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Introduction

Lung cancer is the leading cause of cancer-related death worldwide and accounts for one quarter of all cancer mortalities in the US (1). Non-small cell lung cancer (NSCLC) accounts for approximately 80% of all lung cancer cases and can be classified by histotypes as adenocarcinoma (AC), squamous cell carcinoma (SCC), and large-cell lung cancer (LCLC). The high mortality rate of lung cancer is mainly attributed to the disease not being diagnosed until it is in advanced stages. Chemotherapy with platinum-based drugs in combination with taxanes, camptothecins, or vinca alkaloids, the first-line treatment for patients with NSCLC, has made little progress in improving prognoses in recent decades (1).

Similar to other malignancies, tumorigenesis in NSCLC depends on the clustering of gene dysfunction as a result of genetic susceptibility and/or the accumulation of noxious environmental factors. The discoveries of recurrent mutations in the epidermal growth-factor receptor (EGFR) kinase and fusions, such as EML4-ALK, involving anaplastic lymphoma kinase (ALK) led to a dramatic change in the treatment of lung AC (2,3). Recent data suggest that substance CI1040 can bind to MEK and mutated BRAF, resulting in the shrinkage of lung ACs that harbor mutated KRAS and BRAF, respectively (4). Other recent data show that targeting mutations in AKT1, ERBB2 and PIK3CA and fusions involving ROS1 and RET may also be successful (5). Unfortunately, activating mutations in EGFR, EML4-ALK fusions, and mutations in KRAS are only detected in lung AC, and are not present in the second most-common type of lung cancer, SCC (6). Thus, targeted agents developed for lung AC are largely ineffective against lung SCC (7).

Lung SCC accounts for 45% of NSCLC, and is therefore a main cause of lung cancer mortality. Lung SCC is different from AC in terms of its clinical features, response to therapies, and, most importantly, its genetic-variation profiles. Research on the molecular mechanisms of lung SCC is limited with few encouraging outcomes. Previous candidate-gene studies of lung SCC reported recurring mutations in several genes including TP53, NFE2L2, KEAP1, BAI3, FBXW7, GRM8, MUC16, RUNX1T1, STK11 and ERBB4(8,9). Other recent data showed that lung SCC with FGFR1 amplification and DDR1 mutations would be responsive to targeted agents (10–12).

We performed whole-exome sequencing of lung SCC tissue and adjacent normal lung tissue from one patient to identify new mutations involved in lung SCC tumorigenesis. We annotated our results by comparing them with those of previous matched tumor/normal sequencing studies in the Catalogue of Somatic Mutation in Cancer (COSMIC) database.

Materials and methods

Sample collection and DNA extraction

We obtained 98 paired tumor-tissue and adjacent normal-tissue samples including 44 lung SCCs, 49 lung ACs, and 5 LCLCs from patients diagnosed with NSCLC who underwent definitive surgical resection prior to receiving chemotherapy or radiation at the First Hospital Affiliated to Bengbu Medical College or at Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine. The Ethics Committee of Ruijin Hospital approved the study and we also provided written informed consent. We performed all our experiments according to the Helsinki Declaration. We conducted a pathology review of each sample to establish a histologic diagnosis. The median age of the patients was 53 years (range 27–83). We extracted genomic DNA from the tissue samples using the Automatic Nucleic Acid Isolation System (QuickGene-610L, Fujifilm Life Science, Tokyo, Japan). We selected tumor and adjacent normal-tissue samples from one 55-year-old male patient with lung SCC for whole-exome sequencing.

Targeted sequence capture

We captured the genomic DNA on a NimbleGen 2.1M human-exome array according to the manufacturer’s protocols (Roche/NimbleGen). We aimed to capture most of the human exome from the DNA sample with the NimbleGen chip, which contains 24 Mb CCDS (~85% of the US National Center for Biotechnology Information CCDS Database) region across approximately 17,000 genes in 34 Mb targeted nucleotides. The DNA was sheared by sonication and the adaptors ligated to the fragments. The adaptor-ligated templates were fractioned by agarose-gel electrophoresis and the fragments were excised to the desired size. We hybridized the extracted DNA to the capture array at 42°C using the manufacturer’s buffer. The array was washed twice at 47.5°C and three more times at room temperature (20–25°C) with the manufacturer’s buffers. The bound genomic DNA was eluted in 125 mM NaOH for 10 min at room temperature. The selected DNA fragments were amplified by ligation-mediated PCR, purified and sequenced on the Illumina platform.

