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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, leading to an estimated 1.4 million deaths in 2010 (ref. 1). The discovery of recurrent mutations in the epidermal growth factor receptor (EGFR) kinase, as well as fusions involving anaplastic lymphoma kinase (ALK), has led to a marked change in the treatment of patients with lung adenocarcinoma, the most common type of lung cancer2–5. More recent data have suggested that targeting mutations in BRAF, AKT1, ERBB2 and PIK3CA and fusions that involve ROS1 and RET may also be successful6,7. Unfortunately, activating mutations in EGFR and ALK fusions are typically not present in the second most common type of lung cancer, lung squamous cell carcinoma (SQCC)8, and targeted agents developed for lung adenocarcinoma are largely ineffective against lung SQCC. Although no comprehensive genomic analysis of lung SQCCs has been reported, single-platform studies have identified regions of somatic copy number alterations in lung SQCCs, including amplification of SOX2, PDGFRA and FGFR1 and/or WHSC1L1 and deletion of CDKN2A9,10. DNA sequencing studies of lung SQCCs have reported recurrent mutations in several genes, including TP53, NFE2L2, KEAP1, BAI3, FBXW7, GRM8, MUC16, RUNX1T1, STK11 and ERBB4 (refs 11, 12). DDR2 mutations and FGFR1 amplification have been nominated as therapeutic targets13–15. We have conducted a comprehensive study of lung SQCCs from a large cohort of patients as part of The Cancer Genome Atlas (TCGA) project. The twin aims are to characterize the genomic and epigenomic landscape of lung SQCC and to identify potential opportunities for therapy. We report an integrated analysis based on DNA copy number, somatic exonic mutations, messenger RNA sequencing, mRNA expression and promoter methylation for 178 histopathologically reviewed lung SQCCs, in addition to whole genome sequencing (WGS) of 19 samples and microRNA sequencing of 159 samples (Supplementary Table 1.1). Demographic and clinical data and results of the genomic analyses can be downloaded from the TCGA data portal (https://tcga-data.nci.nih.gov/docs/publications/lusc_2012/).

adjacent, histologically normal tissues resected at the time of surgery (n 5 137) or from peripheral blood (n 5 41). All patients provided written informed consent to conduct genomic studies in accordance with local Institutional Review Boards. The demographic characteristics are described in Supplementary Table 1.2. The median follow-up for the cohort was 15.8 months, and 60% of patients were alive at the time of the last follow-up (data updated in November 2011). Ninety-six per cent of the patients had a history of tobacco use, similar to previous reports for North American patients with lung SQCC16. DNA and RNA were extracted from patient specimens and measured by several genomic assays, which included standard quality-control assessments (Supplementary Methods, sections 2–8). A committee of experts in lung cancer pathology performed a further review of all samples to confirm the histological subtype (Supplementary Fig. 1.1 and Supplementary Methods, section 1).

Somatic DNA alterations
The lung SQCCs analysed in this study display a large number and variety of DNA alterations, with a mean of 360 exonic mutations, 323 altered copy number segments and 165 genomic rearrangements per tumour. Copy number alterations were analysed using several platforms. Analysis of single nucleotide polymorphism (SNP) 6.0 array data across the set of 178 lung SQCCs identified a high rate of copy number alteration (mean of 323 segments) when compared with other TCGA projects (as of 1 February 2012), including ovarian cancer (477 segments)17, glioblastoma multiforme (282 segments)18, colorectal carcinoma (213 segments), breast carcinoma (282 segments) and renal cell carcinoma (156 segments) (P , 1 3 10215 by Fisher’s exact test). These segments gave rise to regions of both focal and broad somatic copy number alterations (SCNAs), with a mean of 47 focal and 23 broad events per tumour (broad events defined as $50% of the length of the chromosome arm). There was strong concordance between the three independent copy number assays for all regions of SCNA (Supplementary Figs 2.1–2.4). At the level of whole chromosome arm SCNAs, lung SQCCs exhibit many similarities to 205 cases of lung adenocarcinoma analysed by

Samples and clinical data
Tumour samples were obtained from 178 patients with previously untreated stage I–IV lung SQCC. Germline DNA was obtained from
*

Lists of participants and their affiliations appear at the end of the paper. 2 7 S E P T E M B E R 2 0 1 2 | VO L 4 8 9 | N AT U R E | 5 1 9

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RESEARCH ARTICLE
TCGA (Supplementary Fig. 2.1a). The most notable difference between these cancers is selective amplification of chromosome 3q in lung SQCC, as has been reported9,19. Using the SNP 6.0 array platform and GISTIC 2.0 (refs 20, 21), we identified regions of significant copy number alteration (Supplementary Methods, section 2). There were 50 peaks of significant amplification or deletion (Q , 0.05), several of which included SCNAs previously seen in lung SQCCs including SOX2, PDGFRA and/or KIT, EGFR, FGFR1 and/or WHSC1L1, CCND1 and CDKN2A9,10,19 (Supplementary Fig. 2.1b and Supplementary Data 2.1 and 2.2). Other peaks defined regions of SCNA reported for the first time, including amplifications of chromosomal segments containing NFE2L2, MYC, CDK6, MDM2, BCL2L1 and EYS and deletions of FOXP1, PTEN and NF1 (Supplementary Fig. 2.1b). Whole exome sequencing of 178 lung SQCCs and matched germline DNA targeted 193,094 exons from 18,863 genes. The mean sequencing coverage across targeted bases was 1213, with 83% of target bases above 303 coverage. We identified a total of 48,690 non-silent mutations with 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 megabase (Mb) and median of 8.4 per Mb. That rate is higher than rates observed in other TCGA projects including acute myelogenous leukaemia (0.56 per Mb), breast carcinoma (1.0 per Mb), ovarian cancer17 (2.1 per Mb), glioblastoma multiforme18 (2.3 per Mb) and colorectal carcinoma (3.2 per Mb) (data as of 1 February 2012, P , 2.2 3 10216 by t-test or Wilcoxon’s rank sum test for lung SQCC versus all others). In lung SQCC, CpG transitions and transversions were the most commonly observed mutation types, with mean rates of 9.9 and 10.7 per sequenced megabase of CpG context, respectively, for a total mutation rate of 20.6 per Mb. At non-CpG sites, transversions at C:G sites were more common than transitions (7.3 versus 2.9 per Mb; total 5 10.2 per Mb) and more common than transversions or transitions at A:T sites (1.5 versus 1.3 per Mb; total 5 2.8 per Mb). Significantly mutated genes were identified using a modified version of the MutSig algorithm (Supplementary Methods, section 3)22,23. We identified 10 genes with a false discovery rate (FDR) Q value , 0.1 (Supplementary Table 3.1): TP53, CDKN2A, PTEN, PIK3CA, KEAP1, MLL2, HLA-A, NFE2L2, NOTCH1 and RB1, all of which demonstrated robust evidence of gene expression as defined by reads per kilobase of exon model per million mapped reads (RPKM) . 1 (Fig. 1). TP53 mutation was observed in 81% of samples by automated analysis; visual review of sequencing reads identified a further 9% of samples with potential mutations in regions of sub-optimal coverage or in samples with low purity. Most observed mutations in NOTCH1 (8 out of 17) were truncating alterations, suggesting loss-of-function, as has recently been reported for head and neck SQCCs22,24. Mutations in HLA-A were also almost exclusively nonsense or splice site events (7 out of 8). To increase our statistical power to detect mutated genes in the setting of the observed high background mutation rate, we performed a secondary MutSig analysis only considering genes previously observed to be mutated in cancer according to the COSMIC database.
100 80 60 40 20 0 81% 15% 8% 16% 12% 20% 3% 15% 8% 7% 50 30 10 132

