Risk of extracolonic cancers for people with biallelic and monoallelic mutations in <i>MUTYH</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it