Genetic polymorphisms in 15q25 and 19q13 loci, cotinine levels and risk of lung cancer in EPIC

Abstract

Backgrounds

Multiple polymorphisms affecting smoking behavior have been identified through genome-wide association (GWA) studies. Circulating levels of the nicotine metabolite cotinine is a marker of recent smoking exposure. Hence, genetic variants influencing smoking behavior are expected to be associated with cotinine levels.

Methods

We conducted an analysis in a lung cancer (LC) case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. We investigated the effects of SNPs previously associated with smoking behavior on i) circulating cotinine, and ii) LC risk. 894 cases and 1805 controls were analyzed for cotinine and genotyped for 10 polymorphisms on 7p14, 8p11, 10q23, 15q25 and 19q13.

Results

Two variants in the nicotinic acetylcholine receptor subunit genes CHRNA5 and CHRNA3 on 15q25, rs16969968 and rs578776, were associated with cotinine (P=0.001 and 0.03, respectively) in current smokers, and with LC risk (P<0.001 and P=0.001, respectively). Two 19q13 variants, rs7937 and rs4105144 were associated with increased cotinine (P=0.003 and <0.001, respectively), but decreased LC risk (P=0.01 for both, after adjusting for cotinine). Variants in 7p14, 8p11 and 10q23 were not associated with cotinine or LC.

Conclusions

15q25 and 19q13 SNPs were associated with circulating cotinine. The directions of association for 15q25 variants with cotinine were in accordance with that expected of LC risk, whereas SNPs on 19q13 displayed contrasting associations of cotinine and LC that require further investigation.

Impact

This study is the largest to date investigating the effects of polymorphisms affecting smoking behavior on lung cancer risk using circulating cotinine measures as proxies for recent smoking behavior.

INTRODUCTION

Smoking is the main risk factor for lung cancer, accounting for nearly 85% of cases in men and 50% of cases in women worldwide (1,2). Smoking exposure is usually assessed through questionnaires (e.g. cigarettes per day (CPD) and duration of smoking); measures that have several limitations (3). Circulating levels of cotinine, the nicotine metabolite, provide an accurate measure of recent tobacco smoke exposure and are able to account to some degree for individual differences in smoking practices, such as depth of inhalation and how completely each cigarette is smoked (4,5). Cotinine has a half-life of approximate 17 hours and reflects active and second-hand smoking and smoking intensity over the last 1-2 days (4,5).

Several genome-wide association (GWA) studies on smoking behavior have identified multiple loci associated with the number of cigarettes smoked per day (CPD) and other measures of tobacco addiction (6-13). Genetic variants influencing CPD would be expected to have a more prominent effect on cotinine levels in current smokers, and such variants are also expected to influence the risk of lung cancer.

The 15q25 locus encodes the nicotinic acetylcholine receptor (nAChR) subunit α5, α3 and β4, members of the family of ligand-gated ion channels, which play an important role in the development of nicotine addiction (14,15). Nicotine binds to the nAChR causing its activation and the release of neurotransmitters. Variants on the 15q25 locus are associated with increased vulnerability to tobacco addiction, changed smoking behavior including increasing CPD (7,8,12,16), and were also identified as the main susceptibility locus in several lung cancer GWA studies (13,17,18). Some other loci associated with CPD identified through GWAS, including the CHRNB3 - CHRNA6 locus on 8p11, the CYP2A6 - CYP2B6 locus on 19q13 and the 7p14 locus, have also been found to be associated with a small increase in lung cancer risk (8).

The effects of these loci on lung cancer risk might be mediated by their effect on smoking behavior. However, in the case of the 15q25 locus, adjusting for self-reported smoking (smoking status, pack years, CPD) only partially attenuates the risk effect(18,19) and the remaining ~30% increase in risk observed per risk allele appears to be in excess of that expected from the increase in CPD conferred by the missense variant. Nevertheless, as CPD is a crude measure of how 15q25 variants influence propensity to smoke, additional aspects of smoking such as differences in inhalation may explain this association. Using cotinine measurements together with self-reported information might increase the reliability of smoking exposure data and allow for a more thorough (although by no means complete) adjustment for recent smoking behavior.

In order to investigate how loci modifying smoking behavior influence circulating cotinine levels and lung cancer risk, we conducted an analysis within a nested lung cancer case-control study from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Circulating cotinine level measured in serum or plasma was included as a proxy of pre-diagnosis smoking behavior, together with traditional questionnaire-based smoking measures.

MATERIAL AND METHODS

Study description

The current case-control study was nested within the EPIC cohort, which is an ongoing multi-centre prospective study that recruited more than 520,000 healthy individuals between 1992 and 2000. Baseline non-dietary and dietary questionnaires were completed at enrolment, as well as anthropometric measurements and blood samples which were collected during an enrolment examination at a study centre. Detailed study descriptions of recruitment, follow-up, collection of questionnaire data and blood samples in EPIC have been provided elsewhere (20).

