A decade of GWAS results in lung cancer

Abstract

Genome-wide association studies (GWAS) were successful to identify genetic factors robustly associated with lung cancer. This review aims to synthesize the literature in this field and accelerate the translation of GWAS discoveries into results that are closer to clinical applications. A chronological presentation of published GWAS on lung cancer susceptibility, survival and response to treatment is presented. The most important results are tabulated to provide a concise overview in one read. GWAS have reported 45 lung cancer susceptibility loci with varying strength of evidence and highlighted suspected causal genes at each locus. Some genetic risk loci have been refined to more homogeneous subgroups of lung cancer patients in terms of histological subtypes, smoking status, gender, and ethnicity. Overall, these discoveries are an important step for future development of new therapeutic targets and biomarkers to personalize and improve quality of care for patients. GWAS results are on the edge of offering new tools for targeted screening in high-risk individuals, but more research is needed if GWAS are to pay off the investment. Complementary genomic datasets and functional studies are needed to refine the underlying molecular mechanisms of lung cancer preliminarily revealed by GWAS and reach results that are medically actionable.

Introduction

Lung cancer is the leading cause of cancer-related deaths worldwide in both men and women (1, 2). Our molecular understanding of this disease is in progress. Although it has long been recognized that lung cancer runs strongly in families (3–5), the specific genes that are responsible for enhanced risk are just starting to be revealed. Identifying genes responsible for lung cancer before the era of GWAS has been limited. Candidate susceptibility genes coding for enzymes involved in the activation, detoxification and repair of damages caused by tobacco smoke as well as genes in inflammatory and cell-cycle pathways have been extensively studied (6, 7). Many of these candidate gene studies are either preliminary or controversial (8). Rare germline mutations in TP53, RB1 and EGFR have been shown to confer inherited predisposition to lung cancer (9–11). Fine mapping of genome-wide linkage peak on 6q23–25 also identified RGS17 as a predisposing gene (12). With the arrival of GWAS approximately 10 years ago, it became possible to interrogate the human genome more comprehensively for lung cancer susceptibility genes.

Chronological presentation of published GWAS on lung cancer

GWAS have identified genetic factors robustly associated with lung cancer. Tables 1 and 2 provide a chronological presentation of published GWAS on lung cancer in European and Asian populations, respectively, and summarize susceptibility loci identified. During the last decade, GWAS have evolved from finding lung cancer loci per se, to a more refined search strategy focused on specific subgroups of lung cancer patients. Advances in analysis strategies were also achieved moving from single marker analyses to pathway-based and variant prioritization approaches. GWAS have also been performed to find genetic loci associated with lung cancer survival, response to conventional therapies, and multiple sites of cancer. GWAS by environmental exposures and genome-wide epistasis analyses are also emerging. This review aims to provide a concise overview of this literature. All GWAS on lung cancer susceptibility, survival and response to treatment reported in the literature were cumulated and manually curated by the authors. At the time of writing this review, a search on pubmed with the keywords “GWAS” and “lung cancer” was performed to identify any missing GWAS in the field. Finally, GWAS and susceptibility loci on lung cancer were further refined with the GWAS catalogue (13). Please note that we have attempted to include all loci reported in the literature without quality assessment or exclusion criteria based on the magnitude of effects, sample size or other criteria. Lung cancer susceptibility loci were reported based on the interpretation of the authors in the original manuscripts.

Table 1.

Susceptibility loci for lung cancer identified by GWAS in European ancestry populations

