Comprehensive molecular profiling of lung adenocarcinoma.
Journal: 2014/September - Nature
ISSN: 1476-4687
Abstract:
Adenocarcinoma of the lung is the leading cause of cancer death worldwide. Here we report molecular profiling of 230 resected lung adenocarcinomas using messenger RNA, microRNA and DNA sequencing integrated with copy number, methylation and proteomic analyses. High rates of somatic mutation were seen (mean 8.9 mutations per megabase). Eighteen genes were statistically significantly mutated, including RIT1 activating mutations and newly described loss-of-function MGA mutations which are mutually exclusive with focal MYC amplification. EGFR mutations were more frequent in female patients, whereas mutations in RBM10 were more common in males. Aberrations in NF1, MET, ERBB2 and RIT1 occurred in 13% of cases and were enriched in samples otherwise lacking an activated oncogene, suggesting a driver role for these events in certain tumours. DNA and mRNA sequence from the same tumour highlighted splicing alterations driven by somatic genomic changes, including exon 14 skipping in MET mRNA in 4% of cases. MAPK and PI(3)K pathway activity, when measured at the protein level, was explained by known mutations in only a fraction of cases, suggesting additional, unexplained mechanisms of pathway activation. These data establish a foundation for classification and further investigations of lung adenocarcinoma molecular pathogenesis.
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Nature. Jul/30/2014; 511(7511): 543-550
Published online Jul/8/2014

Comprehensive molecular profiling of lung adenocarcinoma

Abstract

Adenocarcinoma of the lung is the leading cause of cancer death worldwide. Here we report molecular profiling of 230 resected lung adenocarcinomas using messenger RNA, microRNA and DNA sequencing integrated with copy number, methylation and proteomic analyses. High rates of somatic mutation were seen (mean 8.9 mutations per megabase). Eighteen genes were statistically significantly mutated, including RIT1 activating mutations and newly described loss-of-function MGA mutations which are mutually exclusive with focal MYC amplification. EGFR mutations were more frequent in female patients, whereas mutations in RBM10 were more common in males. Aberrations in NF1, MET, ERBB2 and RIT1 occurred in 13% of cases and were enriched in samples otherwise lacking an activated oncogene, suggesting a driver role for these events in certain tumours. DNA and mRNA sequence from the same tumour highlighted splicing alterations driven by somatic genomic changes, including exon 14 skipping in MET mRNA in 4% of cases. MAPK and PI(3)K pathway activity, when measured at the protein level, was explained by known mutations in only a fraction of cases, suggesting additional, unexplained mechanisms of pathway activation. These data establish a foundation for classification and further investigations of lung adenocarcinoma molecular pathogenesis.

Lung cancer is the most common cause of global cancer-related mortality, leading to over a million deaths each year and adenocarcinoma is its most common histological type. Smoking is the major cause of lung adenocarcinoma but, as smoking rates decrease, proportionally more cases occur in never-smokers (defined as less than 100 cigarettes in a life-time). Recently, molecularly targeted therapies have dramatically improved treatment for patients whose tumours harbour somatically activated oncogenessuch as mutant EGFR1 or translocated ALK, RET, orROS1 (refs 24). Mutant BRAF and ERBB2 (ref. 5) are also investigational targets. However, mostlung adenocarcinomas either lack an identifiable driver oncogene, or harbour mutations in KRAS and are therefore still treated with conventional chemotherapy. Tumour suppressor gene abnormalities, such as those in TP53 (ref. 6), STK11 (ref. 7), CDKN2A8, KEAP1 (ref. 9), and SMARCA4 (ref. 10) are also common but are not currently clinically actionable. Finally, lung adenocarcinoma shows high rates of somatic mutation and genomic rearrangement, challenging identification of all but the most frequent driver gene alterations because of a large burden of passenger events per tumour genome1113. Our efforts focused on comprehensive, multiplatform analysis of lung adenocarcinoma, with attention towards pathobiology and clinically actionable events.

Clinical samples and histopathologic data

We analysed tumour and matched normal material from 230 previously untreated lung adenocarcinoma patients who provided informed consent (Supplementary Table 1). All major histologic types of lung adenocarcinoma were represented: 5% lepidic, 33% acinar, 9% papillary, 14% micropapillary, 25% solid, 4% invasive mucinous, 0.4% colloid and 8% unclassifiable adenocarcinoma (Supplementary Fig. 1)14. Median follow-up was 19 months, and 163 patients were alive at the time of last follow-up. Eighty-one percent of patients reported pastor present smoking. Supplementary Table 2 summarizes demographics. DNA, RNA and protein were extracted from specimens and quality-control assessments were performed as described previously15. Supplementary Table 3 summarizes molecular estimates of tumour cellularity16.