The current EPIC-lung study, including selection criteria, has also been described in detail previously (18,21,22). The study included lung cancer cases diagnosed on average 62 months, and a minimum 1 of month, after blood collection together with matching controls from eight of the ten participating countries: France, Italy, Spain, United Kingdom, The Netherlands, Greece, Germany and Sweden (excluding the Malmö centre). Where possible, two controls were matched to each case by study center, gender, date of blood collection (± 1 month, relaxed to ± 5 months for sets without available controls), and date of birth (± 1 year, relaxed to ± 5 years for sets without available controls). Overall, 894 cases and 1805 controls were included in the analysis (Table 1). Information on tobacco consumption was collected in a non-dietary questionnaire as a part of the recruitment procedure in the EPIC cohort. Study participants were classified as never, current or ex-smokers. Duration of smoking was calculated based on collected information on age of smoking initiation and age at recruitment for current smokers or age at smoking cessation for ex-smokers. Additionally, information on the number of cigarettes currently smoked and smoked at ages 20, 30, 40, and 50 was collected. Based on this information, the average number of cigarettes per day (CPD) was calculated. Information on smoking interruptions was available only for four coordinating centres in EPIC and therefore not taken into account.

Table 1.

Distribution of Selected Demographic Variables by Case-Control Status in the EPIC Lung Cancer Study

		All cases and controls
		Controls		Cases
		N	%	N	%
All		1,805		894
Smoking statusa	never smokers	705	39.8	96	10.9
	former smokers	659	37.2	258	29.3
	current smokers	409	23.0	526	59.8
Gender	men	1,117	61.9	556	62.2
	women	688	38.1	338	37.8
Ageb	<40	37	2.1	19	2.1
	40-49	276	15.3	133	14.8
	50-59	722	40	355	39.7
	60-69	610	33.8	307	34.3
	>70	160	8.9	80	9.0
Country	France	48	2.7	24	2.7
	Italy	278	15.4	139	15.6
	Spain	259	14.4	130	14.5
	United Kingdom	355	19.7	175	19.6
	Netherlands	241	13.4	121	13.5
	Greece	186	10.3	90	10.1
	Germany	312	17.3	157	17.6
	Sweden	126	7.0	58	6.5
Histology	SCLC			108	12.1
	Adenocarcinoma			270	30.2
	LCLC			50	5.6
	SCC			199	22.3
	Otherc			29.87	26.7

Abbreviations: EPIC, European Prospective Investigation into Cancer and Nutrition study; LCLC, large cell lung carcinoma; SCC, squamous cell carcinoma; SCLC, small cell lung carcinoma

^aInformation on smoking status was missing for 32 controls and 14 cases

^bAt the date of blood collection

^cIncluding missing histology for French study

All participants gave written informed consent to participate in the study, which was approved by the local ethics committees in the participating countries and the Institutional Review Board of the International Agency for Research on Cancer (IARC).

Genotyping methods and biochemical analysis

Biochemical measurement of cotinine was performed at Bevital A/S, Bergen, Norway. Cotinine levels were measured in serum by liquid chromatography-tandem mass spectrometry(23). For the Swedish cohort cotinine levels were measured in plasma. The laboratory coefficients of variations were 2.0 – 3.0% for repeated analyses within the same day, and were approximatelly 6% between-days. Cases and controls were analyzed in random order and laboratory personnel were blinded to case-control status.

We reviewed the literature and identified several GWAS investigating smoking behavior, as assessed by CPD, as an outcome (6-10,12,13). Overall 10 single nucleotide polymorphisms (SNPs) from 5 loci which were found to be genome-wide significant for CPD: 7p14, 8p11, 10q23, 15q25 and 19q13 were selected for genotyping (Table 2). Genotyping was performed using the 5′ exonuclease assay (TaqMan) at IARC. PCR primers and TaqMan probes were synthesized by Applied Biosystems (Foster City, CA, U.S.A.). Highly correlated proxies (r²=1.0) were genotyped in place of assays that were unable to be designed as TaqMan assays. Only one SNP of any correlated group of variants (r²>0.5) was genotyped.

Table 2.