Reference	Studya	Sample size (cases/controls)	Disease/trait	Platform (# SNPs)	Region (size)	Gene	Key SNPs
Hung et al. (14)	IARC  Toronto  EPIC  CARET  Liverpool  HUNT/Tromsø	1926/2522 330/453 781/1578 764/1515 403/814 235/392	Lung cancer	Illumina HumanHap300 (310,023)	15q25 (182 kb)	CHRNA5 CHRNA3 CHRNB4 IREB2 PSMA4 HYKK (AGPHD1)	rs1051730 rs8034191 rs16969968 (D398N)
Thorgeirsson et al. (15)	Icelandic smokers  Icelandic smokers  Iceland/Spain/The Netherlands	10995 2950 1024/32244	Smoking quantity  Nicotine dependence  Lung cancer	Illumina HumanHap300 (306,207)	15q24	CHRNA5 CHRNA3 CHRNB4	rs1051730
Amos et al. (16)	Texas  Texas replication  UK	1154/1137 711/632 2013/3062	Non-small cell lung cancer	Illumina HumanHap300 (315,450)	15q25.1 (88 kb)	CHRNA5 CHRNA3 PSMA4 HYKK	rs1051730 rs8034191 rs931794
Liu et al. (17)	GELCC	194/219	Familial lung cancer	Affymetrix 500K (399,377) or 6.0 (722,376)	15q24–25.1 (160 kb)	CHRNA5 CHRNA3 CHRNB4 IREB2 PSMA4 HYKK	rs8034191 rs1051730 rs16969968 (D398N) rs578776
McKay et al. (19)	Central Europe/ Toronto/ HUNT2-Tromso/ CARET  EPIC/Szczecin/ CARET2/ Liverpool	2971/3746 2899/5573	Lung cancer	Illumina HumanHap300 (315,194)	15q25.1			rs1051730
					5p15.33	TERT CLPTM1L		rs402710 rs2736100
Wang et al. (18)	British cohort  IARC  Texas  UK replication	1952/1438 1989/2625 1154/1137 2448/2983	Lung cancer	Illumina HumanHap550 (511,919)	15q25.1			rs8042374
					6p21.33 (627 kb)	BAG6 MSH5		rs3117582 rs3131379
					5p15.33 (60 kb)	CLPTM1L		rs401681
Broderick et al. (20)	GELCAPS phase 1  GELCAPS phase 2  Meta-analysis (GELCAPS, IARC, Texas)	1952/1438 2465/3005 7560/8205	Lung cancer	Illumina HumanHap550 (511,919)	15q25.1 (248 kb)	CHRNA3		rs12914385 rs938682 rs8042374 rs8034191
					5p15.33 (60 kb)	CLPTM1L TERT		rs4975616
					6p21.33	BAG6 TNXB		rs3117582 rs1150752
Landi et al. (21)	NCI (EAGLE, ATBC, PLCO, CPS-II)  Meta-analysis (UK, Central Europe, Texas, DeCODE Genetics, HGF Germany, CARET, HUNT2/Tromso, Canada, France, Estonia)	5739/5848 13,300/19,666	Lung cancer  Adenocarcinoma  Squamous cell  Small cell	Illumina (515,922)	15q25	CHRNA3 CHRNA5 HYKK		rs12914385 rs1051730 rs8034191
					5p15	TERT CLPTM1L		rs2736100 rs4635969 rs31489
					6p21	BAG6 APOM		rs3117582
Timofeeva et al. (22)	TRICLb  Han Chinese	14900/29485 2338/3077	Lung cancer	Illumina HumanHap300 (318,094) + HumanHap550 or 610Quad (217,914)	15q25	CHRNA5 CHRNA3 CHRNB4 HYKK		rs1051730 rs8034191 rs6495309 rs680244 rs6495306 rs951266
					5p15.33	TERT CLPTM1L		rs2736100 rs401681 rs2853677 rs465498
					6p21–22	BAG6 MSH5		rs3117582 rs2523546 rs2523571
			Squamous cell carcinoma		12p13.33	RAD52		rs10849605 rs3748522
					9p21.3	CDKN2A CDKN2B ANRIL (CDKN2B-AS1)		rs1333040 rs1537372
					2q32.1	NUP35		rs11683501
Wang et al. (23)	MDACC  ICR  NCI  IARC  EPIC  ICR  IARC  Toronto	11348/15861 10246/38295	Lung cancer, Adenocarcinoma, Squamous cell carcinoma	Illumina 317, 317+240S, 370Duo, 550, 610 or 1M	13q13.1	BRCA2 FRY		rs11571833 (K3326X) rs56084662
					22q12.1	CHEK2		rs17879961 (I157T)
					3q28	TP63		rs13314271 rs4488809
McKay, Hung et al. (25)	OncoArrayc	29863/55586	Lung cancer	Oncoarray (10,439,017)	1p31.1	FUBP1		rs71658797
					6q27	RNASET2		rs6920364
					8p21.1	EPHX2 CHRNA2		rs11780471
					13q13.1	BRCA2		rs11571833
					15q21.1	SEMA6D		rs66759488
					15q25.1	CHRNA5		rs55781567
					19q13.2	CYP2A6		rs56113850
		11245/54619	Adenocarcinoma		3q28	TP63		rs13080835
					5p15.33	TERT		rs7705526
					8p12	NRG1		rs4236709
					9p21.3	MTAP CDKN2A		rs885518
					10q24.3	OBFC1		rs11591710
					11q23.3	MPZL3 AMICA1		rs1056562
					15q21.1	SECISBP2L		rs77468143
					20q13.33	RTEL1		rs41309931
		7704/54763	Squamous cell carcinoma		6p21.33	MHC		rs116822326
					12p13.33	RAD52		rs7953330
					22q12.1	CHEK2		rs17879961
Never smokers
Li et al. (34)	Mayo  MDACC  Harvard  UCLA	377/377 328/407 92/161 9¼39	Lung cancer in never smokers	Illumina HumanHap370 and HumanHap610 (331,918)	13q31.3	GPC5		rs2352028 rs2352029
Pathway-based GWAS
Shi et al. (40)	NCI  UK1  Texas  UK2	5,355/4,344 592/2699 306/1137 1038/933	Lung cancer, Squamous cell carcinoma, Adenocarcinoma, small cell	Illumina (19,082) (pathway-based analysis)	12p13.33	RAD52		rs6489769
Spitz et al. (41)	Texas  Mayo	451/508 303/311	NSCLC in never smokers	Illumina (11,737) (pathway-based analysis)	12q13	ACVR1B NR4A1		rs12809597 rs2701129 rs1882119
Wang et al. (42)	ICR  MDACC  IARC  NCI  Toronto  HGF Germany	12160/16838	Lung cancer	Illumina (826 functional SNPs) (pathway-based analysis)	6p21.33	MSH5 GTF2H4		rs3115672 rs114596632
					5q14.2	XRCC4		rs1056503 rs2035990
Variant prioritization approaches
Li et al. (44)	Texas	1154/1137	Lung cancer + intermediate phenotype (cigarettes per day)	Illumina HumanHap300	15q24–25.1	HYKK CHRNA3 CHRNA5 CHRNB4		rs12914385
					19q13	TGFB1 B9D2		rs1800469 rs1982072 rs2241714
					3p26			rs1444056 rs1403124
Poirier et al. (45)	Toronto  IARC  MDACC  HMGU  NCI  MSH-PMH  MEC  Harvard	33¼99 1964/2610 1154/1137 504/484 5699/5815 1073/939 215/225 52¾97	Lung cancer + Family history	Illumina HumanHap300, 550	10q23.33	FFAR4		rs12415204
					4p15.2	KCNIP4		rs1158970
Brenner et al. (46)	TRICLb  ILCCO	5061 (SQ)/ 6756 (AD)/ 2216 (SCLC)/ 33456 625 (SQ)/ 1417 (AD)/ 369 (SCLC)/ 2966	Squamous cell carcinoma	Illumina HumanHap300, 550, 610	4p15.2	KCNIP4		rs6448050 rs9799795
			Adenocarcinoma		18q12.1	GAREM		rs11662168 rs3786309
Cross-cancer loci
Hung et al. (55)	GAME-ON / GECCO	64591/74467	Cross-cancer	Illumina, Affymetrix (12,370) (pathway-based analysis)	12q24	SH2B3		rs3184504 (W262R)
					5p15	TERT		rs2736100
Fehringer et al. (56)	GAME-ON / GECCO Europeans Others	61851/61820 55789/330490 18152/21410	Cross-cancer	Illumina, Affymetrix (9,916,564)	1q22	MUC1 ADAM15 THBS3		rs1057941 rs4072037
					9p21.3	CDKN2B-AS1		rs62560775
					13q13.1	BRCA2		rs11571833

