Background

Established psychosocial risk factors increase the risk for experimentation among Mexican-origin youth. Now we comprehensively investigate the added contribution of select polymorphisms in candidate genetic pathways associated with sensation seeking, risk taking, and smoking phenotypes to predict experimentation.

Methods

Participants, (N=1,118 Mexican origin youth) recruited from a large population-based cohort study in Houston, Texas, provided prospective data on cigarette experimentation over three years. Psychosocial data were elicited twice—baseline and final follow-up. Participants were genotyped for 672 functional and tagging variants in the dopamine, serotonin and opioid pathways.

Results

After adjusting for gender and age, with a Bayesian False Discovery Probability set at 0.8 and prior probability of 0.05, six gene variants were significantly associated with risk of experimentation. After controlling for established risk factors, multivariable analyses revealed that participants with six or more risk alleles were 2.25 (95%CI: 1.62–3.13) times more likely to have experimented since baseline compared to participants with five or fewer. Among committed never smokers (N=872), three genes (OPRM1, SNAP25, HTR1B) were associated with experimentation as were all psychosocial factors. Among susceptible youth (N=246) older age at baseline, living with a smoker, and three different genes (HTR2A, DRD2, SLC6A3) predicted experimentation.

Conclusions

Our findings, which have implications for development of culturally-specific interventions, need to be validated in other ethnic groups.

Impact

These results suggest that variations in select genes interact with a cognitive predisposition toward smoking. In susceptible adolescents, the impact of the genetic variants appears to be larger compared to committed never smokers.

BACKGROUND

Tobacco-induced lung cancer is characterized by a deregulated inflammatory microenvironment. Variants in multiple genes in inflammation pathways may contribute to risk of lung cancer.

METHODS

We therefore conducted a three-stage comprehensive pathway analysis (discovery, replication and meta-analysis) of inflammation gene variants in ever smoking lung cancer cases and controls. A discovery set (1096 cases; 727 controls) and an independent and non-overlapping internal replication set (1154 cases; 1137 controls) were derived from an ongoing case-control study. For discovery, we used an iSelect BeadChip to interrogate a comprehensive panel of 11737 inflammation pathway SNPs and selected nominally significant (p<0.05) SNPs for internal replication.

RESULTS

There were 6 SNPs that achieved statistical significance (p<0.05) in the internal replication dataset with concordant risk estimates for former smokers and 5 concordant and replicated SNPs in current smokers. Replicated hits were further tested in a subsequent meta-analysis using external data derived from two published GWAS and a case-control study. Two of these variants (a BCL2L14 SNP in former smokers and a SNP in IL2RB in current smokers) were further validated. In risk score analyses, there was a 26% increase in risk with each additional adverse allele when we combined the genotyped SNP and the most significant imputed SNP in IL2RB in current smokers and a 36% similar increase in risk for former smokers associated with genotyped and imputed BCL2L14 SNPs.

CONCLUSIONS/IMPACT

Before they can be applied for risk prediction efforts, these SNPs should be subject to further external replication and more extensive fine mapping studies.

Introduction

The purpose of this study was to examine the extent to which adolescent reports on mother’s smoking status and mother’s self-reports on smoking are concordant with one another.

Methods

Mothers self-reported on their smoking at two timepoints (first query and second query), while the adolescents reported on their mother’s smoking status at one timepoint. Kappa values and percent exact agreement as well as sensitivity and specificity were calculated to examine the degree of agreement between child and mother’s reports at the two timepoints.

Results

Overall, the results indicated good concordance between mothers’ self-reports and adolescent reports on smoking. Specifically, higher concordance was observed for mother’s first query compared with mother’s second query (Κ = 0.69 vs. Κ = 0.51). Younger adolescents and girls provided more concordant reports than older adolescents and boys.

Discussion

The results indicate that adolescent reports on mothers’ smoking behavior can be used as a proxy to obtain data if mothers’ self-report data are not available. Our results further suggest that when reports are not collected concurrently, self-report data obtained from the mothers prior to the proxy report obtained from her adolescent may be more reliable than the other way around.

Risk prediction models are useful in clinical decision making. We have published an internally validated prediction tool for lung cancer based on easily obtainable epidemiologic and clinical data. Because the precision of the model was modest, we now estimate the improvement obtained by adding two markers of DNA repair capacity.

Assay data (host-cell reactivation and mutagen sensitivity) were available for 725 White lung cancer cases and 615 controls, all former or current smokers, a subset of cases and controls from the previous analysis. Multivariable models were constructed from the original variables with addition of the biomarkers separately and together. Pairwise comparisons of the area under the receiver operating characteristic curves (AUC) and 3-fold cross-validations were done.

For former smokers, the AUC and 95% confidence intervals were 0.67 (0.63–0.71) for the baseline model and 0.70 (0.66–0.74) for the expanded model. For current smokers, the comparable AUC values were 0.68 (0.64–0.72) and 0.73 (0.69–0.77). For both groups, the expanded models were statistically significantly better than the baseline models (P = 0.006 and P = 0.0048, respectively), although the increases in the concordance statistics were modest. We also recomputed 1-year absolute risks of lung cancer as described previously for two different risk profiles and showed that individuals who exhibited poor repair capacity or heightened mutagen sensitivity had increased absolute risks of lung cancer.

