Anthony M. D'Amelio

Development and validation of a lung cancer risk prediction model for African-Americans.

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.

Comparison of discriminatory power and accuracy of three lung cancer risk models.

Background:Three lung cancer (LC) models have recently been constructed to predict an individuals absolute risk of LC within a defined period. Given their potential application in prevention strategies, a comparison of their accuracy in an independent population is important.Methods:We used data for 3197 patients with LC and 1703 cancer-free controls recruited to an ongoing case–control study at the Harvard School of Public Health and Massachusetts General Hospital. We estimated the 5-year LC risk for each risk model and compared the discriminatory power, accuracy, and clinical utility of these models.Results:Overall, the Liverpool Lung Project (LLP) and Spitz models had comparable discriminatory power (0.69), whereas the Bach model had significantly lower power (0.66; P=0.02). Positive predictive values were highest with the Spitz models, whereas negative predictive values were highest with the LLP model. The Spitz and Bach models had lower sensitivity but better specificity than did the LLP model.Conclusion:We observed modest differences in discriminatory power among the three LC risk models, but discriminatory powers were moderate at best, highlighting the difficulty in developing effective risk models.

Hodgkin disease risk: Role of genetic polymorphisms and gene-gene interactions in inflammation pathway genes

Inflammation is a critical component of cancer development. The clinical and pathological features of Hodgkin disease (HD) reflect an abnormal immunity that results from cytokines secreted by Reed–Sternberg cells and the surrounding tumor. Numerous studies have reported the association between genetic polymorphisms in cytokine genes and the susceptibility to different hematologic cancers. However, the effects of such SNPs on modulating HD risk have not yet been investigated. We hypothesized that gene–gene interactions between candidate genes in the anti‐ and pro‐inflammatory pathways carrying suspicious polymorphisms may contribute to susceptibility to HD. To test this hypothesis, we conducted a study on 200 HD cases and 220 controls to assess associations between HD risk and 38 functional SNPs in inflammatory genes. We evaluated potential gene–gene interactions using a multi‐analytic strategy combining logistic regression, multi‐factor dimensionality reduction, and classification and regression tree (CART) approaches. We observed that, in combination, allelic variants in the COX2, IL18, ILR4, and IL10 genes modify the risk for developing HD. Moreover, the cumulative genetic risk score (CGRS) revealed a significant trend where the risk for developing HD increases as the number of adverse alleles in the cytokine genes increase. These findings support the notion that epigenetic‐interactions between these cytokines may influence pathogenesis of HD modulating the proliferation of regulatory T cells. In this way, the innate and adaptative immune responses may be altered and defy their usual functions in the host anti‐tumor response. Our study is the first to report the association between polymorphisms in inflammation genes and HD susceptibility risk. Mol. Carcinog.

Re: Discriminatory Accuracy From Single-Nucleotide Polymorphisms in Models to Predict Breast Cancer Risk

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. Gail (1) had previously shown that adding seven single-nucleotide polymorphisms (SNPs) identified from genome-wide association analyses to the original Breast Cancer Risk Assessment Tool had yielded only a modest improvement in area under the curve (AUC) from 0.607 to 0.632. Gail (2) now reports that inclusion of 11 SNPs exhibits an even smaller improvement in the AUC (0.637) than that of the BRACTplus 7 model. The receiver operating characteristics curve may not be sensitive to differences in probabilities between models and, therefore, may be insufficient to assess the impact of adding new predictors. A very large independent association of the new marker is required for a meaningful improvement in AUC, and a substantial gain in performance may not yield a substantial increase in AUC. One suggested statistic for comparing nested models is the net reclassification index that is useful when risk categories are defined, and there is a consensus as to clinically meaningful cut points (3). The net reclassification index quantifies overall improvement in model sensitivity and specificity. A net improvement in risk classification implies upward reclassification of case patients and downward reclassification of control subjects. We evaluated these metrics in our own internally validated risk prediction model for lung cancer that incorporated easily attainable epidemiological and clinical variables (4). In a genome-wide association analysis of 315 450 tagging SNPs in 1154 patients with lung cancer who were current and former smokers and were of European ancestry and 1137 frequency-matched control subjects (5), two SNPs, rs1051730 and rs8034191, that mapped to a region within 15q25.1 (which encompasses the nicotinic acetylcholine receptor subunit genes CHRNA3 and CHRNA5) were strongly associated with risk (odds ratio [OR] = 1.32, 95% confidence interval [CI] = 1.24 to 1.41, P = 3.15 × 10−18 for rs8034191; and OR = 1.32, 95% CI = 1.23 to 1.39, P = 7.00 × 10−18 for rs1051730). In a subsequent meta-analysis (6) involving the UK genome-wide association study, the International Agency for Research on Cancer genome-wide association study, and our Texas genome-wide association study, the strongest associations remained for SNPs mapping to 15q25.1 (ie, rs1051730, P = 2.83 × 10−19; and rs8034191, P = 4.03 × 10−19). There was also consistent evidence for a new disease locus at 5p15.33 (ie, rs401681, P = 4.40 × 10−6). This locus contains two known genes: TERT (human telomerase reverse transcriptase) gene and CLPTM1L (cleft lip and palate transmembrane 1-like) gene. We therefore added one SNP from the 15q25.1 locus (ie, rs1051730, which was used because it was in strong linkage disequilibrium with rs8034191) and two SNPs from the 5p15.33 region (ie, rs2736100 and rs401681) to the baseline model and assessed discrimination improvement. Our AUC for the baseline epidemiological–clinical model including 1016 case patients and 1111 control subjects was 0.661 (95% CI = 0.64 to 0.68). With addition of the three SNPs, the AUC showed modest, yet statistically significant, improvement to 0.673 (95% CI = 0.65 to 0.70, P = .01). We defined risk categories on the basis of the lower and upper quartiles of predicted risk from our baseline model as proposed by Bach et al. (7): low (predicted risk 50%). The resulting net reclassification indices were 0.152 (95% CI = 0.112 to 0.193) overall, 0.089 (95% CI = 0.048 to 0.130) for case patients, and 0.064 (95% CI = 0.023 to 0.105) for control subjects (all statistically significant at the 0.2% level), indicating that the SNPs modestly improved both sensitivity (9%) and specificity (6%). Although it could be argued that models providing a continuous score are more appropriate in the clinical setting, it is likely that a variety of additional summary measures evaluating model performance will be needed to assess these multigenic models.