Abbreviations: ATBC: Alpha-Tocopherol, Beta-Carotene Cancer Prevention study; CARET: Beta-Carotene and Retinol Efficacy Trial; CPS-II: Cancer Prevention Study II nutrition cohort; EAGLE: Environment and Genetics in Lung Cancer Etiology; EPIC: European Prospective Investigation in Cancer and Nutrition; GAME-ON: The Genetic Associations and Mechanisms in Oncology; GECCO: The Genetic and Epidemiology of Colorectal Cancer Consortium; GELCAPS: Genetic Lung Cancer Predisposition Study; GELCC: Genetic Epidemiology of Lung Cancer Consortium; HGF Germany: Helmholtz-Gemeinschaft Deutscher Forschungszentren Lung Cancer GWAS; HMGU: Germany study; HUNT: Health Study of North-Trondelag; IARC: International Agency for Research on Cancer; ICR: Institute of Cancer Research; ILCCO: International Lung Cancer Consortium; MDACC: MD Anderson Cancer Centre; MEC: Multi-Ethnic Cohort; MSH-PMH: Mount Sinai Hospital and Princess Margaret Hospital in Toronto; NCI: National Cancer Institute; PLCO: Prostate, Lung, Colon, Ovary screening trial; TRICL: Transdisciplinary Research In Cancer of the Lung; UCLA: University of California in Los Angeles.

^aItalic indicates replication cohorts.

^bTRICL includes the following cohorts: MDACC, Liverpool, ICR, Toronto, IARC (Central Europe, CARET, Estonia, France, HUNT2/Tromso), DeCODE Genetics, HGF Germany, Harvard, NCI (EAGLE, ATBC, PLCO, CPS-II).

^cOncoArray includes the following cohorts: CARET, PLCO, MEC, NELCS (New England Lung Cancer Study), Harvard, MDACC, Tampa (Tampa Lung Cancer Study), BioVU (Vanderbilt 2), LCRI-DOD, TLC (Total Lung Cancer: Molecular Epidemiology of Lung Cancer Survival), Canada (Canadian screening study), MSH-PMH, CAPUA (Cancer de Pulmon en Asturias), ATBC, NSHDC (Northern Sweden Health and Disease Cohort), MDCS (The Malmö Diet and Cancer Study), EPIC, Liverpool, Norway (Norway Lung Cancer Study), EAGLE, Nijmegen (The Nijmegen Lung Cancer Study), NICCC-LCA (Clalit National Israeli Cancer Control Center- lung cancer study), L2 (The IARC L2 Study), Copenhagen (Copenhagen lung cancer study), Germany, ReSoLuCENT.

Table 2.

Susceptibility loci for lung cancer identified by GWAS in Asian ancestry populations