Addition of biomarker assays improved the sensitivity of the expanded models.

The multi-endpoint cytokinesis-blocked micronucleus assay is used for assessing chromosome aberrations. We have recently reported that this assay is extremely sensitive to genetic damage caused by the tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyr-idyl)-1-butanone (NNK) and that the binucleated cells with micronuclei, nucleoplasmic bridges, and nuclear buds in lymphocytes (chromosome damage endpoints measured by the assay) are strong predictors of lung cancer risk. In the current study, we refined our analysis to include toxicity endpoints (micronuclei in mononucleated cells, apoptosis, necrosis, and nuclear division index) to investigate the benefit of including these variables on improving the predictive value of the assay. Baseline and NNK-induced micronuclei in mononucleated cells were significantly higher in patients (n = 139) than controls (n = 130; P < 0.001). Baseline apoptosis was higher among cases; however, the controls showed a significant higher fold increase in NNK-induced apoptosis compared with baseline (P < 0.001). Principal components analysis was used to derive a summary measure for all endpoints and calculate the positive predictive value (PPV) and negative predictive value (NPV) for disease status. First principal component for NNK-induced chromosome damage endpoints (binucleated cells with micronuclei, nucleoplasmic bridges, and nuclear buds) had an area under the curve = 97.9 (95% confidence interval, 95.9-99.0), PPV = 94.8, and NPV = 92.6. The discriminatory power improved when micronuclei in mononucleated cells were included: area under the curve = 99.1 (95% confidence interval, 97.9- 100.0), PPV = 98.7 and NPV = 95.6. The simplicity, rapidity, and sensitivity of the assay together with potential for automation make it a valuable tool for screening and prioritizing potential cases for intensive screening.

Because existing risk prediction models for lung cancer were developed in white populations, they may not be appropriate for predicting risk among African-Americans. Therefore, a need exists to construct and validate a risk prediction model for lung cancer that is specific to African-Americans. We analyzed data from 491 African-Americans with lung cancer and 497 matched African-American controls to identify specific risks and incorporate them into a multivariable risk model for lung cancer and estimate the 5-year absolute risk of lung cancer. We performed internal and external validations of the risk model using data on additional cases and controls from the same ongoing multiracial/ethnic lung cancer case-control study from which the model-building data were obtained as well as data from two different lung cancer studies in metropolitan Detroit, respectively. We also compared our African-American model with our previously developed risk prediction model for whites. The final risk model included smoking-related variables [smoking status, pack-years smoked, age at smoking cessation (former smokers), and number of years since smoking cessation (former smokers)], self- reported physician diagnoses of chronic obstructive pulmonary disease or hay fever, and exposures to asbestos or wood dusts. Our risk prediction model for African-Americans exhibited good discrimination [75% (95% confidence interval, 0.67−0.82)] for our internal data and moderate discrimination [63% (95% confidence interval, 0.57−0.69)] for the external data group, which is an improvement over the Spitz model for white subjects. Existing lung cancer prediction models may not be appropriate for predicting risk for African-Americans because (a) they were developed using white populations, (b) level of risk is different for risk factors that African-American share with whites, and (c) unique group-specific risk factors exist for African-Americans. This study developed and validated a risk prediction model for lung cancer that is specific to African-Americans and thus more precise in predicting their risks. These findings highlight the importance of conducting further ethnic-specific analyses of disease risk.

Magnesium (Mg) is required for maintenance of genomic stability; however, data on the relationship between dietary Mg intake and lung cancer are lacking. In an ongoing lung cancer case–control study, we identified 1139 cases and 1210 matched healthy controls with data on both diet and DNA repair capacity (DRC). Dietary intake was assessed using a modified Block-NCI food frequency questionnaire and DRC was measured using the host-cell reactivation assay to assess repair in lymphocyte cultures. After adjustment for potential confounding factors including DRC, the odds ratios (ORs) and 95% confidence intervals (CIs) for lung cancer with increasing quartiles of dietary Mg intake were 1.0, 0.83 (0.66–1.05), 0.64 (0.50–0.83) and 0.47 (0.36–0.61), respectively, for all subjects (P-trend < 0.0001). Similar results were observed by histology and clinical stage of lung cancer. Low dietary Mg intake was associated with poorer DRC and increased risk of lung cancer. In joint effects analyses, compared with those with high dietary Mg intake and proficient DRC, the OR (95% CI) for lung cancer in the presence of both low dietary Mg and suboptimal DRC was 2.36 (1.83–3.04). Similar results were observed for men and women. The effects were more pronounced among older subjects (>60 years), current or heavier smokers, drinkers, those with a family history of cancer in first-degree relatives, small cell lung cancer and late-stage disease. These intriguing results need to be confirmed in prospective studies.