Meng Zhu

Ph.D.

Nanjing Medical University

Associate Professor

PLCO (Learn more about this study)

PLCO-892

Jan 7, 2022

Modeling risk for lung cancer using trans-ancestry polygenic risk score, blood biomarkers, and lifestyle behaviors

Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer death, which is responsible for more than 1.7 million deaths worldwide each year. Identification of lung cancer at an early stage and taking intervention can significantly reduce lung cancer mortality. Although the NLST criteria (mainly including age and smoking amount) were widely used to identify high-risk populations for lung cancer screening, epidemiological studies have shown that risk prediction model usually have a better performance than the NLST criteria. However, joint utilizations of the risk prediction model with genetic testing are expected to help further optimize the definition of lung cancer high-risk population and facilitate the development of precision intervention strategies.

So far, a total of 45 genetic susceptibility loci have been identified by genome-wide association studies (GWAS) of lung cancer. Our previous study had successfully constructed a PRS-19, which was effective to assess lung cancer risk in Chinese population. Hung RJ et al. and colleagues also developed a PRS-114 for white populations, recently. However, it remains unclear whether a trans-ancestry PRS would be more effective than ethnic-specific PRS for the risk stratification of lung cancer. After integrating GWASs of lung cancer from East Asians and Europeans, we have developed a trans-ancestry PRS for lung cancer.

A combination of PRS, environmental factors and clinical factors had shown good performance in identification of high-risk populations for breast cancer and prostate cancer. Therefore, we assumed that using the trans-ancestry PRS and combining it with blood biomarkers and lifestyle predictors will improve the power to identify high-risk populations of lung cancer. Therefore, we will construct a trans-ancestry PRS for lung cancer, and evaluate its effectiveness in the UK Biobank as well as the PLCO cohort study respectively. At last, we plan to develop a lung cancer risk prediction model based on the trans-ancestry PRS, blood biomarkers and lifestyle behaviors based on the UK Biobank, and evaluated its application value in the PLCO cohort as well as the NLST clinical screening trial.