This yielded 12 other genes with FDR , 0.1: FAM123B (also known as WTX), HRAS, FBXW7, SMARCA4, NF1, SMAD4, EGFR, APC, TSC1, BRAF, TNFAIP3 and CREBBP (Supplementary Table 3.1). Both the spectrum and the frequency of EGFR mutations differed from those seen in lung adenocarcinomas. The two most common alterations in lung adenocarcinoma, Leu858Arg and inframe deletions in exon 19, were absent, whereas two Leu861Gln mutations were detected in EGFR. As described in Supplementary Fig. 3.1, we verified somatic mutations by performing an independent hybrid-recapture of 76 genes in all samples. A total of 1,289 mutations were assayed, and we achieved satisfactory coverage to have power to verify at 1,283 positions. We validated 1,235 mutations (96.2%) (Supplementary Fig. 3.1 and Supplementary Methods, section 3). We also verified mutation calls using WGS and RNA sequencing data with similar results (Supplementary Figs 3.1, 4.3 and Supplementary Methods, sections 3 and 4). WGS was performed for 19 tumour/normal pairs with a mean computed coverage of 543. A mean of 165 somatic rearrangements was found per lung SQCC tumour pair (Supplementary Fig. 3.2), a value in excess of that reported for WGS studies of other tumour types including colorectal carcinoma (75)25, prostate carcinoma (108)26, multiple myeloma (21)23 and breast cancer (90)27. Although most inframe coding fusions detected in WGS were validated by RNA sequencing, no recurrent rearrangements predicted to generate fusion proteins were identified (Supplementary Data 3.1 and 4.1).

Somatically altered pathways
Many of the somatic alterations we have identified in lung SQCCs seem to be drivers of pathways important to the initiation or progression of the cancer. Specifically, genes involved in the oxidative stress response and squamous differentiation were frequently altered by mutation or SCNA. We observed mutations and copy number alterations of NFE2L2 and KEAP1 and/or deletion or mutation of CUL3 in 34% of cases (Fig. 2). NFE2L2 and KEAP1 code for proteins that bind to each other, have been shown to regulate the cell response to oxidative damage, chemo- and radiotherapy, and are somatically altered in a variety of cancer types28,29. We found mutations in NFE2L2 almost exclusively in one of two KEAP1 interaction motifs, DLG or ETGE. Mutations in KEAP1 and CUL3 showed a pattern consistent with lossof-function and were mutually exclusive with mutations in NFE2L2 (Figs 1c and 2). PARADIGM SHIFT30 analysis predicts that mutations in NFE2L2 and KEAP1 exert a considerable functional effect (Supplementary Fig. 7.C.1, 7.C.2 and Supplementary Methods, section 7). We also found alterations in genes with known roles in squamous cell differentiation in 44% of samples, including overexpression and amplification of SOX2 and TP63, loss-of-function mutations in NOTCH1, NOTCH2 and ASCL4 and focal deletions in FOXP1 (Fig. 2). Although NOTCH1 has been well characterized as an oncogene in haematological cancers31, NOTCH1 and NOTCH2 truncating mutations have been reported in cutaneous SQCCs and lung SQCCs32. Truncating mutations in ASCL4 are the first to be reported in human
Syn. Missense Splice site Nonsense Frame shift Inframe indel Other non syn.

Syn. Non syn.

155

TP53 CDKN2A PTEN PIK3CA KEAP1 MLL2 HLA-A NFE2L2 NOTCH1 RB1 0.5 2.0 3.5

Figure 1 | Significantly mutated genes in lung SQCC. Significantly mutated genes (Q value , 0.1) identified by exome sequencing are listed vertically by Q value. The percentage of lung SQCC samples with a mutation detected by automated calling is noted at the left. Samples displayed as columns, with the overall number of mutations plotted at the top, and samples are arranged to emphasize mutual exclusivity among mutations. Syn., synonymous.

70

Individuals with mutation
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Mutations per Mb

–log10 (Q value)

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ARTICLE RESEARCH
Oxidative stress response 34% altered (62% in classical subtype) KEAP1
12%

Squamous differentiation 44% altered

SOX2
21%

TP63
16%

CUL3
7%

NFE2L2
19%

NOTCH1
8%

NOTCH2
5%

ASCL4
3%

FOXP1
4%

Oxidative stress response 60 cases with at least one alteration NFE2L2 KEAP1 CUL3
Cases (%)
50 0 50

78 cases with at least one alteration SOX2 TP63 NOTCH1 NOTCH2 ASCL4 FOXP1 Inhibition Homozygous deletion Amplification Overexpression Truncating mutation Missense mutation

Figure 2 | Somatically altered pathways in squamous cell lung cancer. Left, alterations in oxidative stress response pathway genes as defined by somatic mutation, copy number alteration or up- or downregulation. Frequencies of alteration are expressed as a percentage of all cases, with background in red for activated genes and blue for inactivated genes. Right, alterations in genes that regulate squamous differentiation, as defined in the left panel.