Somatically acquired DNA alterations

We performed whole-exome sequencing (WES) on tumour and germ-line DNA, with a mean coverage of 97.6× and 95.8×, respectively, as performed previously17. The mean somatic mutation rate across the TCGA cohort was 8.87 mutations per megabase (Mb) of DNA (range: 0.5–48, median: 5.78). The non-synonymous mutation rate was 6.86 per Mb. MutSig2CV18 identified significantly mutated genes among our 230 cases along with 182 similarly-sequenced, previously reported lung adenocarcinomas12. Analysis of these 412 tumour/normal pairs highlighted 18 statistically significant mutated genes (Fig. 1 a shows co-mutation plot of TCGA samples (n =230), Supplementary Fig. 2 shows co-mutation plot of all samples used in the statistical analysis (n =412) and Supplementary Table 4 contains complete MutSig2CV results, which also appear on the TCGA Data Portal along with many associated data files (https://tcga-data.nci.nih.gov/docs/publications/luad_2014/). TP53 was commonly mutated (46%). Mutations in KRAS (33%) were mutually exclusive with those in EGFR (14%). BRAF was also commonly mutated (10%), as were PIK3CA (7%), MET (7%) and the small GTPase gene, RIT1 (2%). Mutations in tumour suppressor genes including STK11 (17%), KEAP1 (17%), NF1 (11%), RB1 (4%) and CDKN2A (4%) were observed. Mutations in chromatin modifying genes SETD2(9%), ARID1A(7%) and SMARCA4 (6%) and the RNA splicing genes RBM10 (8%) and U2AF1 (3%) were also common. Recurrent mutations in the MGA gene (which encodes a Max-interacting protein on the MYC pathway19) occurred in 8% of samples. Loss-of-function (frameshift and nonsense) mutations in MGA were mutually exclusive with focal MYC amplification (Fisher’s exact test P =0.04), suggesting a hitherto unappreciated potential mechanism of MYC pathway activation. Coding single nucleotide variants and indel variants were verified by resequencing at a rate of 99% and 100%, respectively (Supplementary Fig. 3a, Supplementary Table 5). Tumour purity was not associated with the presence of false negatives identified in the validation data (P =0.31; Supplementary Fig. 3b).

Past or present smoking associated with cytosine to adenine (C >A) nucleotide transversions as previously described both in individual genes and genome-wide12,13. C >A nucleotide transversion fraction showed two peaks; this fraction correlated with total mutation count (R2 =0.30) and inversely correlated with cytosine to thymine (C >T) transition frequency (R2 =0.75) (Supplementary Fig. 4). We classified each sample (Supplementary Methods) into one of two groups named transversion-high (TH, n =269), and transversion-low (TL, n =144). The transversion-high group was strongly associated with past or present smoking (P < 2.2 ×10−16), consistent with previous reports13. The transversion-high and transversion-low patient cohorts harboured different gene mutations. Whereas KRAS mutations were significantly enriched in the transversion-highcohort (P=2.1×10−13), EGFR mutations were significantly enriched in the transversion-low group (P =3.3 ×10−6). PIK3CA and RB1 mutations were likewise enriched in transversion-low tumours (P <0.05). Additionally, the transversion-low tumours were specifically enriched for in-frame insertions in EGFR and ERBB2 (ref. 5) and for frameshift indels in RB1 (Fig. 1b). RB1 is commonly mutated in small-cell lung carcinoma (SCLC). We found RB1 mutations in transversion-low adenocarcinomas were enriched for frameshift indels versus single nucleotide substitutions compared to SCLC (P <0.05)20,21 suggesting a mutational mechanism in transversion-low adenocarcinoma that is probably distinct from smoking in SCLC.

Gender is correlated with mutation patterns in lung adenocarcinoma22. Only a fraction of significantly mutated genes from the complete set reported in this study (Fig. 1a) were enriched in men or women (Fig. 1c). EGFR mutations were enriched in tumours from the female cohort (P =0.03) whereas loss-of-function mutations within RBM10, an RNA-binding protein located on the X chromosome23 were enriched in tumours from men (P =0.002). When examining the transversion-high group, 16 out of 21 RBM10 mutations were observed in males (P =0.003, Fisher’s exact test).

Somatic copy number alterations were very similar to those previously reported for lung adenocarcinoma24 (Supplementary Fig. 5, Supplementary Table 6). Significant amplifications included NKX2-1, TERT, MDM2, KRAS, EGFR, MET, CCNE1, CCND1, TERC and MECOM (Supplementary Table 6), as previously described24, 8q24 near MYC, and a novel peak containing CCND3 (Supplementary Table 6). The CDKN2A locus was the most significant deletion (Supplementary Table 6). Supplementary Table 7 summarizes molecular and clinical characteristics by sample. Low-pass whole-genome sequencing on a subset (n =93) of the samples revealed an average of 36 gene–gene and gene–inter-gene rearrangements per tumour. Chromothripsis25 occurred in six of the 93 samples (6%) (Supplementary Fig. 6, Supplementary Table 8). Low-pass whole genome sequencing-detected rearrangements appear in Supplementary Table 9.

Description of aberrant RNA transcripts

Gene fusions, splice site mutations or mutations in genes encoding splicing factors promote or sustain the malignant phenotype by generating aberrant RNA transcripts. Combining DNA with mRNA sequencing enabled us to catalogue aberrant RNA transcripts and, in many cases, to identify the DNA-encoded mechanism for the aberration. Seventy-five per cent of somatic mutations identified by WES were present in the RNA transcriptome when the locus in question was expressed (minimum 5×) (Supplementary Fig. 7a) similar to prior analyses15. Previously identified fusions involving ALK (3/230 cases), ROS1 (4/230) and RET (2/230) (Fig. 2a, Supplementary Table 10), all occurred in transversion-low tumours (P =1.85 × 10−4, Fisher’s exact test).

MET activation can occur by exon 14 skipping, which results in a stabilized protein26. Ten tumours had somatic MET DNA alterations with MET exon 14 skipping in RNA. In nine of these samples, a 5′ or 3′ splice site mutation or deletion was identified27. MET exon 14 skipping was also found in the setting of a MET Y1003* stop codon mutation (Fig. 2b, Supplementary Fig. 8a). The codon affected by the Y1003* mutation is predicted to disrupt multiple splicing enhancer sequences, but the mechanism of skipping remains unknown in this case.