Reference	Studya	Sample size (cases/controls)	Disease/trait	Platform (# SNPs)	Region (size)	Gene	Key SNPs
Yoon et al. (26)	Korea  Korea replication	621/1541 804/1470	NSCLC	Affymetrix 5.0 (246,758)	3q29	C3orf21		rs2131877 rs10433328 rs952481 rs4677657
					5p15	TERT CLPTM1L		rs2736100 rs402710 rs401681
Miki et al. (27)	Japanese  Japanese  Korean	1004/1900 525/7678 569/1470	Lung adenocarcinoma	Illumina HumanHap610-Quad and HumanHap550 (432,024)	5p15	TERT		rs2736100
					3q28	TP63		rs10937405 rs4488809 rs9816619 rs4600802
Hu et al. (28)	Han Chinese (Nanjing, Beijing, Shanghai)  1st stage replication  2nd stage replication	2331/3077 2283/2243 4030/4166	Lung cancer	Affymetrix 6.0 (591,370)	3q28	TP63		rs4488809 rs10937405
					5p15.33	TERT CLPTM1L		rs465498 rs2736100
					13q12.12	MIPEP TNFRSF19		rs753955
					22q12.2	MTMR3 HORMAD2 LIF		rs17728461 rs36600
Dong et al. (29)	Chinese (GWAS)  Chinese (stage 1)  Chinese (stage 2)	2331/3077 2283/2243 5153/5240	Lung cancer	Affymetrix 6.0 (591,370)	10p14	GATA3		rs1663689
					5q32	PPP2R2B STK32A DPYSL3		rs2895680
					20q13.2	CYP24A1		rs4809957 rs2296239
					5q31.1	IL3 CSF2 P4HA2 SLC22A5 ACSL6		rs247008
					1p36.32	AJAP1 NPHP4		rs9439519
Shiraishi et al. (32)	Japanese  1st validation  2nd validation	1695/5333 2955/7036 1379/1166	Adenocarcinoma	Illumina OmniExpress & Omni1-Quad (538,166)	5p15.33	TERT		rs2736100 rs2853677
					3q28	TP63		rs10937405
					17q24.3	BPTF		rs7216064
					6p21.3	BTNL2		rs3817963
Dong et al. (30)	Han Chinese  Replication 1  Replication 2	833/3094 822/2243 140¼166	Squamous cell carcinoma	Affymetrix 6.0 (570,009)	12q23.1	NR1H4 SLC17A8		rs12296850
Jin et al. (31)	Han Chinese  Replication 1  Replication 2	1341/1982 1115/1246 3584/3669	Lung cancer	Illumina HumanExome (72,423)	6p21.33	PRRC2A (BAT2) FKBPL		rs9469031 (P515L) rs200847762 (P137L)
					20q11.21	BPIFB1		rs6141383 (V284M)
					6p22.2	HIST1H1E		rs2298090 (L152R)
Never smokers
Hsiung et al. (35)	GELAC (Han Chinese)  GELAC (replication)  CAMSCH  SNU  SWHS  WHLCS  KNUH  KUMC  GEL-S  NJLCS	584/585 610/560 287/287 259/293 209/213 207/207 121/119 95/87 193/546 203/203	Lung adenocarcinoma in never-smoking Asian females	Illumina HumanCNV370-Duo and HumanHap610 Quad (457,504)	5p15.33	CLPTM1L TERT		rs2736100
Lan et al. (36)	Female Lung cancer Consortium in Asia	5510/4544 1099/2913	Lung cancer in never smokers	Illumina (512,226)	10q25.2	VTI1A		rs7086803 rs11196080
					6q22.2	ROS1, DCBLD1		rs9387478
					6p21.32	HLA class II region		rs2395185
					5p15.33	TERT		rs2736100
					3q28	TP63		rs4488809
					17q24.3	BPTF		rs7216064
Wang et al. (37)	Female Lung cancer Consortium in Asia	6877/6277 5878/7046	Lung cancer in never smokers	Illumina (7,564,751)	6p21.1	FOXP4 FOXP4-AS1		rs7741164
					9p21.3	CDKN2B CDKN2B-AS1		rs72658409
					12q13.13	ACVR1B		rs116101143
Ahn et al. (38)	Korean	446/497 434/1000	NSCLC in never smokers	Affymetrix 6.0 (474,503)	18p11.22	FAM38B (PIEZO2) APCDD1 NAPG		rs11080466 rs11663246
Kim et al. (39)	Korean  Replication 1  Replication 2	285/1455 29¾95 546/744	Lung cancer in never smoker women	Affymetrix 5.0 (331,088)	2p16.3	NRXN1		rs10187911
Genome-wide epistasis
Chu et al. (52)	Han Chinese  Replication 1  Replication 2	2331/3077 1534/1489 2512/2449	Lung cancer	Affymetrix 6.0 (591,370) (epistatis)	2q32.2	HIBCH INPP1 PMS1 STAT1		rs2562796 rs16832404
Cross-cancer loci
Jin et al. (54)	Han Chinese  Han Chinese	5368/4006 9001/11436	Cross-cancer	Affymetrix 6.0	6p21.1	LRFN2		rs2494938
					7p15.3	SP4 DNAH11		rs2285947

Abbreviations: CAMSCH: Chinese Academy of Medical Sciences Cancer Hospital Study; GELAC: Genetic Epidemiological Study of Lung Adenocarcinoma; GEL-S: Genes and Environment in Lung Cancer, Singapore study; KNUH: Kyungpook National University Hospital Study; KUMC: Korea University Medical Center Study; NJLCS: Nanjing Lung Cancer Study; SNU: Seoul National University study; SWHS: Shanghai Women’s Health Cohort Study; WHLCS: Wuhan Lung Cancer Study.

^aItalic indicates replication cohorts.

GWAS on lung cancer susceptibility

GWAS in European populations.

The first GWAS on lung cancer were reported in 2008. Three independent studies identified a susceptibility locus on chromosome 15q. Hung et al. (14) found two SNPs strongly associated with lung cancer on chromosome 15q25. Further genotyping in this region revealed many SNPs in tight linkage disequilibrium (LD) showing evidence of association. Six genes are located in this region including three nicotinic acetylcholine receptor subunits (CHRNA5, CHRNA3, and CHRNB4). Interestingly, no appreciable variation in the risk was found across smoking categories or histological subtypes of lung cancer. In a second GWAS, a SNP within the CHRNA3 gene was strongly associated with smoking quantity and nicotine dependence (15). The same SNP was also strongly associated with lung cancer. The results suggest that the variant on chromosome 15q25 confers risk of lung cancer through its effect on tobacco addiction. In contrast, a third study showed weak evidence that the 15q25 locus influences smoking behavior and is mostly directly associated with lung cancer (16). However, it should be emphasized that the later GWAS was conducted in cases and controls matched on smoking status, thus limiting variation between the two groups and the power to detect any smoking association. Further analyses from the same study suggest that SNPs and smoking have independent effects on risk. Together, these three studies unequivocally support the 15q25 locus as harboring susceptibility variants for lung cancer or smoking behavior.

A GWAS performed in familial lung cancer confirmed the susceptibility locus on 15q24–25.1 (17). A subsequent GWAS identified two newly associated risk loci for lung cancer (18). In this study, 15q25 was again the most strongly associated locus. However, by pooling the results with other studies (14, 16), new cancer risk loci were found. Two intronic SNPs located in different genes (BAG6, previously known as BAT3, and MSH5) and separated by more than 600 kilobases on chromosome 6p21 were significantly associated with lung cancer. The strongest association, aside 15q25 and 6p21, was found on chromosome 5p15 within the CLPTM1L gene. The 5p15 locus was further supported by an expanded GWAS from previous populations (19). Two uncorrelated SNPs in that region were strongly associated with lung cancer. These SNPs are located within or in proximity to two biologically relevant genes namely CLPTM1L and TERT. Together, by the end of 2008, three susceptibility loci for lung cancer were identified, i.e. 15q25, 6p21 and 5p15.

