Effect of <i>CHEK2</i> missense variant I157T on the risk of breast cancer in carriers of other <i>CHEK2</i> or <i>BRCA1</i> mutations
Bibliographic record
Abstract
BACKGROUND: Carriers of heterozygous mutations in CHEK2 or BRCA1 are at increased risk of breast cancer. These mutations are rare and a very small number of women in a population will carry two mutations. However, it is of interest to estimate the breast cancer risks associated with carrying two mutations because this information may be informative for genetic counsellors and may provide clues to the carcinogenic process. METHODS: We genotyped 7782 Polish breast cancer patients and 6233 controls for seven founder mutations in BRCA1 and CHEK2. Odds ratios (OR) and 95% confidence intervals (CI) were estimated for the mutations, singly and in combination. RESULTS: Of the 7782 women with breast cancer, 1091 had one mutation (14.0%) and 37 had two mutations (0.5%). Compared to controls, the odds ratio for a BRCA1 mutation in isolation was 13.1 (95% CI 8.2 to 21). The odds ratio was smaller for BRCA1 mutation carriers who also carried a CHEK2 mutation (OR 6.6, 95% CI 1.5 to 29), but the difference was not statistically significant. In contrast, the odds ratio for women who carried two CHEK2 mutations (OR 3.9, 95% CI 1.5 to 10) was greater than that for women who carried one CHEK2 mutation (OR 1.9, 95% CI 1.6 to 2.1). The odds ratio for women who carried both a truncating mutation and the missense mutation in CHEK2 was 7.0 (95% CI 0.9 to 56) and was greater than for women who carried the truncating mutation alone (OR 3.3, 95% CI 2.4 to 4.3) or the missense mutation alone (OR 1.6, 95% CI 1.4 to 1.9), but the difference was not statistically significant. CONCLUSION: Our study suggests that the risk of breast cancer in carriers of a deleterious CHEK2 mutation is increased if the second allele is the I157T missense variant. However, the presence of a CHEK2 mutation in women with a BRCA1 mutation may not increase their risk beyond that of the BRCA1 mutation alone. These suggestive findings need to be verified in other studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".