S34F mutations in U2AF1 have recently been reported in lung adenocarcinoma12 but their contribution to oncogenesis remains unknown. Eight samples harboured U2AF1S34F. We identified 129 splicing events strongly associated with U2AF1S34F mutation, consistent with the role of U2AF1 in 3′-splice site selection28. Cassette exons and alternative 3′ splice sites were most commonly affected (Fig. 2c, Supplementary Table 11)29. Among these events, alternative splicing of the CTNNB1 protooncogene was strongly associated with U2AF1 mutations (Supplementary Fig. 8b). Thus, concurrent analysis of DNA and RNA enabled delineation of both cis and trans mechanisms governing RNA processing in lung adenocarcinoma.

Candidate driver genes

The receptor tyrosine kinase (RTK)/RAS/RAF pathway is frequently mutated in lung adenocarcinoma. Striking therapeutic responses are often achieved when mutant pathway components are successfully inhibited. Sixty-two percent (143/230) of tumours harboured known activating mutations in known driver oncogenes, as defined by others30. Cancer-associated mutations in KRAS (32%, n =74), EGFR (11%, n =26) and BRAF (7%, n =16) were common. Additional, previously uncharacterized KRAS, EGFR and BRAF mutations were observed, but were not classified as driver oncogenes for the purposes of our analyses (see Supplementary Fig. 9a for depiction of all mutations of known and unknown significance); explaining the differing mutation frequencies in each gene between this analysis and the overall mutational analysis described above. We also identified known activating ERBB2 in-frame insertion and point mutations (n =5)6, as well as mutations in MAP2K1 (n =2), NRAS and HRAS (n =1 each). RNA sequencing revealed the aforementioned MET exon 14 skipping (n =10) and fusions involving ROS1 (n =4), ALK (n =3) and RET (n =2). We considered these tumours collectively as oncogene-positive, as they harboured a known activating RTK/RAS/ RAF pathway somatic event. DNA amplification events were not considered to be driver events before the comparisons described below.

We sought to nominate previously unrecognized genomic events that might activate this critical pathway in the 38% of samples without a RTK/RAS/RAF oncogene mutation. Tumour cellularity did not differ between oncogene-negative and oncogene-positive samples (Supplementary Fig. 9b). Analysis of copy number alterations using GISTIC31 identified unique focal ERBB2 and MET amplifications in the oncogene-negative subset (Fig. 3a, Supplementary Table 6); amplifications in other wild-type proto-oncogenes, including KRAS and EGFR, were not significantly different between the two groups.

We next analysed WES data independently in the oncogene-negative and oncogene-positive subsets. We found that TP53, KEAP1, NF1 and RIT1 mutations were significantly enriched in oncogene-negative tumours (P <0.01; Fig. 3b, Supplementary Table 12). NF1 mutations have previously been reported in lung adenocarcinoma11, but this is the first study, to our knowledge, capable of identifying all classes of loss-of-function NF1 defects and to statistically demonstrate that NF1 mutations, as well as KEAP1 and TP53 mutations are enriched in the oncogene-negative subset of lung adenocarcinomas (Fig. 3c). All RIT1 mutations occurred in the oncogene-negative subset and clustered around residue Q79 (homologous to Q61 in the switch II region of RAS genes). These mutations transform NIH3T3 cells and activate MAPK and PI(3)K signalling32, supporting a driver role for mutant RIT1 in 2% of lung adenocarcinomas. This analysis increases the rate at which putative somatic lung adenocarcinoma driver events can be identified within the RTK/RAS/RAF pathway to 76% (Fig. 3d).

Recurrent alterations in key pathways

Recurrent aberrations in multiple key pathways and processes characterize lung adenocarcinoma (Fig. 4a). Among these were RTK/RAS/ RAF pathway activation (76% of cases), PI(3)K-mTOR pathway activation (25%), p53 pathway alteration (63%), alteration of cell cycle regulators (64%, Supplementary Fig. 10), alteration of oxidative stress pathways (22%, Supplementary Fig. 11), and mutation of various chromatin and RNA splicing factors (49%).

We then examined the phenotypic sequelae of some key genomic events in the tumours in which they occurred. Reverse-phase protein arrays provided proteomic and phosphoproteomic phenotypic evidence of pathway activity. Antibodies on this platform are listed in Supplementary Table 13. This analysis suggested that DNA sequencing did not identify all samples with phosphoprotein evidence of activation of a given signalling pathway. For example, whereas KRAS-mutant lung adenocarcinomas had higher levels of phosphorylated MAPK than KRAS wild-type tumours had on average, many KRAS wild-type tumours displayed significant MAPK pathway activation (Fig. 4b, Supplementary Fig. 10). The multiple mechanisms by which lung adenocarcinomas achieve MAPK activation suggest additional, still undetected RTK/RAS/ RAF pathway alterations. Similarly, we found significant activation of mTOR and its effectors (p70S6kinase, S6, 4E-BP1) in a substantial fraction of the tumours (Fig. 4c). Analysis of mutations in PIK3CA and STK11, STK11 protein levels, and AMPK and AKT phosphorylation33 led to the identification of three major mTOR patterns in lung adenocarcinoma: (1) tumours with minimal or basal mTOR pathway activation, (2) tumours showing higher mTOR activity accompanied by either STK11-inactivating mutation or combined low STK11 expression and low AMPK activation and (3) tumours showing high mTOR activity accompanied by either phosphorylated AKT activation, PIK3CA mutation, or both. As with MAPK, many tumours lack an obvious underlying genomic alteration to explain their apparent mTOR activation.

