A Polygenic Risk Score for Late Bladder Toxicity Following Radiotherapy for Non-Metastatic Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Late bladder toxicity is a concern for patients receiving prostate cancer radiotherapy and negatively affects survivors. Few risk factors are known beyond the radiation dose and volume of bladder exposed. A polygenic risk score (PRS) could identify susceptible patients. METHODS: A PRS was built using genome-wide association results from the Radiogenomics Consortium (N = 3,988) and then tested in the prospective REQUITE and URWCI studies (N = 2,034). The primary outcome was time to patient-reported gross [grade ≥2, (≥G2)] hematuria, analyzed using Cox proportional hazards regression. Secondary outcomes were ≥G2 urinary retention and frequency. The PRS was externally validated for clinically diagnosed irradiation cystitis in the UK Biobank (N = 8,430). A gene-burden test evaluated rare coding variants. RESULTS: A 115-variant PRS was associated with a significantly increased risk of ≥G2 hematuria [hazard ratio (HR) per SD = 1.22; P = 0.009] as well as urinary retention (HR per SD = 1.18; P = 0.016) and frequency (HR per SD = 1.14; P = 0.036). When binarized, men in the upper decile (PRShigh) had a >2-fold increased risk of hematuria after adjusting for clinical risk factors [HR = 2.12; P = 0.002; Harrel's concordance index = 0.71 (95% confidence interval, 0.65-0.76)]. A similar effect size was seen in the UK Biobank for clinically diagnosed irradiation cystitis [odds ratio (OR) = 2.15; P = 0.026]. The burden test identified BOD1L1 as a putative novel radiosensitivity gene. CONCLUSIONS: This PRS identifies susceptible patients and could guide the selection of those needing reoptimized treatment plans that spare the bladder beyond currently recommended constraints. IMPACT: PRS-guided treatment planning in radiation oncology could lower the incidence of clinically relevant bladder toxicity and reduce the impact of this outcome on prostate cancer survivors.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".