Genetic and lifestyle factors for breast cancer risk assessment in Southeast China

Abstract

Background: Despite the rising incidence and mortality of breast cancer among women in China, there are currently few predictive models for breast cancer in the Chinese population and with low accuracy. This study aimed to identify major genetic and life-style risk factors in a Chinese population for potential application in risk assessment models.

Methods: A case-control study in southeast China was conducted including 1321 breast cancer patients and 2045 controls during 2013-2016, in which the data were randomly divided into a training set and a test set on a 7:3 scale. The association between genetic and life-style factors and breast cancer was examined using logistic regression models. Using AUC curves, we also compared the performance of the logistic model to machine learning models, namely LASSO regression model and support vector machine (SVM), and the scores calculated from CKB, Gail and Tyrer-Cuzick models in the test set.

Results: Among all factors considered, the best model was achieved when polygenetic risk score, lifestyle, and reproductive factors were considered jointly in the logistic regression model (AUC = 0.73; 95% CI: 0.70-0.77). The models created in this study performed better than those using scores calculated from the CKB, Gail, and Tyrer-Cuzick models. However, the logistic model and machine learning models did not significantly differ from one another.

Conclusion: In summary, we have found genetic and lifestyle risk predictors for breast cancer with moderate discrimination, which might provide reference for breast cancer screening in southeast China. Further population-based studies are needed to validate the model for future applications in personalized breast cancer screening programs.

Keywords: breast cancer; machine learning; risk assessment.

FIGURE 1

Receiver‐operating characteristic (ROC) curves for different risk assessment models of breast cancer. (A): ROC curves of logistic model that include only traditional risk factors compared with the models based on CKB score, Gail score and Tyrer–Cuzick score. (B): ROC curves of logistic model that include only traditional risk factors compared with logistics regression model that include both traditional risk factors and PRS. (C): ROC curves of logistic model that include only traditional risk factors compared with LASSO regression model and SVM m.