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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.

Background: Although cigarette smoking is the primary risk factor for lung cancer, other risk factors have been shown to be associated with lung cancer including prior history of respiratory diseases. The purpose of this analysis was to assess the association between prior history of respiratory disease (i.e., asthma, chronic obstructive pulmonary disease (COPD), hay fever, and pneumonia) and lung cancer among Mexican Americans using data from a multi-racial/ethnic lung cancer case-control study. Methods: The data were collected between 1991 and 2010. Cases (n = 217) were patients who presented with newly diagnosed, histopathologically confirmed and previously untreated lung cancer. Healthy control participants (n = 339) were recruited from a large multispecialty physician group practice. Demographics, cigarette use, and history of respiratory disease were collected by personal interview using a validated questionnaire. Multivariable logistic regression models were used to estimate relative risk. All analyses were restricted to self-report Mexican Americans. Results: Prior history of COPD (OR = 2.33; 95% CI = 1.14–4.79) and pneumonia (OR = 2.88; 95% CI = 1.47–5.63) were associated with a statistically significant increased risk of lung cancer. Conversely, self-reported history of asthma (OR = 0.71, 95% CI = 0.20–2.45) or hay fever (OR = 0.59, 95% CI = 0.29–1.21) were inversely associated with lung cancer risk but the odds ratios were not statistically significant. Conclusions: These findings illustrate that COPD and pneumonia are associated with an increased for lung cancer among Mexican Americans. Identifying risk factors such as C. pneumonia infections and COPD may induce targeted treatments to help attenuate progression to lung cancer. At present this study is one of largest case-control analyses assessing respiratory disease and lung cancer risk among Mexican-Americans. Citation Information: Cancer Prev Res 2011;4(10 Suppl):B91.

Dietary magnesium intake is significantly less in African Americans than Caucasians. Deficiency of magnesium influences immune-inflammatory response and induces oxidative stress that leads to increased cell proliferation and DNA damage. Low magnesium level also increases the physiologic effects of calcium. Therefore, we hypothesize that low dietary intake of magnesium and high calcium intake will increase the risk of developing prostate cancer among African American men. To test this hypothesis, we conducted a pilot case-control study to determine whether deficiency of dietary magnesium and a high calcium intake are associated with an increased risk of prostate cancer. This study included 209 cases with histologically confirmed adenocarcinoma of the prostate (110 African American and 99 Caucasian men). 199 population-based controls were frequency matched by age (±5 years) and race. Risk factor questionnaire and nutrition data were collected via telephone interview. Dietary Assessment was performed using Modified Block-NCI Food Frequency Questionnaire with 129 food items, questions about restaurant intake and vitamin mineral supplementation. Dietary intake was calculated using DIETSYS + Plus version 5.9 dietary analysis program. Nutrition data for 166 cases and 155 controls were available for this analysis. Mean magnesium and calcium intake did not vary significantly between cases and controls. The odds ratios for magnesium was 0.79 (95% CI 0.39–1.61) and for calcium 1.35 (95% CI 0.87–2.09). However, among African Americans, the odds ratio for magnesium was 0.28 (95% CI 0.08–1.05) and for calcium 1.9 (95% CI 1.05–3.40). These preliminary data suggest that high dietary magnesium intake is inversely associated with prostate cancer, while higher calcium intake is associated with increased risk among African American men. These data may have significant public health implications since the identification of African American men deficient of magnesium can be targeted for preventive interventions. Results of this study needs to be confirmed in future studies with larger sample size. Citation Information: Cancer Prev Res 2011;4(10 Suppl):A114.

Mexican American youth have the highest rates of smoking experimentation. We have previously confirmed several psychosocial factors including cognitive susceptibility to smoking, outcome expectations, risk taking tendencies, family influence and demographic characteristics, as risk factors for experimentation in these youth. In this study, we evaluated the incremental value of adding select variants of two candidate genes in the dopamine pathway implicated in the smoking phenotype. Participants were Mexican origin youth recruited from a large population-based cohort study in Houston, TX, who provided prospective data on cigarette experimentation yearly for five years. Psychosocial data on family influence, risk taking tendencies and outcome expectations were elicited at two time points-baseline and final follow-up. At baseline, all participants provided buccal samples, from which DNA was extracted and genotyped for 27 genetic variants in the dopamine receptor DRD2, and Dopamine Transporter (DAT) genes. Of the 1002 youth in the current analysis, 52% were girls, 66% were born in the US, average age at baseline was 11.8 (SD=0.8) years, 78% reported being highly acculturated, and 22% reported being new experimenters (i.e. first experimented with cigarettes only subsequent to the baseline survey). Two SNPs were associated with experimentation: rs10052016 (p Citation Information: Cancer Prev Res 2010;3(1 Suppl):A86.