Characteristics of SNPs Selected for Genotyping

SNPs	Locus	Gene	minor allele	MAF	Previously observed effects of SNPs on CPD (8)(6) or CPD levels (1-10 CPD, 11-20, 21- 30 and >=31) (7)
					ß	P	Ref
rs215614a	7p14	PDE1C	G	0.36	0.22	2*10−7	(8)
rs13273442b	8p11	CHNB3	A	0.23	−0.29	4*10−8	(8)
rs1329650	10q23	LOC100188947	T	0.29	−0.37	6*10−10	(6)
rs1028936	10q23	LOC100188947	C	0.19	−0.45	1*10−9	(6)
rs16969968	15q25	CHRNA5	A	0.39	1.00 0.08	6*10−72 4*10−65	(6) (7)
rs578776	15q25	CHRNA3	A	0.27	−0.06	7*10−37	(7)
rs4105144	19q13	2Kb 5′ CYP2A6	T	0.35	−0.39	2*10−12	(8)
rs3733829	19q13	EGLN	G	0.36	0.33	1*10−8	(6)
rs7937	19q13	UTR RAB4B	C	0.46	−0.24	2*10−9	(8)
rs7260329	19q13	Intron, CYP2B6	T	0.32	−0.20	5*10−6	(8)

Abbreviations: CPD, number of cigarettes per day; MAF, Minor Allele Frequency; SNP, single-nucleotide polymorphism

^aGenotyped instead of proxy rs215605 (r²=1.0; d′=1.0 )

^bGenotyped instead of proxy SNPs rs6474412 (r²=1.0; d′=1, effect on CPD ß= 0.30, s.e.=0.05, P= 1.7 × 10^{−8 ((8))}) and rs13280604 (r²=1.0; d′=1.0, effect on CPD ß= 0.31, s.e.=0.05, P= 1.3 × 10^{−8 ((8))}).

Cases and controls were randomly mixed when genotyped and laboratory staff were blinded to case-control status. A random selection of 5% of the study subjects were genotyped twice for quality control. Genotyping success rate per SNP in the present study ranged between 93% and 100%. Internal duplicate concordance was >98.7% for all variants. All variants showed genotype distributions consistent with that expected under Hardy-Weinberg equilibrium (HWE) using a P threshold of 0.005 (Bonferroni correction for 10 tests).

Statistical methods

The distribution of cotinine levels between smoking cases and controls were compared using the Kruskal-Walis test. The associations between SNPs and cotinine levels or CPD were investigated in current smokers using multivariate linear regression models with smoking variables (CPD or cotinine) as outcomes, adjusting for study center, gender and case-control status. The mean cotinine levels adjusted for study center, gender and case-control status were calculated for each genotype.

Risk analysis was performed using conditional logistic regression by estimating odds ratios (ORs) and their 95% confidence intervals (CI). Risk effects of smoking measured as cotinine and CPD on lung cancer risk were analyzed for ten categories of increasing cotinine levels (76-200 nmol/L, 201-400 nmol/L, 401-600 nmol/L etc) with subjects with cotinine levels below 75nmol/L as a reference category equivalent to never smokers(24), and also for deciles of CPD defined by control individuals using never smokers as a reference category. ORs for SNPs were calculated using the per rare allele log-additive model as overall significance test (P). We subsequently adjusted for various smoking variables, including cotinine levels, average CPD, and duration of smoking. We conducted exploratory analysis using two models: i) adjusted by quartiles of smoking variables defined by the distribution among corresponding controls and ii) adjusted by continuous smoking variables. The results for both models are presented. Further, unconditional logistic regression models were used to allow stratification by smoking status (current, former and never smokers), adjusting for matching variables (gender, date of birth, date of blood collection and country). To investigate if the risk effect of genotypes is constant across different levels of smoking exposure we tested for multiplicative interaction of genotype with quartiles of cotinine levels/CPD. Likelihood ratio tests, comparing the models with and without the interaction terms, were used to evaluate statistical significance.

The nominal and reported significance level for the present study was set up to α=0.05. Regression calibration was used to correct for some of the dilution effects due to day-to-day variation in cotinine levels. We obtained repeat measurements one and 3 years apart for 502 individuals, including 96 current smokers who had not changed their smoking status, from the placebo arm of a randomized trial from Norway (WENBIT)(25) . The samples were analyzed in the same laboratory and using the same protocol as the EPIC samples. We used these measurements to estimate the within-individual variance of cotinine, assuming that the long term average was the ideal predictor of lung cancer. This allowed us to calculate regression dilution ratios (RDR) and obtain the adjusted ORs for the effect of cotinine on lung cancer risk by multiplying the observed regression coefficients with the RDR, as described by Clarke et al.(26). To account for the effect of regression dilution in the adjustment of the SNPs OR’s for lung cancer we applied the method described by Rosner et al.(27), modified to the fact that the genotype data were not available for the participants with repeated cotinine measurements. Further details are provided in the Supplementary Methods.

All statistical analyses were conducted using SAS version 9.2 (Raleigh, NC, USA). Power calculations were performed using QUANTO version 1.2 for the main effect of gene and log-additive model of inheritance (28).