Cigarette Experimentation in Mexican Origin Youth: Psychosocial and Genetic Determinants

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, TX, provided prospective data on cigarette experimentation over 3 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% confidence interval (CI), 1.62–3.13] times more likely to have experimented since baseline than 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 than committed never-smokers. Cancer Epidemiol Biomarkers Prev; 21(1); 228–38. ©2011 AACR.

The Cytokinesis-Blocked Micronucleus Assay as a Strong Predictor of Lung Cancer: Extension of a Lung Cancer Risk Prediction Model

Background: There is an urgent need to improve lung cancer outcome by identifying and validating markers of risk. We previously reported that the cytokinesis-blocked micronucleus assay (CBMN) is a strong predictor of lung cancer risk. Here, we validate our findings in an independent external lung cancer population and test discriminatory power improvement of the Spitz risk prediction model upon extension with this biomarker. Methods: A total of 1,506 participants were stratified into a test set of 995 (527 cases/468 controls) from MD Anderson Cancer Center (Houston, TX) and a validation set of 511 (239 cases/272 controls) from Massachusetts General Hospital (Boston, MA). An epidemiologic questionnaire was administered and genetic instability was assessed using the CBMN assay. Results: Excellent concordance was observed between the two populations in levels and distribution of CBMN endpoints [binucleated-micronuclei (BN-MN), binucleated-nucleoplasmic bridges (BN-NPB)] with significantly higher mean BN-MN and BN-NPB values among cases (P < 0.0001). Extension of the Spitz model led to an overall improvement in the AUC (95% confidence intervals) from 0.61 (55.5–65.7) with epidemiologic variables to 0.92 (89.4–94.2) with addition of the BN-MN endpoint. The most dramatic improvement was observed with the never-smokers extended model followed by the former and current smokers. Conclusions: The CBMN assay is a sensitive and specific predictor of lung cancer risk, and extension of the Spitz risk prediction model led to an AUC that may prove useful in population screening programs to identify the “true” high-risk individuals. Impact: Identifying high-risk subgroups that would benefit from screening surveillance has immense public health significance. Cancer Epidemiol Biomarkers Prev; 23(11); 2462–70. ©2014 AACR.

A Novel Approach to Exploring Potential Interactions among Single-Nucleotide Polymorphisms of Inflammation Genes in Gliomagenesis: An Exploratory Case-Only Study

Background: Despite extensive research on the topic, glioma etiology remains largely unknown. Exploration of potential interactions between single-nucleotide polymorphisms (SNP) of immune genes is a promising new area of glioma research. The case-only study design is a powerful and efficient design for exploring possible multiplicative interactions between factors that are independent of one another. The purpose of our study was to use this exploratory design to identify potential pair wise SNP–SNP interactions from genes involved in several different immune-related pathways for investigation in future studies. Methods: The study population consisted of two case groups: 1,224 histologic confirmed, non-Hispanic white glioma cases from the United States and a validation population of 634 glioma cases from the United Kingdom. Polytomous logistic regression, in which one SNP was coded as the outcome and the other SNP was included as the exposure, was utilized to calculate the ORs of the likelihood of cases simultaneously having the variant alleles of two different SNPs. Potential interactions were examined only between SNPs located in different genes or chromosomes. Results: Using this data mining strategy, we found 396 significant SNP–SNP interactions among polymorphisms of immune-related genes that were present in both the U.S. and U.K. study populations. Conclusion: This exploratory study was conducted for the purpose of hypothesis generation, and thus has provided several new hypotheses that can be tested using traditional case–control study designs to obtain estimates of risk. Impact: This is the first study, to our knowledge, to take this novel approach to identifying SNP–SNP interactions relevant to glioma etiology. Cancer Epidemiol Biomarkers Prev; 20(8); 1683–9. ©2011 AACR.