Activation

Inactivated

Activated

cancer and may have a lineage role given the requirement for ASCL1 for survival of small-cell lung cancer cells33. Alterations in NOTCH1, NOTCH2 and ASCL4 were mutually exclusive and exhibited minimal overlap with amplification of TP63 and/or SOX2 (Fig. 2), suggesting that aberrations in those modulators of squamous cell differentiation have overlapping functional consequences.

mRNA expression profiling and subtype classification
Whole-transcriptome expression profiles were generated by RNA sequencing for the entire cohort and by microarrays for a 121-sample subset. Of 20,502 genes analysed, the mean RNA coverage indices were 193 and 6,420 RPKM (Supplementary Fig. 4.1 and Supplementary Methods, section 4). Previously reported lung SQCC gene expressionsubtype signatures34 were applied to both of the expression platforms, yielding four subtypes designated as classical (36%), basal (25%), secretory (24%) and primitive (15%). The concordance of subtypes between the two platforms was high (94% agreement) (Supplementary Fig. 4.2). Considerable correlations were found between the expression subtypes and genomic alterations in copy number, mutation and methylation (Fig. 3). The classical subtype was characterized by alterations in KEAP1, NFE2L2 and PTEN, as well as pronounced hypermethylation and chromosomal instability. The 3q26 amplicon was present in all of the subtypes, but it was most characteristic of the classical subtype, which also showed the greatest overexpression of three known oncogenes on 3q: SOX2, TP63 and PIK3CA. RNA sequencing data suggested that high expression levels of TP63, in samples with and without amplification of TP63, were associated with dominant expression of the deltaN isoform (also called p40), which lacks the aminoterminal transactivation domain, compared with the longer isoform, called tap63 (89% of tumours overexpressed deltaN compared with
Classical expression subtype Primitive expression subtype

tap63; P , 2.2 3 10216). The short deltaN isoform is thought to function as an oncogene35,36, and its expression was most enriched in the classical subtype. By contrast, the primitive expression subtype more commonly exhibited RB1 and PTEN alterations, and the basal expression subtype showed NF1 alterations (Fig. 3). Amplification of FGFR1 and WHSC1L1 was anticorrelated with the classical subtype and specifically with NFE2L2 or KEAP1 mutated samples. Although CDKN2A alterations are common in lung SQCCs, they are not associated with any particular expression subtype (Fig. 3). Independent clustering of miRNA and methylation data indicated association with expression subtypes. The highest overall methylation was seen in the classical subtype (Fig. 3, Supplementary Figs 5.1 and 6.1, Supplementary Methods, sections 5 and 6, Supplementary Data 6.1 and 6.2 and Supplementary Table 5.1). Integrative clustering (iCluster)37 of mRNA, miRNA, methylation, SCNA and mutation data demonstrated concordance with the mRNA expression subtypes and associated alterations (Fig. 3, Supplementary Fig. 7.A.1 and Supplementary Methods, section 7). Independent correlation of somatic mutations, copy number alterations and gene expression signatures revealed notable subtype associations with alterations in the TP53, PI3K, RB1 and NFE2L2/KEAP1 pathways (Supplementary Fig. 7.B.1 and Supplementary Methods, section 7).

Analysis of the CDKN2A locus
Integrated multiplatform analyses showed that CDKN2A, a known tumour suppressor gene in lung SQCC38 that encodes the p16INK4A and p14ARF proteins, is inactivated in 72% of cases of lung SQCC (Fig. 4a and Supplementary Data 7.1)—by epigenetic silencing by methylation (21%), inactivating mutation (18%), exon 1b skipping (4%) and homozygous deletion (29%).
Secretory expression subtype

Basal expression subtype

CN PIK3CA expr. TP63 expr. deltaN % SOX2 expr. Mut. KEAP1 Expr. Mut. NFE2L2 CN Expr. CN PTEN Expr. Mut. RB1 CN NF1 Expr. CIN Hypermethylation Mut. CDKN2A CN Expr. iCluster 3q26 Gene sequence: DNA copy number, CIN, deltaN %: Expression and methylation: WT
–0.3 –0.1 0.1

Mut.
0.3

–0.75 –0.25 0.25 0.75

Figure 3 | Gene expression subtypes integrated with genomic alterations. Tumours are displayed as columns, grouped by gene expression subtype. Subtypes were compared by Kruskal–Wallis tests for continuous features and by Fisher’s exact tests for categorical features. Displayed features showed significant association with gene expression subtype (P , 0.05), except for CDKN2A alterations. deltaN percentage represents transcript isoform usage between the TP63 isoforms, deltaN and tap63, as determined by RNA sequencing. Chromosomal instability (CIN) is defined by the mean of the absolute values of chromosome arm copy numbers (CN) from the GISTIC23,24 output. Absolute values are used so that amplification and deletion alterations are counted equally. Hypermethylation scores and iCluster assignments are described in Supplementary Figs 6.1 and 7.A1, respectively. CIN, methylation, gene expression and deltaN values were standardized for display using z-score transformation. Expr., expression; mut., mutation; WT, wild type.

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RESEARCH ARTICLE
a
CDKN2A locus Exon 2 skipping: 4% Mutation: 17%
Missense Truncating

b
p16INK4a mRNA expression (exon 1α log2 RPKM) 4

p16INK4a alteration rate: 72% Epigenetic silencing: 21%
p16INK4: exons 1, 2 and 3

p16INK4a mutated p16INK4a methylated p16INK4a fusion (TCGA-21-1078)

RB1 altered

2

Exon 1β
ARF: exons 1β, 2 and 3

Exon 1α

Exon 2

Exon 3

0

Homozygous deletion: 30%

–2

c
Exon 17 ORF

KIAA1797 Exon 18 Chr9: 20863778

p16INK4 Exon 1α Chr9: 21972267

–4

–6

mRNA

KIAA1797 exons 1–18

p16INK4 exon 1α

–8 Homoz. del. Het. loss Diploid
p16INK4a low expression

d
p16INK4 RB1 CDK6 CCND1 Homozygous deletion Amplification Downregulation Missense mutation Truncating mutation Methylation

Figure 4 | Multi-faceted characterization of mechanisms of CDKN2A loss. a, Schematic view of the exon structure of CDKN2A demonstrating the types of alterations identified in the study. The locations of point mutation are denoted by black and green circles. b, CDKN2A expression (y axis) versus CDKN2A copy number (x axis). Samples are represented by circles and colour-coded by specific type of CDKN2A alteration. Del., deletion; het., heterozygous; homoz., homozygous. c, Diagram of the KIAA1797-p16INK4 fusion identified by WGS. ORF, open reading frame. d, CDKN2A alterations and expression levels (binary) in each sample.