RESULTS

Genetic variation, circulating cotinine and cigarettes per day

The effect of SNPs on cotinine levels and CPD were investigated among current smokers only (n=935). We did not observe a significant association of any of the investigated SNPs with CPD (Table 3). In contrast, increased cotinine levels were associated with the minor alleles of rs578776 and rs16969968 on 15q25 (P_trend=0.03, and P_trend=0.001, respectively), as well as rs4105144 and rs7937 on 19q13 (P_trend=0.0001, and P_trend=0.003, respectively, Table 3). The direction of effects of 15q25 variants on circulating cotinine levels were consistent with that expected based on previously published results for CPD (Table 2). In contrast, the 19q13 variants showed opposite effects on circulating cotinine level compared to those reported for CPD previously (Table 2). No other SNPs were significantly associated with cotinine levels.

Table 3.

Cotinine Level and CPD per Allele in Current Smokers in the EPIC Lung Cancer Study

SNP	Expected effect on CPD and cotinine(7,8)	CPD (n)		Cotinine level (nmol/L)
		N	Mean (95% CI)	N	Mean (95% CI)
rs215614 (7p14, PDE1C)
AA	Low level	280	16.8 (15.7-17.9)	3	1317.1 (1205.3-1429)
AG		352	15.7 (14.7-16.7)	4	1268.4 (1159.6-1377.3)
GG	High level	88	15.9 (14-17.7)	1	1269.9 (1120.5-1419.3)
	Trend testa:	ß=−0.7, s.e.=0.5, P=0.15		ß=−31.7, s.e.=32.2, P=0.33
rs13273442 (8p11, CHRNB3)
GG	High level	447	16.3 (15.4-17.2)	5	1295.2 (1190.9-1399.5)
GA		227	16.2 (15-17.4)	2	1268.5 (1156.2-1380.7)
AA	Low level	42	17.2 (14.7-19.7)	4	1328.5 (1127.5-1529.4)
	Trend testa:	ß=0.2, s.e.=0.5, P=0.72		ß= −6.4, s.e.= 35.6 , P= 0.86
rs1329650 (10q23)
GG	High level	383	16.1 (15.1-17)	4	1292 (1186.1-1397.8)
GT		255	16.4 (15.3-17.5)	3	1291.1 (1179.6-1402.6)
TT	Low level	64	17.1 (15.1-19.2)	7	1302.4 (1134.1-1470.7)
	Trend testa:	ß=0.5, s.e.=0.5, P=0.32		ß=2.8, s.e.=32.5, P=0.93
rs1028936 (10q23)
AA	High level	495	16 (15.1-16.9)	5	1298.4 (1194.9-1401.9)
AC		195	16.3 (15-17.5)	2	1254.4 (1135.1-1373.7)
CC	Low level	31	17.3 (14.5-20.2)	3	1215.6 (990.7-1440.5)
	Trend testa:	ß=0.4, s.e.=0.5, P=0.42		ß=−42.9, s.e.=37.9, P=0.26
rs16969968 (15q25, CHRNA5)
GG	Low level	280	15.9 (14.8-17)	3	1176.7 (1063.9-1289.4)
GA		348	16.4 (15.4-17.3)	4	1301 (1195-1406.9)
AA	High level	131	16.6 (15.2-18.1)	1	1357.1 (1231-1483.2)
	Trend testa:	ß=0.4, s.e.=0.4, P=0.35		ß=96, s.e.=28.8, P=0.001
rs578776 (15q25, CHRNA3)
GG	High level	394	16.7 (15.8-17.7)	4	1339 (1231.6-1446.4)
GA		281	15.6 (14.5-16.6)	3	1229.7 (1113.5-1346)
AA	Low level	32	15.3 (12.5-18)	3	1276 (1058.8-1493.1)
	Trend testaa:	ß=−1, s.e.=0.5, P=0.06		ß=−77.4, s.e.=36.2 , P= 0.03
rs4105144 (19q13, CYP2A6 )
CC	High level	335	16.1 (15.1-17.1)	3	1193.2 (1086.3-1300.2)
CT		287	16.5 (15.4-17.6)	3	1330.5 (1218.6-1442.3)
TT	Low level	81	16.4 (14.5-18.3)	1	1413.6 (1277.2-1550.1)
	Trend testa:	ß=0.2, s.e.=0.5, P=0.65		ß=118.3, s.e.=30.2, P=0.0001
rs3733829 (19q13)
AA	Low level	299	16.2 (15.1-17.2)	3	1312.1 (1201.5-1422.6)
AG		314	16.5 (15.4-17.5)	3	1283.1 (1172.9-1393.3)
GG	High level	98	15.9 (14.3-17.6)	1	1168.4 (1019.7-1317)
	Trend testa:	ß=−0.02, s.e.=0.4, P=0.97		ß=−59.5, s.e.=31.1, P=0.06
rs7937 (19q13, RAB4B)
TT	High level	227	16.3 (15.2-17.5)	2	1176.1 (1059.9-1292.2)
TC		339	16.3 (15.3-17.3)	4	1340.1 (1231.2-1448.9)
CC	Low level	153	16.3 (14.9-17.7)	1	1332.1 (1211.8-1452.3)
	Trend test*:	ß=−0.01, s.e.=0.4, P=0.98		ß=86.3, s.e.=29.3, P=0.003
rs7260329 (19q13, CYP2B6)
CC	High level	383	16.2 (15.2-17.1)	4	1275.2 (1168.3-1382)
CT		273	16.7 (15.6-17.8)	3	1304.6 (1194.1-1415.2)
TT	Low level	66	15.6 (13.5-17.6)	7	1320.1 (1156.6-1483.6)
	Trend testa:	ß=0.02, s.e.=0.5, P=0.97		ß=25.2, s.e.=32.2, P=0.43

