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Comprehensive genomic characterization of squamous cell lung cancers

Comprehensive genomic characterization of squamous cell lung cancers ARTICLE doi:10.1038/nature11404 Comprehensive genomic characterization of squamous cell lung cancers The Cancer Genome Atlas Research Network* Lung squamous cell carcinoma is a common type of lung cancer, causing approximately 400,000 deaths per year worldwide. Genomic alterations in squamous cell lung cancers have not been comprehensively characterized, and no molecularly targeted agents have been specifically developed for its treatment. As part of The Cancer Genome Atlas, here we profile 178 lung squamous cell carcinomas to provide a comprehensive landscape of genomic and epigenomic alterations. We show that the tumour type is characterized by complex genomic alterations, with a mean of 360 exonic mutations, 165 genomic rearrangements, and 323 segments of copy number alteration per tumour. We find statistically recurrent mutations in 11 genes, including mutation of TP53 in nearly all specimens. Previously unreported loss-of-function mutations are seen in the HLA-A class I major histocompatibility gene. Significantly altered pathways included NFE2L2 and KEAP1 in 34%, squamous differentiation genes in 44%, phosphatidylinositol-3-OH kinase pathway genes in 47%, and CDKN2A and RB1 in 72% of tumours. We identified a potential therapeutic target in most tumours, offering new avenues of investigation for the treatment of squamous cell lung cancers. Lung cancer is the leading cause of cancer-related mortality worldwide, adjacent, histologically normal tissues resected at the time of surgery leading to an estimated 1.4 million deaths in 2010 (ref. 1). The discovery (n5 137) or from peripheral blood (n5 41). All patients provided of recurrent mutations in the epidermal growth factor receptor (EGFR) written informed consent to conduct genomic studies in accordance kinase, as well as fusions involving anaplastic lymphoma kinase (ALK), with local Institutional Review Boards. The demographic characteris- has led to a marked change in the treatment of patients with lung tics are described in Supplementary Table 1.2. The median follow-up 2–5 adenocarcinoma, the most common type of lung cancer . More recent for the cohort was 15.8 months, and 60% of patients were alive at the data have suggested that targeting mutations in BRAF, AKT1, ERBB2 time of the last follow-up (data updated in November 2011). Ninety-six and PIK3CA and fusions that involve ROS1 and RET may also be suc- per cent of the patients had a history of tobacco use, similar to previous 6,7 cessful . Unfortunately, activating mutations in EGFR and ALK fusions reports for North American patients with lung SQCC . DNA and are typically not present in the second most common type of lung cancer, RNA were extracted from patient specimens and measured by several lung squamous cell carcinoma (SQCC) , and targeted agents developed genomic assays, which included standard quality-control assessments for lung adenocarcinoma are largely ineffective against lung SQCC. (Supplementary Methods, sections 2–8). A committee of experts in Although no comprehensive genomic analysis of lung SQCCs has lung cancer pathology performed a further review of all samples to been reported, single-platform studies have identified regions of confirm the histological subtype (Supplementary Fig. 1.1 and somatic copy number alterations in lung SQCCs, including amplifica- Supplementary Methods, section 1). tion of SOX2, PDGFRA and FGFR1 and/or WHSC1L1 and deletion of 9,10 CDKN2A . DNA sequencing studies of lung SQCCs have reported Somatic DNA alterations recurrent mutations in several genes, including TP53, NFE2L2, The lung SQCCs analysed in this study display a large number and variety KEAP1, BAI3, FBXW7, GRM8, MUC16, RUNX1T1, STK11 and of DNA alterations, with a mean of 360 exonic mutations, 323 altered ERBB4 (refs 11, 12). DDR2 mutations and FGFR1 amplification have copy number segments and 165 genomic rearrangements per tumour. 13–15 been nominated as therapeutic targets . Copy number alterations were analysed using several platforms. We have conducted a comprehensive study of lung SQCCs from a Analysis of single nucleotide polymorphism (SNP) 6.0 array data large cohort of patients as part of The Cancer Genome Atlas (TCGA) across the set of 178 lung SQCCs identified a high rate of copy number project. The twin aims are to characterize the genomic and epigenomic alteration (mean of 323 segments) when compared with other TCGA landscape of lung SQCC and to identify potential opportunities for projects (as of 1 February 2012), including ovarian cancer (477 therapy. We report an integrated analysis based on DNA copy number, 17 18 segments) , glioblastoma multiforme (282 segments) , colorectal somatic exonic mutations, messenger RNA sequencing, mRNA carcinoma (213 segments), breast carcinoma (282 segments) and renal expression and promoter methylation for 178 histopathologically cell carcinoma (156 segments) (P, 13 10 by Fisher’s exact test). reviewed lung SQCCs, in addition to whole genome sequencing These segments gave rise to regions of both focal and broad somatic (WGS) of 19 samples and microRNA sequencing of 159 samples copy number alterations (SCNAs), with a mean of 47 focal and 23 (Supplementary Table 1.1). Demographic and clinical data and results broad events per tumour (broad events defined as$50% of the length of the genomic analyses can be downloaded from the TCGA data of the chromosome arm). There was strong concordance between the portal (https://tcga-data.nci.nih.gov/docs/publications/lusc_2012/). three independent copy number assays for all regions of SCNA Samples and clinical data (Supplementary Figs 2.1–2.4). At the level of whole chromosome arm SCNAs, lung SQCCs exhibit Tumour samples were obtained from 178 patients with previously untreated stage I–IV lung SQCC. Germline DNA was obtained from many similarities to 205 cases of lung adenocarcinoma analysed by Lists of participants and their affiliations appear at the end of the paper. 27 SE P T EM BE R 2 01 2 | V O L 4 8 9 | N AT U R E | 5 1 9 ©2012 Macmillan Publishers Limited. All rights reserved RESEARCH ARTICLE TCGA (Supplementary Fig. 2.1a). The most notable difference This yielded 12 other genes with FDR, 0.1: FAM123B (also known as between these cancers is selective amplification of chromosome 3q WTX), HRAS, FBXW7, SMARCA4, NF1, SMAD4, EGFR, APC, TSC1, 9,19 in lung SQCC, as has been reported . Using the SNP 6.0 array BRAF, TNFAIP3 and CREBBP (Supplementary Table 3.1). Both the spectrum and the frequency of EGFR mutations differed from those platform and GISTIC 2.0 (refs 20, 21), we identified regions of sig- nificant copy number alteration (Supplementary Methods, section 2). seen in lung adenocarcinomas. The two most common alterations in There were 50 peaks of significant amplification or deletion lung adenocarcinoma, Leu858Arg and inframe deletions in exon 19, (Q, 0.05), several of which included SCNAs previously seen in lung were absent, whereas two Leu861Gln mutations were detected in EGFR. SQCCs including SOX2, PDGFRA and/or KIT, EGFR, FGFR1 and/or As described in Supplementary Fig. 3.1, we verified somatic muta- 9,10,19 WHSC1L1, CCND1 and CDKN2A (Supplementary Fig. 2.1b and tions by performing an independent hybrid-recapture of 76 genes in Supplementary Data 2.1 and 2.2). Other peaks defined regions of SCNA all samples. A total of 1,289 mutations were assayed, and we achieved reported for the first time, including amplifications of chromosomal satisfactory coverage to have power to verify at 1,283 positions. We segments containing NFE2L2, MYC, CDK6, MDM2, BCL2L1 and EYS validated 1,235 mutations (96.2%) (Supplementary Fig. 3.1 and Sup- and deletions of FOXP1, PTEN and NF1 (Supplementary Fig. 2.1b). plementary Methods, section 3). We also verified mutation calls using Whole exome sequencing of 178 lung SQCCs and matched germline WGS and RNA sequencing data with similar results (Supplementary Figs 3.1, 4.3 and Supplementary Methods, sections 3 and 4). DNA targeted 193,094 exons from 18,863 genes. The mean sequencing coverage across targeted bases was 1213, with 83% of target bases above WGS was performed for 19 tumour/normal pairs with a mean 303 coverage. We identified a total of 48,690 non-silent mutations with computed coverage of 543. A mean of 165 somatic rearrangements was found per lung SQCC tumour pair (Supplementary Fig. 3.2), a a mean of 228 non-silent and 360 total exonic mutations per tumour, corresponding to a mean somatic mutation rate of 8.1 mutations per value in excess of that reported for WGS studies of other tumour types 25 26 megabase (Mb) and median of 8.4 per Mb. That rate is higher than rates including colorectal carcinoma (75) , prostate carcinoma (108) , 23 27 observed in other TCGA projects including acute myelogenous leuk- multiple myeloma (21) and breast cancer (90) . Although most aemia (0.56 per Mb), breast carcinoma (1.0 per Mb), ovarian cancer inframe coding fusions detected in WGS were validated by RNA (2.1 per Mb), glioblastoma multiforme (2.3 per Mb) and colorectal sequencing, no recurrent rearrangements predicted to generate fusion carcinoma (3.2 per Mb) (data as of 1 February 2012, P, 2.23 10 by proteins were identified (Supplementary Data 3.1 and 4.1). t-test or Wilcoxon’s rank sum test for lung SQCC versus all others). In Somatically altered pathways lung SQCC, CpG transitions and transversions were the most com- monly observed mutation types, with mean rates of 9.9 and 10.7 per Many of the somatic alterations we have identified in lung SQCCs sequenced megabase of CpG context, respectively, for a total mutation seem to be drivers of pathways important to the initiation or progres- rate of 20.6 per Mb. At non-CpG sites, transversions at C:G sites were sion of the cancer. Specifically, genes involved in the oxidative stress more common than transitions (7.3 versus 2.9 per Mb; total5 10.2 per response and squamous differentiation were frequently altered by Mb) and more common than transversions or transitions at A:T sites mutation or SCNA. We observed mutations and copy number altera- (1.5 versus 1.3 per Mb; total5 2.8 per Mb). tions of NFE2L2 and KEAP1 and/or deletion or mutation of CUL3 in Significantly mutated genes were identified using a modified version 34% of cases (Fig. 2). NFE2L2 and KEAP1 code for proteins that bind 22,23 of the MutSig algorithm (Supplementary Methods, section 3) .We to each other, have been shown to regulate the cell response to oxid- identified 10 genes with a false discovery rate (FDR) Q value , 0.1 ative damage, chemo- and radiotherapy, and are somatically altered in 28,29 (Supplementary Table 3.1): TP53, CDKN2A, PTEN, PIK3CA, KEAP1, a variety of cancer types . We found mutations in NFE2L2 almost MLL2, HLA-A, NFE2L2, NOTCH1 and RB1, all of which demonstrated exclusively in one of two KEAP1 interaction motifs, DLG or ETGE. robust evidence of gene expression as defined by reads per kilobase of Mutations in KEAP1 and CUL3 showed a pattern consistent with loss- exon model per million mapped reads (RPKM) . 