A more extensive follow-up on a previous GWAS (18) further supports the contribution of the three loci (20). The latter study supports the possibility that two independent loci are acting on 15q25. The latter locus was also associated with smoking behavior, with risk alleles correlated with higher tobacco consumption. In contrast, the 5p15 and 6p21 loci were not associated with smoking behavior. However, DNA variants at 5p15 were associated with histological subtypes of lung cancer, with an increased frequency of the risk allele in cases with adenocarcinoma. This observation was subsequently confirmed in a meta-analysis published in 2009 (21). This study provides compelling evidence that the 5p15 susceptibility locus for lung cancer is confined to a more specific subtype of lung cancer, i.e. adenocarcinoma. Particularly intriguing in that study of more than 30,000 subjects is the absence of new genomic regions associated with lung cancer. In 2010, a meta-analysis of 16 GWAS confirmed lung cancer loci on 15q25, 5p15, and 6p21 (22). Again, the association at 5p15 was confined to adenocarcinoma, whereas the 6p21 locus was more strongly associated with squamous cell carcinoma. Stratification by histology identified three loci for squamous cell carcinoma including 12q13.33, 9p21.3, and 2q32.1. In 2014, another GWAS meta-analysis taking advantage of the imputation based on the 1000 Genomes Project was performed (23), which allowed testing for less frequent SNPs not measured in earlier studies. The top nine signals were followed-up and rare genetic variants were associated with squamous cell carcinoma in the BRCA2 gene on 13q13.1 and in CHEK2 on 22q12.1. CHEK2 was previously associated with lung cancer (24), but this was the first time using a GWAS approach. The 3q28 locus was associated with lung adenocarcinoma, which has been previously found in Asian populations (see below). Finally, the latest and largest lung cancer GWAS in individuals of European ancestry was performed in 29,266 cases and 56,450 controls (25). This GWAS highlighted the genetic heterogeneity across histological subtypes of lung cancer and reported novel loci for lung cancer per se (1p31.1, 6q27, 8p21.1, and 15q21.1) and adenocarcinoma (8p12, 10q24.3, 11q23.3, and 20q13.33). Previously reported lung cancer loci were more specifically associated with squamous cell carcinoma in this study including 6p21.33, 12p13.33, and 22q12.1.

GWAS in Asian populations.

Genetic heterogeneity in lung cancer susceptibility is observed between populations of European and Asian descent. For example, the strongest lung cancer susceptibility variants on 15q25 have very low allele frequencies in Asian populations. Similarly, variants on 6p21 found in Europeans are not polymorphic in Asians. Accordingly, GWAS specific for Asian populations were required.

In a Korean population, a GWAS on NSCLC revealed a new locus on chromosome 3q29 (26). This study also confirmed the 5p15 susceptibility locus in Koreans. Other GWAS in Asian populations have followed. Two susceptibility loci were identified in Japanese and Korean populations confirming 5p15 and elucidating a new locus on 3q28 (27). The 5p15 and 3q28 were subsequently confirmed in a larger GWAS in Han Chinese (28). In addition, two new loci on 13q12.12 and 12q12.2 were identified. In the same GWAS, but with an extended validation sample size, five new lung cancer loci were identified including 10p14, 5q32, 20q13.2, 5q31.1, and 1p36.32 (29). A subsequent GWAS specifically for lung squamous cell carcinoma in Han Chinese revealed a new locus on 12q23.1 (30). Using the exome genotyping chip, rare variants on 6p21.33, 20q11.21, and 6p22.2 were also found in the Chinese populations (31). On 6p21.33, two missense variants, one in PRRC1A (also known as BAT2) and the other in FKBPL, were independently associated with the risk of lung cancer, suggesting more than one genetic signal in this region. This study also demonstrated that 6p21.33 is also a susceptibility locus for Asian populations, but with different risk variants. The aforementioned GWAS in Japanese population (27) was later expanded in terms of sample size and SNP coverage to identify a new locus on 17q24.3 (32). The association with lung adenocarcinoma was also confirmed for 5p15, 3q28, and 6p21, but not for 13q12.12 and 22q12.2.

GWAS in never smokers.

Lung cancer in never smokers is known to be a distinct entity (33). The first GWAS on lung cancer in never smokers was reported in 2010 (34). A single locus on chromosome 13q31.3 was identified. The lung cancer-associated SNPs were located in the GPC5 gene and were also associated with mRNA expression levels of this gene in human lung tissues. A subsequent GWAS was performed in never-smoking females from Asia (35). The 5p15 locus was confirmed with an effect size greater than the estimates reported in populations of European background. Interestingly, the 15q25 and 6p21 loci were not associated with lung cancer in this study and no new loci were identified. In a larger GWAS of the same population forming the Female Lung Cancer Consortium in Asia, new susceptibility loci were revealed at 10q25.2 and 6q22.2 (36). The 6p21 was also associated with lung cancer in this study, but significant markers were not in LD with those previously reported, suggesting again more than one independent genetic signal at 6p21. This study in Asian females also confirmed other loci reported before including 5p15, 3q28, and 17q24.3. A recent meta-analysis was reported with an extended sample size of the Female Lung Cancer Consortium in Asia (37). A new locus on 12q13.13 was identified as well as genetic variants not correlated with lung cancer-SNPs previously associated with lung cancer on 6p21.1 and 9p21.3. In never smokers from Korea, a new NSCLC locus on 18p11 was identified (38). The 2p16.3 locus was also suggested in non-smoking Korean women (39). However, previous loci identified in never smoker populations were not replicated in these Korean studies including 5p15 and 13q31.3.

Pathway-based GWAS.