Abbreviations: CPD, number of cigarettes per day; CI, confidence interval; EPIC, European Prospective Investigation into Cancer and Nutrition study; SNP, single-nucleotide polymorphism

^aLinear trends in CPD and cotinine levels were assessed by linear regression models adjusted for center, gender and case-control status.

R² between the SNPs are less than 0.50.

Circulating cotinine levels, cigarettes per day and lung cancer risk

In risk analysis both cotinine levels and CPD were positively associated with lung cancer risk (OR=1.34 95%CI 1.29-1.39 P_trend =2*10⁻⁵³ per decile of CPD , and OR=1.36 95%CI 1.3-1.4; P_trend =3*10⁻⁷³ per 200 nmol/L of cotinine. Figure 1, Supplementary Tables 1 and 2). The risk increased monotonically with increasing cotinine levels, and reached an OR of 19.6 (95%CI 12.5-30.8) for subjects having cotinine levels above 1800nmol/L. The estimated RDR taking into account within-person variation was 0.86 and correction for regression dilution resulted in notably higher ORs than from uncorrected measurements (Figure1). In contrast, the risk increase associated with CPD deciles reached a plateau at 20-21 CPD and the maximum observed OR was 16.4 (95%CI 10.3-26.1) for CPD levels of between 21 and 26.9 (Figure 1, Supplementary Table 1). Mutual adjustments of cotinine and CPD attenuated the maximum risk association for CPD from OR=16.4 to OR=6.5 which is a considerably greater attenuation than seen for the maximum odds ratio for cotinine (OR=19.6 to OR=12.4, Figure 1).

Figure 1.

ORs for the Risk of Lung Cancer by CPD (a.) and Cotinine Level (b.)

a. ORs for the risk of lung cancer for deciles of CPD are presented before adjustment (P_trend=2*10⁻⁵³) and after adjustment for cotinine level (P_trend=4.5*10⁻²⁰). Corresponding mean cotinine level for each percentile of CPD is given. Non smokers were used as reference group. Corresponding ORs and 95%CIs are presented in the Supplementary Table 1. The analysis includes only individuals with available cotinine and CPD measurements.

b. ORs for the risk of lung cancer for 200nmol/L intervals of cotinine level before P_trend=3*10⁻⁷³) and after correction for RDR and adjustment for CPD (P_trend=1.5*10⁻²⁹) are presented. Reference group for the cotinine level – individuals with less then 75nmol/L. Corresponding ORs and 95%CIs are presented in the Supplementary Table 2.

Genetic variation and lung cancer risk

Both rs16969968 (OR=1.31, 95%CI 1.16-1.48, P<0.001)(18) and rs578776 on 15q25 (OR=0.79, 95%CI 0.69–0.91,P=0.001) were associated with risk (Table 4), as well as rs7937 on 19q13 (OR=0.88, 95%CI 0.77-1.00, P=0.05, Table 4). No other SNPs showed evidence of association with lung cancer risk.

Table 4.

ORs and 95%CI for the Risk of Lung Cancer for All Cases and Controls and for Current Smokers in the EPIC Lung Cancer Study

