Risk of Non-Melanoma Cancers in First-Degree Relatives of CDKN2A Mutation Carriers
Bibliographic record
Abstract
The purpose of this study was to quantify the risk of cancers other than melanoma among family members of CDKN2A mutation carriers using data from the Genes, Environment and Melanoma study. Relative risks (RRs) of all non-melanoma cancers among first-degree relatives (FDRs) of melanoma patients with CDKN2A mutations (n = 65) and FDRs of melanoma patients without mutations (n = 3537) were calculated as the ratio of estimated event rates (number of cancers/total person-years) in FDRs of carriers vs noncarriers with exact Clopper-Pearson-type tests and 95% confidence intervals (CIs). All statistical tests were two-sided. There were 56 (13.1%) non-melanoma cancers reported among 429 FDRs of mutation carriers and 2199 (9.4%) non-melanoma cancers in 23 452 FDRs of noncarriers. The FDRs of carriers had an increased risk of any cancer other than melanoma (56 cancers among 429 FDRs of carrier probands vs 2199 cancers among 23 452 FDRs of noncarrier probands; RR = 1.5, 95% CI = 1.2 to 2.0, P = .005), gastrointestinal cancer (20 cancers among 429 FDRs of carrier probands vs 506 cancers among 23 452 FDRs of noncarrier probands; RR = 2.4, 95% CI = 1.4 to 3.7, P = .001), and pancreatic cancer (five cancers among 429 FDRs of carrier probands vs 41 cancers among 23 452 FDRs of noncarrier probands; RR = 7.4, 95% CI = 2.3 to 18.7, P = .002). Wilms tumor was reported in two FDRs of carrier probands and three FDRs of noncarrier probands (RR = 40.4, 95% CI = 3.4 to 352.7, P = .005). The lifetime risk of any cancer other than melanoma among CDKN2A mutation carriers was estimated as 59.0% by age 85 years (95% CI = 39.0% to 75.4%) by the kin-cohort method, under the standard assumptions of Mendelian genetics on the genotype distribution of FDRs conditional on proband genotype.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".