Carol J. Etzel

Genetic Variations in Radiation and Chemotherapy Drug Action Pathways Predict Clinical Outcomes in Esophageal Cancer

PURPOSE Understanding how specific genetic variants modify drug action pathways may provide informative blueprints for individualized chemotherapy. METHODS We applied a pathway-based approach to examine the impact of a comprehensive panel of genetic polymorphisms on clinical outcomes in 210 esophageal cancer patients. RESULTS In the Cox proportional hazards model, MTHFR Glu429Ala variant genotypes were associated with significantly improved survival (hazard ratio [HR] = 0.56; 95% CI, 0.35 to 0.89) in patients treated with fluorouracil (FU). The 3-year survival rates for patients with the variant genotypes and the wild genotypes were 65.26% and 46.43%, respectively. Joint analysis of five polymorphisms in three FU pathway genes showed a significant trend for reduced recurrence risk and longer recurrence-free survival as the number of adverse alleles decreased (P = .004). For patients receiving platinum drugs, the MDR1 C3435T variant allele was associated with significantly reduced recurrence risk (HR = 0.25; 95% CI, 0.10 to 0.64) and improved survival (HR = 0.44; 95% CI, 0.23 to 0.85). In nucleotide excision repair genes, there was a significant trend for a decreasing risk of death with a decreasing number of high-risk alleles (P for trend = .0008). In base excision repair genes, the variant alleles of XRCC1 Arg399Gln were significantly associated with the absence of pathologic complete response (odds ratio = 2.75; 95% CI, 1.14 to 6.12) and poor survival (HR = 1.92; 95% CI, 1.00 to 3.72). CONCLUSION Several biologically plausible associations between individual single nucleotide polymorphisms and clinical outcomes were found. Our data also strongly suggest that combined pathway-based analysis may provide valuable prognostic markers of clinical outcomes.

Cytokinesis-Blocked Micronucleus Assay as a Novel Biomarker for Lung Cancer Risk

In this case-control study, we modified the cytokinesis-block micronucleus (CBMN) assay, an established biomarker for genomic instability, to evaluate susceptibility to the nicotine-derived nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) by measuring the frequency of NNK-induced chromosomal damage endpoints (micronuclei, nucleoplasmic bridges, and nuclear buds) per 1,000 binucleated lymphocytes. Spontaneous and NNK-induced chromosomal damage were significantly higher in lung cancer patients compared with controls. Forty-seven percent of cases (versus 12% of controls) had >or=4 spontaneous micronuclei, 66% of cases (and no controls) had >or=4 spontaneous nucleoplasmic bridges, and 25% of cases (versus 5% of controls) had >or=1 spontaneous nuclear bud (P < 0.001). Similarly, 40% of cases (versus 6% of the controls) had >or=5 NNK-induced micronuclei, 89% of cases (and no controls) had >or=6 induced nucleoplasmic bridges, and 23% of cases (versus 2% of controls) had >or=2 induced nuclear buds (P < 0.001). When analyzed on a continuous scale, spontaneous micronuclei, nucleoplasmic bridges, and nuclear buds were associated with 2-, 29-, and 6-fold increases in cancer risk, respectively. Similarly, NNK-induced risks were 2.3-, 45.5-, and 10-fold, respectively. We evaluated the use of CBMN assay to predict cancer risk based on the numbers of micronuclei, nucleoplasmic bridges, and nuclear buds defined by percentile cut points in controls. Probabilities of being a cancer patient were 96%, 98%, and 100% when using the 95th percentiles of spontaneous and NNK-induced micronuclei, nucleoplasmic bridges, and nuclear buds, respectively. Our study indicates that the CBMN assay is extremely sensitive to NNK-induced genetic damage and may serve as a strong predictor of lung cancer risk.

