Population-based estimates of breast cancer risks associated with<i>ATM</i>gene variants c.7271T>G and c.1066-6T>G (IVS10-6T>G) from the Breast Cancer Family Registry
Bibliographic record
Abstract
The ATM gene variants segregating in ataxia-telangiectasia families are associated with increased breast cancer risk, but the contribution of specific variants has been difficult to estimate. Previous small studies suggested two functional variants, c.7271T>G and c.1066-6T>G (IVS10-6T>G), are associated with increased risk. Using population-based blood samples we found that 7 out of 3,743 breast cancer cases (0.2%) and 0 out of 1,268 controls were heterozygous for the c.7271T>G allele (P=0.1). In cases, this allele was more prevalent in women with an affected mother (odds ratio [OR]=5.5, 95% confidence interval [CI]=1.2-25.5; P=0.04) and delayed child-bearing (OR=5.1; 95% CI=1.0-25.6; P=0.05). The estimated cumulative breast cancer risk to age 70 years (penetrance) was 52% (95% CI=28-80%; hazard ratio [HR]=8.6; 95% CI=3.9-18.9; P<0.0001). In contrast, 13 of 3,757 breast cancer cases (0.3%) and 10 of 1,268 controls (0.8%) were heterozygous for the c.1066-6T>G allele (OR=0.4; 95% CI=0.2-1.0; P=0.05), and the penetrance was not increased (P=0.5). These findings suggest that although the more common c.1066-6T>G variant is not associated with breast cancer, the rare ATM c.7271T>G variant is associated with a substantially elevated risk. Since c.7271T>G is only one of many rare ATM variants predicted to have deleterious consequences on protein function, an effective means of identifying and grouping these variants is essential to assess the contribution of ATM variants to individual risk and to the incidence of breast cancer in the population.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".