The single-nucleotide variants (SNVs) and INDELs discovered by the whole-exome sequencing was confirmed by sequencing the PCR amplification with specific primers on ABI3703 (data no shown).

Alignment, SNV/INDEL calling and quality control

We aligned the paired-end reads to the reference human genome (hg19, http://genome.ucsc.edu/) using third-party software, BWA, with the default parameters. The average sequencing depth of the case and control samples was more than 50X, and the coverage of the target area was approximately 80% (data no shown). Approximately 70% of the nucleotides within the coding region were covered by at least 10 different reads.

We re-aligned the INDEL regions of the bam file using GATK software (version 1.1–30). The SNVs and INDELs were extracted using the unified genotyper function in accordance with the default parameters. To call an SNV or an INDEL, the mapping quality had to be no less than 40, the mutation had to be measured at least five times, and the allelic heterozygosity had to be >12.5%.

Mutation annotation based on COSMIC

We confirmed the mutations by ABI 3730 sequencing and annotated them using the COSMIC database. The latest version of the COSMIC database contains 14,819 articles on tumor somatic-mutation research, including 2,556 whole-genome sequencing studies of tumor tissues which scanned 22,170 genes for mutations, and a total of 773,098 tissue samples. The database contains 405,271 mutation sites with 224,649 single-site mutations (there is no reproducible variation in the tumor samples), 8,931 fusion-gene variants, and 7,503 genomic rearrangements.

Molecular modeling of TP53

To further investigate the influence of the R249S mutation on the TP53 structure, a three-dimensional computer model was constructed with the NOC program. TP53 (residues 219–292) was modeled with the SWISS-MODEL software (http://swissmodel.expasy.org/) using the crystal structure of human TP53 (PDB accession code 2qxa, chain B) as a template (13).

Validation of sequencing results

Four genes (EP300, CADM2, CEP63 and MAP3K3) that harbored amino acid replacements in the whole-exome sequencing sample were selected based on their functions and sequenced their exons and exon/intron junctions in the 98 paired lung cancer/normal tissue samples. EP300 and MAP3K3 are, respectively, involved in the TP53 and RAS singnal pathway, which plays an important role in the pathogenesis of lung cancer. CEP63 binds to and recruits Cdk1 to centrosomes, and thus regulates mitotic entry (14). Although the function of CADM2 remained elusive, it has been reported that CADM2 was recurrently disrupted in prostate cancer (15).

Results

Identification of somatic mutations from lung SCC

By comparing the whole-exome sequencing data between the tumor and normal lung tissues from a single patient with lung SCC, we identified 293 somatic SNVs and 62 INDELs (29 deletions and 33 insertions) (Fig. 1). The majority of the SNVs were located in inter-genic regions or introns. We identified 101 SNVs, including 77 non-synonymous SNVs (67 missense mutations and 10 nonsense mutations) and 11 INDELs, in the coding regions of genes (data no shown). We also found four SNVs in splicing sites (within three nucleotides of a splicing adaptor or receptor) (data no shown).

Figure 1 Non-synonymous somatic mutations discovered by whole-exome sequencing. From outside to inside: the colored ring and number represent chromosome number and partition; the blue ring and letters indicate genes with non-synonymous SNVs and their corresponding chromosomes; the gray ring shows non-synonymous INDELs and their corresponding chromosomes.

Confirmation of the somatic non-synonymous variants in lung SCC

We designed specific primers to verify the 77 non-synonymous SNVs, 11 INDELS, and 4 splicing-site mutations by sequencing on ABI3730. We confirmed 51 missense mutations, 10 nonsense mutations and 1 splicing-site mutation to be somatic mutations in the lung SCC tissue by ABI 3730 sequencing (Table I). We also verified 10 of the 11 INDELs as somatic mutations in lung SCC tissue (Table I and Fig. 1).