Association and Interactions between DNA Repair Gene Polymorphisms and Adult Glioma

It is generally accepted that glioma develops through accumulation of genetic alterations. We hypothesized that polymorphisms of candidate genes involved in the DNA repair pathways may contribute to susceptibility to glioma. To address this possibility, we conducted a study on 373 Caucasian glioma cases and 365 cancer-free Caucasian controls to assess associations between glioma risk and 18 functional single-nucleotide polymorphisms in DNA repair genes. We evaluated potential gene-gene and gene-environment interactions using a multianalytic strategy combining logistic regression, multifactor dimensionality reduction and classification and regression tree approaches. In the single-locus analysis, six single-nucleotide polymorphisms [ERCC1 3′ untranslated region (UTR), XRCC1 R399Q, APEX1 E148D, PARP1 A762V, MGMT F84L, and LIG1 5′UTR] showed a significant association with glioma risk. In the analysis of cumulative genetic risk of multiple single-nucleotide polymorphisms, a significant gene-dosage effect was found for increased glioma risk with increasing numbers of adverse genotypes involving the aforementioned six single-nucleotide polymorphisms (Ptrend = 0.0004). Furthermore, the multifactor dimensionality reduction and classification and regression tree analyses identified MGMT F84L as the predominant risk factor for glioma and revealed strong interactions among ionizing radiation exposure, PARP1 A762V, MGMT F84L, and APEX1 E148D. Interestingly, the risk for glioma was dramatically increased in ionizing radiation exposure individuals who had the wild-type genotypes of MGMT F84L and PARP1 A762V (adjusted odds ratios, 5.95; 95% confidence intervals, 2.21-16.65). Taken together, these results suggest that polymorphisms in DNA repair genes may act individually or together to contribute to glioma risk. (Cancer Epidemiol Biomarkers Prev 2009;18(1):204–14)

An expanded risk prediction model for lung cancer.

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.

Polymorphisms of LIG4, BTBD2, HMGA2, and RTEL1 Genes Involved in the Double-Strand Break Repair Pathway Predict Glioblastoma Survival

PURPOSE Glioblastoma (GBM) is the most common and aggressive type of glioma and has the poorest survival. However, a small percentage of patients with GBM survive well beyond the established median. Therefore, identifying the genetic variants that influence this small number of unusually long-term survivors may provide important insight into tumor biology and treatment. PATIENTS AND METHODS Among 590 patients with primary GBM, we evaluated associations of survival with the 100 top-ranking glioma susceptibility single nucleotide polymorphisms from our previous genome-wide association study using Cox regression models. We also compared differences in genetic variation between short-term survivors (STS; <or= 12 months) and long-term survivors (LTS; >or= 36 months), and explored classification and regression tree analysis for survival data. We tested results using two independent series totaling 543 GBMs. RESULTS We identified LIG4 rs7325927 and BTBD2 rs11670188 as predictors of STS in GBM and CCDC26 rs10464870 and rs891835, HMGA2 rs1563834, and RTEL1 rs2297440 as predictors of LTS. Further survival tree analysis revealed that patients >or= 50 years old with LIG4 rs7325927 (V) had the worst survival (median survival time, 1.2 years) and exhibited the highest risk of death (hazard ratio, 17.53; 95% CI, 4.27 to 71.97) compared with younger patients with combined RTEL1 rs2297440 (V) and HMGA2 rs1563834 (V) genotypes (median survival time, 7.8 years). CONCLUSION Polymorphisms in the LIG4, BTBD2, HMGA2, and RTEL1 genes, which are involved in the double-strand break repair pathway, are associated with GBM survival.

Matched-Pair Analysis of Survival of Never Smokers and Ever Smokers With Squamous Cell Carcinoma of the Head and Neck

PURPOSE To compare survival rates between patients with squamous cell carcinoma of the head and neck (SCCHN) without a history of smoking (never smokers) and those with a current or previous history of smoking (ever smokers). PATIENTS AND METHODS Fifty never smokers with newly diagnosed SCCHN were matched to 50 ever smokers according to sex, age, tumor site, overall stage, nodal stage, and treatment. Survival analysis was performed using Kaplan-Meier estimates. Matched-pair survival was compared using the Cox proportional hazards model. RESULTS The never smokers had a greater overall survival (P =.020), disease-specific survival (P =.022), and recurrence-free survival (P =.016). Furthermore, matched-pair analysis demonstrated smoking was associated with a significant increase in risk of overall death (relative risk [RR] = 3.50; 95% CI, 1.14 to 10.77; P =.029), risk of death owing to disease (RR = 3.98; 95% CI, 1.11 to 14.33; P =.034), and risk of disease recurrence (RR = 3.29; 95% CI, 1.18 to 9.14; P =.023). Smoking was associated with three-fold increases in risk for overall death, death owing to disease, and recurrence after adjustment for cancer-associated symptom severity and alcohol use, but the 95% CI for these adjusted risk estimates each included the null. CONCLUSION Survival differed significantly between never smokers and ever smokers with SCCHN. These results are not substantively explained by differences in cancer-associated symptoms or alcohol use, but the CIs are wide and some imprecision remains. Regardless, possible fundamental differences in SCCHN between ever smokers and never smokers may exist, and further molecular characterization of these tumors is needed to determine whether biologic differences needing targeted therapies exist.