Molecular subtypes of lung adenocarcinoma

Broad transcriptional and epigenetic profiling can reveal downstream consequences of driver mutations, provide clinically relevant classification and offer insight into tumours lacking clear drivers. Prior unsupervised analyses of lung adenocarcinoma gene expression have used varying nomenclature for transcriptional subtypes of the disease3437. To coordinate naming of the transcriptional subtypes with the histopathological38, anatomic and mutational classifications of lung adenocarcinoma, we propose an updated nomenclature: the terminal respiratory unit (TRU, formerly bronchioid), the proximal-inflammatory (PI, formerly squamoid), and the proximal-proliferative (PP, formerly magnoid)39 transcriptional subtypes (Fig. 5a). Previously reported associations of expression signatures with pathways and clinical outcomes34,36,39 were observed (Supplementary Fig. 7b) and integration with multi-analyte data revealed statistically significant genomic alterations associated with these transcriptional subtypes. The PP subtype was enriched for mutation of KRAS, along with inactivation of the STK11 tumour suppressor gene by chromosomal loss, inactivating mutation, and reduced gene expression. In contrast, the PI subtype was characterized by solid histopathology and co-mutation of NF1 and TP53. Finally, the TRU subtype harboured the majority of the EGFR-mutated tumours as well as the kinase fusion expressing tumours. TRU subtype membership was prognostically favourable, as seen previously34 (Supplementary Fig. 7c). Finally, the subtypes exhibited different mutation rates, transition frequencies, genomic ploidy profiles, patterns of large-scale aberration, and differed in their association with smoking history (Fig. 5a). Unsupervised clustering of miRNA sequencing-derived or reverse phase protein array (RPPA)-derived data also revealed significant heterogeneity, partially overlapping with the mRNA-based subtypes, as demonstrated in Supplementary Figs 12 and 13.

Mutations in chromatin-modifying genes (for example, SMARCA4, ARID1A and SETD2) suggest a major role for chromatin maintenance in lung adenocarcinoma. To examine chromatin states in an unbiased manner, we selected the most variable DNA methylation-specific probes in CpG island promoter regions and clustered them by methylation intensity (Supplementary Table 14). This analysis divided samples into two distinct subsets: a significantly altered CpG island methylator phenotype-high (CIMP-H(igh)) cluster and a more normal-like CIMP-L(ow) group, with a third set of samples occupying an intermediate level of methylation at CIMP sites (Fig. 5b). Our results confirm a prior report40 and provide additional insights into this epigenetic program. CIMP-H tumours often showed DNA hypermethylation of several key genes: CDKN2A, GATA2, GATA4, GATA5, HIC1, HOXA9, HOXD13, RASSF1, SFRP1, SOX17 and WIF1 among others (Supplementary Fig. 14). WNT pathway genes are significantly over-represented in this list (P value =0.0015) suggesting that this is a key pathway with an important driving role within this subtype. MYC overexpression was significantly associated with the CIMP-H phenotype as well (P =0.003).

Although we did not find significant correlations between global DNA methylation patterns and individual mutations in chromatin remodelling genes, there was an intriguing association between SETD2 mutation and CDKN2A methylation. Tumours with low CDKN2A expression due to methylation (rather than due to mutation or deletion) had lower ploidy, fewer overall mutations (Fig. 5c) and were significantly enriched for SETD2 mutation, suggesting an important role for this chromatin-modifying gene in the development of certain tumours.

Integrative clustering41 of copy number, DNA methylation and mRNA expression data found six clusters (Fig. 5c). Tumour ploidy and mutation rate are higher in clusters 1–3 than in clusters 4–6. Clusters 1–3 frequently harbour TP53 mutations and are enriched for the two proximal transcriptional subtypes. Fisher’s combined probability tests revealed significant copy number associated gene expression changes on 3q in cluster one, 8q in cluster two, and chromosome 7 and 15q in cluster three (Supplementary Fig. 15). The low ploidy and low mutation rate clusters four and five contain many TRU samples, whereas tumours in cluster 6 have comparatively lower tumour cellularity, and few other distinguishing molecular features. Significant copy number-associated gene expression changes are observed on 6q in cluster four and 19p in cluster five. The CIMP-H tumours divided into a high ploidy, high mutation rate, proximal-inflammatory CIMP-H group (cluster 3) and a low ploidy, low mutation rate, TRU-associated CIMP-H group (cluster 4), suggesting that the CIMP phenotype in lung adenocarcinoma can occur in markedly different genomic and transcriptional contexts. Furthermore, cluster four is enriched for CDKN2A methylation and SETD2 mutations, suggesting an interaction between somatic mutation of SETD2andderegulated chromatin maintenance in this subtype. Finally, cluster membership was significantly associated with mutations in TP53, EGFR and STK11 (Supplementary Fig. 15, Supplementary Table 6).

Conclusions

We assessed the mutation profiles, structural rearrangements, copy number alterations, DNA methylation, mRNA, miRNA and protein expression of 230 lung adenocarcinomas. In recent years, the treatment of lung adenocarcinoma has been advanced by the development of multiple therapies targeted against alterations in the RTK/RAS/RAF pathway. We nominate amplifications in MET and ERBB2 as well as mutations of NF1 and RIT1 as driver events specifically in otherwise oncogene-negative lung adenocarcinomas. This analysis increases the fraction of lung adenocarcinoma cases with somatic evidence of RTK/RAS/RAF activation from 62% to 76%. While all lung adenocarcinomas may activate this pathway by some mechanism, only a subset show tonic pathway activation at the protein level, suggesting both diversity between tumours with seemingly similar activating events and as yet undescribed mechanisms of pathway activation. Therefore, the current study expands the range of possible targetable alterations within the RTK/RAS/RAF pathway in general and suggests increased implementation of MET and ERBB2/HER2 inhibitors in particular. Our discovery of inactivating mutations of MGA further underscores the importance of the MYC pathway in lung adenocarcinoma.

