Cancer risks associated with germline PALB2 pathogenic variants - an international study of 524 families
Bibliographic record
Abstract
PURPOSE To estimate age-specific relative and absolute cancer risks for breast cancer, and to estimate risks of ovarian, pancreatic, male breast, prostate, and colorectal cancers associated with germline PALB2 pathogenic variants (PVs) as these risks have not been extensively characterized. METHODS We analysed data from 524 families with PALB2 PVs from 21 countries. Complex segregation analysis was used to estimate relative risks (RRs, relative to country-specific population incidences) and absolute risks of cancers. The models allowed for residual familial aggregation of breast and ovarian cancer and were adjusted for the family-specific ascertainment schemes. RESULTS We found associations between PALB2 PVs and risk of female breast cancer (RR=7.18; 95% CI: 5.82-8.85, p=6.5×10-76), ovarian cancer (RR=2.91; 95% CI: 1.40-6.04, p=4.1×10-3), pancreatic cancer (RR=2.37; 95% CI: 1.24-4.50; p=8.7×10-3), and male breast cancer (RR=7.34, 95% CI: 1.28-42.18, p=2.6×10-2). There no evidence for increased risks of prostate or colorectal cancer. The breast cancer RRs declined with age (p-trend=2.0×10-3). After adjusting for family-ascertainment, breast cancer risk estimates based on multiple case families were similar to the estimates from families ascertained through population-based studies (p-difference=0.41). Based on the combined data, the estimated risks to age 80 years were 53% (95% CI: 44-63%) for female breast cancer, 5% (95% CI: 2-10%) for ovarian cancer, 2-3% (95% CI: females: 1-4%; males: 2-5%) for pancreatic cancer, and 1% (95% CI: 0.2-5%) for male breast cancer. CONCLUSION These results confirm PALB2 as a key breast cancer susceptibility gene and establish substantial associations between germline PALB2 PVs and ovarian, pancreatic, and male breast cancers. These findings will facilitate incorporation of PALB2 into risk prediction models and optimise the clinical cancer risk management of PALB2 PV carriers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".