Polygenic Risk Score Improves Risk Stratification and Prediction of Subsequent Thyroid Cancer after Childhood Cancer

Background: Subsequent thyroid cancer (STC) is one of the most common malignancies in childhood cancer survivors. We aimed to evaluate the polygenic contributions to STC risk and potential utility in improving risk prediction. Methods: A polygenic risk score (PRS) was calculated from 12 independent SNPs associated with thyroid cancer risk in the general population. Associations between PRS and STC risk were evaluated among survivors from St. Jude Lifetime Cohort (SJLIFE) and were replicated in survivors from Childhood Cancer Survivor Study (CCSS). A risk prediction model integrating the PRS and clinical factors, initially developed in SJLIFE, and its performance were validated in CCSS. Results: Among 2,370 SJLIFE survivors with a median follow-up of 28.8 [interquartile range (IQR) = 21.9–36.1] years, 65 (2.7%) developed STC. Among them, the standardized PRS was associated with an increased rate of STC [relative rate (RR) = 1.57; 95% confidence interval (CI) = 1.24–1.98; P < 0.001]. Similar associations were replicated in 6,416 CCSS survivors, among whom 121 (1.9%) developed STC during median follow-up of 28.9 (IQR = 22.6–34.6) years (RR = 1.52; 95% CI = 1.25–1.83; P < 0.001). A risk prediction model integrating the PRS with clinical factors showed better performance than the model considering only clinical factors in SJLIFE (P = 0.004, AUC = 83.2% vs. 82.1%, at age 40), which was further validated in CCSS (P = 0.010, AUC = 72.9% vs. 70.6%). Conclusions: Integration of the PRS with clinical factors provided a statistically significant improvement in risk prediction of STC, although the magnitude of improvement was modest. Impact: PRS improves risk stratification and prediction of STC, suggesting its potential utility for optimizing screening strategies in survivorship care.