Pathway-based analyses have been used to identify lung cancer loci. Using GWAS data, genes listed under the category of inflammation were evaluated by lung cancer histological subtypes (40). This analysis identified a risk locus on chromosome 12p13.33 harboring the RAD12 gene. A similar approach was used to evaluate SNPs in inflammatory-pathway genes in lifetime never smokers (41). SNPs on chromosome 12q13 in the ACVR1B and NR4A1 genes were associated with lung cancer, particularly in women and those who reported environmental tobacco smoke exposure. Focused on DNA repair genes, a recent study revealed variants in GTF2H4 on 6p21 and in XRCC4 on 5q14.2 associated with lung cancer risk (42). Pathway-based analyses of GWAS data have also identified groups of genes linked by known biological pathways (ABC transporters, VEGF signaling, G1/S check point, and NRAGE signals death through JNK) that were modestly, but coordinately associated with the risk of developing lung cancer (43).

Variant prioritization approaches.

New lung cancer loci were also revealed by incorporating an intermediate phenotype, i.e. smoked cigarettes per day, into the analyses (44). By combining the estimates derived from the case-control analysis and the intermediate phenotype, a stronger signal was observed on 15q25 locus compared to the case-control study alone. Genetic associations with lung cancer were also detected on 19q13 and 3p26, which demonstrated improved power to identify genetic loci by combining different types of data from a single population. Studying cohorts of patients well-characterized for lung cancer may thus be very promising using this approach. A similar approach was used by assigning higher priors to SNPs associated with family history of lung cancer (45). By focusing on SNPs missed by traditional GWAS, this study identified 30 variants that showed evidence of association with lung cancer risk. The strongest associations were found on 10q23.33 and 4p15.2. Biological priors within a Bayesian framework were also applied to histology-specific analyses (46). In this study, the 4p15.2 locus was assigned more specifically to squamous cell carcinoma and a new adenocarcinoma locus was identified on 18q12.1.

GWAS-by-exposure interaction.

Accounting for environmental exposure is challenging owing to the large number of possible factors as well as the level of measurement accuracy that can be achieved for each exposure. Despite these challenges, some genome-wide gene-environment interaction studies are starting to emerge in the field of lung cancer. The first attempt of a genome-wide gene-smoking interaction study identified two SNPs on 14q22.1 and 15q22.32 influencing the risk of lung cancer (47). For asbestos exposure, interacting loci were suggested on 2q34, 7q32.1, and 11q13 (48). A risk locus for asbestos-associated lung cancer was also discovered on 22q13.31 (49). Interacting loci with household air pollution caused by solid fuel burning for heating and cooking were also evaluated in never smoker women from Asia (50). Interestingly, interactions were reported for GWAS-nominated loci previously identified in this population (36), but no new loci reached significance at the genome-wide scale level. Exploratory analyses of gene-occupation interactions in determining lung cancer susceptibility were also performed for 17 established or suspected lung carcinogens and 49 additional occupational agents (51). A large number of gene-environment interactions were reported in that study. However, the results could not be validated in an independent population because of the uniqueness of the dataset with detailed occupational exposure data. So far, results from genome-wide gene-environment studies in lung cancer have been more hypothesis-generating owing to limited sample size and power as well as the lack of appropriate replication sets. To make further progress, extra care will be needed to build large cohorts that are well-characterized for environmental exposures.

Genome-wide epistasis.

The effects of genetic variants on lung cancer are likely to be amplified when multiple variants synergize together. Gene-gene interactions may identify genetic determinants of lung cancer. The first and only genome-wide two-locus interaction analysis performed so far revealed a significant interaction between two SNPs 60 kilobases apart on 2q32.2 (52). Individually, the two interacting SNPs were not significantly associated with the risk of lung cancer. Further investigations of gene-gene interactions will be needed to understand the genetic architecture of lung cancer.

Cross-cancer susceptibility loci.

Large-scale GWAS across cancer sites have been conducted to identify pleiotropic loci. For lung cancer, the first pleiotropic locus was identified on 5p15 (TERT-CLPTM1L) (53). A novel pleiotropic association at 7p15.3 was found in Han Chinese involving lung cancer, non-cardia gastric cancer, and esophageal squamous-cell carcinoma (54). The GAME-ON/GECCO Network on lung, ovary, breast, prostate and colorectal cancer then identified novel pleiotropic associations involving lung cancer on 12q24 (55) and 1q22 (56). Known lung cancer loci were also identified in cross-cancer analyses including 6p21 (54) and 5p15 (55) as well as 9p21.3 and 13q13.1 (56). These loci are particularly promising to reveal shared carcinogenesis mechanisms across multiple cancer sites.

Integration of GWAS on lung cancer susceptibility

Excluding gene-environment loci that are more suggestive at this point, GWAS reported 45 loci associated with lung cancer. Figure 1 shows the chronological and cumulative number of lung cancer susceptibility loci identified. Loci are also listed based on chromosome number in Table 3. Note that these loci are an evolving list. The strength of evidence for association with lung cancer and effect size vary by loci (Figure 2). Evidence supporting some loci is relatively modest and will require validation in independent studies. The magnitude of genetic associations reported in publications also varies within loci. For example, the largest OR for chromosome 15q25 was 7.2 reported in familial form of lung cancer with a relatively modest p value of 1.03 × 10⁻³ (17). On the other hand, the same locus was reported highly significant (p = 3.08 × 10⁻¹⁰³) with an OR of ~1.3 (25), which is an effect size more consistent with most studies on sporadic form of lung cancer. The maximum odds ratio and p value per locus as well as variability between studies are illustrated in Figure 2. Refining susceptibility loci by clinically relevant subgroups is a critical step to reveal functional variants and causative genes (57). Table 3 summarizes the evidence supporting the specificity of lung cancer risk loci by histology, smoking status, gender, ethnicity, and age of onset. For example, convincing evidence supports that the 5p15 locus is specific to lung adenocarcinoma and more strongly associated with never smokers and women (21, 22, 28). Similar genetic association patterns are also emerging for 2p16.3, 5q32, and 6q22. Accordingly, a number of studies have started to delineate the effects of genetic variants in specific subgroups of patients with lung cancer, which is important to reveal the true nature of genetic effects detected in GWAS and narrow the set of genetic variants and genes worthy of functional studies. It is also important to know whether independent variants in the same loci are associated with lung cancer. Convincing evidence supports at least two independent loci on 15q25, 5p15, and 6p21 (Table 3). Two independent loci were also reported at 9p21.3 (37) and 22q12.2 (28), but will require further validation. This knowledge is lacking for other lung cancer loci. Table 3 also provides a glimpse of suspected causal genes at each locus. Further functional and biological analyses will be needed to understand the role of these genes in lung cancer development.

