component analysis, and analysis of variance (Supplementary Methods). Although some differences among batches were detected, we did not correct them computationally because the differences were generally modest and because some of them may reflect biological phenomena (Supplementary Methods).
We used Affymetrix SNP 6.0 microarrays to detect copy-number alterations. A subset of samples was subjected to low pass (2–5X) whole genome sequencing (Illumina HiSeq), in part for detection of SCNA and chromosomal translocations43,44. Gene expression profiles were generated using Agilent microarrays and RNA-Seq. DNA methylation data were obtained using Illumina Infinium (HumanMethylation27) arrays. DNA sequencing of coding regions was performed by exome capture followed by sequencing on the SOLiD or Illumina HiSeq platforms. Details of the analytical methods used are described in Supplementary Methods.
All of the primary sequence files are deposited in dbGap and all other data are deposited at the Data Coordinating Center (DCC) for public access (http://cancergenome.nih.gov/). Data matrices and supporting data can be found at http://tcga-data.nci.nih.gov/docs/publications/coadread_2012/. The data can also be explored via the ISB Regulome Explorer (http://explorer.cancerregulome.org/) and the cBio Cancer Genomics Portal (http://cbioportal.org). Descriptions of the data can be found at https://wiki.nci.nih.gov/x/j5dXAg and in Supplementary Methods.
We thank Charles Fuchs for reviewing the manuscript prior to submission. This work was supported by the following grants from the USA National Institutes of Health: U24CA143799, U24CA143835, U24CA143840, U24CA143843, U24CA143845, U24CA143848, U24CA143858, U24CA143866, U24CA143867, U24CA143882, U24CA143883, U24CA144025, U54HG003067, U54HG003079, U54HG003273.
Author Contributions: The TCGA research network contributed collectively to this study. Biospecimens were provided by the Tissue Source Sites and processed by the Biospecimen Core Resource. Data generation and analyses were performed by the Genome Sequencing Centers, Cancer Genome Characterization Centers, and Genome Data Analysis Centers. All data were released through the Data Coordinating Center. Project activities were coordinated by the NCI and NHGRI Project Teams. Project Leaders: Raju Kucherlapati and David A. Wheeler; Writing Team: Todd Auman, Adam J. Bass, Timothy A. Chan, Lawrence Donehower, Angela Hadjipanayis, Stanley R. Hamilton, Raju Kucherlapati, Peter W. Laird, Matthew Meyerson, Nikolaus Schultz, Ilya Shmulevich, Joshua M. Stuart, Joel Tepper, Vesteinn Thorsson, David A. Wheeler. Mutations: Michael S. Lawrence, Lisa R. Trevino, David A. Wheeler, Gad Getz; Copy-Number and Structural Aberrations: Alex H. Ramos, Adam J. Bass, Angela Hadjipanayis, Peng-Chieh Chen; DNA Methylation: Toshinori Hinoue; Expression: J. Todd Auman; miRNA: Gordon Robertson, Andy Chu; Pathways: Chad J. Creighton, Lawrence Donehower, Ted Goldstein, Sam Ng, Jorma de Ronde, Chris Sander, Nikolaus Schultz, Joshua M. Stuart, & Vesteinn Thorsson.