1 (Fig. 1). TP53 of-function and were mutually exclusive with mutations in NFE2L2 mutation was observed in 81% of samples by automated analysis; visual (Figs 1c and 2). PARADIGM SHIFT analysis predicts that muta- review of sequencing reads identified a further 9% of samples with tions in NFE2L2 and KEAP1 exert a considerable functional effect (Sup- potential mutations in regions of sub-optimal coverage or in samples plementary Fig. 7.C.1, 7.C.2 and Supplementary Methods, section 7). with low purity. Most observed mutations in NOTCH1 (8 out of 17) We also found alterations in genes with known roles in squamous were truncating alterations, suggesting loss-of-function, as has recently cell differentiation in 44% of samples, including overexpression and 22,24 been reported for head and neck SQCCs . Mutations in HLA-A were amplification of SOX2 and TP63, loss-of-function mutations in also almost exclusively nonsense or splice site events (7 out of 8). NOTCH1, NOTCH2 and ASCL4 and focal deletions in FOXP1 To increase our statistical power to detect mutated genes in the (Fig. 2). Although NOTCH1 has been well characterized as an onco- setting of the observed high background mutation rate, we performed gene in haematological cancers , NOTCH1 and NOTCH2 truncating a secondary MutSig analysis only considering genes previously mutations have been reported in cutaneous SQCCs and lung SQCCs . observed to be mutated in cancer according to the COSMIC database. Truncating mutations in ASCL4 are the first to be reported in human 100 Figure 1 | Significantly mutated genes in lung Syn. Frame shift Missense Inframe indel SQCC. Significantly mutated genes Syn. Splice site Other non syn. (Q value, 0.1) identified by exome sequencing are Non syn. Nonsense listed vertically by Q value. The percentage of lung SQCC samples with a mutation detected by 81% TP53 automated calling is noted at the left. Samples 15% CDKN2A displayed as columns, with the overall number of 8% PTEN mutations plotted at the top, and samples are 16% PIK3CA 12% KEAP1 arranged to emphasize mutual exclusivity among 20% MLL2 mutations. Syn., synonymous. 3% HLA-A 15% NFE2L2 8% NOTCH1 7% RB1 70 50 30 10 0.5 2.0 3.5 Individuals with –log mutation (Q value) 52 0 | NAT U R E | V OL 4 8 9 | 27 SE P T E M BE R 2 01 2 ©2012 Macmillan Publishers Limited. All rights reserved Mutations per Mb 132 ARTICLE RESEARCH Figure 2 | Somatically altered Oxidative stress response Squamous differentiation pathways in squamous cell lung 34% altered (62% in classical subtype) 44% altered SOX2 TP63 cancer. Left, alterations in oxidative 21% 16% KEAP1 CUL3 stress response pathway genes as 12% 7% defined by somatic mutation, copy number alteration or up- or NFE2L2 downregulation. Frequencies of NOTCH1 NOTCH2 ASCL4 FOXP1 19% alteration are expressed as a 8% 5% 3% 4% percentage of all cases, with 78 cases with at least one alteration background in red for activated genes Oxidative stress response SOX2 and blue for inactivated genes. Right, TP63 60 cases with at least one alteration alterations in genes that regulate NOTCH1 NFE2L2 squamous differentiation, as defined NOTCH2 KEAP1 ASCL4 in the left panel. CUL3 FOXP1 Cases (%) Homozygous Truncating Missense 50 0 50 Activation Inhibition Amplification Overexpression deletion mutation mutation Inactivated Activated cancer and may have a lineage role given the requirement for ASCL1 tap63; P, 2.23 10 ). The short deltaN isoform is thought to func- 33 35,36 for survival of small-cell lung cancer cells . Alterations in NOTCH1, tion as an oncogene , and its expression was most enriched in the NOTCH2 and ASCL4 were mutually exclusive and exhibited minimal classical subtype. By contrast, the primitive expression subtype more overlap with amplification of TP63 and/or SOX2 (Fig. 2), suggesting commonly exhibited RB1 and PTEN alterations, and the basal express- that aberrations in those modulators of squamous cell differentiation ion subtype showed NF1 alterations (Fig. 3). Amplification of FGFR1 have overlapping functional consequences. and WHSC1L1 was anticorrelated with the classical subtype and spe- cifically with NFE2L2 or KEAP1 mutated samples. Although CDKN2A mRNA expression profiling and subtype classification alterations are common in lung SQCCs, they are not associated with any particular expression subtype (Fig. 3). Whole-transcriptome expression profiles were generated by RNA sequencing for the entire cohort and by microarrays for a 121-sample Independent clustering of miRNA and methylation data indicated association with expression subtypes. The highest overall methylation subset. Of 20,502 genes analysed, the mean RNA coverage indices were 193 and 6,420 RPKM (Supplementary Fig. 4.1 and Supplementary was seen in the classical subtype (Fig. 3, Supplementary Figs 5.1 and 6.1, Supplementary Methods, sections 5 and 6, Supplementary Data Methods, section 4). Previously reported lung SQCC gene expression- 6.1 and 6.2 and Supplementary Table 5.1). Integrative clustering subtype signatures were applied to both of the expression platforms, (iCluster) of mRNA, miRNA, methylation, SCNA and mutation yielding four subtypes designated as classical (36%), basal (25%), data demonstrated concordance with the mRNA expression subtypes secretory (24%) and primitive (15%). The concordance of subtypes and associated alterations (Fig. 3, Supplementary Fig. 7.A.1 and between the two platforms was high (94% agreement) (Supplemen- Supplementary Methods, section 7). Independent correlation of tary Fig. 4.2). Considerable correlations were found between the somatic mutations, copy number alterations and gene expression expression subtypes and genomic alterations in copy number, mutation signatures revealed notable subtype associations with alterations in and methylation (Fig. 3). The classical subtype was characterized by alterations in KEAP1, NFE2L2 and PTEN, as well as pronounced hyper- the TP53, PI3K, RB1 and NFE2L2/KEAP1 pathways (Supplementary Fig. 7.B.1 and Supplementary Methods, section 7). methylation and chromosomal instability. The 3q26 amplicon was pre- sent in all of the subtypes, but it was most characteristic of the classical Analysis of the CDKN2A locus subtype, which also showed the greatest overexpression of three known oncogenes on 3q: SOX2, TP63 and PIK3CA. RNA sequencing data Integrated multiplatform analyses showed that CDKN2A, a known 38 INK4A suggested that high expression levels of TP63, in samples with and tumour suppressor gene in lung SQCC that encodes the p16 ARF without amplification of TP63, were associated with dominant expres- and p14 proteins, is inactivated in 72% of cases of lung SQCC sion of the deltaN isoform (also called p40), which lacks the amino- (Fig. 4a and Supplementary Data 7.1)—by epigenetic silencing by terminal transactivation domain, compared with the longer isoform, methylation (21%), inactivating mutation (18%), exon 1b skipping called tap63 (89% of tumours overexpressed deltaN compared with (4%) and homozygous deletion (29%). Primitive Figure 3 | Gene expression subtypes integrated Classical expression subtype expression subtype Basal expression subtype Secretory expression subtype with genomic alterations. Tumours are displayed as columns, grouped by gene expression subtype. CN Subtypes were compared by Kruskal–Wallis tests for PIK3CA expr. TP63 expr. 3q26 continuous features and by Fisher’s exact tests for deltaN % categorical features. Displayed features showed SOX2 expr. Mut. KEAP1 significant association with gene expression subtype Expr. Mut. (P, 0.05), except for CDKN2A alterations. deltaN NFE2L2 CN percentage represents transcript isoform usage Expr. CN between the TP63 isoforms, deltaN and tap63, as PTEN Expr. determined by RNA sequencing. Chromosomal RB1 Mut. CN instability (CIN) is defined by the mean of the NF1 Expr. absolute values of chromosome arm copy numbers CIN 23,24 Hypermethylation (CN) from the GISTIC output. Absolute values Mut. are used so that amplification and deletion alterations CDKN2A CN Expr. are counted equally. Hypermethylation scores and iCluster iCluster assignments are described in Supplementary Gene sequence: WT Mut. Figs 6.1 and 7.A1, respectively. CIN, methylation, gene expression and deltaN values were standardized DNA copy number, CIN, deltaN %: –0.3 –0.1 0.1 0.3 Expression and methylation: –0.75 –0.25 0.25 0.75 for display using z-score transformation. Expr., expression; mut., mutation; WT, wild type. 27 S EPTEMBER 2 01 2 | V O L 489 | N A TU R E | 5 2 1 ©2012 Macmillan Publishers Limited. All rights reserved RESEARCH ARTICLE INK4a INK4a Figure 4 | Multi-faceted b p16 mutated p16 methylated RB1 altered a CDKN2A locus INK4a p16 fusion (TCGA-21-1078) characterization of mechanisms of INK4a p16 alteration rate: 72% CDKN2A loss. a, Schematic view of Exon 2 skipping: 4% Epigenetic silencing: 21% Mutation: 17% the exon structure of CDKN2A Missense Truncating p16INK4: exons 1, 2 and 3 demonstrating the