Risk of extracolonic cancers for people with biallelic and monoallelic mutations in <i>MUTYH</i>
Bibliographic record
Abstract
Germline mutations in the DNA base excision repair gene MUTYH are known to increase a carrier's risk of colorectal cancer. However, the risks of other (extracolonic) cancers for MUTYH mutation carriers are not well defined. We identified 266 probands (91% Caucasians) with a MUTYH mutation (41 biallelic and 225 monoallelic) from the Colon Cancer Family Registry. Mutation status, sex, age and histories of cancer from their 1,903 first- and 3,255 second-degree relatives were analyzed using modified segregation analysis conditioned on the ascertainment criteria. Compared with incidences for the general population, hazard ratios (HRs) (95% confidence intervals [CIs]) for biallelic MUTYH mutation carriers were: urinary bladder cancer 19 (3.7-97) and ovarian cancer 17 (2.4-115). The HRs (95% CI) for monoallelic MUTYH mutation carriers were: gastric cancer 9.3 (6.7-13); hepatobiliary cancer 4.5 (2.7-7.5); endometrial cancer 2.1 (1.1-3.9) and breast cancer 1.4 (1.0-2.0). There was no evidence for an increased risk of cancers at the other sites examined (brain, pancreas, kidney or prostate). Based on the USA population incidences, the estimated cumulative risks (95% CI) to age 70 years for biallelic mutation carriers were: bladder cancer 25% (5-77%) for males and 8% (2-33%) for females and ovarian cancer 14% (2-65%). The cumulative risks (95% CI) for monoallelic mutation carriers were: gastric cancer 5% (4-7%) for males and 2.3% (1.7-3.3%) for females; hepatobiliary cancer 3% (2-5%) for males and 1.4% (0.8-2.3%) for females; endometrial cancer 3% (2%-6%) and breast cancer 11% (8-16%). These unbiased estimates of both relative and absolute risks of extracolonic cancers for people, mostly Caucasians, with MUTYH mutations will be important for their clinical management.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".