Skipped exon

Analysis of mRNA expression across the CDKN2A locus revealed four distinct patterns of expression: complete absence of both p16INK4 and ARF (33%); expression of high levels of both p16INK4 and ARF (31%); high expression of ARF and absence of p16INK4 (31%); or expression of a transcript that represents a splicing of exon 1b from ARF with the shared exon 3 of ARF and p16INK4, generating a premature stop codon (4%) (Supplementary Fig. 4.4). Almost all of the cases completely lacking p16INK4 and ARF expression showed homozygous deletion (Fig. 4b and Supplementary Data 7.1). In one case, p16INK4 expression was detected but analysis of WGS data demonstrated an intergenic fusion event that resulted in detectable transcription between exon 1a p16INK4 and exon 18 of KIAA1797 (Fig. 4b, c). Interestingly, combined analysis of WGS and RNA sequencing data identified tumour suppressor gene inactivation by intra- or interchromosomal rearrangement in PTEN, NOTCH1, ARID1A, CTNNA2, VHL and NF1, in eight further cases (Supplementary Data 3.1 and 4.1). In addition to homozygous deletion, there are frequent mutational events in CDKN2A (Fig. 4b and Supplementary Data 7.1). These account for 45% of the 56 cases with high p16INK4 and ARF expression. Furthermore, methylation of the exon 1a promoter accounts for many other cases of CDKN2A inactivation (70% of lung SQCCs with ARF expression in the absence of detectable p16INK4). Seven other tumours in the high-ARF/low-INK4A group had documented mutations of INK4A, primarily nonsense mutations, suggesting nonsense-mediated decay as a mechanism. Of the 28% of tumours without CDKN2A alterations, RB1 mutations were identified in eight cases and CDK6 amplification in one case (Fig. 4d).

Therapeutic targets
Molecularly targeted agents are now commonly used in patients with adenocarcinoma of the lung, whereas no effective targeted agents have been developed specifically for lung SQCCs13. We analysed our genomic data for evidence of the two common genomic alterations in adenocarcinomas of the lung: EGFR and KRAS mutations. Only one sample had a KRAS codon 61 mutation, and there were no exon 19 deletions or Leu858Arg mutations in EGFR. However, amplifications of EGFR were found in 7% of cases, as were two instances of the Leu861Gln EGFR mutation, which confers sensitivity to erlotinib and gefitinib39. The presence of new potential therapeutic targets in lung SQCC was suggested by the observation that 96% (171 out of 178) of tumours
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contain one or more mutations in tyrosine kinases, serine/threonine kinases, phosphatidylinositol-3-OH kinase (PI(3)K) catalytic and regulatory subunits, nuclear hormone receptors, G-protein-coupled receptors, proteases and tyrosine phosphatases (Supplementary Fig. 7.D.1a and Supplementary Data 7.2 and 7.3). From 50 to 77% of the mutations were predicted to have a medium or high functional effect as determined by the mutation assessor score40 (Supplementary Fig. 7.D.1a), and 39% of tyrosine and 42% of serine/threonine kinase mutations were located in the kinase domain. Many of the alterations were in known oncogenes and tumour suppressors, as defined in the COSMIC database (Supplementary Data 7.3). We selected potential therapeutic targets based on several features, including (1) availability of a US Food and Drug Administration (FDA)-approved targeted therapeutic agent or one under study in current clinical trials (Supplementary Data 7.2); (2) confirmation of the altered allele in RNA sequencing; and (3) the mutation assessor score40. Using those criteria, we identified 114 cases with somatic alteration of a potentially targetable gene (64%) (Supplementary Fig. 7.D.1b and Supplementary Data 7.4). Among these, we identified three families of tyrosine kinases, the erythroblastic leukaemia viral oncogene homologues (ERBBs), fibroblast growth factor receptors (FGFRs) and Janus kinases (JAKs), all of which were found to be mutated and/or amplified41. As discussed for EGFR, the mutational spectra in these potential therapeutic targets differed from those in lung adenocarcinoma (Supplementary Fig. 7.D.2)42. To complement a gene-centred search for potential therapeutic targets, we analysed core cellular pathways known to represent potential therapeutic vulnerabilities: PI(3)K/AKT, receptor tyrosine kinase (RTK) and RAS. Analysis of the 178 lung SQCCs revealed alteration in at least one of those pathways in 69% of samples after restriction of the analysis to mutations confirmed by RNA sequencing and to amplifications associated with overexpression of the target gene (Fig. 5). Mutational events that have been curated in COSMIC are also shown in Supplementary Fig. 7D.2, as is the distribution of mutations, amplifications and overexpression of the genes depicted in Fig. 5. (A summary of all samples and their significant mutations and copy number alterations, including alterations in Fig. 5, is shown in Supplementary Data 7.5.) Specifically, one of the components of the PI(3)K/AKT pathway was altered in 47% of tumours and RTK signalling probably affected by events such as EGFR amplification, BRAF mutation or FGFR amplification or mutation in 26% of tumours

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PI(3)K/RTK/RAS signalling 69% altered
PTEN
15%

EGFR ERBB2 ERBB3 FGFR1 FGFR2 FGFR3
9% 4% 2% 7% 3% 2%

data could thereby help to facilitate effective personalized therapy for this deadly disease.

PIK3CA
16%

METHODS SUMMARY
All specimens were obtained from patients with appropriate consent from the relevant Institutional Review Board. DNA and RNA were collected from samples using the Allprep kit (Qiagen). We used commercial technology for capture and sequencing of exomes from tumour DNA and normal DNA and whole-genome shotgun sequencing. Significantly mutated genes were identified by comparing them with expectation models based on the exact measured rates of specific sequence lesions. GISTIC23,24 analysis of the circular-binary-segmented Affymetrix SNP 6.0 copy number data was used to identify recurrent amplification and deletion peaks. Consensus clustering approaches were used to analyse mRNA, miRNA and methylation subtypes using previous approaches20,21,34,38,41,44.
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RASA1 STK11
2%

AKT1 AKT2 AKT3
<1% 4% 16%

KRAS HRAS NRAS
3% 3% <1%

4%

NF1
11%

AMPK

TSC1 TSC2
3% 3%

BRAF
4% Cases (%) 50 0 50 Activated Inactivated Activation Inhibition

MTOR

Proliferation, cell survival, translation Alteration pattern
RTK 26% RAS 24% PI(3)K 47%

Figure 5 | Alterations in targetable oncogenic pathways in lung SQCCs. Pathway diagram showing the percentage of samples with alterations in the PI(3)K/RTK/RAS pathways. Alterations are defined by somatic mutations, homozygous deletions, high-level, focal amplifications, and, in some cases, by significant up- or downregulation of gene expression (AKT3, FGFR1, PTEN).