This study further implicates both chromatin modifications and splicing alterations in lung adenocarcinoma through the integration of DNA, transcriptome and methylome analysis. We identified alternative splicing due to both splicing factor mutations in trans and mutation of splice sites in cis, the latter leading to activation of the MET gene by exon 14 skipping. Cluster analysis separated tumours based on single-gene driver events as well as large-scale aberrations, emphasizing lung adenocarcinoma’s molecular heterogeneity and combinatorial alterations, including the identification of coincident SETD2 mutations and CDKN2A methylation in a subset of CIMP-H tumours, providing evidence of a somatic event associated with a genome-wide methylation phenotype. These studies provide new knowledge by illuminating modes of genomic alteration, highlighting previously unappreciated altered genes, and enabling further refinement in sub-classification for the improved personalization of treatment for this deadly disease.

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 standard approaches for capture and sequencing of exomes from tumour DNA and normal DNA15 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 lesions42. GISTIC analysis of the circular-binary-segmented Affymetrix SNP 6.0 copy number data was used to identify recurrent amplification and deletion peaks31. Consensus clustering approaches were used to analyse mRNA, miRNA and methylation subtypes using previous approaches15. The publication web page is (https://tcga-data.nci.nih.gov/docs/publications/luad_2014/). Sequence files are in CGHub (https://cghub.ucsc.edu/).

Supplementary Material

Figure 1

Somatic mutations in lung adenocarcinoma

a, Co-mutation plot from whole exome sequencing of 230 lung adenocarcinomas. Data from TCGA samples were combined with previously published data12 for statistical analysis. Co-mutation plot for all samples used in the statistical analysis (n =412) can be found in Supplementary Fig. 2. Significant genes with a corrected P value less than 0.025 were identified using the MutSig2CV algorithm and are ranked in order of decreasing prevalence. b, c, The differential patterns of mutation between samples classified as transversion high and transversion low samples (b) or male and female patients (c) are shown for all samples used in the statistical analysis (n =412). Stars indicate statistical significance using the Fisher’s exact test (black stars: q <0.05, grey stars: P <0.05) and are adjacent to the sample set with the higher percentage of mutated samples.

Figure 2

Aberrant RNA transcripts in lung adenocarcinoma associated with somatic DNA translocation or mutation

a, Normalized exon level RNA expression across fusion gene partners. Grey boxes around genes mark the regions that are removed as a consequence of the fusion. Junction points of the fusion events are also listed in Supplementary Table 9. Exon numbers refer to reference transcripts listed in Supplementary Table 9. b,MET exon 14 skipping observed in the presence of exon 14 splice site mutation (ss mut), splice site deletion (ss del) or a Y1003* mutation. A total of 22 samples had insufficient coverage around exon 14 for quantification. The percentage skipping is (total expression minus exon 14 expression)/total expression. c, Significant differences in the frequency of 129 alternative splicing events in mRNA from tumours with U2AF1 S34F tumours compared to U2AF1 WT tumours (q value <0.05). Consistent with the function of U2AF1 in 3′ splice site recognition, most splicing differences involved cassette exon and alternative 3′ splice site events (chi-squared test, P <0.001).

Figure 3

Identification of novel candidate driver genes

a, GISTIC analysis of focal amplifications in oncogene-negative (n =87) and oncogene-positive (n =143) TCGA samples identifies focal gains of MET and ERBB2 that are specific to the oncogene-negative set (purple). b,TP53, KEAP1, NF1 and RIT1 mutations are significantly enriched in samples otherwise lacking oncogene mutations (adjusted P <0.05 by Fisher’s exact test). c, Co-mutation plot of variants of known significance within the RTK/RAS/RAF pathway in lung adenocarcinoma. Not shown are the 63 tumours lacking an identifiable driver lesion. Only canonical driver events, as defined in Supplementary Fig. 9, and proposed driver events, are shown; hence not every alteration found is displayed. d, New candidate driver oncogenes (blue: 13% of cases) and known somatically activated drivers events (red: 63%) that activate the RTK/RAS/RAF pathway can be found in the majority of the 230 lung adenocarcinomas.

Figure 4

Pathway alterations in lung adenocarcinoma

a, Somatic alterations involving key pathway components for RTK signalling, mTOR signalling, oxidative stress response, proliferation and cell cycle progression, nucleosome remodelling, histone methylation, and RNA splicing/processing. b, c, Proteomic analysis by RPPA (n =181) P values by two-sided t-test. Box plots represent 5%, 25%, 75%, median, and 95%. PP, proximal proliferative; TRU, terminal respiratory unit; PI, proximal inflammatory. c, mTOR signalling may be activated, by either Akt (for example, via PI(3)K) or inactivation of AMPK (for example, via STK11 loss). Tumours were separated into three main groups: those with PI(3)K-AKT activation, through either PIK3CA activating mutation or unknown mechanism (high p-AKT); those with LKB1-AMPK inactivation, through either STK11 mutation or unknown mechanism with low levels of LKB1 and p-AMPK; and those showing none of the above features.

Figure 5

Integrative analysis

ac, Integrating unsupervised analyses of 230 lung adenocarcinomas reveals significant interactions between molecular subtypes. Tumours are displayed as columns, grouped by mRNA expression subtypes (a), DNA methylation subtypes (b), and integrated subtypes by iCluster analysis (c). All displayed features are significantly associated with subtypes depicted. The CIMP phenotype is defined by the most variable CpG island and promoter probes.

