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1.  Global assessment of genetic variation influencing response to retinoid chemoprevention in head and neck cancer patients 
Head and neck squamous cell carcinoma (HNSCC) patients are at an increased risk of developing a second primary tumor (SPT) or recurrence following curative treatment. 13-cis-retinoic acid (13-cRA) has been tested in chemoprevention clinical trials but the results have been inconclusive. We genotyped 9,465 SNPs in 450 patients from the Retinoid Head and Neck Second Primary Trial. SNPs were analyzed for associations with SPT/recurrence in patients receiving placebo to identify prognosis markers and further analyzed for effects of 13-cRA in patients with these prognostic loci. Thirteen loci identified a majority subgroup of patients at a high risk of SPT/recurrence and in whom 13-cRA was protective. Patients carrying the common genotype of rs3118570 in the retinoid X receptor (RXRA) were at a 3.33-fold increased risk (95% confidence interval [CI], 1.67–6.67) and represented over 70% of the study population. This locus also identified individuals who received benefit from chemoprevention with a 38% reduced risk (95% CI, 0.43–0.90). Analyses of cumulative effect and potential gene-gene interactions also implicated CDC25C:rs6596428 and JAK2:rs1887427 as two other genetic loci with major roles in prognosis and 13-cRA response. Patients with all three common genotypes had a 76% reduction in SPT/recurrence (95% CI, 0.093–0.64) following 13-cRA chemoprevention. Carriers of these common genotypes constituted a substantial percentage of the study population, indicating that a pharmacogenetics approach could help select patients for 13-cRA chemoprevention. The lack of any alternatives for reducing risk in these patients highlights the need for future clinical trials to prospectively validate our findings.
doi:10.1158/1940-6207.CAPR-10-0125
PMCID: PMC3955084  PMID: 21292633
HNSCC; SPT; single nucleotide polymorphisms; retinoids
2.  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.
doi:10.1158/1940-6207.CAPR-08-0060
PMCID: PMC2854404  PMID: 19138968
3.  Novel Susceptibility Loci for Second Primary Tumors/Recurrence in Head and Neck Cancer Patients: Large Scale Evaluation of Genetic Variants 
Background
This study was aimed to identify novel susceptibility variants for second primary tumor (SPT) or recurrence in curatively treated early stage head and neck squamous cell carcinoma (HNSCC) patients.
Methods
We constructed a custom chip containing a comprehensive panel of 9645 chromosomal and mitochondrial single nucleotide polymorphisms (SNPs) representing 998 cancer-related genes selected by a systematic prioritization schema. Using this chip, we genotyped 150 early-stage HNSCC patients with and 300 matched patients without SPT/recurrence from a prospectively conducted randomized trial and assessed the association of these SNPs with risk of SPT/recurrence.
Results
Individually, six chromosomal SNPs and seven mitochondrial SNPs (mtSNPs) were significantly associated with risk of SPT/recurrence after adjustment for multiple comparisons. A strong gene-dosage effect was observed these SNPs were combined, as evidenced by a progressively increasing SPT/recurrence risk as the number of unfavorable genotypes increased (P for trend < 1.00×10−20). Several polygenic analyses suggest an important role of interconnected functional network and gene-gene interaction in modulating SPT/recurrence. Furthermore, incorporation of these genetic markers into a multivariate model improved significantly the discriminatory ability over the models containing only clinical and epidemiologic variables.
Conclusions
This is the first large scale systematic evaluation of germline genetic variants for their roles in HNSCC SPT/recurrence. The study identified several promising susceptibility loci and demonstrated the cumulative effect of multiple risk loci in HNSCC SPT/recurrence. Furthermore, this study underscores the importance of incorporating germline genetic variation data with clinical and risk factor data in constructing prediction models for clinical outcomes.
doi:10.1158/1940-6207.CAPR-09-0025
PMCID: PMC2964280  PMID: 19584075
iSelect Infinium; Single nucleotide polymorphisms; Head and neck cancer; Secondary primary tumor; recurrence
4.  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.
doi:10.1158/1940-6207.CAPR-08-0082
PMCID: PMC2854402  PMID: 19138969

Results 1-4 (4)