Radiation Treatment, <i>ATM</i>, <i>BRCA1/2</i>, and <i>CHEK2</i>*1100delC Pathogenic Variants and Risk of Contralateral Breast Cancer
Bibliographic record
Abstract
Whether radiation therapy (RT) affects contralateral breast cancer (CBC) risk in women with pathogenic germline variants in moderate- to high-penetrance breast cancer-associated genes is unknown. In a population-based case-control study, we examined the association between RT; variants in ATM, BRCA1/2, or CHEK2*1100delC; and CBC risk. We analyzed 708 cases of women with CBC and 1399 controls with unilateral breast cancer, all diagnosed with first invasive breast cancer between 1985 and 2000 and aged younger than 55 years at diagnosis and screened for variants in breast cancer-associated genes. Rate ratios (RR) and 95% confidence intervals (CIs) were estimated using multivariable conditional logistic regression. RT did not modify the association between known pathogenic variants and CBC risk (eg, BRCA1/2 pathogenic variant carriers without RT: RR = 3.52, 95% CI = 1.76 to 7.01; BRCA1/2 pathogenic variant carriers with RT: RR = 4.46, 95% CI = 2.96 to 6.71), suggesting that modifying RT plans for young women with breast cancer is unwarranted. Rare ATM missense variants, not currently identified as pathogenic, were associated with increased risk of RT-associated CBC (carriers of ATM rare missense variants of uncertain significance without RT: RR = 0.38, 95% CI = 0.09 to 1.55; carriers of ATM rare missense variants of uncertain significance with RT: RR = 2.98, 95% CI = 1.31 to 6.80). Further mechanistic studies will aid clinical decision-making related to RT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".