The Cancer Genome Atlas Research Network

Disease analysis working group Eric A. Collisson1, Joshua D. Campbell2, Angela N. Brooks2,3, Alice H. Berger2, William Lee4, Juliann Chmielecki2, David G. Beer5, Leslie Cope6, Chad J. Creighton7, Ludmila Danilova6, Li Ding8, Gad Getz2,9,10, Peter S. Hammerman2, D. Neil Hayes11, Bryan Hernandez2, James G. Herman6, John V. Heymach12, Igor Jurisica13, Raju Kucherlapati9, David Kwiatkowski14, Marc Ladanyi4, Gordon Robertson15, Nikolaus Schultz4, Ronglai Shen4, Rileen Sinha12, Carrie Sougnez2, Ming-Sound Tsao13, William D. Travis4, John N. Weinstein12, Dennis A. Wigle16, Matthew D. Wilkerson11, Andy Chu15, Andrew D. Cherniack2, Angela Hadjipanayis9, Mara Rosenberg2, Daniel J. Weisenberger17, Peter W. Laird17, Amie Radenbaugh18, Singer Ma18, Joshua M. Stuart18, Lauren Averett Byers12, Stephen B. Baylin6, Ramaswamy Govindan8, Matthew Meyerson2,3

Genome sequencing centres: The Eli & Edythe L. Broad Institute Mara Rosenberg2, Stacey B. Gabriel2, Kristian Cibulskis2, Carrie Sougnez2, Jaegil Kim2, Chip Stewart2, Lee Lichtenstein2, Eric S. Lander2,19, Michael S. Lawrence2, Getz2,9,10; Washington University in St. Louis Cyriac Kandoth8, Robert Fulton8, Lucinda L. Fulton8, Michael D. McLellan8, Richard K. Wilson8, Kai Ye8, Catrina C. Fronick8, Christopher A. Maher8, Christopher A. Miller8, Michael C. Wendl8, Christopher Cabanski8, Li Ding8, Elaine Mardis8, Ramaswamy Govindan8; Baylor College of Medicine Chad J. Creighton7, David Wheeler7

Genome characterization centres: Canada’s Michael Smith Genome Sciences Centre, British Columbia Cancer Agency Miruna Balasundaram15, Yaron S. N. Butterfield15, Rebecca Carlsen15, Andy Chu15, Eric Chuah15, Noreen Dhalla15, Ranabir Guin15, Carrie Hirst15, Darlene Lee15, Haiyan I. Li15, Michael Mayo15, Richard A. Moore15, Andrew J. Mungall15, Jacqueline E. Schein15, Payal Sipahimalani15, Angela Tam15, Richard Varhol15, A. Gordon Robertson15, Natasja Wye15, Nina Thiessen15, Robert A. Holt12, Steven J. M. Jones15, Marco A. Marra15; The Eli & Edythe L. Broad Institute Joshua D. Campbell2, Angela N. Brooks2,3, Juliann Chmielecki2, Marcin Imielinski2,9,10, Robert C. Onofrio2, Eran Hodis9, Travis Zack2, Carrie Sougnez2, Elena Helman2, Chandra Sekhar Pedamallu2, Jill Mesirov2, Andrew D. Cherniack2, Gordon Saksena2, Steven E. Schumacher2, Scott L. Carter2, Bryan Hernandez2, Levi Garraway2,3,9, Rameen Beroukhim2,3,9, Stacey B. Gabriel2, Gad Getz2,9,10, Matthew Meyerson2,3,9; Harvard Medical School/Brigham & Women’s Hospital/MD Anderson Cancer Center Angela Hadjipanayis9,14, Semin Lee9,14, Harshad S. Mahadeshwar12, Angeliki Pantazi9,14, Alexei Protopopov12, Xiaojia Ren9, Sahil Seth12, Xingzhi Song12, Jiabin Tang12, Lixing Yang9, JianhuaZhang12, Peng-Chieh Chen9, Michael Parfenov9,14, Andrew Wei Xu9,14, Netty Santoso9,14, Lynda Chin12, Peter J. Park9,14 & Raju Kucherlapati9,14; University of North Carolina, Chapel Hill Katherine A. Hoadley11, J. Todd Auman11, Shaowu Meng11, Yan Shi11, Elizabeth Buda11, Scot Waring11, Umadevi Veluvolu11, Donghui Tan11, Piotr A. Mieczkowski11, Corbin D. Jones11, Janae V. Simons11, Matthew G. Soloway11, Tom Bodenheimer11, Stuart R. Jefferys11, Jeffrey Roach11, Alan P. Hoyle11, Junyuan Wu11, Saianand Balu11, Darshan Singh11, Jan F. Prins11, J.S. Marron11, Joel S. Parker11, D. Neil Hayes11, Charles M. Perou11; University of Kentucky Jinze Liu20; The USC/JHU Epigenome Characterization Center Leslie Cope6, Ludmila Danilova6, Daniel J. Weisenberger17, Dennis T. Maglinte17, Philip H. Lai17, Moiz S. Bootwalla17, David J. Van Den Berg17, Timothy Triche Jr17, Stephen B. Baylin6, Peter W. Laird17