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.

Bias in Estimates of Quantitative-Trait–Locus Effect in Genome Scans: Demonstration of the Phenomenon and a Method-of-Moments Procedure for Reducing Bias

An attractive feature of variance-components methods (including the Haseman-Elston tests) for the detection of quantitative-trait loci (QTL) is that these methods provide estimates of the QTL effect. However, estimates that are obtained by commonly used methods can be biased for several reasons. Perhaps the largest source of bias is the selection process. Generally, QTL effects are reported only at locations where statistically significant results are obtained. This conditional reporting can lead to a marked upward bias. In this article, we demonstrate this bias and show that its magnitude can be large. We then present a simple method-of-moments (MOM)-based procedure to obtain more-accurate estimates, and we demonstrate its validity via Monte Carlo simulation. Finally, limitations of the MOM approach are noted, and we discuss some alternative procedures that may also reduce bias.

Increased risk of lung cancer in individuals with a family history of the disease: A pooled analysis from the International Lung Cancer Consortium

BACKGROUND AND METHODS Familial aggregation of lung cancer exists after accounting for cigarette smoking. However, the extent to which family history affects risk by smoking status, histology, relative type and ethnicity is not well described. This pooled analysis included 24 case-control studies in the International Lung Cancer Consortium. Each study collected age of onset/interview, gender, race/ethnicity, cigarette smoking, histology and first-degree family history of lung cancer. Data from 24,380 lung cancer cases and 23,305 healthy controls were analysed. Unconditional logistic regression models and generalised estimating equations were used to estimate odds ratios and 95% confidence intervals. RESULTS Individuals with a first-degree relative with lung cancer had a 1.51-fold increase in the risk of lung cancer, after adjustment for smoking and other potential confounders (95% CI: 1.39, 1.63). The association was strongest for those with a family history in a sibling, after adjustment (odds ratios (OR) = 1.82, 95% CI: 1.62, 2.05). No modifying effect by histologic type was found. Never smokers showed a lower association with positive familial history of lung cancer (OR = 1.25, 95% CI: 1.03, 1.52), slightly stronger for those with an affected sibling (OR = 1.44, 95% CI: 1.07, 1.93), after adjustment. CONCLUSIONS The occurrence of lung cancer among never smokers and similar magnitudes of the effect of family history on lung cancer risk across histological types suggests familial aggregation of lung cancer is independent of those risks associated with cigarette smoking. While the role of genetic variation in the aetiology of lung cancer remains to be fully characterised, family history assessment is immediately available and those with a positive history represent a higher risk group.

Projecting Individualized Probabilities of Developing Bladder Cancer in White Individuals

PURPOSE There has been no risk assessment model for bladder cancer (BC). We developed the first model incorporating mutagen sensitivity and epidemiologic factors to predict BC risk. PATIENTS AND METHODS We used epidemiologic and genetic data from a large case-control study to build the models and constructed receiver operating characteristic curves. The area under the curve (AUC) was used to evaluate model discriminatory ability. We also projected absolute risk of developing BC by taking into account competing causes of death. RESULTS The study included 678 white BC patients and 678 controls. Significant risk factors in the epidemiologic model included pack-years smoked and exposures to diesel, aromatic amines, dry cleaning fluids, radioactive materials, and arsenic. This model yielded good discriminatory ability (AUC = 0.70; 95% CI, 0.67 to 0.73). When mutagen sensitivity data were incorporated, the AUC increased to 0.80 (95% CI, 0.72 to 0.82). The models showed excellent concordance in the internal validation. We also computed an easy to use ordinal risk score and provided examples for projecting absolute risk. CONCLUSION We have developed the first risk prediction model for BC. The enhanced model integrating the genetic factor exhibited excellent discriminatory ability. Our model only requires an individual to answer a few simple questions during a clinic visit to project individualized probability. This model may be used as a basis for developing a Web-based tool for BC risk assessment. Validation of our model in an external population is an essential next step towards practical use in the clinical setting.