(Fig. 5 and Supplementary Fig. 7.D.3). Alterations in the PI(3)K/AKT pathway genes were mutually exclusive with EGFR alterations as identified by MEMo43 (Supplementary Fig. 7.D.4.). Although the dependence of lung SQCC on many of these individual alterations remains to be defined functionally, this analysis suggests new areas for potential therapeutic development in this cancer.

Discussion
Lung SQCCs are characterized by a high overall mutation rate of 8.1 mutations per megabase and marked genomic complexity. Similar to high-grade serous ovarian carcinoma17, almost all lung SQCCs display somatic mutation of TP53. There were also frequent alterations in the following pathways: CDKN2A/RB1, NFE2L2/KEAP1/ CUL3, PI3K/AKT and SOX2/TP63/NOTCH1 pathways, providing evidence of common dysfunction in cell cycle control, response to oxidative stress, apoptotic signalling and/or squamous cell differentiation. Pathway alterations clustered according to expression-subtype in many cases, suggesting that those subtypes have a biological basis. A role for somatic mutation in the cancer hallmark of avoiding immune destruction44 is suggested by the presence of inactivating mutations in the HLA-A gene. Somatic loss-of-function alterations of HLA-A have not been reported previously in genomic studies of lung cancer. Given the recently reported efficacy of anti-programmed death 1 (PD1)45 and anti-cytotoxic T-lymphocyte antigen 4 (CTLA4) antibodies in non-small-cell lung cancer46, these HLA-A mutations suggest a possible role for genotypic selection of patients for immunotherapies. Targeted kinase inhibitors have been successfully used for the treatment of lung adenocarcinoma but minimally so in lung SQCC. The observations reported here suggest that a detailed understanding of the possible targets in lung SQCCs may identify targeted therapeutic approaches. Whereas EGFR and KRAS mutations, the two most common oncogenic aberrations in lung adenocarcinoma, are extremely rare in lung SQCC, alterations in the FGFR kinase family are common. Lung SQCCs also share many alterations in common with head and neck squamous cell carcinomas without evidence of human papilloma virus infection, including mutation in PIK3CA, PTEN, TP53, CDKN2A, NOTCH1 and HRAS22,24, suggesting that the biology of these two diseases may be similar. The current study has identified a potentially targetable gene or pathway alteration in most lung SQCC samples studied. The data presented here can help to organize efforts to analyse lung SQCC clinical tumour specimens for a panel of specific, actionable mutations to select patients for appropriately targeted clinical trials. These

