Cancer risks in first-degree relatives of CHEK2 mutation carriers: effects of mutation type and cancer site in proband
Bibliographic record
Abstract
It is important to have accurate knowledge of the range of cancers associated with various CHEK2 mutations, and of the lifetime risks of cancer associated with each. We wished to establish the relationship between family history, mutation type and cancer risk in families with a CHEK2 mutation. We obtained a blood sample and pedigree information from 2012 unselected women with breast cancer, from 2007 men with prostate cancer and from 1934 patients with colon cancer, from hospitals throughout Poland. Genetic testing was carried out for four founder CHEK2 mutations on all 5953 specimens and 533 carriers were identified. We estimated the risk to age 75 for any cancer in the 2544 first-degree relatives to be 22.3%. After adjusting for mutation type, the risk of breast cancer was much higher among relatives of probands with breast cancer than among relatives of patients with prostate or colon cancer (HR=3.6; 95% CI=2.1-6.2; P=0.0001). Similarly, the risk of prostate cancer was higher among relatives of probands with prostate cancer than among relatives of patients with breast or colon cancer (HR=4.4; 95% CI=2.2-8.7; P=0.0001) and the risk of colon cancer was higher among relatives of probands with colon cancer than among relatives of patients with prostate or breast cancer (HR=4.2; 95% CI=2.4-7.8; P=0.0001). These analyses suggest that the risk of cancer in a carrier of a CHEK2 mutation is dependent on the family history of cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".