types of Exon 1β Exon 1α Exon 3 Exon 2 alterations identified in the study. ARF: exons 1β, 2 and 3 The locations of point mutation are Homozygous deletion: 30% denoted by black and green circles. b, CDKN2A expression (y axis) –2 versus CDKN2A copy number (x axis). Samples are represented by KIAA1797 p16INK4 –4 circles and colour-coded by specific Exon 17 Exon 18 Exon 1α type of CDKN2A alteration. Del., Chr9: 20863778 Chr9: 21972267 deletion; het., heterozygous; homoz., ORF –6 homozygous. c, Diagram of the KIAA1797-p16INK4 fusion –8 p16INK4 exon 1α KIAA1797 exons 1–18 identified by WGS. ORF, open mRNA reading frame. d, CDKN2A Homoz. del. Het. loss Diploid INK4a p16 low alterations and expression levels expression (binary) in each sample. p16INK4 RB1 CDK6 CCND1 Homozygous Missense Truncating Skipped Amplification Downregulation Methylation deletion mutation mutation exon Analysis of mRNA expression across the CDKN2A locus revealed contain one or more mutations in tyrosine kinases, serine/threonine four distinct patterns of expression: complete absence of both p16INK4 kinases, phosphatidylinositol-3-OH kinase (PI(3)K) catalytic and and ARF (33%); expression of high levels of both p16INK4 and ARF regulatory subunits, nuclear hormone receptors, G-protein-coupled (31%); high expression of ARF and absence of p16INK4 (31%); or receptors, proteases and tyrosine phosphatases (Supplementary expression of a transcript that represents a splicing of exon 1b from Fig. 7.D.1a and Supplementary Data 7.2 and 7.3). From 50 to 77% ARF with the shared exon 3 of ARF and p16INK4, generating a pre- of the mutations were predicted to have a medium or high functional mature stop codon (4%) (Supplementary Fig. 4.4). Almost all of the effect as determined by the mutation assessor score (Supplementary cases completely lacking p16INK4 and ARF expression showed homo- Fig. 7.D.1a), and 39% of tyrosine and 42% of serine/threonine kinase zygous deletion (Fig. 4b and Supplementary Data 7.1). In one case, mutations were located in the kinase domain. Many of the alterations p16INK4 expression was detected but analysis of WGS data demon- were in known oncogenes and tumour suppressors, as defined in the strated an intergenic fusion event that resulted in detectable transcrip- COSMIC database (Supplementary Data 7.3). tion between exon 1a p16INK4 and exon 18 of KIAA1797 (Fig. 4b, c). We selected potential therapeutic targets based on several features, Interestingly, combined analysis of WGS and RNA sequencing data including (1) availability of a US Food and Drug Administration identified tumour suppressor gene inactivation by intra- or interchro- (FDA)-approved targeted therapeutic agent or one under study in mosomal rearrangement in PTEN, NOTCH1, ARID1A, CTNNA2, VHL current clinical trials (Supplementary Data 7.2); (2) confirmation of and NF1, in eight further cases (Supplementary Data 3.1 and 4.1). the altered allele in RNA sequencing; and (3) the mutation assessor In addition to homozygous deletion, there are frequent mutational score . Using those criteria, we identified 114 cases with somatic events in CDKN2A (Fig. 4b and Supplementary Data 7.1). These alteration of a potentially targetable gene (64%) (Supplementary account for 45% of the 56 cases with high p16INK4 and ARF expres- Fig. 7.D.1b and Supplementary Data 7.4). Among these, we identified sion. Furthermore, methylation of the exon 1a promoter accounts for three families of tyrosine kinases, the erythroblastic leukaemia viral many other cases of CDKN2A inactivation (70% of lung SQCCs with oncogene homologues (ERBBs), fibroblast growth factor receptors ARF expression in the absence of detectable p16INK4). Seven other (FGFRs) and Janus kinases (JAKs), all of which were found to be tumours in the high-ARF/low-INK4A group had documented mutated and/or amplified . As discussed for EGFR, the mutational mutations of INK4A, primarily nonsense mutations, suggesting spectra in these potential therapeutic targets differed from those in nonsense-mediated decay as a mechanism. Of the 28% of tumours lung adenocarcinoma (Supplementary Fig. 7.D.2) . without CDKN2A alterations, RB1 mutations were identified in eight To complement a gene-centred search for potential therapeutic cases and CDK6 amplification in one case (Fig. 4d). targets, we analysed core cellular pathways known to represent poten- tial therapeutic vulnerabilities: PI(3)K/AKT, receptor tyrosine kinase Therapeutic targets (RTK) and RAS. Analysis of the 178 lung SQCCs revealed alteration Molecularly targeted agents are now commonly used in patients with in at least one of those pathways in 69% of samples after restriction of adenocarcinoma of the lung, whereas no effective targeted agents have the analysis to mutations confirmed by RNA sequencing and to been developed specifically for lung SQCCs . We analysed our genomic amplifications associated with overexpression of the target gene data for evidence of the two common genomic alterations in adeno- (Fig. 5). Mutational events that have been curated in COSMIC are carcinomas of the lung: EGFR and KRAS mutations. Only one also shown in Supplementary Fig. 7D.2, as is the distribution of muta- sample had a KRAS codon 61 mutation, and there were no exon 19 tions, amplifications and overexpression of the genes depicted in deletions or Leu858Arg mutations in EGFR. However, amplifications Fig. 5. (A summary of all samples and their significant mutations of EGFR were found in 7% of cases, as were two instances of the and copy number alterations, including alterations in Fig. 5, is shown Leu861Gln EGFR mutation, which confers sensitivity to erlotinib in Supplementary Data 7.5.) Specifically, one of the components of the and gefitinib . PI(3)K/AKT pathway was altered in 47% of tumours and RTK sig- The presence of new potential therapeutic targets in lung SQCC nalling probably affected by events such as EGFR amplification, BRAF was suggested by the observation that 96% (171 out of 178) of tumours mutation or FGFR amplification or mutation in 26% of tumours 5 2 2 | N ATUR E | V OL 48 9 | 2 7 SEP TEM B E R 2 012 ©2012 Macmillan Publishers Limited. All rights reserved INK4a p16 mRNA expression (exon 1α log RPKM) 2 ARTICLE RESEARCH PI(3)K/RTK/RAS signalling data could thereby help to facilitate effective personalized therapy for 69% altered this deadly disease. EGFR ERBB2 ERBB3 FGFR1 FGFR2 FGFR3 9% 4% 2% 7% 3% 2% PTEN PIK3CA METHODS SUMMARY 15% 16% All specimens were obtained from patients with appropriate consent from the RASA1 relevant Institutional Review Board. DNA and RNA were collected from samples 4% KRAS HRAS NRAS STK11 AKT1 AKT2 AKT3 using the Allprep kit (Qiagen). We used commercial technology for capture and 2% <1% 4% 16% 3% 3% <1% NF1 sequencing of exomes from tumour DNA and normal DNA and whole-genome 11% shotgun sequencing. Significantly mutated genes were identified by comparing them TSC1 TSC2 AMPK BRAF with expectation models based on the exact measured rates of specific sequence 3% 3% 4% 23,24 lesions. GISTIC analysis of the circular-binary-segmented Affymetrix SNP 6.0 copy number data was used to identify recurrent amplification and deletion peaks. MTOR Cases (%) Consensus clustering approaches were used to analyse mRNA, miRNA and methy- 20,21,34,38,41,44 50 0 50 lation subtypes using previous approaches . Inactivated Activated Proliferation, cell survival, translation Activation Inhibition Received 9 March; accepted 9 July 2012. Alteration pattern Published online 9 September 2012. RTK 26% RAS 24% PI(3)K 47% 1. World Health Organization. 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Scott , Christina Yau ,Sam Ng ,Ted 36 36 36 36 39 CA126544, U24 CA143845, U24 CA143858, U24 CA144025, U24 CA143882, U24 Goldstein , Peter Waltman , Artem Sokolov ,Kyle Ellrott , Eric A. Collisson , 36 36 36 36 CA143866, U24 CA143867, U24 CA143848, U24 CA143840, U24 CA143835, U24 Daniel Zerbino , Christopher Wilks ,Singer Ma , Brian Craft ; University of North 23 19,20 CA143799, U24 CA143883, U24 CA143843, U54 HG003067, U54 HG003079 and Carolina at Chapel Hill Matthew D. Wilkerson , J. Todd Auman , Katherine A. 21,22,23 23 23 23 23 U54 HG003273. Hoadley , Ying Du , Christopher Cabanski ,VonnWalter , Darshan Singh , 23 23 23 23 Junyuan Wu , Anisha Gulabani , Tom Bodenheimer ,AlanP.Hoyle , Janae V. 23 23 22 22 23 Author Contributions The TCGA research network contributed collectively to this Simons , Matthew G. Soloway , Lisle E. Mose , Stuart R. Jefferys , Saianand Balu , 40 24 27 27 23 23,28 study. Biospecimens were provided by the tissue source sites and processed by the J. S. Marron , Yufeng Liu , Kai Wang , Jinze Liu , Jan F. Prins , D. Neil Hayes , 21,22,23 41 biospecimen core resource. Data generation and analyses were performed by the Charles M. Perou ; Baylor College of Medicine Chad J. Creighton ,Yiqun genome sequencing centres, cancer genome characterization centres and genome Zhang data analysis centres. All data were released through the data coordinating centre. Project activities were coordinated by the National Cancer Institute and National 42 42 43 Pathology committee William D. Travis , Natasha Rekhtman ,JoanneYi ,Marie C. Human Genome Research Institute project teams. We also acknowledge the following 43 44 45 46 47 Aubry , Richard Cheney , Sanja Dacic , Douglas Flieder , William Funkhouser , TCGA investigators who made substantial contributions to the project: P.S.H. and 48 49 50 Peter Illei ,Jerome Myers ,Ming-Sound Tsao D.N.H. (manuscript coordinators); M.D.W. (data coordinator); P.S.H. and N.S. (analysis coordinators); P.S.H., M.S.L., A. Sivachecnko, B.H. and G.G. (DNA sequence analysis); M.D.W., J.L. and D.N.H. (mRNA sequence analysis); L. Cope, J.G.H. and L. Danilova (DNA Biospecimen core resources: International Genomics Consortium Robert Penny , 51 51 51 51 51 methylation analysis); A.C., G.S., N.H.P., R.K. and M.L. (copy number analysis); N.S., R. David Mallery , Troy Shelton , Martha Hatfield , Scott Morris , Peggy Yena , 51 51 51 Bose, C.J.C., R. Sinha, C.M., S.N., E.A.C., R. Shen, J.N.W. and C. Sander (pathway analysis); Candace Shelton , Mark Sherman , Joseph Paulauskis A.C. and G.R. (miRNA sequence analysis); W.D.T., B.E.J., D.A.W. and M.