Table I The 62 confirmed somatic SNVs and 10 INDELs in lung cancer tissues and the effect of missense mutation on protein function.

Table I

The 62 confirmed somatic SNVs and 10 INDELs in lung cancer tissues and the effect of missense mutation on protein function.

Chromosome	Positiona	Exon	Wild-type sequence	Mutant sequence	Amino acid variation	Mutation type	Certification	SIFT	Gene
1	16258094	11	GCA	CCA	A1787P	Missense	Y	Tolerated	SPEN
1	26357058	4	GTG	GCG	V358A	Missense	Y	Tolerated	EXTL1
1	43651012	5	GAG	GAC	E318D	Missense	Y	Tolerated	WDR65
1	82431854	10	CAG	CAC	Q693H	Missense	Y	Damagingb	LPHN2
1	119467295	4	AAC	GAC	N117D	Missense	N	Not scored	TBX15
1	155263101	9	CGG	TGG	R435W	Missense	Y	Not scored	PKLR
2	48809658	2	CAG	CGG	Q629R	Missense	Y	Tolerated	STON1-GTF2A1L
2	173429766	5	ATA	ACA	I219T	Missense	N	Damaging	PDK1
3	50332472	2	GCC	TCC	A188S	Missense	Y	Not scored	HYAL3
3	85851331	2	TTT	ATT	F68I	Missense	Y	Tolerated	CADM2
3	109049529	5	GGG	GTG	G174V	Missense	Y	Not scored	DPPA4
3	134269082	12	GAC	TAC	D454Y	Missense	Y	Damaging	CEP63
3	164758812	18	CGC	CAC	R692H	Missense	N	Not scored	SI
3	183211938	5	GGA	TGA	G427_	Nonsense	Y	Not scored	KLHL6
3	197597029	17–18	-	-	-	Splice	N	-	LRCH3
4	23815373	8	TGT	TAT	C578Y	Missense	N	Not scored	PPARGC1A
4	107154130	17	TGG	TTG	W535L	Missense	Y	Not scored	TBCK
4	159052121	5	CGC	CAC	R398H	Missense	Y	Not scored	FAM198B
4	177084348	23	GCA	GAA	A989E	Missense	N	Damaging	WDR17
5	56526793	3	GGA	TGA	G69_	Nonsense	Y	N/A	GPBP1
5	80511767	24	CTT	TTT	L1143F	Missense	Y	Damaging	RASGRF2
6	7862569	4	GGC	TGC	G348C	Missense	Y	Damaging	BMP6
6	71508430	6	ACA	AGA	T189R	Missense	N	Tolerated	SMAP1
6	133014234	4	TCA	TAA	S252_	Nonsense	Y	Not scored	VNN1
7	33312787	8	TGT	TCT	C289S	Missense	Y	Tolerated	BBS9
7	94057137	49	AGA	GGA	R1156G	Missense	Y	Damaging	COL1A2
7	94897906	13	GCA	CCA	A904P	Missense	Y	Damaging	PPP1R9A
7	119915111	1	CGA	CTA	R142L	Missense	Y	Not scored	KCND2
7	127954893	17	GAG	TAG	E657_	Nonsense	Y	Not scored	RBM28
7	143018515	4	TGG	TAG	W164_	Nonsense	N	N/A	CLCN1
8	25325856	6	GTA	GCA	V221A	Missense	Y	Tolerated	CDCA2
8	87645122	10–11	-	-	-	Splice	N	-	CNGB3
8	124195471	1	GAG	GAT	E125D	Missense	Y	Tolerated	FAM83A
9	21206945	1	TCC	TTC	S51F	Missense	N	Not scored	IFNA10
9	33280855	8	TAG	CAG	_220Q	Missense	Y	Not scored	CHMP5
9	97563158	4	CAA	CGA	Q413R	Missense	Y	Tolerated	C9orf3
9	104187214	8	CGG	TGG	R304W	Missense	N	Not scored	ALDOB
9	108061571	2	ATC	ACC	I36T	Missense	Y	Tolerated	SLC44A1
9	137710511	55	GGC	CGC	G1414R	Missense	Y	Damaging	COL5A1
10	119013997	6	ATG	ACG	M230T	Missense	Y	Damaging	SLC18A2
10	127409947	2	CTT	TTT	L95F	Missense	Y	Tolerated	C10orf137
11	674771	10	CTG	CAG	L423Q	Missense	N	Not scored	DEAF1