Figure 1.

Chronological and cumulative lung cancer susceptibility loci identified by GWAS. New loci identified yearly are on a black background. The cumulative number of loci is shown with grey background.

Table 3.

Lung cancer susceptibility loci derived from GWAS, genetic risk specificity by subgroups, and suspected causal genes

Abbreviations: AD: adenocarcinoma; a: Asians; e: Europeans; m: men; n: never-smokers; o: older; s: smokers; SQ: squamous cell carcinoma; w: women; y: younger. The color of the background illustrates the strength of evidence on a black and white scale where black indicates convincing evidence and white indicates no evidence so far. The strength of evidence was assigned based on the content of publications cited in the first column and our best possible judgment and comprehension of each locus considering the number of studies that replicated the associations, the level of statistically significance, and the quality of the studies, e.g. sample size. No evidence (white) does not delineate the lack of associations that have been studied from those not yet examined and also highlights knowledge gaps.

Figure 2.

Magnitude of the genetic associations for the 45 lung cancer susceptibility loci reported in the literature. P values and odds ratio for key SNPs indicated in Tables 1 and 2 were collected from reported studies. The upper and lower panels show p values and odds ratio, respectively. The x-axis shows the 45 lung cancer susceptibility loci ordered based on chromosome position. Bars illustrate the maximum p value (smallest p values) or the maximum OR reported per locus. Tick symbols inside bars are the results of the individual studies. For the upper panel, the y-axis represents p values in -log10 scale. The dashed horizontal line shows the typical GWAS significance threshold, i.e. 5 × 10⁻⁸. For the lower panel, the y-axis represents odds ratio. Note that OR lower than 1 were converted into their reciprocal (1/OR) for illustration purpose. OR for locus 3p26 was not available.

GWAS on lung cancer survival

Inter-individual differences in lung cancer survival are observed among lung cancer patients, even among those with the same tumor stage and treatment regimen. The identification of genetic factors associated with lung cancer survival has the potential to guide adjuvant therapy after surgery in early-stage disease, but also to refine prognosis and personalize clinical care in advanced-stages disease. So far, GWAS on lung cancer survival were performed in patients with early-stage NSCLC (58, 59), advanced-stage NSCLC (60–63), and SCLC (64). GWAS were also performed in more specific subgroups of lung cancer patients including never smokers with NSCLC (65) and patients with lung adenocarcinoma (66). Together, these studies have identified 23 loci associated with lung cancer survival (Supplementary Table S1). However, none of these loci was reported in more than one study. The lack of replication may be explained by heterogeneity in treatment regimens. There is also no overlap with GWAS lung cancer susceptibility loci. While the 9p21.3 locus was associated with both susceptibility (22, 37) and survival (62), sentinel SNPs are located more than 1 Mb away from each other, indicating that they are likely not reflecting the same association. GWAS susceptibility loci were specifically evaluated for association with survival in SCLC (67). Briefly, three loci on 20q13.2, 22q12.2, and 5p15 demonstrated some evidence of association with survival. However, none reached genome-wide significance. It should be noted that clinical follow-up of patients are needed to conduct survival analyses and GWAS based on this outcome have thus been performed with much smaller sample sizes compared to studies focused on cancer susceptibility. Larger-scale studies are needed to identify robust lung cancer survival loci.

GWAS on response to lung cancer therapies

Somatic alterations in the tumor genome are known to modulate the response to anticancer therapy. Less is known about the influence of the host genome on treatment response. The effect of germline variants on sensitivity and toxicity to platinum-based chemotherapy have been examined by GWAS. In patients with SCLC, seven loci demonstrated some evidence of association with treatment response in a discovery set, but were not convincingly replicated in a validation set (68). In NSCLC patients, a locus on 21q22.3 was associated with platinum-induced hepatotoxicity (69) and two loci on 2q24.3 and 17p12 were associated with the risk of platinum-induced myelosuppression (70). More and larger studies are needed to effectively delineate chemosensitive patients that will benefit from treatment and nonresponders that may be spared the adverse side effects associated with chemotherapy.

Future directions

Important progress was made to understand host susceptibility to lung cancer using GWAS. This approach is also starting to reveal inherited variants associated with lung cancer survival and response to treatment. During the last decade, progress was driven by enlarging sample sizes, improving methods to genotype and impute SNPs with more comprehensive reference sets (e.g. 1000 Genomes Project), and the creation of large-scale international collaborations and consortia. Additional developments were made by studying patients of different ancestries, never smokers, women, and specific lung cancer histology. Progress was also made by refining results by pathway-based analysis and variant prioritization approaches. The next important step in the field of genomics of lung cancer is to identify the causal genetic variants and genes underpinning GWAS-nominated loci. In addition, the new genomic knowledge must be translated into real benefits for patients. These must be achieved if GWAS are to pay off the huge investment. We foresee different strategies to reach these goals.

eQTL & TWAS.