-S.T. (pathology and clinical expertise); S.B.B., R. Govindan and M. Meyerson (project chairs). 1,2,6 29 Disease working group Matthew Meyerson , Stephen B. Baylin , Ramaswamy 52 33 53 54 52 Govindan , Rehan Akbani , Ijeoma Azodo , David Beer , Ron Bose ,Lauren A. Author Information The primary and processed data used to generate the analyses 55 56 52 21,23 7 Byers , David Carbone , Li-Wei Chang , Derek Chiang , Andy Chu , Elizabeth presented here can be downloaded by registered users from The Cancer Genome Atlas 7 39 29 41 29 Chun , Eric Collisson , Leslie Cope , Chad J. Creighton , Ludmila Danilova ,Li (https://tcga-data.nci.nih.gov/tcga/tcgaDownload.jsp, https://cghub.ucsc.edu/ and 52 1,5 1,2 23,28 1 Ding , Gad Getz , Peter S. Hammerman , D. Neil Hayes , Bryan Hernandez , https://tcga-data.nci.nih.gov/docs/publications/lusc_2012/). Reprints and 29 55 43 1,6 James G. Herman ,John Heymach , Cristiane Ida , Marcin Imielinski ,Bruce permissions information is available at www.nature.com/reprints. This paper is 2 57 56 53 11,12 Johnson ,Igor Jurisica , Jacob Kaufman ,Farhad Kosari ,Raju Kucherlapati , distributed under the terms of the Creative Commons 2 17,18 1 52 David Kwiatkowski , Marc Ladanyi , Michael S. Lawrence , Christopher A. Maher , Attribution-Non-Commercial-Share Alike licence, and the online version of the paper is 7 36 56 58,59 51 freely available to all readers. The authors declare no competing financial interests. Andy Mungall ,Sam Ng , William Pao , Martin Peifer , Robert Penny , Gordon 7 60 16 16 31 Robertson , Valerie Rusch , Chris Sander ,NikolausSchultz , Ronglai Shen , Jill Readers are welcome to comment on the online version of the paper. Correspondence 61 16 1 4 7 and requests for materials should be addressed to M. Meyerson Siegfried ,RileenSinha , Andrey Sivachenko ,CarrieSougnez , Dominik Stoll , 36 58,59,62 53 50 ([email protected]). Joshua Stuart , Roman K. Thomas , Sandra Tomaszek , Ming-Sound Tsao , 5 2 4 | NA TU RE | V OL 489 | 2 7 S EPTEMBER 2 01 2 ©2012 Macmillan Publishers Limited. All rights reserved ARTICLE RESEARCH 42 36 33,35 30 28 William D. Travis , Charles Vaske , John N. Weinstein , Daniel Weisenberger , Kentucky, Lexington, Kentucky 40506, USA. Department of Internal Medicine, Division 63 53 23 30 David Wheeler ,DennisA.Wigle , Matthew D. Wilkerson , Christopher Wilks , Ping of Medical Oncology, University of North Carolina at Chapel Hill, Chapel Hill, North 53 9,10 29 Yang , Jianjua John Zhang Carolina 27599, USA. Cancer Biology Division, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, Maryland 21231, USA. 64 64 64 University of Southern California Epigenome Center, University of Southern California, Data coordination centre Mark A. Jensen , Robert Sfeir , Ari B. Kahn ,Anna L. 64 64 64 64 64 Los Angeles, California 90033, USA. Department of Epidemiology and Biostatistics, Chu , Prachi Kothiyal , Zhining Wang ,Eric E.Snyder ,Joan Pontius ,Todd D. 64 64 64 64 64 Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. Department Pihl , Brenda Ayala , Mark Backus , Jessica Walton ,JulienBaboud , Dominique 64 64 64 64 of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. L. Berton , Matthew C. Nicholls ,Deepak Srinivasan , Rohini Raman , Stanley 64 64 64 64 Department of Bioinformatics and Computational Biology, The University of Texas MD Girshik , Peter A. Kigonya , Shelley Alonso , Rashmi N. Sanbhadti , Sean P. 64 64 64 Anderson Cancer Center, Houston, Texas 77030, USA. Division of Pathology and Barletta , John M. Greene , David A. Pot Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA. Department of Systems Biology, The University of Texas MD 50 50 46 Tissuesourcesites Ming-Sound Tsao , Bizhan Bandarchi-Chamkhaleh , Jeff Boyd , 36 Anderson Cancer Center, Houston, Texas 77030, USA. Department of Biomolecular 46 53 53 53 JoEllen Weaver ,Dennis A.Wigle ,Ijeoma A.Azodo ,Sandra C. Tomaszek ,Marie Engineering and Center for Biomolecular Science and Engineering, University of 65 65 66 53 67 Christine Aubry ,ChristianeM.Ida ,PingYang , Farhad Kosari , MalcolmV. Brock , 37 California Santa Cruz, Santa Cruz, California 95064, USA. Howard Hughes Medical 67 68 67 68 69 Kristen Rodgers ,MarianRutledge ,TravisBrown ,Beverly Lee ,James Shin , 38 Institute, University of California Santa Cruz, Santa Cruz, California 95064, USA. Buck 69 70 61 71 Dante Trusty , Rajiv Dhir , Jill M. Siegfried , Olga Potapova , Konstantin V. 39 Institute for Age Research, Novato, California 94945, USA. Division of Hematology/ 72 71 60 73 Fedosenko , Elena Nemirovich-Danchenko , Valerie Rusch , Maureen Zakowski , Oncology, University of California San Francisco, San Francisco, California 94143, USA 74 74 74 74 Mary V. Iacocca , Jennifer Brown ,BrendaRabeno , Christine Czerwinski , Nicholas Department of Statistics and Operations Research, University of North Carolina Medical 74 75 75 49 49 Petrelli ,Zhen Fan , Nicole Todaro ,JohnEckman ,Jerome Myers ,W. Kimryn Center, Chapel Hill, North Carolina 27599, USA. Human Genome Sequencing Center 23 76 76 76 23 51 Rathmell ,Leigh B. Thorne ,Mei Huang ,LoriBoice , Ashley Hill , Robert Penny , and Dan L. Duncan Cancer Center Division of Biostatistics, Baylor College of Medicine, 51 51 51 51 44 David Mallery ,Erin Curley , Candace Shelton ,Peggy Yena , Carl Morrison , Houston, Texas 77030, USA. Department of Pathology, Memorial Sloan Kettering 44 77 77 77 Carmelo Gaudioso , John M. S. Bartlett , Sugy Kodeeswaran , Brent Zanke ,Harman Cancer Center, New York, New York 10065 USA. Department of Pathology, Mayo Clinic, 78 79 80 81 81 Sekhon , Kerstin David ,Hartmut Juhl ,XuanVan Le ,Bernard Kohl ,Richard Rochester, Minnesota 55905, USA. Department of Pathology, Roswell Park Cancer 81 82 83 84 83 Thorp , Nguyen Viet Tien ,NguyenVan Bang , Howard Sussman ,Bui Duc Phu , 85 86 87 88 Institute, Buffalo, New York 14263, USA. Department of Pathology, University of Richard Hajek ,Nguyen Phi Hung , Khurram Z. Khan , Thomas Muley Pittsburgh Cancer Center, Pittsburgh, Pennsylvania 15213, USA. Department of Pathology, Fox Chase Cancer Center, Philadelphia, Pennsylvania 19111, USA. 89 89 Project team: National Cancer Institute Kenna R. Mills Shaw , Margi Sheth , Liming Department of Pathology, University of North Carolina Medical Center, Chapel Hill, 89 90 90 89 90 Yang , Ken Buetow , Tanja Davidsen ,John A. Demchok , Greg Eley ,Martin North Carolina 27599, USA. Department of Pathology, Johns Hopkins University School 91 89 90 Ferguson , Laura A. L. Dillon , Carl Schaefer ; National Human Genome Research of Medicine, Baltimore, Maryland 21287, USA. Department of Pathology, Penrose-St. 92 92 92 Institute Mark S. Guyer , Bradley A. Ozenberger , Jacqueline D. Palchik ,Jane Francis Health System, Colorado Springs, Colorado 80907, USA. Department of 92 92 92 Peterson , Heidi J. Sofia , Elizabeth Thomson Pathology and Medical Biophysics, Ontario Cancer Institute and Princess Margaret Hospital, Toronto, Ontario M5G 2MY, Canada. International Genomics Consortium, 1,2 23,28 Phoenix, Arizona 85004, USA. Division of Oncology, Department of Medicine and The Writing committee Peter S. Hammerman , D. Neil Hayes , Matthew D. 23 16 52 7 39 Genome Institute, Washington University School of Medicine, St. Louis, Missouri 63110, Wilkerson , Nikolaus Schultz , Ron Bose ,Andy Chu , Eric A. Collisson ,Leslie 29 41 1,5 29 2 USA. Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, Cope , Chad J. Creighton , Gad Getz , James G. Herman , Bruce E. Johnson ,Raju 11,12 17,18 52 7 USA. Department of Surgery, University of Michigan, Ann Arbor, Michigan 48109, USA. Kucherlapati , Marc Ladanyi , Christopher A. Maher , Gordon Robertson , 16 16 16 1 The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA. Chris Sander , Ronglai Shen , Rileen Sinha , Andrey Sivachenko , Roman K. 58,59,62 42 50 33,35 Departments of Hematology/Oncology and Cancer Biology, Vanderbilt University Thomas , WilliamD. Travis , Ming-Sound Tsao , John N. Weinstein ,Dennis 53 29 52 1,2,6 School of Medicine,Nashville, Tennessee 37232, USA. Ontario Cancer Institute, IBM Life A. Wigle , Stephen B. Baylin , Ramaswamy Govindan , Matthew Meyerson Sciences Discovery Centre, Toronto, Ontario M5G 1L7, Canada. Department of 1 Translational Genomics, University of Cologne, Cologne D-50931, Germany. Max The Eli and Edythe L. Broad Institute of Massachusetts Institute of Technology and 2 Planck Institute for Neurological Research, Cologne D-50866, Germany. Department of Harvard University Cambridge, Massachusetts 02142, USA. Department of Medical Surgery, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA. Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA. 3 Department of Pharmacology and Chemical Biology, University of Pittsburgh Medical Department of Biology, Massachusetts Institute of Technology, Cambridge, 4 Center, Pittsburgh, Pennsylvania 15232, USA. Department of Translational Cancer Massachusetts 02142, USA. Department of Systems Biology, Harvard University, 5 Genomics, Center of Integrated Oncology, University of Cologne, Cologne D-50924, Boston, Massachusetts 02115, USA. Genetic Analysis Platform, The Eli and Edythe L. Germany. Human Genome Sequencing Center, Baylor College of Medcine, Houston, Broad Institute of Massachusetts Institute of Technology and Harvard University, 64 65 6 Texas 77030, USA. SRA International, Fairfax, Virginia 22033, USA. Department of Cambridge, Massachusetts 02142, USA. 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Department of Pathology, Johns Hopkins School of Medicine, 600 North Wolfe Street, Institute for Applied Cancer Science, Department of Genomic Medicine, The University Baltimore, Maryland 21287, USA. Department of Pathology, University of Pittsburgh, of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA. Department of Pittsburgh, Pennsylvania 15213, USA. Cureline, South San Francisco, California 94080, Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA. Division of 72 73 USA. City Clinical Oncology Dispensary, St Petersburg 197022, Russia. Department Genetics, Brigham and Women’s Hospital, Boston, Massachusetts 02115, USA. The of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA. Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 74 75 Helen F. Graham Cancer Center, Newark, Delaware 19713, USA. St Joseph Medical 02115, USA. Department of Dermatology, Harvard Medical School, Boston, Center, Towson, Maryland 21204, USA. UNC Tissue Procurement Facility, Department Massachusetts 02115, USA. Informatics Program, Children’s Hospital, Boston, of Pathology, UNC Lineberger Cancer Center, Chapel Hill, North Carolina 27599, USA. Massachusetts 02115, USA. Computational Biology Center, Memorial Sloan-Kettering Ontario Tumour Bank, Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Cancer Center, New York, New York 10065, USA. Department of Molecular Oncology, Canada. Ontario Tumour Bank – Ottawa site, The Ottawa Hospital, Ottawa, Ontario K1H Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. Department 8L6, Canada. Indivumed GmbH, Hamburg, Falkenried 88, Haus D D-20251, Germany. of Pathology and Human Oncology & Pathogenesis Program, Memorial Sloan-Kettering 80 81 Indivumed Inc, Kensington, Maryland 20895, USA. ILSBio, LLC, Chestertown, Cancer Center, New York, New York 10065, USA. 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ThoraxKlinik, Heidelberg University Hospital, Heidelberg 69126, Chapel Hill, Chapel Hill, Chapel Hill, North Carolina 27599, USA. Lineberger Germany. The Cancer Genome Atlas Program Office, National Cancer Institute, National Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, Institutes of Health, Bethesda, Maryland 20892, USA. Center for Biomedical Informatics North Carolina 27599, USA. Carolina Center for Genome Sciences, University of North and Information Technology (CBIIT), National Cancer Institute, National Institutes of Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. Health, Rockville, Maryland 20852, USA. MLF Consulting, Arlington, Maryland 02474, 26 92 Department of Computer Science, University of North Carolina at Chapel Hill, Chapel USA. National Human Genome Research Institute, National Institutes of Health, Hill, North Carolina 27599, USA. Department of Computer Science, University of Bethesda, Maryland 20892, USA. 27 S EPTEMBER 2 01 2 | V O L 4 89 | N AT U R E | 5 2 5 ©2012 Macmillan Publishers Limited. All rights reserved CORRECTIONS & AMENDMENTS CORRIGENDUM doi:10.1038/nature11666 Corrigendum: Comprehensive genomic characterization of squamous cell lung cancers The Cancer Genome Atlas Research Network Nature 489, 519–525 (2012); doi:10.1038/nature11404 In this Article, author Kristen Rodgers was spelt incorrectly. This error has been corrected in the HTML and PDF of the original paper. 288 | N ATUR E | V OL 4 9 1 | 8 N OVEM B E R 2 012 ©2012 Macmillan Publishers Limited. All rights reserved http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nature Springer Journals

Comprehensive genomic characterization of squamous cell lung cancers

The Cancer Genome Atlas Research Network
Nature , Volume 489 (7417) – Sep 9, 2012
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Science, Humanities and Social Sciences, multidisciplinary; Science, Humanities and Social Sciences, multidisciplinary; Science, multidisciplinary
ISSN
0028-0836
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1476-4687
DOI
10.1038/nature11404
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Abstract

ARTICLE doi:10.1038/nature11404 Comprehensive genomic characterization of squamous cell lung cancers The Cancer Genome Atlas Research Network* Lung squamous cell carcinoma is a common type of lung cancer, causing approximately 400,000 deaths per year worldwide. Genomic alterations in squamous cell lung cancers have not been comprehensively characterized, and no molecularly targeted agents have been specifically developed for its treatment. As part of The Cancer Genome Atlas, here we profile 178 lung squamous cell carcinomas to provide a comprehensive landscape of genomic and epigenomic alterations. We show that the tumour type is characterized by complex genomic alterations, with a mean of 360 exonic mutations, 165 genomic rearrangements, and 323 segments of copy number alteration per tumour. We find statistically recurrent mutations in 11 genes, including mutation of TP53 in nearly all specimens. Previously unreported loss-of-function mutations are seen in the HLA-A class I major histocompatibility gene. Significantly altered pathways included NFE2L2 and KEAP1 in 34%, squamous differentiation genes in 44%, phosphatidylinositol-3-OH kinase pathway genes in 47%, and CDKN2A and RB1 in 72% of tumours. We identified a potential therapeutic target in most tumours, offering new avenues of investigation for the treatment of squamous cell lung cancers. Lung cancer is the leading cause of cancer-related mortality worldwide, adjacent, histologically normal tissues resected at the time of surgery leading to an estimated 1.4 million deaths in 2010 (ref. 1). The discovery (n5 137) or from peripheral blood (n5 41). All patients provided of recurrent mutations in the epidermal growth factor receptor (EGFR) written informed consent to conduct genomic studies in accordance kinase, as well as fusions involving anaplastic lymphoma kinase (ALK), with local Institutional Review Boards. The demographic characteris- has led to a marked change in the treatment of patients with lung tics are described in Supplementary Table 1.2. The median follow-up 2–5 adenocarcinoma, the most common type of lung cancer . More recent for the cohort was 15.8 months, and 60% of patients were alive at the data have suggested that targeting mutations in BRAF, AKT1, ERBB2 time of the last follow-up (data updated in November 2011). Ninety-six and PIK3CA and fusions that involve ROS1 and RET may also be suc- per cent of the patients had a history of tobacco use, similar to previous 6,7 cessful . Unfortunately, activating mutations in EGFR and ALK fusions reports for North American patients with lung SQCC . DNA and are typically not present in the second most common type of lung cancer, RNA were extracted from patient specimens and measured by several lung squamous cell carcinoma (SQCC) , and targeted agents developed genomic assays, which included standard quality-control assessments for lung adenocarcinoma are largely ineffective against lung SQCC. (Supplementary Methods, sections 2–8). A committee of experts in Although no comprehensive genomic analysis of lung SQCCs has lung cancer pathology performed a further review of all samples to been reported, single-platform studies have identified regions of confirm the histological subtype (Supplementary Fig. 1.1 and somatic copy number alterations in lung SQCCs, including amplifica- Supplementary Methods, section 1). tion of SOX2, PDGFRA and FGFR1 and/or WHSC1L1 and deletion of 9,10 CDKN2A . DNA sequencing studies of lung SQCCs have reported Somatic DNA alterations recurrent mutations in several genes, including TP53, NFE2L2, The lung SQCCs analysed in this study display a large number and variety KEAP1, BAI3, FBXW7, GRM8, MUC16, RUNX1T1, STK11 and of DNA alterations, with a mean of 360 exonic mutations, 323 altered ERBB4 (refs 11, 12). DDR2 mutations and FGFR1 amplification have copy number segments and 165 genomic rearrangements per tumour. 13–15 been nominated as therapeutic targets . Copy number alterations were analysed using several platforms. We have conducted a comprehensive study of lung SQCCs from a Analysis of single nucleotide polymorphism (SNP) 6.0 array data large cohort of patients as part of The Cancer Genome Atlas (TCGA) across the set of 178 lung SQCCs identified a high rate of copy number project. The twin aims are to characterize the genomic and epigenomic alteration (mean of 323 segments) when compared with other TCGA landscape of lung SQCC and to identify potential opportunities for projects (as of 1 February 2012), including ovarian cancer (477 therapy. We report an integrated analysis based on DNA copy number, 17 18 segments) , glioblastoma multiforme (282 segments) , colorectal somatic exonic mutations, messenger RNA sequencing, mRNA carcinoma (213 segments), breast carcinoma (282 segments) and renal expression and promoter methylation for 178 histopathologically cell carcinoma (156 segments) (P, 13 10 by Fisher’s exact test). reviewed lung SQCCs, in addition to whole genome sequencing These segments gave rise to regions of both focal and broad somatic (WGS) of 19 samples and microRNA sequencing of 159 samples copy number alterations (SCNAs), with a mean of 47 focal and 23 (Supplementary Table 1.1). Demographic and clinical data and results broad events per tumour (broad events defined as$50% of the length of the genomic analyses can be downloaded from the TCGA data of the chromosome arm). There was strong concordance between the portal (https://tcga-data.nci.nih.gov/docs/publications/lusc_2012/). three independent copy number assays for all regions of SCNA Samples and clinical data (Supplementary Figs 2.1–2.4). At the level of whole chromosome arm SCNAs, lung SQCCs exhibit Tumour samples were obtained from 178 patients with previously untreated stage I–IV lung SQCC. Germline DNA was obtained from many similarities to 205 cases of lung adenocarcinoma analysed by Lists of participants and their affiliations appear at the end of the paper. 