The predictive ability of the 313 variant–based polygenic risk score for contralateral breast cancer risk prediction in women of European ancestry with a heterozygous BRCA1 or BRCA2 pathogenic variant
Bibliographic record
Abstract
Purpose To evaluate the association between a previously published 313 variant–based breast cancer (BC) polygenic risk score (PRS 313 ) and contralateral breast cancer (CBC) risk, in BRCA1 and BRCA2 pathogenic variant heterozygotes. Methods We included women of European ancestry with a prevalent first primary invasive BC ( BRCA1 = 6,591 with 1,402 prevalent CBC cases; BRCA2 = 4,208 with 647 prevalent CBC cases) from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA), a large international retrospective series. Cox regression analysis was performed to assess the association between overall and ER-specific PRS 313 and CBC risk. Results For BRCA1 heterozygotes the estrogen receptor (ER)-negative PRS 313 showed the largest association with CBC risk, hazard ratio (HR) per SD = 1.12, 95% confidence interval (CI) (1.06–1.18), C-index = 0.53; for BRCA2 heterozygotes, this was the ER-positive PRS 313 , HR = 1.15, 95% CI (1.07–1.25), C-index = 0.57. Adjusting for family history, age at diagnosis, treatment, or pathological characteristics for the first BC did not change association effect sizes. For women developing first BC < age 40 years, the cumulative PRS 313 5th and 95th percentile 10-year CBC risks were 22% and 32% for BRCA1 and 13% and 23% for BRCA2 heterozygotes, respectively. Conclusion The PRS 313 can be used to refine individual CBC risks for BRCA1/2 heterozygotes of European ancestry, however the PRS 313 needs to be considered in the context of a multifactorial risk model to evaluate whether it might influence clinical decision-making.
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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 | 0.007 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".