SNPs	Expected effect on lung cancer (8,13,17,18)	Effect allele	Cases	Controls	Unadjusted model		Model adjusted for cotininea		Model adjusted for regression -dilution bias corrected cotininea
					OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P
rs215614 (7p14, PDE1C)	↑	G	819	1,643	1.03 (0.91-1.18)	0.61	1.06 (0.91-1.23)	0.47	1.07 (0.91-1.24)	0.42
rs13273442 (8p11, CHRNB3)	↓	A	820	1,646	0.98 (0.85-1.14)	0.83	1.03 (0.87-1.22	0.73	1.03 (0.87- 1.23)	0.72
rs1329650 (10q23)	n/a	T	801	1,568	1.07 (0.93-1.24)	0.34	1.06 (0.89-1.25)	0.53	1.05(0.89-1.25)	0.55
rs1028936 (10q23)	n/a	C	819	1,649	1.02 (0.87-1.19)	0.85	1.04 (0.86-1.26)	0.67	1.05 (0.87-1.27)	0.62
rs16969968 (15q25, CHRNA5)	↑	A	868	1,749	1.31 (1.16-1.48)	<0.001	1.26 (1.09-1.45)	0.002	1.23 (1.06-1.42)	0.01
rs578776 (15q25, CHRNA3)	↓	A	812	1,658	0.79 (0.69-0.91)	0.001	0.85 (0.72-1)	0.06	0.87 (0.74-1.03)	0.10
rs4105144 (19q13, CYP2A6)	↓	T	810	1,635	0.93 (0.82-1.06)	0.29	0.87 (0.75-1.02)	0.09	0.86 (0.74-1.01)	0.06
rs3733829 (19q13)	no effect	G	811	1,647	1.05 (0.92-1.19)	0.46	1.14 (0.98-1.33)	0.09	1.15 (0.99-1.34)	0.07
rs7937 (19q13, RAB4B)	↓	C	811	1,638	0.88 (0.77-1.00)	0.05	0.84 (0.72-0.98)	0.02	0.83 (0.71-0.97)	0.02
rs7260329 (19q13, CYP2B6)	↓	T	821	1,640	0.90 (0.79-1.03)	0.12	0.98 (0.84-1.14)	0.76	0.99 (0.84-1.15)	0.86
current smokersb
rs215614 (7p14, PDE1C)	↑	G	484	368	0.95 (0.77-1.17)	0.62	0.99 (0.79-1.23)	0.92	1.03 (0.81-1.31)	0.81
rs13273442 (8p11, CHRNB3)	↓	A	480	373	0.98 (0.78-1.24)	0.90	1.00 (0.77-1.28)	0.98	1.01 (0.78-1.30)	0.96
rs1329650 (10q23)	n/a	T	470	364	1.16 (0.94-1.44)	0.18	1.14 (0.90-1.43)	0.28	1.08 (0.85-1.38)	0.53
rs1028936 (10q23)	n/a	C	482	375	1.05 (0.82-1.34)	0.72	1.07 (0.82-1.4)	0.62	1.11 (0.85-1.46)	0.44
rs16969968 (15q25, CHRNA5)	↑	A	511	398	1.39 (1.15-1.69)	<0.001	1.22 (0.99-1.5)	0.06	1.06 (0.77-1.46)	0.72
rs578776 (15q25, CHRNA3)	↓	A	469	375	0.68 (0.53-0.86)	0.001	0.74 (0.57-0.95)	0.02	0.84 (0.60-1.18)	0.32
rs4105144 (19q13, CYP2A6)	↓	T	480	365	0.85 (0.70-1.04)	0.12	0.74 (0.59-0.92)	0.01	0.65 (0.48-0.88)	0.005
rs3733829 (19q13)	no effect	G	475	371	1.13 (0.92-1.39)	0.23	1.23 (0.99-1.54)	0.07	1.31 (1.03-1.68)	0.03
rs7937 (19q13, RAB4B)	↓	C	482	371	0.85 (0.70-1.03)	0.10	0.76 (0.62-0.94)	0.01	0.71 (0.56-0.90)	0.005
rs7260329 (19q13, CYP2B6)	↓	T	486	366	0.85 (0.69-1.05)	0.14	0.8 (0.65-1.03)	0.09	0.81 (0.64-1.02)	0.07

Abbreviations: CI, confidence interval; EPIC, European Prospective Investigation into Cancer and Nutrition study; OR, odds ratio; SNP, single-nucleotide polymorphism

n/a - no available information on effect of the SNP on lung cancer risk.

ORs and 95%CIs for the SNPs were calculated using conditional to matching variables logistic regression

^aModel was adjusted by continous cotinine variable.

^bUnconditional logistic regression adjusted for matching variables (year of birth, year of blood donation, gender and country)

Adjusting for cotinine and CPD separately attenuated these associations to varying degrees (maximum attenuation 29% for adjustment of rs578776 for cotinine) , as did adjustments for both cotinine and duration of smoking (maximum attenuation a substantial 43% for adjustment of rs578776 for as-measured cotinine and duration of smoking, Figure 2). Adjustment for regression-dilution bias corrected cotinine led to attenuation from OR=1.31 to OR=1.23 for rs16969968 and from OR=0.79 to OR=0.87 for rs578776 (Table 4).

Figure 2.

Effect of 15q25 Locus (rs16969968 and rs57876) and 19q13 Locus (rs7937 and rs4105144) on the Risk of Lung Cancer

ORs and 95%CIs for the risk of lung cancer are calculated applying conditional logistic regression adjusted for quintiles of cotinine levels/CPD/duration of smoking. Effect of SNPs in smoking strata (current, former and never smokers) was calculated using unconditional logistic regression adjusted for matching variables (date of birth, country, date of blood collection and gender).