We need to continue to mine GWAS data using more advanced statistical techniques that leverage other sources of data. So far, expression quantitative trait loci (eQTL) mapping studies in a variety of tissues have been used to extent the functional meaning of GWAS in lung cancer (23, 25, 29, 30, 32, 34, 37, 45, 56, 71). More comprehensive methods of colocalisation of GWAS and eQTL signals were recently developed and must be performed to reveal genetic associations explained by regulatory effects on gene expression (72). The identification of lung cancer-associated genetic variants associated with the expression of specific genes in a disease relevant tissue is an important step forward to understand the molecular mechanisms underpinning GWAS signals. In addition, the relationships between genetic variants, RNA expression levels and lung cancer must be further delineated by causality models and Mendelian randomization approaches (73, 74). Large-scale lung cancer GWAS (25) and lung eQTL (75) are also available to perform the first transcriptome-wide association study (TWAS) in lung cancer. In this approach, the cis genetic component of expression derived from the eQTL dataset is used to impute expression data for cases and controls used in the GWAS. Imputed genome-wide gene expression levels of sample size orders of magnitude larger than any of the transcriptomic datasets generated so far can then be used to identify genes whose expression is significantly associated with the disease. This approach has the potential to elucidate the most likely molecular drivers of lung cancer in GWAS-nominated loci, but also yield molecular drivers of lung cancer outside GWAS loci. GWAS, eQTL and TWAS results will also need to be integrated with genes differentially expressed in lung tumor compared to adjacent non-tumor lung tissues. For example, we have recently derived a robust list of genes differentially expressed in lung tumor from our own transcriptomic dataset (76) as well as two publically available datasets (77, 78). These results identified genes consistently deregulated in lung tumor and revealed important insights about the molecular transitions that occur between normal and tumor lung tissues. Accordingly, very promising research is underway exploiting GWAS and gene expression datasets to identify causal genes and molecular drivers of lung cancer.

Deep molecular profiling and biobanking.

To make further progress, relevant tissues must be profiled beyond gene expression. GWAS variants of lung cancer may not exert their effects through gene regulation, but other molecular phenotypes such as protein expression, protein state, metabolite levels, and epigenetic marks. Accordingly, deep molecular profiling of human lung tissues will be needed to comprehend the molecular impact of inherited variants on lung cancer. Current biobanking activities to collect high-quality and large numbers of well-annotated lung specimens are the essence of this future development.

Exposome.

As depicted in Table 3, the independent contribution of GWAS loci on lung cancer, smoking behavior and nicotine dependence is still not clearly delineated. Larger-scale gene-exposure interaction studies with established environmental risk factors including tobacco smoke and solid fuel burning are warranted. More comprehensive assessment of environmental factors including radon, asbestos, household and outdoor pollution, and occupational agents will be critically important, but at the same time very challenging to measure accurately in large sample size. A well-orchestrated community effort thus seems necessary (79).

Exome and genome sequencing.

New genomic approaches from next-generation of sequencers are also expected to refine GWAS loci and discover new variants unlikely to be found by GWAS. To this effect, whole-exome sequencing in three members of a five-generation family affected by lung cancer has revealed a rare variants in PARK2 resulting in a loss-of-function of this tumor suppressor gene (80). Although rare, the effect size of this mutation was greater than those reported in GWAS. Similarly, exome-sequencing of sporadic and familial cases of lung cancer identified rare deleterious mutations in GWAS-nominated loci located in the CDC147 and DBH (81) genes. Whole-genome sequencing in a family with very high aggregation of lung adenocarcinoma revealed a functional missense variant in the oncogene YAP1 (82) associated with the risk of developing the disease. We expect this type of discoveries using exome and genome sequencing to multiply in a near future.

En route for a genetic risk score.

Identified lung cancer susceptibility loci provide hope to build tools for targeted screening of high-risk individuals. To date, cumulative effects of loci have shown promising results to improve the discriminatory performance of risk prediction models, but not sufficiently to merit clinical implementation (83). For example, a recent report combining GWAS loci demonstrated only small improvement in lung cancer risk prediction in models including basic clinical factors such as age and smoking (84). Interestingly, the best model may not come from considering only the top GWAS loci. A genetic risk score built from seven telomere-length associated genetic variants was associated with lung cancer risk (85). More recently, it was demonstrated that the cumulative effects of susceptibility variants were better predictors when organized in biological pathways (86). These examples demonstrated the variety of strategies that are currently used to develop new clinical tools to predict lung cancer. Such tools are urgently needed to enable earlier diagnosis. The task is challenging and will require major efforts, but seems more realistically feasible with the outcomes of GWAS in hands. We hope that this compendium of lung cancer GWAS loci will facilitate further progress in building a clinically useful genetic risk score.

Conclusions

Understanding the genetic factors underlying the development of lung cancer is important to elucidate the etiology of the disease. This genetic knowledge is a prerequisite to develop and improve future clinical strategies for lung cancer management. Discovered loci summarized in this review testify progress made in this field during the last decade. This review also highlights knowledge gaps about causal variants and genes responsible for the underlying genetic associations and proposes some short term solutions to ensure further progress through eQTL, colocalization, causality models and TWAS. The specificity of many lung cancer loci in terms of histological subtypes, gender and ethnicity have been discovered for some loci, but will demand large studies with well-characterized individuals for others. Although smoking and other environmental factors, notably solid fuel burning, are clearly interacting with host factors to cause the disease, the specific variants that come into play are still elusive. Preliminary data provides some clues about inherited variants associated with lung cancer survival and response to treatment, but will require validation in larger-scale studies. On the other hand, robust genetic factors associated with lung cancer derived from GWAS give hope for possible clinical translation. In short term, a genetic risk score to screen high-risk individuals seems realistically achievable and would allow more effective treatments available at earlier stages of the disease. In mid and longer terms, discovering the causal genes underpinning GWAS signals will propel results one step closer to clinical applications by revealing new therapeutic targets and biomarkers to personalize quality of care.

Abbreviation lists

eQTL

expression quantitative trait loci

GWAS

genome-wide association study

LD

linkage disequilibrium

Mb

megabase

NSCLC

non-small cell lung cancer

SCLC

small cell lung cancer

SNP

single nucleotide polymorphism

TWAS

transcriptome-wide association study