11	5510732	1	CGC	TGC	R266C	Missense	Y	Damaging	OR52D1
11	6023708	1	TGT	TAT	C224Y	Missense	N	Not scored	OR56A4
11	14991481	3	AGC	ATC	S76I	Missense	N	-	CALCA
11	22396340	9	ATT	TTT	I361F	Missense	Y	Tolerated	SLC17A6
11	55432790	1	AGT	GGT	S50G	Missense	N	Damaging	OR4C6
11	59830067	3	GGT	TGT	G95C	Missense	Y	Damaging	MS4A3
11	68773662	3	GCG	GTG	A39V	Missense	Y	Not scored	MRGPRF
11	70256067	5–6	-	-	-	Splice	Y	-	CTTN
12	118462797	6	CAT	CGT	H188R	Missense	Y	Tolerated	RFC5
13	99098929	26	CCC	ACC	P972T	Missense	Y	Damaging	FARP1
13	103438665	9	GAG	TAG	E470_	Nonsense	Y	Not scored	KDELC1
14	21992519	2	AGT	ATT	S448I	Missense	Y	Not scored	SALL2
16	718670	4	GAC	TAC	D65Y	Missense	Y	Damaging	RHOT2
16	4016671	11	AGC	ATC	S1056I	Missense	Y	Not scored	ADCY9
16	69170741	3	GGA	GTA	G101V	Missense	Y	Damaging	CIRH1A
17	7577534	7	AGG	AGT	R249S	Missense	Y	Not scored	TP53
17	7950699	11–12	-	-	-	Splice	Y	-	ALOX15B
17	8701111	1	GGG	GAG	G443E	Missense	Y	Not scored	MFSD6L
17	10426681	38	GAG	TAG	E1841_	Nonsense	Y	Not scored	MYH2
17	40821598	12	TTG	TTC	L685F	Missense	N	Not scored	PLEKHH3
17	40844603	17	GAC	AAC	D873N	Missense	Y	Tolerated	CNTNAP1
17	56811546	9	ACC	AAC	T365N	Missense	N	Tolerated	RAD51C
17	61771046	17	CGG	CTG	R628L	Missense	Y	Damaging	MAP3K3
18	5395661	20	CAG	GAG	Q1007E	Missense	Y	Not scored	EPB41L3
18	7949302	6	CGC	TGC	R263C	Missense	Y	Tolerated	PTPRM
18	54424249	15	GCC	TCC	A809S	Missense	Y	Tolerated	WDR7
18	58038807	1	GCC	GAC	A259D	Missense	Y	Not scored	MC4R
19	1059027	40	TAC	TAG	Y1802_	Nonsense	Y	N/A	ABCA7
20	10601998	7	GCT	TCT	A148S	Missense	Y	Tolerated	C20orf94
20	18296358	4	CAC	CGC	H287R	Missense	N	Damaging	ZNF133
20	36770575	7	CGC	AGC	R296S	Missense	Y	Not scored	TGM2
21	28327109	2	GTG	CTG	V396L	Missense	Y	Not scored	ADAMTS5
22	41568590	28	GAA	AAA	E1514K	Missense	Y	Damaging	EP300
X	5821872	5	GCC	ACC	A283T	Missense	Y	Not scored	NLGN4X
X	34962529	1	TAC	TAA	Y527_	nonsense	Y	N/A	FAM47B
X	47432323	13	GAG	GAC	E686D	Missense	Y	Not scored	SYN1
X	83362646	13	GCA	ACA	A366T	Missense	Y	Not scored	RPS6KA6
X	96136620	5	CAA	GAA	Q164E	Missense	N	Tolerated	DIAPH2
X	153175487	19	GAG	TAG	E777_	Nonsense	Y	Not scored	ARHGAP4
1	12711337	2	GC	G	NA	Frameshift	Y	Not scored	AADACL4
2	88472701	2	CACGGGT CAACTTT	C	NA	Frameshift	Y	Not scored	THNSL2
3	154802107	2	AC	A	NA	Frameshift	Y	Not scored	MME
5	127474317	8	CG	C	NA	Frameshift	Y	Not scored	SLC12A2
6	35610514	2	C	CT	NA	Frameshift	Y	Not scored	FKBP5
6	135314894	8–9	AC	A	NA	Splice-5	Y	-	HBS1L
12	130898840	14	GC	G	NA	Frameshift	Y	Not scored	RIMBP2
17	10348353	37	AT	A	NA	Frameshift	Y	Not scored	MYH4
17	43332710	4	AGG	A	NA	Frameshift	Y	Not scored	C17orf46
18	47091805	2	GC	G	NA	Frameshift	N	Not scored	LIPG
22	21346634	10	GC	G	NA	Frameshift	Y	Not scored	LZTR1