Genome data analysis centres: The Eli & Edythe L. Broad Institute Mara Rosenberg2, Lynda Chin12, Jianhua Zhang12, Juok Cho2, Daniel DiCara2, David Heiman2, Pei Lin2, William Mallard2, Douglas Voet2, Hailei Zhang2, Lihua Zou2, Michael S. Noble2, Michael S. Lawrence2, Gordon Saksena2, Nils Gehlenborg2, Helga Thorvaldsdottir2, Jill Mesirov2, Marc-Danie Nazaire2, Jim Robinson2, Gad Getz2,9,10; Memorial Sloan-Kettering Cancer Center William Lee4, B. Arman Aksoy4, Giovanni Ciriello4, Barry S. Taylor1, Gideon Dresdner4, Jianjiong Gao4, Benjamin Gross4, Venkatraman E. Seshan4, Marc Ladanyi4, Boris Reva4, Rileen Sinha4, S. Onur Sumer4, Nils Weinhold4, Nikolaus Schultz4, Ronglai Shen4, Chris Sander4; University of California, Santa Cruz/ Buck Institute Sam Ng18, Singer Ma18, Jingchun Zhu18, Amie Radenbaugh18, Joshua M. Stuart18, Christopher C. Benz21, Christina Yau21 & David Haussler18,22; Oregon Health & Sciences University Paul T. Spellman23; University of North Carolina, Chapel Hill Matthew D. Wilkerson11, Joel S. Parker11, Katherine A. Hoadley11, Patrick K. Kimes11, D. Neil Hayes11, Charles M. Perou11; The University of Texas MD Anderson Cancer Center Bradley M. Broom12, Jing Wang12, Yiling Lu12, Patrick Kwok Shing Ng12, Lixia Diao12, Lauren Averett Byers12, Wenbin Liu12, John V. Heymach12, Christopher I. Amos12, John N. Weinstein12, Rehan Akbani12, Gordon B. Mills12

Biospecimen core resource: International Genomics Consortium Erin Curley24, Joseph Paulauskis24, Kevin Lau24, Scott Morris24, Troy Shelton24, David Mallery24, Johanna Gardner24, Robert Penny24

Tissue source sites: Analytical Biological Service, Inc. Charles Saller25, Katherine Tarvin25; Brigham & Women’s Hospital William G. Richards14; University of Alabama at Birmingham Robert Cerfolio26, Ayesha Bryant26; Cleveland Clinic: Daniel P. Raymond27, Nathan A. Pennell27, Carol Farver27; Christiana Care Christine Czerwinski28, Lori Huelsenbeck-Dill28, Mary Iacocca28, Nicholas Petrelli28, Brenda Rabeno28, Jennifer Brown28, Thomas Bauer28; Cureline Oleg Dolzhanskiy29, Olga Potapova29, Daniil Rotin29, Olga Voronina29, Elena Nemirovich-Danchenko29, Konstantin V. Fedosenko29; Emory University Anthony Gal30, Madhusmita Behera30, Suresh S. Ramalingam30, Gabriel Sica30; Fox Chase Cancer Center Douglas Flieder31, Jeff Boyd31, JoEllen Weaver31; ILSbio Bernard Kohl32, Dang Huy Quoc Thinh32; Indiana University George Sandusky33; Indivumed Hartmut Juhl34; John Flynn Hospital Edwina Duhig35,36; Johns Hopkins University Peter Illei6, Edward Gabrielson6, James Shin6, Beverly Lee6, Kristen Rogers6, Dante Trusty6, Malcolm V. Brock6; Lahey Hospital & Medical Center Christina Williamson37, Eric Burks37, Kimberly Rieger-Christ37, Antonia Holway37, Travis Sullivan37; Mayo Clinic Dennis A. Wigle16, Michael K. Asiedu16, Farhad Kosari16; Memorial Sloan-Kettering Cancer Center William D. Travis4, Natasha Rekhtman4, Maureen Zakowski4, Valerie W. Rusch4; NYU Langone Medical Center Paul Zippile38, James Suh38, Harvey Pass38, Chandra Goparaju38, Yvonne Owusu-Sarpong38; Ontario Tumour Bank John M. S. Bartlett39, Sugy Kodeeswaran39, Jeremy Parfitt39, Harmanjatinder Sekhon39, Monique Albert39; Penrose St. Francis Health Services John Eckman40, Jerome B. Myers40; Roswell Park Cancer Institute Richard Cheney41, Carl Morrison41, Carmelo Gaudioso41; Rush University Medical Center Jeffrey A. Borgia42, Philip Bonomi42, Mark Pool42, Michael J. Liptay42; St. Petersburg Academic University Fedor Moiseenko43, Irina Zaytseva43; Thoraxklinik am Universitätsklinikum Heidelberg, Member of Biomaterial Bank Heidelberg (BMBH) & Biobank Platform of the German Centre for Lung Research (DZL) Hendrik Dienemann44, Michael Meister44, Philipp A. Schnabel45, Thomas R. Muley44; University of Cologne Martin Peifer46; University of Miami Carmen Gomez-Fernandez47, Lynn Herbert47, Sophie Egea47; University of North Carolina Mei Huang11, Leigh B. Thorne11, Lori Boice11, Ashley Hill Salazar11, William K. Funkhouser11, W. Kimryn Rathmell11; University of Pittsburgh Rajiv Dhir48, Samuel A. Yousem48, Sanja Dacic48, Frank Schneider48, Jill M. Siegfried48; The University of Texas MD Anderson Cancer Center Richard Hajek12; Washington University School of Medicine Mark A. Watson8, Sandra McDonald8, Bryan Meyers8; Queensland Thoracic Research Center Belinda Clarke35, Ian A. Yang35, Kwun M. Fong35, Lindy Hunter35, Morgan Windsor35, Rayleen V. Bowman35; Center Hospitalier Universitaire Vaudois Solange Peters49, Igor Letovanec49; Ziauddin University Hospital Khurram Z. Khan50

Data Coordination Centre Mark A. Jensen51, Eric E. Snyder51, Deepak Srinivasan51, Ari B. Kahn51, Julien Baboud51, David A. Pot51

Project team: National Cancer Institute Kenna R. Mills Shaw52, Margi Sheth52, Tanja Davidsen52, John A. Demchok52, Liming Yang52, Zhining Wang52, Roy Tarnuzzer52, Jean Claude Zenklusen52; National Human Genome Research Institute Bradley A. Ozenberger53, Heidi J. Sofia53

Expert pathology panel William D. Travis4, Richard Cheney41, Belinda Clarke35, Sanja Dacic48, Edwina Duhig36,35, William K. Funkhouser11, Peter Illei6, Carol Farver27, Natasha Rekhtman4, Gabriel Sica30, James Suh38 & Ming-Sound Tsao13

Footnotes

1University of California San Francisco, San Francisco, California 94158, USA.