Baylor College of Medicine – Donna M. Muzny(1), Matthew N. Bainbridge(1), Kyle Chang(1), Huyen H. Dinh(1), Jennifer A. Drummond(1), Gerald Fowler(1), Christie L. Kovar(1), Lora R. Lewis(1), Margaret B. Morgan(1), Irene F. Newsham(1), Jeffrey G. Reid(1), Jireh Santibanez(1), Eve Shinbrot(1), Lisa R. Trevino(1), Yuan-Qing Wu(1), Min Wang(1), Preethi Gunaratne(1,2), Lawrence A. Donehower(1,3), Chad J. Creighton(1,3), David A. Wheeler(1), Richard A. Gibbs(1), Broad Institute – Michael S. Lawrence(4), Douglas Voet(4), Rui Jing(4), Kristian Cibulskis(5), Andrey Sivachenko(3), Petar Stojanov(4), Aaron McKenna(4), Eric S. Lander(4,6,7), Stacey Gabriel(8), Gad Getz(4), Washington University in St. Louis – Li Ding(9,10), Robert S. Fulton(9), Daniel C. Koboldt(9), Todd Wylie(9), Jason Walker(9), David J. Dooling(9,10), Lucinda Fulton(9), Kim D. Delehaunty(9), Catrina C. Fronick(9), Ryan Demeter(9), Elaine R. Mardis(9,10,11), & Richard K. Wilson(9,10,11)
BC Cancer Agency – Andy Chu(12), Hye-Jung E. Chun(12), Andrew J. Mungall(12), Erin Pleasance(12), A. Gordon Robertson(12), Dominik Stoll(12), Miruna Balasundaram(12), Inanc Birol(12), Yaron S.N. Butterfield(12), Eric Chuah(12), Robin J.N. Coope(12), Noreen Dhalla(12), Ranabir Guin(12), Carrie Hirst(12), Martin Hirst(12), Robert A. Holt(12), Darlene Lee(12), Haiyan I. Li(12), Michael Mayo(12), Richard A. Moore(12), Jacqueline E. Schein(12), Jared R. Slobodan(12), Angela Tam(12), Nina Thiessen(12), Richard Varhol(12), Thomas Zeng(12), Yongjun Zhao(12), Steven J.M. Jones(12), Marco A. Marra(12), Broad Institute – Adam J. Bass(4,13), Alex H. Ramos(4,13), Gordon Saksena(4), Andrew D. Cherniack(4), Stephen E. Schumacher(4,13), Barbara Tabak(4,13), Scott L. Carter(4,13), Nam H. Pho(4), Huy Nguyen(4), Robert C. Onofrio(4), Andrew Crenshaw(4), Kristin Ardlie(4), Rameen Beroukhim(4,13), Wendy Winckler(4), Gad Getz(4), Matthew Meyerson(4,13,14), Brigham and Women’s Hospital and Harvard Medical School – Alexei Protopopov (15), Juinhua Zhang (15), Angela Hadjipanayis (16,17), Eunjung Lee (17,18), Ruibin Xi (18), Lixing Yang(18), Xiaojia Ren(15), Hailei Zhang (15), Narayanan Sathiamoorthy (19), Sachet Shukla (15), Peng-Chieh Chen (16,17), Psalm Haseley (17,18), Yonghong Xiao (15), Semin Lee (18), Jonathan Seidman (16), Lynda Chin (4,15,20), Peter J. Park (17,18,19), Raju Kucherlapati (16,17), University of North Carolina, Chapel Hill: J. Todd Auman(21,22), Katherine A. Hoadley(23,24,25), Ying Du(25), Matthew D. Wilkerson(25), Yan Shi(25), Christina Liquori (25), Shaowu Meng(25), Ling Li(25), Yidi J. Turman(25), Michael D. Topal(24,25), Donghui Tan(26), Scot Waring(25), Elizabeth Buda(25), Jesse Walsh(25), Corbin D. Jones(27), Piotr A. Mieczkowski(23), Darshan Singh(28), Junyuan Wu(25), Anisha Gulabani(25), Peter Dolina(25), Tom Bodenheimer(25), Alan P. Hoyle(25), Janae V. Simons(25), Matthew Soloway(25), Lisle E. Mose(24), Stuart R. Jefferys(24), Saianand Balu(25), Brian D. O’Connor(25), Jan F. Prins(28), Derek Y. Chiang(23,25), D. Neil Hayes(25,29), Charles M. Perou(23,24,25), University of Southern California / Johns Hopkins – Toshinori Hinoue(30), Daniel J. Weisenberger(30), Dennis T. Maglinte(30), Fei Pan(30), Benjamin P. Berman(30), David J. Van Den Berg(30), Hui Shen(30), Timothy Triche Jr(30), Stephen B. Baylin(31), & Peter W. Laird(30)