Background: Since lung cancer only occurs in a small fraction of long-term smokers, there is a need to develop risk prediction models to identify high-risk subgroups. Three lung cancer models, constructed using clinical and epidemiological variables, predicted absolute risk of lung cancer: one based on a cohort of patients recruited for the CARET study, and two constructed from case-control studies conducted in Houston, Texas and Liverpool, England. Given their potential application to primary chemo-prevention strategies and screening trials, it is important to compare the accuracy of these three models in an independent population. Methods: We used data for 3197 lung cancer patients and 1703 cancer-free controls recruited to an ongoing case-control study of lung cancer at Harvard School of Public Health and Massachusetts General Hospital (Boston, MA). We estimated 5-year lung cancer risk for each risk model and compared the discriminatory power, as measured by the area under the the receiver-operator characteristic curve, accuracy, as measured by the positive predictive value and negative predictive value, and clinical utility of these models, as measured with scaled rectangles. Results: Overall, the discriminatory power for the Liverpool Lung Project (LLP) (AUC = 0.69, 95% CI = 0.67–0.71) and Spitz models (AUC = 0.69, 95%CI = 0.66–0.71) were comparable, while the Bach model had significantly lower power (AUC =0.66, 95% CI = 0.64–0.69; P=0.02). Positive predictive values were highest with the Spitz model (0.882) compared to 0.809 for the Bach model and 0.759 for the LLP model. In contrast, the negative predictive values were highest for the LLP model (0.560) compared to 0.450 for the Spitz model and 0.447 for the Bach. The Spitz and Bach models had lower sensitivity but higher specificity compared to the LLP model. For instance, 26.6% of all lung cancer cases have a five-year absolute risk of lung cancer ≥ 2.5% for the Spitz model compared to 66.7% of all cases for the LLP model. However, only 5.6% of all healthy controls have a five-year absolute risk of lung cancer ≥ 2.5% with the Spitz model compared to 33.4% of all controls for the LLP model. Conclusion: We observed modest differences in discriminatory among the three lung cancer risk models. The level of the discriminatory powers of these three lung cancer risk models was moderate at best, which highlights the difficulty in developing effective risk models. There is considerable room for improvement in model performance by incorporating additional risk factors, such as genetic risk factors, to increase discriminatory power and accuracy, while still maintaining clinical utility. Citation Information: Cancer Prev Res 2010;3(1 Suppl):A127.

Background: With increasing amounts of genes available for study, more opportunities are available to discover key associations between genetic factors and disease. In a previous study on Hodgkin disease, we observed powerful positive associations between genes on chromosomes 3 and 19. In this study, we expanded the previous analyses to include 62 single nucleotide polymorphisms (SNPs) in DNA repair and inflammation pathway genes located throughout the genome and evaluated possible associations for Hodgkin disease by inferring haplotypes at the individual gene level. Methods: Genetic and epidemiological data were obtained from a case-control study conducted at M.D. Anderson Cancer Center between 1987 and 1992 including 200 cases and 220 controls. Haplotypes were initially inferred with Haploview using linkage disequilibrium plots, and differences in haplotypes between cases and controls were determined with PHASE. Linear modeling with haplotypes, through both joint and separate effects haplotype modeling, with sex, race, smoking status, and age as confounders, were conducted with Haplo.stats. A joint-effects linear model allows each haplotype to be compared to the most frequent haplotype. A separate-effects linear model compares a haplotype to all of the other haplotypes possible for study. Results: With the joint effects haplotype model, some significant haplotype associations with Hodgkin disease were observed with DNA repair gene XPC on chromosome 3 and inflammation pathway gene ILR4 on Chromosome 16. For DNA repair gene XPC, susceptible associations with Hodgkin disease were discovered with the use of an additive genetic model with haplotype CT from SNPs rs2228001 (XPC 499 A>C) and rs2228000 (XPC 939 C>T) (OR = 1.49, 95% CI = 1.03–2.16) and a dominant genetic model with the same haplotype (OR = 1.75, 95% CI = 1.15–2.13). For inflammation pathway gene ILR4, protective associations with Hodgkin disease were discovered with the use of an additive genetic model with haplotype TC from SNPs rs1805012 (XPC 499 T>C) and rs1805015 (XPC 939 T>C) (OR = 0.63, 95% CI = 0.33–0.99) and a dominant genetic model with the same haplotype (OR = 0.48, 95% CI = 0.26–0.88). The separate effects haplotype model also highlighted associations between haplotypes and Hodgkin disease on these same genes. Other significant associations between haplotypes and Hodgkin disease were observed on chromosomes 5 with inflammation pathway gene IL4 and chromosome 19 with DNA repair gene. Conclusions: The basis for haplotype analysis to analyze the effects of linked SNPs associated with Hodgkin disease has been demonstrated. Furthermore, the interactions between specific SNPs in both the inflammation and the DNA repair pathway have been shown to play an important role in possible Hodgkin disease development. By targeting specific aspects of the genome that show either an increase or decrease in Hodgkin risk, identification of high risk individuals could be achieved. Citation Information: Cancer Prev Res 2010;3(1 Suppl):A7.