Association between the Lynch syndrome gene MSH2 and breast cancer susceptibility in a Canadian familial cancer registry
Bibliographic record
Abstract
Background Previous studies assessing breast cancer risk in families with Lynch syndrome (LS) have yielded conflicting results. Furthermore, conclusions are limited by small sample size and few breast cancer outcomes. This study assesses breast cancer risk in a large prospectively followed LS cohort. Methods Pedigrees of 325 unrelated families with LS within the Familial Gastrointestinal Cancer Registry in Canada were examined for breast cancer diagnoses. Standardised incidence ratios (SIR) and lifetime cumulative incidence calculations were used to compare the incidence of breast cancer in mutation carriers with the general population. Results Forty-one mutation carriers diagnosed with breast cancer belonging to 34 unrelated families were identified. Mean age at diagnosis was 54 years. The mutation distribution among the LS patients with breast cancer was statistically different from those without breast cancer (p=0.015), reflecting the predominance of MSH2 mutations among affected patients (74%). Eighty-eight per cent of LS families with breast cancer met Amsterdam criteria, compared with 49% of LS families without breast cancer (p=0.03). Lifetime cumulative incidence of breast cancer in female MSH2 mutation carriers in our cohort was 22% (p<0.001). The SIR for breast cancer of female MSH2 mutation carriers in our cohort was 3.11 (95% CI 1.95 to 4.71). Conclusions An increased risk of breast cancer in MSH2 mutation carriers was demonstrated in a Canadian familial cancer registry. Women with breast cancer often had a personal and family history of multiple LS-related malignancies. These results suggest a potential role for intensified breast cancer surveillance among women with LS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".