Risk Prediction for Gastric Cancer Using GWAS-Identifie Polymorphisms, Helicobacter pylori Infection and Lifestyle-Related Risk Factors in a Japanese Population

SIMPLE SUMMARY: Gastric cancer remains the major cancer in Japan and worldwide. It is expected that practical intervention strategies for prevention, such as personalized approaches based on genetic risk models, will be developed. Here, we developed and validated a risk prediction model for gastric cancer using genetic, biological, and lifestyle-related risk factors. Results showed that the combination of selected GWAS-identified SNP polymorphisms and other predictors provided high discriminatory accuracy and good calibration in both the derivation and validation studies; however, the contribution of genetic factors to risk prediction was limited. The greatest contributor to risk prediction was ABCD classification (Helicobacter pylori infection-related factor). ABSTRACT: Background: As part of our efforts to develop practical intervention applications for cancer prevention, we investigated a risk prediction model for gastric cancer based on genetic, biological, and lifestyle-related risk factors. Methods: We conducted two independent age- and sex-matched case–control studies, the first for model derivation (696 cases and 1392 controls) and the second (795 and 795) for external validation. Using the derivation study data, we developed a prediction model by fitting a conditional logistic regression model using the predictors age, ABCD classification defined by H. pylori infection and gastric atrophy, smoking, alcohol consumption, fruit and vegetable intake, and 3 GWAS-identified polymorphisms. Performance was assessed with regard to discrimination (area under the curve (AUC)) and calibration (calibration plots and Hosmer–Lemeshow test). Results: A combination of selected GWAS-identified polymorphisms and the other predictors provided high discriminatory accuracy and good calibration in both the derivation and validation studies, with AUCs of 0.77 (95% confidence intervals: 0.75–0.79) and 0.78 (0.77–0.81), respectively. The calibration plots of both studies stayed close to the ideal calibration line. In the validation study, the environmental model (nongenetic model) was significantly more discriminative than the inclusive model, with an AUC value of 0.80 (0.77–0.82). Conclusion: The contribution of genetic factors to risk prediction was limited, and the ABCD classification (H. pylori infection-related factor) contributes most to risk prediction of gastric cancer.