CHEK2 mutations as markers for high risk of breast cancer
Bibliographic record
Abstract
Genetic testing for the two major breast cancer susceptibility genes, BRCA1 and BRCA2 is widely available in North America and Europe. A few other highly-penetrant breast cancer genes have been found, including p53, BRIP1 and PALB2, but families with mutations in these are exceedingly rare. Arguably, the most relevant of the post-BRCA genes, from a clinical point of view, is CHEK2, which was first linked to breast cancer susceptibility in 2002. In Poland, there are four founder mutations of CHEK2. Three of these (IVS2+1G>A, del5395 and 1100delC) are protein-truncating mutations and one (I157T) is a missense variant. We estimated the lifetime risk of breast cancer for carriers of CHEK2 truncating mutations to be 20% for a woman with no affected relative, 28% for a woman with one second-degree-relative affected, 34% for a woman with one first-degree relative affected, and 44% for a woman with both first- and one-second degree relative affected in the Polish population. In addition we estimated that the lifetime risk for breast cancer for women who carried two different CHEK2 mutations (a truncating mutation and the missense mutation) to be 42%. Our results confirm that CHEK2 mutation screening detects a clinically meaningful risk of breast cancer, and women with a truncating mutation in CHEK2 and a positive family history of breast cancer, and women who carry two different CHEK2 mutations (the missense mutation I157T and a truncating mutation) face a lifetime risk of breast cancer above 25% and are candidates for MRI screening and for tamoxifen chemoprevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".