The <i>CHEK2*1100delC</i> Allelic Variant and Risk of Breast Cancer: Screening Results from the Breast Cancer Family Registry
Bibliographic record
Abstract
CHEK2, a serine-threonine kinase, is activated in response to agents, such as ionizing radiation, which induce DNA double-strand breaks. Activation of CHEK2 can result in cell cycle checkpoint arrest or apoptosis. One specific variant, CHEK2*1100delC, has been associated with an increased risk of breast cancer. In this population-based study, we screened 2,311 female breast cancer cases and 496 general population controls enrolled in the Ontario and Northern California Breast Cancer Family Registries for this variant (all controls were Canadian). Overall, 30 cases and one control carried the 1100delC allele. In Ontario, the weighted mutation carrier frequency among cases and controls was 1.34% and 0.20%, respectively [odds ratio (OR), 6.65; 95% confidence interval (95% CI), 2.37-18.68]. In California, the weighted population mutation carrier frequency in cases was 0.40%. Across all cases, 1 of 524 non-Caucasians (0.19%) and 29 of 1,775 Caucasians (1.63%) were mutation carriers (OR, 0.12; 95% CI, 0.02-0.89). Among Caucasian cases >45 years age at diagnosis, carrier status was associated with history of benign breast disease (OR, 3.18; 95% CI, 1.30-7.80) and exposure to diagnostic ionizing radiation (excluding mammography; OR, 3.21; 95% CI, 1.13-9.14); compared with women without exposure to ionizing radiation, the association was strongest among women exposed >15 years before diagnosis (OR, 4.28; 95% CI, 1.50-12.20) and among those who received two or more chest X-rays (OR, 3.63; 95% CI, 1.25-10.52). These data supporting the biological relevance of CHEK2 in breast carcinogenesis suggest that further studies examining the joint roles of CHEK2*1100delC carrier status and radiation exposure may be warranted.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".