Leveraging Therapy-Specific Polygenic Risk Scores to Predict Restrictive Lung Defects in Childhood Cancer Survivors
Bibliographic record
Abstract
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 = 1.60, P = 3.7 × 10-10) and validation (OR = 1.44, P = 8.5 × 10-4) data sets. The composite model featuring the surPRS showed the best discriminatory accuracy (AUC = 0.81; 95% CI, 0.76-0.87), a significant improvement (P = 9.0 × 10-3) over clinical risk scores only (AUC = 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 = 20.01, P = 2.2 × 10-16), exceeding the comparable estimate considering nongenetic risk factors only (OR = 9.20, P = 7.4 × 10-11). Inclusion of genetic predictors also selectively improved risk stratification for pulmonary complications across at-risk primary cancer diagnoses (AUCclinical = 0.72; AUCcomposite = 0.80, P = 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".