2The Eliand Edythe L. Broad Institute, Cambridge, Massachusetts 02142, USA.

3Dana Farber Cancer Institute, Boston, Massachusetts 02115, USA.

4Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA.

5University of Michigan, Ann Arbor, Michigan 48109, USA.

6Johns Hopkins University, Baltimore, Maryland 21287, USA.

7Baylor College of Medicine, Houston, Texas 77030, USA.

8Washington University, St. Louis, Missouri 63108, USA.

9Harvard Medical School, Boston, Massachusetts 02115, USA.

10Massachusetts General Hospital, Boston, Massachusetts 02114, USA.

11University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.

12University of Texas MD Anderson Cancer Center, Houston, Texas 77054, USA.

13Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9, Canada.

14Brigham and Women’s Hospital Boston, Massachusetts 02115, USA.

15BC Cancer Agency, Vancouver, British Columbia V5Z 4S6, Canada.

16Mayo Clinic, Rochester, Minnesota 55905, USA.

17University of Southern California, Los Angeles, California 90033, USA.

18University of California Santa Cruz, Santa Cruz, California 95064, USA.

19Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.

20University of Kentucky, Lexington, Kentucky 40515, USA.

21Buck Institute for Age Research, Novato, California 94945, USA.

22Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, California 95064, USA.

23Oregon Health and Science University, Portland, Oregon 97239, USA.

24International Genomics Consortium, Phoenix, Arizona 85004, USA.

25Analytical Biological Services, Inc., Wilmington, Delaware 19801, USA.

26University of Alabama at Birmingham, Birmingham, Alabama 35294, USA.

27Cleveland Clinic, Cleveland, Ohio 44195, USA.

28Christiana Care, Newark, Delaware 19713, USA.

29Cureline, Inc., South San Francisco, California 94080, USA.

30Emory University, Atlanta, Georgia 30322, USA.

31Fox Chase Cancer Center, Philadelphia, Philadelphia 19111, USA.

32ILSbio, Chestertown, Maryland 21620, USA.

33Indiana University School of Medicine, Indianapolis, Indiana 46202, USA.

34Individumed, Silver Spring, Maryland 20910, USA.

35The Prince Charles Hospital and the University of Queensland Thoracic Research Center, Brisbane, 4032, Australia.

36Sullivan Nicolaides Pathology & John Flynn Hospital, Tugun 4680, Australia.

37Lahey Hospital and Medical Center, Burlington, Massachusetts 01805, USA.

38NYU Langone Medical Center, New York, New York 10016, USA.

39Ontario Tumour Bank, Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada.

40Penrose St. Francis Health Services, Colorado Springs, Colorado 80907, USA.

41Roswell Park Cancer Center, Buffalo, New York 14263, USA.

42Rush University Medical Center, Chicago, Illinois 60612, USA.

43St. Petersburg Academic University, St Petersburg 199034, Russia.

44Thoraxklinik am Universitätsklinikum Heidelberg, 69126 Heidelberg, Germany.

45University Heidelberg, 69120 Heidelberg, Germany.

46University of Cologne, 50931 Cologne, Germany.

47University of Miami, Sylvester Comprehensive Cancer Center, Miami, Florida 33136, USA.

48University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA.

49Center Hospitalier Universitaire Vaudois, Lausanne and European Thoracic Oncology Platform, CH-1011 Lausanne, Switzerland.

50Ziauddin University Hospital, Karachi, 75300, Pakistan.

51SRA International, Inc., Fairfax, Virginia 22033, USA.

52National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.

53National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.

Supplementary Information is available in the online version of the paper.

Author Contributions The Cancer Genome Atlas 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. The National Cancer Institute and National Human Genome Research Institute project teams coordinated project activities. We also acknowledge the following TCGA investigators who made substantial contributions to the project: E. A. Collisson (manuscript coordinator); J. D. Campbell, J. Chmielecki, (analysis coordinators); C. Sougnez (data coordinator); J. D. Campbell, M. Rosenberg, W. Lee, J. Chmielecki, M. Ladanyi, and G. Getz (DNA sequence analysis); M. D. Wilkerson, A. N. Brooks, and D. N. Hayes (mRNA sequence analysis); L. Danilova and L. Cope (DNA methylation analysis); A. D. Cherniack (copy number analysis); M. D. Wilkerson and A. Hadjipanayis (translocations); N. Schultz, W. Lee, E. A. Collisson, A. H. Berger, J. Chmielecki, C. J. Creighton, L. A. Byers and M. Ladanyi (pathway analysis); A. Chu and A. G. Robertson (miRNA sequence analysis); W. Travis and D. A. Wigle (pathology and clinical expertise); L. A. Byers and G. B. Mills (reverse phase protein arrays); S. B. Baylin, 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 at (https://tcga-data.nci.nih.gov/tcga/tcgaDownload.jsp). All of the primary sequence files are deposited in cgHub and all other data are deposited at the Data Coordinating Center (DCC) for public access (http://cancergenome.nih.gov/), (https://cghub.ucsc.edu/) and (https://tcga-data.nci.nih.gov/docs/publications/luad_2014/). Reprints and permissions information is available at www.nature.com/reprints. 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.M. ().

Acknowledgments

This study was supported by NIH grants: U24 CA126561, U24 CA126551, U24 CA126554, U24 CA126543, U24 CA126546, U24 CA137153, 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. We thank K. Guebert and L. Gaffney for assistance and C. Gunter for review.

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