Broad Institute – Gad Getz(4), Michael Noble(4), Doug Voet(4), Gordon Saksena(4), Nils Gehlenborg (18,4), Daniel DiCara (4), Juinhua Zhang(4,15), Hailei Zhang(4,15), Chang-Jiun Wu(4,15), Spring Yingchun Liu(4,15), Sachet Shukla(4,15), Michael S. Lawrence(4), Lihua Zhou(4), Andrey Sivachenko(4), Pei Lin(4), Petar Stojanov(4), Rui Jing(4), Richard W. Park(18), Marc-Danie Nazaire(4), Jim Robinson(4), Helga Thorvaldsdottir(4), Jill Mesirov(4), Peter J.Park(17,18,19), Lynda Chin(4,15,20), Institute for Systems Biology – Vesteinn Thorsson(32), Sheila M. Reynolds(32), Brady Bernard(32), Richard Kreisberg(32), Jake Lin(32), Lisa Iype(32), Ryan Bressler(32), Timo Erkkilä(32), Madhumati Gundapuneni(32), Yuexin Liu(33), Adam Norberg(32), Tom Robinson (32), Da Yang(33), Wei Zhang (33), Ilya Shmulevich(32), Memorial Sloan-Kettering Cancer Center – Jorma J. de Ronde(34,35), Nikolaus Schultz(34), Ethan Cerami(34), Giovanni Ciriello(34), Arthur P. Goldberg(34), Benjamin Gross(34), Anders Jacobsen(34), Jianjiong Gao(34), Bogumil Kaczkowski(34), Rileen Sinha(34), B. Arman Aksoy(34), Yevgeniy Antipin(34), Boris Reva(34), Ronglai Shen(36), Barry S. Taylor(34), Timothy A. Chan(37), Marc Ladanyi(38), Chris Sander(34), The University of Texas MD Anderson Cancer Center – Rehan Akbani(39), Nianxiang Zhang(39), Bradley M. Broom(39), Tod Casasent(39), Anna Unruh(39), Chris Wakefield(39), Stanley R. Hamilton(33), R. Craig Cason (33), Keith A. Baggerly(39), John N. Weinstein(39,40), University of California, Santa Cruz / Buck Institute – David Haussler(41,42), Christopher C. Benz(43), Joshua M. Stuart(41), Stephen C. Benz(41), J. Zachary Sanborn(41), Charles J. Vaske(41), Jingchun Zhu(41), Christopher Szeto(41), Gary K. Scott(43), & Christina Yau(43). Sam Ng(41), Ted Goldstein(41), Kyle Ellrott(41), Eric Collisson(44), Aaron E. Cozen(41), Daniel Zerbino(41), Christopher Wilks(41), Brian Craft(41) & Paul Spellman (45)
International Genomics Consortium – Robert Penny(46), Troy Shelton(46), Martha Hatfield(46), Scott Morris(46), Peggy Yena(46), Candace Shelton(46), Mark Sherman(46), & Joseph Paulauskis(46)
Nationwide Children’s Hospital Biospecimen Core Resource – Julie M. Gastier-Foster(47,48,49), Jay Bowen(47), Nilsa C. Ramirez(47,48), Aaron Black(47), Robert Pyatt(47,48), Lisa Wise(47), & Peter White(47,49)
Monica Bertagnolli(50), Jen Brown(51), Timothy A. Chan(52), Gerald C. Chu(53), Christine Czerwinski(51), Fred Denstman(54), Rajiv Dhir(55), Arnulf Dörner(56), Charles S. Fuchs(57,58), Jose G. Guillem(59), Mary Iacocca(51), Hartmut Juhl(60), Andrew Kaufman(52), Bernard Kohl III(61), Xuan Van Le(61), Maria C. Mariano(62), Elizabeth N. Medina(62), Michael Meyers(63), Garrett M. Nash(59), Phillip B. Paty(59), Nicholas Petrelli(54), Brenda Rabeno(51), William G. Richards(64), David Solit(66), Pat Swanson(51), Larissa Temple(52), Joel E. Tepper(65), Richard Thorp(61), Efsevia Vakiani(62), Martin R. Weiser(59), Joseph E. Willis (67), Gary Witkin(51), Zhaoshi Zeng(59), Michael J. Zinner(63), & Carsten Zornig(68)
Mark A. Jensen(69), Robert Sfeir(69), Ari B. Kahn(69), Anna L. Chu(69), Prachi Kothiyal(69), Zhining Wang(69), Eric E. Snyder(69), Joan Pontius(69), Todd D. Pihl(69), Brenda Ayala(69), Mark Backus(69), Jessica Walton(69), Jon Whitmore(69), Julien Baboud(69), Dominique L. Berton(69), Matthew C. Nicholls(69), Deepak Srinivasan(69), Rohini Raman(69), Stanley Girshik(69), Peter A. Kigonya(69), Shelley Alonso(69), Rashmi N. Sanbhadti(69), Sean P. Barletta(69), John M. Greene(69) & David A. Pot(69)
National Cancer Institute – Kenna R. Mills Shaw(70), Laura A. L. Dillon(70), Ken Buetow(71), Tanja Davidsen(71), John A. Demchok(70), Greg Eley(72), Martin Ferguson(73), Peter Fielding(70), Carl Schaefer(71), Margi Sheth(70), & Liming Yang(70)
National Human Genome Research Institute: Mark S. Guyer(74), Bradley A. Ozenberger(74), Jacqueline D. Palchik(74), Jane Peterson(74), Heidi J. Sofia(74), & Elizabeth Thomson(74)