Abstract A03: Risk prediction for gastric cancer using the GWAS-identified SNPs, Helicobacter pylori infection and lifestyle-related risk factors in a Japanese population

Advances in molecular genetics have the potential to impact cancer prevention. However, the contribution of this information to determining the risk of cancer of the stomach at the population level in combination with biological and lifestyle-related factors has not been evaluated. Here, we established a risk prediction model of gastric cancer using genetic, biological, and lifestyle-related risk factors as a potential practical application in interventions for cancer prevention. We conducted two independent age- and sex-matched case-control studies, the first for model derivation (697 cases and 1,372 controls) and the second (678 and 678) for external validation. Using the derivation study data, we developed a prediction model by fitting a conditional logistic regression model using the following predictors: age, ABCD classification defined by H.pylori infection and atrophic gastritis, smoking, fruit and vegetable intake, and selected GWAS-identified genotypes. Performance was assessed regarding discrimination (area under the curve, AUC), calibration (calibration plots and Hosmer-Lemeshow test) and reclassification (integrated discrimination improvement (IDI)). We preliminarily found that a combination of rs229400 , one of the GWAS-identified SNPs, H.Pylori infection, atrophic gastritis, smoking and fruit and vegetable intake provided high discriminatory accuracy and good calibration in both the derivation and validation studies, with AUCs of 0.78 (95% confidence intervals, 0.76-0.80) and 0.80 (0.77-0.82), respectively. The calibration plots of both studies stayed close to the ideal calibration line. IDI indices were 0.12 (p rs2294008 , and the other predictors. Citation Format: Hidemi Ito, Isao Oze, Keitaro Matsuo. Risk prediction for gastric cancer using the GWAS-identified SNPs, Helicobacter pylori infection and lifestyle-related risk factors in a Japanese population. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr A03.