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RESEARCH ARTICLE
28. Singh, A. et al. Dysfunctional KEAP1–NRF2 interaction in non-small-cell lung cancer. PLoS Med. 3, e420 (2006). 29. Singh, A., Bodas, M., Wakabayashi, N., Bunz, F. & Biswal, S. Gain of Nrf2 function in non-small-cell lung cancer cells confers radioresistance. Antioxid. Redox Signal. 13, 1627–1637 (2010). 30. Vaske, C. J. et al. Inference of patient-specific pathway activities from multidimensional cancer genomics data using PARADIGM. Bioinformatics 26, i237–i245 (2010). 31. Aster, J. C., Blacklow, S. C. & Pear, W. S. Notch signalling in T-cell lymphoblastic leukaemia/lymphoma and other haematological malignancies. J. Pathol. 223, 263–274 (2011). 32. Wang, N. J. et al. Loss-of-function mutations in Notch receptors in cutaneous and lung squamous cell carcinoma. Proc. Natl Acad. Sci. USA 108, 17761–17766 (2011). 33. Osada, H., Tatematsu, Y., Yatabe, Y., Horio, Y. & Takahashi, T. ASH1 gene is a specific therapeutic target for lung cancers with neuroendocrine features. 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The epidermal growth factor receptorL861Q mutation increases kinase activity without leading to enhanced sensitivity toward epidermal growth factor receptor kinase inhibitors. J. Thorac. Oncol. 6, 387–392 (2011). 40. Reva, B., Antipin, Y. & Sander, C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 39, e118 (2011). 41. Govindan, R. Summary of the proceedings from the 10th annual meeting of molecularly targeted therapy in non-small cell lung cancer. J. Thorac. Oncol. 5, S433 (2010). 42. Ding, L. et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature 455, 1069–1075 (2008). 43. Ciriello, G., Cerami, E., Sander, C. & Schultz, N. Mutual exclusivity analysis identifies oncogenic network modules. Genome Res. 22, 398–406 (2012). 44. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011). 45. Brahmer, J. R. et al. Phase I study of single-agent anti-programmed death-1 (MDX1106) in refractory solid tumors: safety, clinical activity, pharmacodynamics, and immunologic correlates. J. Clin. Oncol. 28, 3167–3175 (2010). 46. Lynch, T. J. et al. Phase II trial of ipilimumab (IPI) and paclitaxel/carboplatin (P/C) in first-line stage IIIb/IV non-small cell lung cancer (NSCLC). J. Clin. Oncol. 28, 7531 (2010). Supplementary Information is available in the online version of the paper. Acknowledgements This study was supported by NIH grants U24 CA126561, U24 CA126551, U24 CA126554, U24 CA126543, U24 CA126546, U24 CA126563, U24 CA126544, U24 CA143845, U24 CA143858, U24 CA144025, U24 CA143882, U24 CA143866, U24 CA143867, U24 CA143848, U24 CA143840, U24 CA143835, U24 CA143799, U24 CA143883, U24 CA143843, U54 HG003067, U54 HG003079 and U54 HG003273. 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 centres, cancer genome characterization centres and genome data analysis centres. All data were released through the data coordinating centre. Project activities were coordinated by the National Cancer Institute and National Human Genome Research Institute project teams. We also acknowledge the following TCGA investigators who made substantial contributions to the project: P.S.H. and 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 methylation analysis); A.C., G.S., N.H.P., R.K. and M.L. (copy number analysis); N.S., R. Bose, C.J.C., R. Sinha, C.M., S.N., E.A.C., R. Shen, J.N.W. and C. Sander (pathway analysis); 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). Author Information The primary and processed data used to generate the analyses presented here can be downloaded by registered users from The Cancer Genome Atlas (https://tcga-data.nci.nih.gov/tcga/tcgaDownload.jsp, https://cghub.ucsc.edu/ and https://tcga-data.nci.nih.gov/docs/publications/lusc_2012/). Reprints and permissions information is available at www.nature.com/reprints. This paper is distributed under the terms of the Creative Commons Attribution-Non-Commercial-Share Alike licence, and the online version of the paper is freely available to all readers. The authors declare no competing financial interests. Readers are welcome to comment on the online version of the paper. Correspondence and requests for materials should be addressed to M. Meyerson (matthew_meyerson@dfci.harvard.edu).
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The Cancer Genome Atlas Research Network (Participants are arranged by area of contribution and then by institution.) Genome sequencing centres: Broad Institute Peter S. Hammerman1,2, Michael S. Lawrence1, Douglas Voet1, Rui Jing1, Kristian Cibulskis1, Andrey Sivachenko1, Petar Stojanov1, Aaron McKenna1, Eric S. Lander1,3,4, Stacey Gabriel5, Gad Getz1,5, Carrie Sougnez5, Marcin Imielinski1,6, Elena Helman1, Bryan Hernandez1, Nam H. Pho1, Matthew Meyerson1,2,6 Genome characterization centres: BC Cancer Agency Andy Chu7, Hye-Jung E. Chun7, Andrew J. Mungall7, Erin Pleasance7, A. Gordon Robertson7, Payal Sipahimalani7, Dominik Stoll7, Miruna Balasundaram7, Inanc Birol7, Yaron S. N. Butterfield7, Eric Chuah7, Robin J. N. Coope7, Richard Corbett7, Noreen Dhalla7, Ranabir Guin7, An He7, Carrie Hirst7, Martin Hirst7, Robert A. Holt7, Darlene Lee7, Haiyan I. Li7, Michael Mayo7, Richard A. Moore7, Karen