After stratifying by smoking status (current, former and never smokers), the associations of rs16969968 and rs578776 on 15q25 with risk were only present in current (OR=1.39, 95%CI 1.15-1.68, P<0.001, and OR=0.68, 95%CI 0.54-0.86, P=0.001, respectively), and former smokers (OR=1.31, 95%CI 1.05-1.63, P=0.01 for rs16969968 only, Figure 2), but not in never smokers (OR=1.18, 95%CI 0.84-1.65, P=0.32 and OR=1.07, 95%CI 0.76-1.51, P=0.70, for rs16969968 and rs578776, respectively). The risk effect in smokers seemed to be constant among different levels of smoking exposure measured as CPD (P_interaction=0.55 and P_interaction=0.11 for rs16969968 and rs578776, respectively) and cotinine (P_interaction=0.92 and P_interaction=0.36 for rs16969968 and rs578776, respectively). rs4105144 and rs7937 on 19q13 were robustly associated with lung cancer risk in current smokers only after adjusting for smoking (cotinine level, CPD, duration of smoking, Table 4, Figure 2). Similarly to SNPs on 15q25, no interaction between these two SNPs and levels of CPD (P_interaction=0.94 and P_interaction=0.88 for rs4105144 and rs7937, respectively) and cotinine (P_interaction=0.90 and P_interaction=0.20 for rs4105144 and rs7937, respectively) was detected in smokers in the present study. Among current smokers adjustment for as-measured cotinine led to attenuations of estimated effects by 44% for rs16969968 and 18% for rs578776; adjustment for regression-dilution bias corrected cotinine led to further attenuation of the estimated effect of the 15q25 locus (OR=1.06 95%CI 0.77-1.46 and OR=0.84 95%CI 0.6-1.18). Inversely, adjustment for cotinine and regression-dilution bias corrected cotinine enhanced the apparent effect of 19q13 SNPs (Table 4).

DISCUSSION

In the present study we investigated whether SNPs on 7p14, 8p11, 10q23, 15q25, and 19q13, previously found to be associated with CPD in GWAS, are related to circulating cotinine, a biomarker of recent smoking exposure, as measured in a prospective case-control study nested within EPIC. Only SNPs 15q25 and 19q13 loci had measurable effects on circulating cotinine levels, but showed no association with CPD. As previously shown (18), variants on 15q25 were also associated with lung cancer risk. Smoking exposure measures, both self-reported (CPD) and circulating cotinine levels, could only partly account for the risk associations of 15q25 variants. However, adjustment for regression-dilution bias corrected cotinine led to substantial attenuation of these estimates. An association with lung cancer risk opposite to that predicted by the association with circulating cotinine levels was detected for the 19q13 locus (rs7937 and rs4105144).

Cotinine and CPD as lung cancer risk predictors

CPD and other self-reported variables reflecting smoking behavior have been used extensively as measures of tobacco exposure in epidemiological studies of lung cancer, including studies on genetic factors. As tobacco smoking is the major risk factor for lung cancer(29), accurate measures of tobacco exposures are essential. However, it is known that assessing smoking exposure using questionnaires will be subject to missclassification(3,30). Studies on the relationship between questionnaire measures of tobacco exposure (e.g. CPD) and biomarkers of tobacco exposure (e.g. cotinine) (4,31-36) have reported a non-linear relationship, particularly among heavy smokers, suggesting misclassification at high CPD or differences in inhalation and other smoking styles between heavy and light smokers (37-39). Accordingly, in epidemiological studies lung cancer risk has been shown to steadily increase up to 20-30 cigarettes per day, but plateau for subjects reporting CPD above 20-30 (38), Consistently, the excess ORs of lung cancer risk for each pack-year of exposure was shown to increase with increasing intensity of smoking only for subjects who smoke up to 20 cigarettes per day (33). We observed similar results in the current study, where little excess in risk was noted for those reporting above 20 CPD (Figure 1). As expected, we also observed that cases reporting similar tobacco consumption levels had higher cotinine levels than controls, even after accounting for number of cigarettes smoked over the last 24 hours hours (mean cotinine level in controls adjusted for number of cigarettes smoked in last 24 hours = 1113 nmol/L vs mean cotinine level in cases = 1433 nmol/L; P < 0.001). In contrast with CPD, the relationship between cotinine and risk increased monotonically, consistent with previous observations reported by Boffetta et al.(37) Similarly, Yuan et al.(40) observed an association of cotinine with lung cancer risk among smokers with comparable smoking history, but no association was detected by Church et al.(41) in current smokers. In mutually adjusted analysis of cotinine and CPD, the association of cotinine with risk was considerably less attenuated than that of CPD. This is consistent with the notion that circulating cotinine captures other aspects of smoking behavior and dose than does CPD, such as inhalation depth and the degree to which each cigarette is smoked. However, the association of CPD remains substantial, suggesting that unlike circulating cotinine it has value for capturing past smoking behavior.