a based on UCSC hg(human genome) 19 version;

b low confidence from SIFT prediction.

We further analyzed the 51 confirmed missense mutations by searching the SIFT database (16) to predict their effects on protein structure. We found that 15 of the missense mutations could dramatically affect protein functions (Table I).

Comparison with COSMIC database

In the samples of lung cancer tissues and other types of solid tumors in the COSMIC database, we found previously identified mutations in all of the genes, except for MRGPRF, containing the 62 SNVs and 10 INDELs confirmed in our study by ABI 3730 sequencing. Fifteen of the genes with non-synonymous SNVs and one with an INDEL (LZTR1) had previously identified mutations in at least one sample of different pathological types of lung cancer; four of those genes (LPHN2, TP53, MYH2 and TGM2) were mutated in close to or more than 10% of the tumor tissues available in the COSMIC database. TP53 and LPHN2 were sequenced in more than 500 tumor samples, and their mutation frequencies were 18.3% (12,142/66,304) and 8.32% (49/589), respectively (Table II). TP53, a well-known oncogene that plays an important role in lung cancer pathogenesis, is mutated in 62.3% (1,404/2,252) of the lung cancer tissue samples in the COSMIC database.

Table II Genes with reoccurring mutations in lung cancer tissues in the COSMIC database.

Table II

Genes with reoccurring mutations in lung cancer tissues in the COSMIC database.

Chromosome	Gene	ACC	SCC	SCLC	Solid tumors	Hematol. cancer
1	SPEN	-	1/11	-	-	-
1	LPHN2	1/57	2/63	-	44/465	2/4
2	PDK1	1/253	2/70	-	4/586	-
3	CADM2	-	1/10	-	10/304	2/2
4	PPARGC1A	-	2/10	-	14/308	-
10	C10orf137	1/57	1/63	-	22/557	-
11	MS4A3	1	-	-	6/96	-
17	TP53	952/1,386	452/866	-	8,756/58,462	1,982/5,590
17	MYH2	-	1/1	36/218	-
17	CNTNAP1	-	1/63	-	19/404	-
18	MC4R	-	1/63	-	4/392	-
20	TGM2	-	-	1/1	13/166	-
22	EP300	-	1/63	-	57/1,495	28/525
X	RPS6KA6	1/16	in	LCC	9/292	-
X	DIAPH2	1/1	-	-	18/132	-
22	LZTR1	2/188	-	-	14/104	-

[i] Results represent positive cases/total cases. SCC, squamous cell carcinoma; ACC, adenocarcinoma; LCC, large-cell cancer; SCLC, small cell lung cancer; Hematol, hematological.