Mungall7, Ka Ming Nip7, Adam Olshen8, Jacqueline E. Schein7, Jared R. Slobodan7, Angela Tam7, Nina Thiessen7, Richard Varhol7, Thomas Zeng7, Yongjun Zhao7, Steven J. M. Jones7, Marco A. Marra7; Broad Institute Gordon Saksena1, Andrew D. Cherniack1,Stephen E. Schumacher1,2, Barbara Tabak1,2, Scott L. Carter1, Nam H. Pho1, Huy Nguyen1, Robert C. Onofrio5, Andrew Crenshaw1, Kristin Ardlie5, Rameen Beroukhim1,2, Wendy Winckler1,5, Peter S. Hammerman1,2, Gad Getz1,5, Matthew Meyerson1,2,6; Brigham & Women’s Hospital/Harvard Medical School Alexei Protopopov9,10, Jianhua Zhang9,10, Angela Hadjipanayis11,12, Semin Lee13, Ruibin Xi13, Lixing Yang13, Xiaojia Ren9,11,12, Hailei Zhang1,9, Sachet Shukla1,9, Peng-Chieh Chen11,12, Psalm Haseley12,13, Eunjung Lee12,13, Lynda Chin1,2,9,10,14, Peter J. Park12,13,15, Raju Kucherlapati11,12; Memorial Sloan-Kettering Cancer Center (TCGA pilot phase only) Nicholas D. Socci16, Yupu Liang16, Nikolaus Schultz16, Laetitia Borsu16, Alex E. Lash16, Agnes Viale16, Chris Sander16, Marc Ladanyi17,18; University of North Carolina at Chapel Hill J. Todd Auman19,20, Katherine A. Hoadley21,22,23, Matthew D. Wilkerson23, Yan Shi23, Christina Liquori23, Shaowu Meng23, Ling Li23, Yidi J. Turman23, Michael D. Topal22,23, Donghui Tan24, Scot Waring23, Elizabeth Buda23, Jesse Walsh23, Corbin D. Jones25, Piotr A. Mieczkowski21, Darshan Singh23, Junyuan Wu23, Anisha Gulabani23, Peter Dolina23, Tom Bodenheimer23, Alan P. Hoyle23, Janae V. Simons23, Matthew G. Soloway23, Lisle E. Mose22, Stuart R. Jefferys22, Saianand Balu23, Brian D. O’Connor23, Jan F. Prins26, Jinze Liu27, Derek Y. Chiang21,23, D. Neil Hayes23,28, Charles M. Perou21,22,23; University of Southern California/Johns Hopkins Leslie Cope29, Ludmila Danilova29, Daniel J. Weisenberger30, Dennis T. Maglinte30, Fei Pan30, David J. Van Den Berg30, Timothy Triche Jr30, James G. Herman29, Stephen B. Baylin29, Peter W. Laird30 Genome data analysis centres: Broad Institute Gad Getz1,5, Michael Noble1, Doug Voet1, Gordon Saksena1, Nils Gehlenborg1,13, Daniel DiCara1, Jinhua Zhang9,10, Hailei Zhang1, Chang-Jiun Wu2,10, Spring Yingchun Liu1, Michael S. Lawrence1, Lihua Zou1, Andrey Sivachenko1, Pei Lin1, Petar Stojanov1, Rui Jing1, Juok Cho1, Marc-Danie Nazaire1, Jim Robinson1, Helga Thorvaldsdottir1, Jill Mesirov1, Peter J. Park12,13,15, Lynda Chin1,2,9,10,14; Memorial Sloan-Kettering Cancer Center Nikolaus Schultz16, Rileen Sinha16, Giovanni Ciriello16, Ethan Cerami16, Benjamin Gross16, Anders Jacobsen16, Jianjiong Gao16, B. Arman Aksoy16, Nils Weinhold16, Ricardo Ramirez16, Barry S. Taylor16, Yevgeniy Antipin16, Boris Reva16, Ronglai Shen31, Qianxing Mo31, Venkatraman Seshan31, Paul K. Paik32, Marc Ladanyi17, 18, Chris Sander16; The University of Texas MD Anderson Cancer Center Rehan Akbani33, Nianxiang Zhang33, Bradley M. Broom33, Tod Casasent33, Anna Unruh33, Chris Wakefield33, R. Craig Cason34, Keith A. Baggerly33, John N. Weinstein33,35; University of California Santa Cruz/Buck Institute David Haussler36,37, Christopher C. Benz38, Joshua M. Stuart36, Jingchun Zhu36, Christopher Szeto36, Gary K. Scott38, Christina Yau38, Sam Ng36, Ted Goldstein36, Peter Waltman36, Artem Sokolov36, Kyle Ellrott36, Eric A. Collisson39, Daniel Zerbino36, Christopher Wilks36, Singer Ma36, Brian Craft36; University of North Carolina at Chapel Hill Matthew D. Wilkerson23, J. Todd Auman19,20, Katherine A. Hoadley21,22,23, Ying Du23, Christopher Cabanski23, Vonn Walter23, Darshan Singh23, Junyuan Wu23, Anisha Gulabani23, Tom Bodenheimer23, Alan P. Hoyle23, Janae V. Simons23, Matthew G. Soloway23, Lisle E. Mose22, Stuart R. Jefferys22, Saianand Balu23, J. S. Marron40, Yufeng Liu24, Kai Wang27, Jinze Liu27, Jan F. Prins23, D. Neil Hayes23,28, Charles M. Perou21,22,23; Baylor College of Medicine Chad J. Creighton41, Yiqun Zhang41 Pathology committee William D. Travis42, Natasha Rekhtman42, Joanne Yi43, Marie C. Aubry43, Richard Cheney44, Sanja Dacic45, Douglas Flieder46, William Funkhouser47, Peter Illei48, Jerome Myers49, Ming-Sound Tsao50 Biospecimen core resources: International Genomics Consortium Robert Penny51, David Mallery51, Troy Shelton51, Martha Hatfield51, Scott Morris51, Peggy Yena51, Candace Shelton51, Mark Sherman51, Joseph Paulauskis51 Disease working group Matthew Meyerson1,2,6, Stephen B. Baylin29, Ramaswamy Govindan52, Rehan Akbani33, Ijeoma Azodo53, David Beer54, Ron Bose52, Lauren A. Byers55, David Carbone56, Li-Wei Chang52, Derek Chiang21,23, Andy Chu7, Elizabeth Chun7, Eric Collisson39, Leslie Cope29, Chad J. Creighton41, Ludmila Danilova29, Li Ding52, Gad Getz1,5, Peter S. Hammerman1,2, D. Neil Hayes23,28, Bryan Hernandez1, James G. Herman29, John Heymach55, Cristiane Ida43, Marcin Imielinski1,6, Bruce Johnson2, Igor Jurisica57, Jacob Kaufman56, Farhad Kosari53, Raju Kucherlapati11,12, David Kwiatkowski2, Marc Ladanyi17,18, Michael S. Lawrence1, Christopher A. Maher52, Andy Mungall7, Sam Ng36, William Pao56, Martin Peifer58,59, Robert Penny51, Gordon Robertson7, Valerie Rusch60, Chris Sander16, Nikolaus Schultz16, Ronglai Shen31, Jill Siegfried61, Rileen Sinha16, Andrey Sivachenko1, Carrie Sougnez4, Dominik Stoll7, Joshua Stuart36, Roman K. Thomas58,59,62, Sandra Tomaszek53, Ming-Sound Tsao50,

