Leveraging Therapy-Specific Polygenic Risk Scores to Predict Restrictive Lung Defects in Childhood Cancer Survivors

Therapy-related pulmonary complications are among the leading causes of morbidity among long-term survivors of childhood cancer. Restrictive ventilatory defects (RVD) are prevalent, with risks increasing after exposures to chest radiotherapy and radiomimetic chemotherapies. Using whole-genome sequencing data from 1,728 childhood cancer survivors in the St. Jude Lifetime Cohort Study, we developed and validated a composite RVD risk prediction model that integrates clinical profiles and polygenic risk scores (PRS), including both published lung phenotype PRSs and a novel survivor-specific pharmaco/radiogenomic PRS (surPRS) for RVD risk reflecting gene-by-treatment (GxT) interaction effects. Overall, this new therapy-specific polygenic risk prediction model showed multiple indicators for superior discriminatory accuracy in an independent data set. The surPRS was significantly associated with RVD risk in both training (OR ¼ 1.60, P ¼ 3.7 × 10^–10) and validation (OR ¼ 1.44, P ¼ 8.5× 10^–4) data sets. The composite model featuring the surPRS showed the best discriminatory accuracy (AUC ¼ 0.81; 95% CI, 0.76–0.87), a significant improvement (P ¼ 9.0 × 10^–3) over clinical risk scores only (AUC ¼ 0.78; 95% CI: 0.72–0.83). The odds of RVD in survivors in the highest quintile of composite model-predicted risk was ~20-fold higher than those with median predicted risk or less (OR ¼ 20.01, P ¼ 2.2 × 10^–16), exceeding the comparable estimate considering nongenetic risk factors only (OR ¼ 9.20, P ¼ 7.4 × 10^–11). Inclusion of genetic predictors also selectively improved risk stratification for pulmonary complications across at-risk primary cancer diagnoses (AUC_clinical ¼ 0.72; AUC_composite ¼ 0.80, P ¼ 0.012). Overall, this PRS approach that leverages GxT interaction effects supports late effects risk prediction among childhood cancer survivors. Significance: This study develops a therapy-specific polygenic risk prediction model to more precisely identify childhood cancer survivors at high risk for pulmonary complications, which could help improve risk stratification for other late effects.