We also identified two missense mutations in C10orf137 and MS4A3, respectively, that also appear in different lung cancer tissues in the COSMIC database. It is worth noting that the mutation in C10orf137 was investigated in more than 500 solid tumor tissues and its frequency is approximately 3.55% (24/677) in COSMIC database (Table II). We identified a somatic mutation in EP300 and found that 4.2% (85/2,020) of the tumor tissues in the COSMIC database also had mutations in EP300 (Table II).

Based on the comparisons of our whole-exome sequencing results with the previously identified mutations in the COSMIC database, we identified seven genes (LPHN2, TP53, MYH2, TGM2, C10orf137, EP300 and MS4A3) as possible drivers of lung cancer pathogenesis (Table II and Fig. 2).

Figure 2 The mutation frequency of seven genes. Arrows indicate the distinct mutation sites discovered in our investigation. TP53 has a mutation ‘hot spot’ in DNA-binding domain.

Computer modeling and analysis of TP53

Our study is the first, however, to identify a C>A substitution in squamous cell lung cancer tissue changing Arg to Ser at amino acid position 249 (R249S) in TP53. By molecular modeling, a charged basic amino acid (Arg) was replaced by an neutral amino acid (Ser) at codon 249, which caused an abnormal electrostatic-charge distribution in the DNA-binding domain of TP53 (Fig. 3).

Figure 3 The effect of the novel somatic mutation (R490S) on the structure and function of TP53. (A and B) Structure of wild-type TP53. (C and D) Structure of mutated TP53. The arrow indicates the mutated amino acid. Red represents positively charged area. Blue represents negatively charged area.

Validation of sequencing results

We sequenced all of the exons of four genes (MAP3K3, CEP63, CADM2 and EP300) in 98 additional lung cancer samples; including 44 lung SCCs, 49 ACs, and 5 LCLCs; and found no mutations in the coding regions. We found a deletion of 2–4 cytosine residues in the 5′UTR of CEP63 in three of the samples, but the mutations did not change the protein sequences. We also identified a C>G variant located at nucleotide position 3207 of MAP3K3 (NM_2033351) in one patient; the variant was located in the 3′UTR, but did not change the protein sequence.

Discussion

We used whole-exome sequencing to identify 72 somatic mutations, including 62 SNVs (51 missense mutations, 10 nonsense mutations, and 1 splicing-site mutation) and 10 INDELs, in the coding regions of different genes from a single case of lung SCC. We found somatic mutations in 71 of the genes in at least one additional tumor sample in the COSMIC database. We found mutations in 16 of the genes in at least one additional lung cancer patient. Four genes (LPHN2, TP53, MYH2 and TGM2) were mutated in approximately 10% of the tumor samples in the COSMIC database.

We found the most mutations in TP53: 68.7% (952/1,386) of lung AD cases and 52.2% (452/866) of lung SCC cases. Although TP53 is frequently mutated in tumor tissues from patients with lung cancer, our study is the first to describe the R249S somatic missense mutation in lung cancer tissues. SIFT analysis showed that the R249S mutation in TP53 could dramatically influence the structure of the TP53 protein (16). It worth noting that the R249S mutation in TP53 is frequently found in HBV-induced hepatic-cell carcinoma, accounting for 90% of the TP53 mutations identified in liver cancer (17). In hepatocellular-carcinoma cell lines, the R249S mutation abolishes the capacity for TP53 to bind p53 response elements and trans-activate p53 target genes. Moreover, in a p53-null Hep3B cell line that constitutively expresses both the R249S variant of TP53 and the hepatitis-B virus antigen HBx (PLC/PRF/5), the silencing of either R249S TP53 or HBx by RNA interference inhibited cellular proliferation, but without additive effects when both genes were silenced (17). Taken together with the previous results, our results suggest that the R249S mutation in TP53 may be play a key role in lung cancer pathogenesis.