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ARTICLE RESEARCH
William D. Travis42, Charles Vaske36, John N. Weinstein33,35, Daniel Weisenberger30, David Wheeler63, Dennis A. Wigle53, Matthew D. Wilkerson23, Christopher Wilks30, Ping Yang53, Jianjua John Zhang9,10 Data coordination centre Mark A. Jensen64, Robert Sfeir64, Ari B. Kahn64, Anna L. Chu64, Prachi Kothiyal64, Zhining Wang64, Eric E. Snyder64, Joan Pontius64, Todd D. Pihl64, Brenda Ayala64, Mark Backus64, Jessica Walton64, Julien Baboud64, Dominique L. Berton64, Matthew C. Nicholls64, Deepak Srinivasan64, Rohini Raman64, Stanley Girshik64, Peter A. Kigonya64, Shelley Alonso64, Rashmi N. Sanbhadti64, Sean P. Barletta64, John M. Greene64, David A. Pot64 Tissue source sites Ming-Sound Tsao50, Bizhan Bandarchi-Chamkhaleh50, Jeff Boyd46, JoEllen Weaver46, Dennis A. Wigle53, Ijeoma A. Azodo53, Sandra C. Tomaszek53, Marie Christine Aubry65, Christiane M. Ida65, Ping Yang66, Farhad Kosari53, Malcolm V. Brock67, Kristen Rogers67, Marian Rutledge68, Travis Brown67, Beverly Lee68, James Shin69, Dante Trusty69, Rajiv Dhir70, Jill M. Siegfried61, Olga Potapova71, Konstantin V. Fedosenko72, Elena Nemirovich-Danchenko71, Valerie Rusch60, Maureen Zakowski73, Mary V. Iacocca74, Jennifer Brown74, Brenda Rabeno74, Christine Czerwinski74, Nicholas Petrelli74, Zhen Fan75, Nicole Todaro75, John Eckman49, Jerome Myers49, W. Kimryn Rathmell23, Leigh B. Thorne76, Mei Huang76, Lori Boice76, Ashley Hill23, Robert Penny51, David Mallery51, Erin Curley51, Candace Shelton51, Peggy Yena51, Carl Morrison44, Carmelo Gaudioso44, John M. S. Bartlett77, Sugy Kodeeswaran77, Brent Zanke77, Harman Sekhon78, Kerstin David79, Hartmut Juhl80, Xuan Van Le81, Bernard Kohl81, Richard Thorp81, Nguyen Viet Tien82, Nguyen Van Bang83, Howard Sussman84, Bui Duc Phu83, Richard Hajek85, Nguyen Phi Hung86, Khurram Z. Khan87, Thomas Muley88 Project team: National Cancer Institute Kenna R. Mills Shaw89, Margi Sheth89, Liming Yang89, Ken Buetow90, Tanja Davidsen90, John A. Demchok89, Greg Eley90, Martin Ferguson91, Laura A. L. Dillon89, Carl Schaefer90; National Human Genome Research Institute Mark S. Guyer92, Bradley A. Ozenberger92, Jacqueline D. Palchik92, Jane Peterson92, Heidi J. Sofia92, Elizabeth Thomson92 Writing committee Peter S. Hammerman1,2, D. Neil Hayes23,28, Matthew D. Wilkerson23, Nikolaus Schultz16, Ron Bose52, Andy Chu7, Eric A. Collisson39, Leslie Cope29, Chad J. Creighton41, Gad Getz1,5, James G. Herman29, Bruce E. Johnson2, Raju Kucherlapati11,12, Marc Ladanyi17,18, Christopher A. Maher52, Gordon Robertson7, Chris Sander16, Ronglai Shen16, Rileen Sinha16, Andrey Sivachenko1, Roman K. Thomas58,59,62, William D. Travis42, Ming-Sound Tsao50, John N. Weinstein33,35, Dennis A. Wigle53, Stephen B. Baylin29, Ramaswamy Govindan52, Matthew Meyerson1,2,6
The Eli and Edythe L. Broad Institute of Massachusetts Institute of Technology and Harvard University Cambridge, Massachusetts 02142, USA. 2Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA. 3 Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA. 4Department of Systems Biology, Harvard University, Boston, Massachusetts 02115, USA. 5Genetic Analysis Platform, The Eli and Edythe L. Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts 02142, USA. 6Department of Pathology, Harvard Medical School, Boston, Massachusetts 02115, USA. 7Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada. 8Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California 94143, USA. 9Belfer Institute for Applied Cancer Science, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. 10 Institute for Applied Cancer Science, Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA. 11Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA. 12Division of Genetics, Brigham and Women’s Hospital, Boston, Massachusetts 02115, USA. 13The Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115, USA. 14Department of Dermatology, Harvard Medical School, Boston, Massachusetts 02115, USA. 15Informatics Program, Children’s Hospital, Boston, Massachusetts 02115, USA. 16Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. 17Department of Molecular Oncology, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. 18Department of Pathology and Human Oncology & Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. 19Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. 20 Institute for Pharmacogenetics and Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. 21Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. 22 Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, Chapel Hill, North Carolina 27599, USA. 23Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. 24Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. 25Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. 26 Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. 27Department of Computer Science, University of
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Kentucky, Lexington, Kentucky 40506, USA. 28Department of Internal Medicine, Division of Medical Oncology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. 29Cancer Biology Division, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, Maryland 21231, USA. 30 University of Southern California Epigenome Center, University of Southern California, Los Angeles, California 90033, USA. 31Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. 32Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. 33 Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA. 34Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA. 35Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA. 36Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, California 95064, USA. 37Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, California 95064, USA. 38Buck Institute for Age Research, Novato, California 94945, USA. 39Division of Hematology/ Oncology, University of California San Francisco, San Francisco, California 94143, USA 40 Department of Statistics and Operations Research, University of North Carolina Medical Center, Chapel Hill, North Carolina 27599, USA. 41Human Genome Sequencing Center and Dan L. Duncan Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, Texas 77030, USA. 42Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York 10065 USA. 43Department of Pathology, Mayo Clinic, Rochester, Minnesota 55905, USA. 44Department of Pathology, Roswell Park Cancer Institute, Buffalo, New York 14263, USA. 45Department of Pathology, University of Pittsburgh Cancer Center, Pittsburgh, Pennsylvania 15213, USA. 46Department of Pathology, Fox Chase Cancer Center, Philadelphia, Pennsylvania 19111, USA. 47 Department of Pathology, University of North Carolina Medical Center, Chapel Hill, North Carolina 27599, USA. 48Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA. 49Department of Pathology, Penrose-St. Francis Health System, Colorado Springs, Colorado 80907, USA. 50Department of Pathology and Medical Biophysics, Ontario Cancer Institute and Princess Margaret Hospital, Toronto, Ontario M5G 2MY, Canada. 51International Genomics Consortium, Phoenix, Arizona 85004, USA. 52Division of Oncology, Department of Medicine and The Genome Institute, Washington University School of Medicine, St. Louis, Missouri 63110, USA. 53Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, USA. 54Department of Surgery, University of Michigan, Ann Arbor, Michigan 48109, USA. 55 The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA. 56 Departments of Hematology/Oncology and Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA. 57Ontario Cancer Institute, IBM Life Sciences Discovery Centre, Toronto, Ontario M5G 1L7, Canada. 58Department of Translational Genomics, University of Cologne, Cologne D-50931, Germany. 59Max Planck Institute for Neurological Research, Cologne D-50866, Germany. 60Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA. 61 Department of Pharmacology and Chemical Biology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania 15232, USA. 62Department of Translational Cancer Genomics, Center of Integrated Oncology, University of Cologne, Cologne D-50924, Germany. 63Human Genome Sequencing Center, Baylor College of Medcine, Houston, Texas 77030, USA. 64SRA International, Fairfax, Virginia 22033, USA. 65Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, USA. 66 Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905, USA. 67Department of Surgery, Johns Hopkins School of Medicine, 600 North Wolfe Street, Baltimore, Maryland 21287, USA. 68Department of Oncology, Johns Hopkins School of Medicine, 600 North Wolfe Street, Baltimore, Maryland 21287, USA. 69 Department of Pathology, Johns Hopkins School of Medicine, 600 North Wolfe Street, Baltimore, Maryland 21287, USA. 70Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA. 71Cureline, South San Francisco, California 94080, USA. 72City Clinical Oncology Dispensary, St Petersburg 197022, Russia. 73Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA. 74 Helen F. Graham Cancer Center, Newark, Delaware 19713, USA. 75St Joseph Medical Center, Towson, Maryland 21204, USA. 76UNC Tissue Procurement Facility, Department of Pathology, UNC Lineberger Cancer Center, Chapel Hill, North Carolina 27599, USA. 77 Ontario Tumour Bank, Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada. 78Ontario Tumour Bank – Ottawa site, The Ottawa Hospital, Ottawa, Ontario K1H 8L6, Canada. 79Indivumed GmbH, Hamburg, Falkenried 88, Haus D D-20251, Germany. 80 Indivumed Inc, Kensington, Maryland 20895, USA. 81ILSBio, LLC, Chestertown, Maryland 21620, USA. 82Ministry of Health, 138A Giang Vo Street, Hanoi, Vietnam. 83Hue Central Hospital, Hue City, 16 Le Loi, Hue, Vietnam. 84Stanford University Medical Center, Stanford, California 94305, USA. 85Center for Minority Health Research, University of Texas, M.D. Anderson Cancer Center, Houston, Texas 77030, USA. 86National Cancer Institute, 43 Quan Su Street, Hanoi, Vietnam. 87ILSBio LLC, Chestertown, Maryland 21620, USA. 88ThoraxKlinik, Heidelberg University Hospital, Heidelberg 69126, Germany. 89The Cancer Genome Atlas Program Office, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA. 90Center for Biomedical Informatics and Information Technology (CBIIT), National Cancer Institute, National Institutes of Health, Rockville, Maryland 20852, USA. 91MLF Consulting, Arlington, Maryland 02474, USA. 92National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.

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