27 SE P T EM BE R 2 01 2 | V O L 4 8 9 | N AT U R E | 5 1 9 ©2012 Macmillan Publishers Limited. All rights reserved RESEARCH ARTICLE TCGA (Supplementary Fig. 2.1a). The most notable difference This yielded 12 other genes with FDR, 0.1: FAM123B (also known as between these cancers is selective amplification of chromosome 3q WTX), HRAS, FBXW7, SMARCA4, NF1, SMAD4, EGFR, APC, TSC1, 9,19 in lung SQCC, as has been reported . Using the SNP 6.0 array BRAF, TNFAIP3 and CREBBP (Supplementary Table 3.1). Both the spectrum and the frequency of EGFR mutations differed from those platform and GISTIC 2.0 (refs 20, 21), we identified regions of sig- nificant copy number alteration (Supplementary Methods, section 2). seen in lung adenocarcinomas. The two most common alterations in There were 50 peaks of significant amplification or deletion lung adenocarcinoma, Leu858Arg and inframe deletions in exon 19, (Q, 0.05), several of which included SCNAs previously seen in lung were absent, whereas two Leu861Gln mutations were detected in EGFR. SQCCs including SOX2, PDGFRA and/or KIT, EGFR, FGFR1 and/or As described in Supplementary Fig. 3.1, we verified somatic muta- 9,10,19 WHSC1L1, CCND1 and CDKN2A (Supplementary Fig. 2.1b and tions by performing an independent hybrid-recapture of 76 genes in Supplementary Data 2.1 and 2.2). Other peaks defined regions of SCNA all samples. A total of 1,289 mutations were assayed, and we achieved reported for the first time, including amplifications of chromosomal satisfactory coverage to have power to verify at 1,283 positions. We segments containing NFE2L2, MYC, CDK6, MDM2, BCL2L1 and EYS validated 1,235 mutations (96.2%) (Supplementary Fig. 3.1 and Sup- and deletions of FOXP1, PTEN and NF1 (Supplementary Fig. 2.1b). plementary Methods, section 3). We also verified mutation calls using Whole exome sequencing of 178 lung SQCCs and matched germline WGS and RNA sequencing data with similar results (Supplementary Figs 3.1, 4.3 and Supplementary Methods, sections 3 and 4). DNA targeted 193,094 exons from 18,863 genes. The mean sequencing coverage across targeted bases was 1213, with 83% of target bases above WGS was performed for 19 tumour/normal pairs with a mean 303 coverage. We identified a total of 48,690 non-silent mutations with computed coverage of 543. A mean of 165 somatic rearrangements was found per lung SQCC tumour pair (Supplementary Fig. 3.2), a a mean of 228 non-silent and 360 total exonic mutations per tumour, corresponding to a mean somatic mutation rate of 8.1 mutations per value in excess of that reported for WGS studies of other tumour types 25 26 megabase (Mb) and median of 8.4 per Mb. That rate is higher than rates including colorectal carcinoma (75) , prostate carcinoma (108) , 23 27 observed in other TCGA projects including acute myelogenous leuk- multiple myeloma (21) and breast cancer (90) . Although most aemia (0.56 per Mb), breast carcinoma (1.0 per Mb), ovarian cancer inframe coding fusions detected in WGS were validated by RNA (2.1 per Mb), glioblastoma multiforme (2.3 per Mb) and colorectal sequencing, no recurrent rearrangements predicted to generate fusion carcinoma (3.2 per Mb) (data as of 1 February 2012, P, 2.23 10 by proteins were identified (Supplementary Data 3.1 and 4.1). t-test or Wilcoxon’s rank sum test for lung SQCC versus all others). In Somatically altered pathways lung SQCC, CpG transitions and transversions were the most com- monly observed mutation types, with mean rates of 9.9 and 10.7 per Many of the somatic alterations we have identified in lung SQCCs sequenced megabase of CpG context, respectively, for a total mutation seem to be drivers of pathways important to the initiation or progres- rate of 20.6 per Mb. At non-CpG sites, transversions at C:G sites were sion of the cancer. Specifically, genes involved in the oxidative stress more common than transitions (7.3 versus 2.9 per Mb; total5 10.2 per response and squamous differentiation were frequently altered by Mb) and more common than transversions or transitions at A:T sites mutation or SCNA. We observed mutations and copy number altera- (1.5 versus 1.3 per Mb; total5 2.8 per Mb). tions of NFE2L2 and KEAP1 and/or deletion or mutation of CUL3 in Significantly mutated genes were identified using a modified version 34% of cases (Fig. 2). NFE2L2 and KEAP1 code for proteins that bind 22,23 of the MutSig algorithm (Supplementary Methods, section 3) .We to each other, have been shown to regulate the cell response to oxid- identified 10 genes with a false discovery rate (FDR) Q value , 0.1 ative damage, chemo- and radiotherapy, and are somatically altered in 28,29 (Supplementary Table 3.1): TP53, CDKN2A, PTEN, PIK3CA, KEAP1, a variety of cancer types . We found mutations in NFE2L2 almost MLL2, HLA-A, NFE2L2, NOTCH1 and RB1, all of which demonstrated exclusively in one of two KEAP1 interaction motifs, DLG or ETGE. robust evidence of gene expression as defined by reads per kilobase of Mutations in KEAP1 and CUL3 showed a pattern consistent with loss- exon model per million mapped reads (RPKM) . 1 (Fig. 1). TP53 of-function and were mutually exclusive with mutations in NFE2L2 mutation was observed in 81% of samples by automated analysis; visual (Figs 1c and 2). PARADIGM SHIFT analysis predicts that muta- review of sequencing reads identified a further 9% of samples with tions in NFE2L2 and KEAP1 exert a considerable functional effect (Sup- potential mutations in regions of sub-optimal coverage or in samples plementary Fig. 7.C.1, 7.C.2 and Supplementary Methods, section 7). with low purity. Most observed mutations in NOTCH1 (8 out of 17) We also found alterations in genes with known roles in squamous were truncating alterations, suggesting loss-of-function, as has recently cell differentiation in 44% of samples, including overexpression and 22,24 been reported for head and neck SQCCs . Mutations in HLA-A were amplification of SOX2 and TP63, loss-of-function mutations in also almost exclusively nonsense or splice site events (7 out of 8). NOTCH1, NOTCH2 and ASCL4 and focal deletions in FOXP1 To increase our statistical power to detect mutated genes in the (Fig. 2). Although NOTCH1 has been well characterized as an onco- setting of the observed high background mutation rate, we performed gene in haematological cancers , NOTCH1 and NOTCH2 truncating a secondary MutSig analysis only considering genes previously mutations have been reported in cutaneous SQCCs and lung SQCCs . observed to be mutated in cancer according to the COSMIC database. Truncating mutations in ASCL4 are the first to be reported in human 100 Figure 1 | Significantly mutated genes in lung Syn. Frame shift Missense Inframe indel SQCC. Significantly mutated genes Syn. Splice site Other non syn. (Q value, 0.1) identified by exome sequencing are Non syn. Nonsense listed vertically by Q value. The percentage of lung SQCC samples with a mutation detected by 81% TP53 automated calling is noted at the left. Samples 15% CDKN2A displayed as columns, with the overall number of 8% PTEN mutations plotted at the top, and samples are 16% PIK3CA 12% KEAP1 arranged to emphasize mutual exclusivity among 20% MLL2 mutations. Syn., synonymous. 3% HLA-A 15% NFE2L2 8% NOTCH1 7% RB1 70 50 30 10 0.5 2.0 3.5 Individuals with –log mutation (Q value) 52 0 | NAT U R E | V OL 4 8 9 | 27 SE P T E M BE R 2 01 2 ©2012 Macmillan Publishers Limited. All rights reserved Mutations per Mb 132 ARTICLE RESEARCH Figure 2 | Somatically altered Oxidative stress response Squamous differentiation pathways in squamous cell lung 34% altered (62% in classical subtype) 44% altered SOX2 TP63 cancer. Left, alterations in oxidative 21% 16% KEAP1 CUL3 stress response pathway genes as 12% 7% defined by somatic mutation, copy number alteration or up- or NFE2L2 downregulation. Frequencies of NOTCH1 NOTCH2 ASCL4 FOXP1 19% alteration are expressed as a 8% 5% 3% 4% percentage of all cases, with 78 cases with at least one alteration background in red for activated genes Oxidative stress response SOX2 and blue for inactivated genes. Right, TP63 60 cases with at least one alteration alterations in genes that regulate NOTCH1 NFE2L2 squamous differentiation, as defined NOTCH2 KEAP1 ASCL4 in the left panel. CUL3 FOXP1 Cases (%) Homozygous Truncating Missense 50 0 50 Activation Inhibition Amplification Overexpression deletion mutation mutation Inactivated Activated cancer and may have a lineage role given the requirement for ASCL1 tap63; P, 2.23 10 ). The short deltaN isoform is thought to func- 33 35,36 for survival of small-cell lung cancer cells . Alterations in NOTCH1, tion as an oncogene , and its expression was most enriched in the NOTCH2 and ASCL4 were mutually exclusive and exhibited minimal classical subtype. By contrast, the primitive expression subtype more overlap with amplification of TP63 and/or SOX2 (Fig. 2), suggesting commonly exhibited RB1 and PTEN alterations, and the basal express- that aberrations in those modulators of squamous cell differentiation ion subtype showed NF1 alterations (Fig. 3). Amplification of FGFR1 have overlapping functional consequences. and WHSC1L1 was anticorrelated with the classical subtype and spe- cifically with NFE2L2 or KEAP1 mutated samples. Although CDKN2A mRNA expression profiling and subtype classification alterations are common in lung SQCCs, they are not associated with any particular expression subtype (Fig. 3). Whole-transcriptome expression profiles were generated by RNA sequencing for the entire cohort and by microarrays for a 121-sample Independent clustering of miRNA and methylation data indicated association with expression subtypes. The highest overall methylation subset. Of 20,502 genes analysed, the mean RNA coverage indices were 193 and 6,420 RPKM (Supplementary Fig. 4.1 and Supplementary was seen in the classical subtype (Fig. 3, Supplementary Figs 5.1 and 6.1, Supplementary Methods, sections 5 and 6, Supplementary Data Methods, section 4). Previously reported lung SQCC gene expression- 6.1 and 6.2 and Supplementary Table 5.1). Integrative clustering subtype signatures were applied to both of the expression platforms, (iCluster) of mRNA, miRNA, methylation, SCNA and mutation yielding four subtypes designated as classical (36%), basal (25%), data demonstrated concordance with the mRNA expression subtypes secretory (24%) and primitive (15%). The concordance of subtypes and associated alterations (Fig. 3, Supplementary Fig. 7.A.1 and between the two platforms was high (94% agreement) (Supplemen- Supplementary Methods, section 7). Independent correlation of tary Fig. 4.2). Considerable correlations were found between the somatic mutations, copy number alterations and