LPHN2 was previously sequenced in more than 500 tumor samples and found to be mutated in 9.46% (44/465) of solid-tumor samples. Moreover, somatic mutations in LPHN2 occurred in 3.17% of lung SCC (2/63) samples in the COSMIC database. LPHN2 encodes a member of the latrophilin subfamily of G-protein coupled receptors (GPCR), and genome-wide association analysis found a significant association between SNVs of LPHN2 and paclitaxel sensitivity in NCI60 cancer cell lines (18).

MYH2 and TGM2 were mutated in 16.5% (36/218) and 7.8% (13/166) of solid tumors; neither mutation, however, was previously investigated in SCC samples.

We identified two missense mutations in C10orf137 and MS4A3, respectively; both were previously identified in different lung cancer tissues. Mutations in C10orf137 were previously investigated in more than 500 solid-tumor samples and found in 3.55% (24/677) of the samples. Recently, Gylfe et al identified one missense germ-line mutation in C10orf137 in 45 familial patients with colorectal cancers, while none of the 890 population-matched healthy controls had the same mutation (19). The function of C10orf137, however, is still unknown. MS4A is a member of the four-transmembrane protein family. MS4A proteins execute diverse functions, acting as cell-surface signaling molecules and intracellular adapter proteins. Tissue microarray analysis showed MS4A3 expression in a wide variety of ACs including breast, prostate, and ovarian cancers (20). Moreover, previous studies showed that MS4A3 forms a functionally relevant complex with cyclin-dependent kinase-associated phosphatase and CDK2 (21), suggesting that MS4S3 may be a novel modulator of the cell cycle. Further study is needed to explain the role of MS4A3 in lung cancer pathogenesis.

We identified a somatic mutation in EP300 that was previously identified in 4.2% (85/2,020) of the tumor samples in the COSMIC database. Recurrent mutations clustered around the histone acetyltransferase domain in EP300 were recently described in small-cell lung cancers (22). EP300 plays an important role in cell proliferation and differentiation by regulating gene transcription via chromatin remodeling (23–25). EP300 is also an important modulator of the TP53 signaling pathway; it helps to maintain TP53 stability by regulating the ubiquitination and degradation of TP53 through both MDM2-dependent and MDM2-independent mechanisms (26,27). Moreover, EP300 is required for the TP53-mediated transactivation of target genes because of its co-activator function and its acetylation of histones (28–30). Together with the previous results, our data suggest that EP300 may be a driver gene in lung cancer tumorigenesis.

Several recent whole-genome or exome-sequencing studies aimed at characterizing the genomic and epigenomic landscapes of different histopathological types of lung cancer (ACC, SCC and small-cell cancer) (31–34). The results included a large number and variety of DNA alterations with a mean of more than 150 exonic non-synonymous mutations per lung cancer type (31–34). Analyses by different algorithms identified some genes with significantly elevated mutational frequencies in different histological types of lung cancer (P<0.05; false-discovery rate ≤0.1). Among these genes, TP53 was confirmed as a tumorigenesis gene and had the highest mutational frequency (29–81%) in all of the independent studies. Four other genes; EGFR, KRAS, KEAP1 and RB1; were also implicated as important tumorigenesis genes in two independent studies (31–34). A somatic mutation in KEAP1 was repeatedly identified in independent studies performed on cohorts of lung SCCs and lung ACs (31,32). Somatic mutations in RB1 were confirmed in patients with SCLC and lung SCCs (31,33). Most of the significantly mutated genes, however, were identified in only one cohort (31–34). These results suggest that genomic variants in lung cancer tissues are complex; the somatic mutations in distinct genes underscore the differences between subgroups of lung cancer, even within a single histological type. More whole-exome sequencing studies with large sample sizes are needed to increase the available somatic-mutation data.

In summary, our results show that whole-exome sequencing is an effective way to detect novel mutations related to lung cancer. Our study indicates seven genes, TP53, EP300, LPHN2, C10orf137, MYH2, TGM2 and MS4A3, that may be drivers of lung cancer tumorigenesis.

Acknowledgements

We thank all the patients who participated in this study. This study was supported in part by the National Natural Science Foundation of China (81071925 and 30900503) and the Shanghai Science and Technology Committee (10ZR1418300).

References