As with all biochemical measurements, cotinine levels are subject to both measurement error and normal day-to-day variations. In regression analysis these variations lead to regression dilution bias and subsequent underestimation of odds ratios(26,42). To correct for this bias we estimated RDRs by use of repeat samples. The RDR corrected ORs of cotinine were, as expected, notably higher than the corresponding uncorrected values, indicating that the underlying risk associated with cotinine is substantially underestimated (Figure 1).

Effect of the studied loci on CPD and circulating cotinine levels and lung cancer risk

Polymorphisms on 7p14, 8p11, 10q23, 15q25, and 19q13 have been associated with smoking behavior in GWAS, typically measured as CPD (6-8,12,13). However, in this study we did not detect any associations between previously implicated SNPs and CPD (Table 3), possibly due to the limited sample size. Indeed, the statistical power to detect the expected effect of SNPs on CPD ranged from 10% to 60% for the variants studied. Conversely, SNPs on 15q25 were clearly associated with cotinine levels, as well as lung cancer risk, consistent with the expected direction of association noted in the original GWAS. Similarly, an association with cotinine levels and other nicotine metabolites was previously described for the 15q25 locus (43). with the effect being stronger for cotinine than CPD(36).

This association of SNPs on 15q25 with lung cancer risk has been suggested to be mediated through changes in propensity to smoke tobacco(13,18). In the present study the estimated risk effect of 15q25 SNPs was attenuated to varying degrees when controlling for various smoking variables, including CPD, duration of smoking and cotinine levels. In this study adjustment for regression-dilution bias corrected cotinine in current smokers led to attenuation of the rs16969968 OR, thus supporting the hypothesis of the 15q25 association with lung cancer risk being mediated by smoking behavior. However, the regression-dilution method is not perfect and relies on several assumptions that may not hold. Firstly, the correction is estimated using measurements taken 3 years apart and assuming a constant mean rate. The issue is further complicated by our incomplete understanding of the relation between life-course smoking and lung cancer risk: a life-time mean may not be the ideal predictor even if we were able to estimate it with precision. In addition, our estimates of regression dilution were obtained from a distinct population, geographically unrepresentative of the EPIC Lung study, for which no genotype data was available. Our method also assumes that the extent of day-to-day variation in smoking is independent of genotype, which may not be correct. Taking these limitations together our regression-dilution corrections may be either an under or over correction, and the result should be interpreted with caution. Naturally, similar concerns of regression dilution apply to self-reported CPD, in this case we had repeated estimates from 5 time points. We used these to calculate an average CPD and this was the variable used in the analysis. Nevertheless, this analysis represents a first attempt to circumvent the limitation inherent in most observational studies using a single measurement. SNPs on 19q13 were also associated with cotinine, but the directions of the observed associations were opposite to those originally observed with CPD. Thus, the rs7937 SNP (T allele) on 19q13 was associated with decreased lung cancer risk, consistent with previous study showing an association with lower CPD(8), but increasing levels of cotinine. Consequently estimates of its effect on lung cancer risk were augmented by correction for regression dilution.

The 19q13 locus contains several CYP2 genes, including CYP2A6 - the major enzyme involved in the metabolism of nicotine. CYP2A6 catalyzes C-oxidation of nicotine to cotinine, which is in turn metabolized to trans-3′-hydroxycotinine (44,45) It would seem plausible that genetic variants in this gene may induce slower nicotine metabolism(12,46) and accumulation of circulating cotinine, and subsequently, a reduction in smoking intensity with a lower lung cancer risk as consequence. While this hypothesis would explain the opposing effects of 19q13 SNPs on cotinine and CPD, circulating measurements of the ratio of 3′-hydroxycotinine to cotinine would be required to further elucidate these complex associations. Overall, these observations highlight the disparate mechanisms of variants on 15q25 and 19q13 in their effects on smoking behavior and subsequent lung cancer risk. To our knowledge, the present study is the largest to date investigating the effects of SNPs on 7p14, 8p11, 10q23, 15q25, and 19q13, on lung cancer risk, that also utilizes circulating cotinine measures as proxies for recent smoking behavior. The study further benefits from several important characteristics, including the prospective study design and detailed information on tobacco exposure. The study was however not adequately powered to detect the small risk effects expected of some of the studied SNPs (OR ranging from 1.05 to 1.12). It would also have been desirable to measure alternative nicotine metabolites to better describe the opposing associations of SNPs on 19q13.

In conclusion, the present study confirms previous associations of SNPs on 15q25 with cotinine levels. The study also indicates that circulating cotinine levels may provide more refined information on recent smoking exposure than does CPD as assessed by questionnaires. The intriguing associations of SNPs on 19q13 with cotinine levels, opposite to that of CPD and lung cancer risk, should be studied further by measuring additional nicotine metabolites. Finally, the present study indicates that the degree to which the established effects of 15q25 SNPs on lung cancer risk are mediated by smoking may be under-estimated by use of crude measures of smoking such as CPD. Further studies with a range of objective smoking measures covering a greater period of lifetime smoking are required to further elucidate this issue.