Margaret R. Spitz

We highlight the value of carefully designed observational epidemiologic analyses in translating basic science discoveries to clinical application and in providing the impetus for exploring underlying mechanisms for observed associations. Coupling epidemiologic data with an in vitro screen of commonly used therapeutic agents may identify novel applications for further clinical testing. Cancer Discovery; 1(1); 21–2. ©2011 AACR.

DNA repair capacity is an important determinant of susceptibility to cancer. The hOGG1 enzyme is crucial for repairing the 8-oxoguanine lesion that occurs either as a byproduct of oxidative metabolism or as a result of exogenous sources such as exposure to cigarette smoke. It has been previously reported that smokers with low hOGG1 activity had significantly higher risk of developing lung cancer as compared to smokers with high hOGG1 activity.

Classical epidemiologic studies have made seminal contributions to identifying the etiology of most common cancers. Molecular epidemiology was conceived of as an extension of traditional epidemiology to incorporate biomarkers with questionnaire data to further our understanding of the mechanisms of carcinogenesis. Early molecular epidemiologic studies employed functional assays. These studies were hampered by the need for sequential and/or prediagnostic samples, viable lymphocytes and the uncertainty of how well these functional data (derived from surrogate lymphocytic tissue) reflected events in the target tissue. The completion of the Human Genome Project and Hapmap Project, together with the unparalleled advances in high-throughput genotyping revolutionized the practice of molecular epidemiology. Early studies had been constrained by existing technology to use the hypothesis-driven candidate gene approach, with disappointing results. Pathway analysis addressed some of the concerns, although the study of interacting and overlapping gene networks remained a challenge. Whole-genome scanning approaches were designed as agnostic studies using a dense set of markers to capture much of the common genome variation to study germ-line genetic variation as risk factors for common complex diseases. It should be possible to exploit the wealth of these data for pharmacogenetic studies to realize the promise of personalized therapy. Going forward, the temptation for epidemiologists to be lured by high-tech 'omics' will be immense. Systems Epidemiology, the observational prototype of systems biology, is an extension of classical epidemiology to include powerful new platforms such as the transcriptome, proteome and metabolome. However, there will always be the need for impeccably designed and well-powered epidemiologic studies with rigorous quality control of data, specimen acquisition and statistical analysis.

The advent of genome-wide association studies to identify low-penetrance common susceptibility alleles heralds the possibility of incorporating panels of gene variants into existing risk prediction models and of assessing improvement in model performance. However, to date, the updated models have shown only modest improvements in discrimination.

Common variants in the nicotinic acetylcholine receptor gene cluster on chromosome 15q24-25.1 were associated with lung cancer risk in three recently published independently conducted genome-wide association studies, with no consensus as to the relative impact of the variants on the propensity to smoke vs a direct carcinogenic effect. To further explore our hypothesis that these variants are indeed associated with both cancer causation and nicotine dependence, we performed a more detailed analysis of the association of these putative risk genotypes with smoking phenotype, as well as in lifetime never smokers, and in other smoking-related cancers. We demonstrate a statistically significant association of the variants with both nicotine dependence, as well as lung cancer phenotypes, including earlier age at lung cancer onset. The variants were associated with higher risks of lung cancer in lower smoking-exposed strata, and in individuals with a strong family history of lung or smoking-related cancers. In contrast, we found no evidence that the variants were associated with elevated risks in 547 lifetime never-smoking lung cancer case subjects, nor in other smoking-related cancers (bladder and renal). Thus, we conclude that the variants are implicated both in smoking behavior and more directly in lung cancer risk.

Survey data suggest that in Texas Latino youth exhibit higher rates of susceptibility to smoking than youth from other ethnic groups. In this analysis we examined the relationship between susceptibility to smoking and well-known risk factors associated with smoking initiation among a cohort of 11 to 13 year old Mexican origin youth residing in Houston, Texas. We analyzed cross-sectional survey data from 1,187 participants who reported they had never smoked, even a puff of a cigarette. The survey assessed peer and family social influence, school and neighborhood characteristics, level of family acculturation and socioeconomic status, and attitudes toward smoking. Bivariate associations, Student's t-tests, and logistic regression analysis were used to examine predictors of susceptibility. Overall, 22.1% of the never-smokers were susceptible to smoking. Boys were more likely to be susceptible than girls (25.6% vs. 18.9%), and susceptible children were slightly older than non-susceptible children (12.1 vs. 11.8 years). In addition, multivariate analyses revealed that positive expectations about smoking exerted the strongest influence on susceptibility status (odds ratio = 4.85). Multivariate analyses further revealed that compared to non-susceptible participants, susceptibles were more likely to report peer influences supportive of smoking, lower subjective social status and more detentions at school, more temptations to try smoking and to have a mother and a brother who smokes. Our findings suggest that interventions that target positive expectations about smoking may be useful in this population. Furthermore, because youth encounter smoking-initiation risk factors in different social environments, our results underscore the continued need for both family- and school-based primary prevention programs to adequately combat their influence. The results also can be used to inform the development of culturally sensitive programs for Mexican origin youth.

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.