Using haplotype analysis to elucidate significant associations between genes and Hodgkin lymphoma.

In this study, we estimated the association between the inferred haplotypes in the inflammation, DNA repair, and folate pathways, and developed risk models for Hodgkin lymphoma. The study population consisted of 200 Hodgkin lymphoma cases and 220 controls. A susceptible association was observed on the XPC gene with haplotype CT (rs2228001 and rs2228000), and a protective association was observed on the IL4R gene with haplotype TCA (rs1805012, rs1805015, and rs1801275). These results can provide the necessary tools to identify high-risk individuals after validation in large data sets.

Abstract 1345: Evidence for genetic mediation of lung cancer through hay fever.

Introduction: In the past decade, advances in genetics have led to the discovery of numerous lung cancer susceptibility variants. The majority of these variants have been found to influence lung cancer susceptibility via tobacco exposure or nicotine addiction; however, recently, researchers have observed lung cancer susceptibility variants that mediate through Chronic Obstructive Pulmonary Disease (COPD). Studies involving potential genetic factors related to other lung-related conditions, such as pneumonia and/or hay fever have been limited. The genetic variants related to underlying mechanism of hay fever on the development of lung cancer are interesting to pursue as hay fever has been showed to be protective against lung cancer. Methods: Cases included 1154 histological-confirmed Caucasian lung cases from MD Anderson Cancer Center in Houston, Texas, and controls included 1137 individuals recruited through the Kelsey-Seybold Clinics in Houston, Texas. These cases and controls were a subset of participants from a lung cancer case-control study conducted that has available Genome-wide association study (GWAS) data (317,498 SNPs). We first conducted an association analysis in PLINK to determine the association of each SNP with both lung cancer and hay fever and find SNPs that showed a joint significance for both hay fever and lung cancer in opposite directions (p-value Results: Two hundred and forty six SNPs were found to be statistically significant (p-value Conclusion: This is the first study to have investigated the mediation effects of hay fever on lung cancer risk. Our data supports the mediation role of certain SNPs in lung cancer through hay fever and points to specific transduction pathway. Future studies are needed to validate these results in external populations. Citation Format: Anthony D9Amelio, Chi H. Nguyen, Randa El-Zein, Margaret R. Spitz, Xifeng Wu, Carol J. Etzel. Evidence for genetic mediation of lung cancer through hay fever. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1345. doi:10.1158/1538-7445.AM2013-1345

Abstract 1899: Validation of the Spitz lung cancer risk prediction model with the Dutch-Belgian randomized lung cancer screening trial (NELSON) cohort

Background: Risk modeling has been shown to be important in detection and prevention of disease, and this is especially true for cancer research. One prominent lung cancer risk model is the Spitz Lung Cancer Risk Model, which was constructed from case-control data and incorporates clinical and epidemiologic variables (smoking status, family history and select co-morbidities). Although the Spitz model is validated using case-control data, the model has not been calibrated. Methods: We used data from 109 lung cancer patients and 4622 controls that were a subset of a large longitudinal cohort study, the Dutch-Belgian randomized lung cancer trial (NELSON). We estimated absolute lung cancer risks from the Spitz risk model to evaluate the model9s calibration, discriminatory power and clinical utility. Results: With the 10 year absolute risk calculations, the Spitz model was well calibrated with NELSON data, with slightly weaker calibration for five year absolute risk calculations. The overall discriminatory power was 0.69 (95% CI = 0.64-0.74), with current smokers having higher discriminatory power (0.73, 95% CI = 0.67-0.79) compared to former smokers (0.61, 95% CI = 0.51-0.70). When examining clinical utility, it peaked at four lung cancer patients correctly identified by the model for every control incorrectly identified as a lung cancer patient. Conclusions: This is the first time the Spitz model has been evaluated for calibration, with very good results except for the lowest and highest risk individuals. The calibration results also confirmed the notion by Dr. Peter Bach9s group that 10 year absolute risk models are better indicators of cancer risk than five year models. The clinical utility results are very similar to those calculated by the Spitz model with Harvard data that was published in the British Journal of Cancer recently. The Spitz model showed moderate discriminatory power, especially among current smokers. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1899. doi:10.1158/1538-7445.AM2011-1899