gene expression expression subtypes and genomic alterations in copy number, mutation signatures revealed notable subtype associations with alterations in and methylation (Fig. 3). The classical subtype was characterized by alterations in KEAP1, NFE2L2 and PTEN, as well as pronounced hyper- the TP53, PI3K, RB1 and NFE2L2/KEAP1 pathways (Supplementary Fig. 7.B.1 and Supplementary Methods, section 7). methylation and chromosomal instability. The 3q26 amplicon was pre- sent in all of the subtypes, but it was most characteristic of the classical Analysis of the CDKN2A locus subtype, which also showed the greatest overexpression of three known oncogenes on 3q: SOX2, TP63 and PIK3CA. RNA sequencing data Integrated multiplatform analyses showed that CDKN2A, a known 38 INK4A suggested that high expression levels of TP63, in samples with and tumour suppressor gene in lung SQCC that encodes the p16 ARF without amplification of TP63, were associated with dominant expres- and p14 proteins, is inactivated in 72% of cases of lung SQCC sion of the deltaN isoform (also called p40), which lacks the amino- (Fig. 4a and Supplementary Data 7.1)—by epigenetic silencing by terminal transactivation domain, compared with the longer isoform, methylation (21%), inactivating mutation (18%), exon 1b skipping called tap63 (89% of tumours overexpressed deltaN compared with (4%) and homozygous deletion (29%). Primitive Figure 3 | Gene expression subtypes integrated Classical expression subtype expression subtype Basal expression subtype Secretory expression subtype with genomic alterations. Tumours are displayed as columns, grouped by gene expression subtype. CN Subtypes were compared by Kruskal–Wallis tests for PIK3CA expr. TP63 expr. 3q26 continuous features and by Fisher’s exact tests for deltaN % categorical features. Displayed features showed SOX2 expr. Mut. KEAP1 significant association with gene expression subtype Expr. Mut. (P, 0.05), except for CDKN2A alterations. deltaN NFE2L2 CN percentage represents transcript isoform usage Expr. CN between the TP63 isoforms, deltaN and tap63, as PTEN Expr. determined by RNA sequencing. Chromosomal RB1 Mut. CN instability (CIN) is defined by the mean of the NF1 Expr. absolute values of chromosome arm copy numbers CIN 23,24 Hypermethylation (CN) from the GISTIC output. Absolute values Mut. are used so that amplification and deletion alterations CDKN2A CN Expr. are counted equally. Hypermethylation scores and iCluster iCluster assignments are described in Supplementary Gene sequence: WT Mut. Figs 6.1 and 7.A1, respectively. CIN, methylation, gene expression and deltaN values were standardized DNA copy number, CIN, deltaN %: –0.3 –0.1 0.1 0.3 Expression and methylation: –0.75 –0.25 0.25 0.75 for display using z-score transformation. Expr., expression; mut., mutation; WT, wild type. 27 S EPTEMBER 2 01 2 | V O L 489 | N A TU R E | 5 2 1 ©2012 Macmillan Publishers Limited. All rights reserved RESEARCH ARTICLE INK4a INK4a Figure 4 | Multi-faceted b p16 mutated p16 methylated RB1 altered a CDKN2A locus INK4a p16 fusion (TCGA-21-1078) characterization of mechanisms of INK4a p16 alteration rate: 72% CDKN2A loss. a, Schematic view of Exon 2 skipping: 4% Epigenetic silencing: 21% Mutation: 17% the exon structure of CDKN2A Missense Truncating p16INK4: exons 1, 2 and 3 demonstrating the types of Exon 1β Exon 1α Exon 3 Exon 2 alterations identified in the study. ARF: exons 1β, 2 and 3 The locations of point mutation are Homozygous deletion: 30% denoted by black and green circles. b, CDKN2A expression (y axis) –2 versus CDKN2A copy number (x axis). Samples are represented by KIAA1797 p16INK4 –4 circles and colour-coded by specific Exon 17 Exon 18 Exon 1α type of CDKN2A alteration. Del., Chr9: 20863778 Chr9: 21972267 deletion; het., heterozygous; homoz., ORF –6 homozygous. c, Diagram of the KIAA1797-p16INK4 fusion –8 p16INK4 exon 1α KIAA1797 exons 1–18 identified by WGS. ORF, open mRNA reading frame. d, CDKN2A Homoz. del. Het. loss Diploid INK4a p16 low alterations and expression levels expression (binary) in each sample. p16INK4 RB1 CDK6 CCND1 Homozygous Missense Truncating Skipped Amplification Downregulation Methylation deletion mutation mutation exon Analysis of mRNA expression across the CDKN2A locus revealed contain one or more mutations in tyrosine kinases, serine/threonine four distinct patterns of expression: complete absence of both p16INK4 kinases, phosphatidylinositol-3-OH kinase (PI(3)K) catalytic and and ARF (33%); expression of high levels of both p16INK4 and ARF regulatory subunits, nuclear hormone receptors, G-protein-coupled (31%); high expression of ARF and absence of p16INK4 (31%); or receptors, proteases and tyrosine phosphatases (Supplementary expression of a transcript that represents a splicing of exon 1b from Fig. 7.D.1a and Supplementary Data 7.2 and 7.3). From 50 to 77% ARF with the shared exon 3 of ARF and p16INK4, generating a pre- of the mutations were predicted to have a medium or high functional mature stop codon (4%) (Supplementary Fig. 4.4). Almost all of the effect as determined by the mutation assessor score (Supplementary cases completely lacking p16INK4 and ARF expression showed homo- Fig. 7.D.1a), and 39% of tyrosine and 42% of serine/threonine kinase zygous deletion (Fig. 4b and Supplementary Data 7.1). In one case, mutations were located in the kinase domain. Many of the alterations p16INK4 expression was detected but analysis of WGS data demon- were in known oncogenes and tumour suppressors, as defined in the strated an intergenic fusion event that resulted in detectable transcrip- COSMIC database (Supplementary Data 7.3). tion between exon 1a p16INK4 and exon 18 of KIAA1797 (Fig. 4b, c). We selected potential therapeutic targets based on several features, Interestingly, combined analysis of WGS and RNA sequencing data including (1) availability of a US Food and Drug Administration identified tumour suppressor gene inactivation by intra- or interchro- (FDA)-approved targeted therapeutic agent or one under study in mosomal rearrangement in PTEN, NOTCH1, ARID1A, CTNNA2, VHL current clinical trials (Supplementary Data 7.2); (2) confirmation of and NF1, in eight further cases (Supplementary Data 3.1 and 4.1). the altered allele in RNA sequencing; and (3) the mutation assessor In addition to homozygous deletion, there are frequent mutational score . Using those criteria, we identified 114 cases with somatic events in CDKN2A (Fig. 4b and Supplementary Data 7.1). These alteration of a potentially targetable gene (64%) (Supplementary account for 45% of the 56 cases with high p16INK4 and ARF expres- Fig. 7.D.1b and Supplementary Data 7.4). Among these, we identified sion. Furthermore, methylation of the exon 1a promoter accounts for three families of tyrosine kinases, the erythroblastic leukaemia viral many other cases of CDKN2A inactivation (70% of lung SQCCs with oncogene homologues (ERBBs), fibroblast growth factor receptors ARF expression in the absence of detectable p16INK4). Seven other (FGFRs) and Janus kinases (JAKs), all of which were found to be tumours in the high-ARF/low-INK4A group had documented mutated and/or amplified . As discussed for EGFR, the mutational mutations of INK4A, primarily nonsense mutations, suggesting spectra in these potential therapeutic targets differed from those in nonsense-mediated decay as a mechanism. Of the 28% of tumours lung adenocarcinoma (Supplementary Fig. 7.D.2) . without CDKN2A alterations, RB1 mutations were identified in eight To complement a gene-centred search for potential therapeutic cases and CDK6 amplification in one case (Fig. 4d). targets, we analysed core cellular pathways known to represent poten- tial therapeutic vulnerabilities: PI(3)K/AKT, receptor tyrosine kinase Therapeutic targets (RTK) and RAS. Analysis of the 178 lung SQCCs revealed alteration Molecularly targeted agents are now commonly used in patients with in at least one of those pathways in 69% of samples after restriction of adenocarcinoma of the lung, whereas no effective targeted agents have the analysis to mutations confirmed by RNA sequencing and to been developed specifically for lung SQCCs . We analysed our genomic amplifications associated with overexpression of the target gene data for evidence of the two common genomic alterations in adeno- (Fig. 5). Mutational events that have been curated in COSMIC are carcinomas of the lung: EGFR and KRAS mutations. Only one also shown in Supplementary Fig. 7D.2, as is the distribution of muta- sample had a KRAS codon 61 mutation, and there were no exon 19 tions, amplifications and overexpression of the genes depicted in deletions or Leu858Arg mutations in EGFR. However, amplifications Fig. 5. (A summary of all samples and their significant mutations of EGFR were found in 7% of cases, as were two instances of the and copy number alterations, including alterations in Fig. 5, is shown Leu861Gln EGFR mutation, which confers sensitivity to erlotinib in Supplementary Data 7.5.) Specifically, one of the components of the and gefitinib . PI(3)K/AKT pathway was altered in 47% of tumours and RTK sig- The presence of new potential therapeutic targets in lung SQCC nalling probably affected by events such as EGFR amplification, BRAF was suggested by the observation that 96% (171 out of 178) of tumours mutation or FGFR amplification or mutation in 26% of tumours 5 2 2 | N ATUR E | V OL 48 9 | 2 7 SEP TEM B E R 2 012 ©2012 Macmillan Publishers Limited. All rights reserved INK4a p16 mRNA expression (exon 1α log RPKM) 2 ARTICLE RESEARCH PI(3)K/RTK/RAS signalling data could thereby help to facilitate effective personalized therapy for 69% altered this deadly disease. EGFR ERBB2 ERBB3 FGFR1 FGFR2 FGFR3 9% 4% 2% 7% 3% 2% PTEN PIK3CA METHODS SUMMARY 15% 16% All specimens were obtained from patients with appropriate consent from the RASA1 relevant Institutional Review Board. DNA and RNA were collected from samples 4% KRAS HRAS NRAS STK11 AKT1 AKT2 AKT3 using the Allprep kit (Qiagen). We used commercial technology for capture and 2% <1% 4% 16% 3% 3% <1% NF1 sequencing of exomes from tumour DNA and normal DNA and whole-genome 11% shotgun sequencing. Significantly mutated genes were identified by comparing them TSC1 TSC2 AMPK BRAF with expectation models based on the exact measured rates of specific sequence 3% 3% 4% 23,24 lesions. GISTIC analysis of the circular-binary-segmented Affymetrix SNP 6.0 copy number data was used to identify recurrent amplification and deletion peaks. MTOR Cases (%) Consensus clustering approaches were used to analyse mRNA, miRNA and methy- 20,21,34,38,41,44 50 0 50 lation subtypes using previous approaches . Inactivated Activated Proliferation, cell survival, translation Activation Inhibition Received 9 March; accepted 9 July 2012. Alteration pattern Published online 9 September 2012. RTK 26% RAS 24% PI(3)K 47% 1. World Health Organization. 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Scott , Christina Yau ,Sam Ng ,Ted 36 36 36 36 39 CA126544, U24 CA143845, U24 CA143858, U24 CA144025, U24 CA143882, U24 Goldstein , Peter Waltman , Artem Sokolov ,Kyle Ellrott , Eric A. Collisson , 36 36 36 36 CA143866, U24 CA143867, U24 CA143848, U24 CA143840, U24 CA143835, U24 Daniel Zerbino , Christopher Wilks ,Singer Ma , Brian Craft ; University of North 23 19,20 CA143799, U24 CA143883, U24 CA143843, U54 HG003067, U54 HG003079 and Carolina at Chapel Hill Matthew D. Wilkerson , J. Todd Auman , Katherine A. 21,22,23 23 23 23 23 U54 HG003273. Hoadley , Ying Du , Christopher Cabanski ,VonnWalter , Darshan Singh , 23 23 23 23 Junyuan Wu , Anisha Gulabani , Tom Bodenheimer ,AlanP.Hoyle , Janae V. 23 23 22 22 23 Author Contributions The TCGA research network contributed collectively to this Simons , Matthew G. Soloway , Lisle E. Mose , Stuart R. Jefferys , Saianand Balu , 40 24 27 27 23 23,28 study. Biospecimens were provided by the tissue source sites and processed by the J. S. Marron , Yufeng Liu , Kai Wang , Jinze Liu , Jan F. Prins , D. Neil Hayes , 21,22,23 41 biospecimen core resource. Data generation and analyses were performed by the Charles M. Perou ; Baylor College of Medicine Chad J. Creighton ,Yiqun genome sequencing centres, cancer genome characterization centres and genome Zhang data analysis centres. All data were released through the data coordinating centre. Project activities were coordinated by the National Cancer Institute and National 42 42 43 Pathology committee William D. Travis , Natasha Rekhtman ,JoanneYi ,Marie C. Human Genome Research Institute project teams. We also acknowledge the following 43 44 45 46 47 Aubry , Richard Cheney , Sanja Dacic , Douglas Flieder , William Funkhouser , TCGA investigators who made substantial contributions to the project: P.S.H. and 48 49 50 Peter Illei ,Jerome Myers ,Ming-Sound Tsao D.N.H. (manuscript coordinators); M.D.W. (data coordinator); P.S.H. and N.S. (analysis coordinators); P.S.H., M.S.L., A. Sivachecnko, B.H. and G.G. (DNA sequence analysis); M.D.W., J.L. and D.N.H. (mRNA sequence analysis); L. Cope, J.G.H. and L. Danilova (DNA Biospecimen core resources: International Genomics Consortium Robert Penny , 51 51 51 51 51 methylation analysis); A.C., G.S., N.H.P., R.K. and M.L. (copy number analysis); N.S., R. David Mallery , Troy Shelton , Martha Hatfield , Scott Morris , Peggy Yena , 51 51 51 Bose, C.J.C., R. Sinha, C.M., S.N., E.A.C., R. Shen, J.N.W. and C. Sander (pathway analysis); Candace Shelton , Mark Sherman , Joseph Paulauskis A.C. and G.R. (miRNA sequence analysis); W.D.T., B.E.J., D.A.W. and M.-S.T. (pathology and clinical expertise); S.B.B., R. Govindan and M. Meyerson (project chairs). 1,2,6 29 Disease working group Matthew Meyerson , Stephen B. Baylin , Ramaswamy 52 33 53 54 52 Govindan , Rehan Akbani , Ijeoma Azodo , David Beer , Ron Bose ,Lauren A. Author Information The primary and processed data used to generate the analyses 55 56 52 21,23 7 Byers , David Carbone , Li-Wei Chang , Derek Chiang , Andy Chu , Elizabeth presented here can be downloaded by registered users from The Cancer Genome Atlas 7 39 29 41 29 Chun , Eric Collisson , Leslie Cope , Chad J. Creighton , Ludmila Danilova ,Li (https://tcga-data.nci.nih.gov/tcga/tcgaDownload.jsp, https://cghub.ucsc.edu/ and 52 1,5 1,2 23,28 1 Ding , Gad Getz , Peter S. Hammerman , D. Neil Hayes , Bryan Hernandez , https://tcga-data.nci.nih.gov/docs/publications/lusc_2012/). Reprints and 29 55 43 1,6 James G. Herman ,John Heymach , Cristiane Ida , Marcin Imielinski ,Bruce permissions information is available at www.nature.com/reprints. This paper is 2 57 56 53 11,12 Johnson ,Igor Jurisica , Jacob Kaufman ,Farhad Kosari ,Raju Kucherlapati , distributed under the terms of the Creative Commons 2 17,18 1 52 David Kwiatkowski , Marc Ladanyi , Michael S. Lawrence , Christopher A. Maher , Attribution-Non-Commercial-Share Alike licence, and the online version of the paper is 7 36 56 58,59 51 freely available to all readers. The authors declare no competing financial interests. Andy Mungall ,Sam Ng , William Pao , Martin Peifer , Robert Penny , Gordon 7 60 16 16 31 Robertson , Valerie Rusch , Chris Sander ,NikolausSchultz , Ronglai Shen , Jill Readers are welcome to comment on the online version of the paper. Correspondence 61 16 1 4 7 and requests for materials should be addressed to M. Meyerson Siegfried ,RileenSinha , Andrey Sivachenko ,CarrieSougnez , Dominik Stoll , 36 58,59,62 53 50 ([email protected]). Joshua Stuart , Roman K. Thomas , Sandra Tomaszek , Ming-Sound Tsao , 5 2 4 | NA TU RE | V OL 489 | 2 7 S EPTEMBER 2 01 2 ©2012 Macmillan Publishers Limited. All rights reserved ARTICLE RESEARCH 42 36 33,35 30 28 William D. Travis , Charles Vaske , John N. Weinstein , Daniel Weisenberger , Kentucky, Lexington, Kentucky 40506, USA. Department of Internal Medicine, Division 63 53 23 30 David Wheeler ,DennisA.Wigle , Matthew D. Wilkerson , Christopher Wilks , Ping of Medical Oncology, University of North Carolina at Chapel Hill, Chapel Hill, North 53 9,10 29 Yang , Jianjua John Zhang Carolina 27599, USA. Cancer Biology Division, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, Maryland 21231, USA. 64 64 64 University of Southern California Epigenome Center, University of Southern California, Data coordination centre Mark A. Jensen , Robert Sfeir , Ari B. Kahn ,Anna L. 64 64 64 64 64 Los Angeles, California 90033, USA. Department of Epidemiology and Biostatistics, Chu , Prachi Kothiyal , Zhining Wang ,Eric E.Snyder ,Joan Pontius ,Todd D. 64 64 64 64 64 Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. Department Pihl , Brenda Ayala , Mark Backus , Jessica Walton ,JulienBaboud , Dominique 64 64 64 64 of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. L. Berton , Matthew C. Nicholls ,Deepak Srinivasan , Rohini Raman , Stanley 64 64 64 64 Department of Bioinformatics and Computational Biology, The University of Texas MD Girshik , Peter A. Kigonya , Shelley Alonso , Rashmi N. Sanbhadti , Sean P. 64 64 64 Anderson Cancer Center, Houston, Texas 77030, USA. Division of Pathology and Barletta , John M. Greene , David A. Pot Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA. Department of Systems Biology, The University of Texas MD 50 50 46 Tissuesourcesites Ming-Sound Tsao , Bizhan Bandarchi-Chamkhaleh , Jeff Boyd , 36 Anderson Cancer Center, Houston, Texas 77030, USA. Department of Biomolecular 46 53 53 53 JoEllen Weaver ,Dennis A.Wigle ,Ijeoma A.Azodo ,Sandra C. Tomaszek ,Marie Engineering and Center for Biomolecular Science and Engineering, University of 65 65 66 53 67 Christine Aubry ,ChristianeM.Ida ,PingYang , Farhad Kosari , MalcolmV. Brock , 37 California Santa Cruz, Santa Cruz, California 95064, USA. Howard Hughes Medical 67 68 67 68 69 Kristen Rodgers ,MarianRutledge ,TravisBrown ,Beverly Lee ,James Shin , 38 Institute, University of California Santa Cruz, Santa Cruz, California 95064, USA. Buck 69 70 61 71 Dante Trusty , Rajiv Dhir , Jill M. Siegfried , Olga Potapova , Konstantin V. 39 Institute for Age Research, Novato, California 94945, USA. Division of Hematology/ 72 71 60 73 Fedosenko , Elena Nemirovich-Danchenko , Valerie Rusch , Maureen Zakowski , Oncology, University of California San Francisco, San Francisco, California 94143, USA 74 74 74 74 Mary V. Iacocca , Jennifer Brown ,BrendaRabeno , Christine Czerwinski , Nicholas Department of Statistics and Operations Research, University of North Carolina Medical 74 75 75 49 49 Petrelli ,Zhen Fan , Nicole Todaro ,JohnEckman ,Jerome Myers ,W. Kimryn Center, Chapel Hill, North Carolina 27599, USA. Human Genome Sequencing Center 23 76 76 76 23 51 Rathmell ,Leigh B. Thorne ,Mei Huang ,LoriBoice , Ashley Hill , Robert Penny , and Dan L. Duncan Cancer Center Division of Biostatistics, Baylor College of Medicine, 51 51 51 51 44 David Mallery ,Erin Curley , Candace Shelton ,Peggy Yena , Carl Morrison , Houston, Texas 77030, USA. Department of Pathology, Memorial Sloan Kettering 44 77 77 77 Carmelo Gaudioso , John M. S. Bartlett , Sugy Kodeeswaran , Brent Zanke ,Harman Cancer Center, New York, New York 10065 USA. Department of Pathology, Mayo Clinic, 78 79 80 81 81 Sekhon , Kerstin David ,Hartmut Juhl ,XuanVan Le ,Bernard Kohl ,Richard Rochester, Minnesota 55905, USA. 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National Human Genome Research Institute, National Institutes of Health, Hill, North Carolina 27599, USA. Department of Computer Science, University of Bethesda, Maryland 20892, USA. 27 S EPTEMBER 2 01 2 | V O L 4 89 | N AT U R E | 5 2 5 ©2012 Macmillan Publishers Limited. All rights reserved CORRECTIONS & AMENDMENTS CORRIGENDUM doi:10.1038/nature11666 Corrigendum: Comprehensive genomic characterization of squamous cell lung cancers The Cancer Genome Atlas Research Network Nature 489, 519–525 (2012); doi:10.1038/nature11404 In this Article, author Kristen Rodgers was spelt incorrectly. This error has been corrected in the HTML and PDF of the original paper. 288 | N ATUR E | V OL 4 9 1 | 8 N OVEM B E R 2 012 ©2012 Macmillan Publishers Limited. All rights reserved

Journal

Nature – Springer Journals

Published: Sep 9, 2012

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