Heterozygous <i>BRCA1</i> and <i>BRCA2</i> and Mismatch Repair Gene Pathogenic Variants in Children and Adolescents With Cancer
Bibliographic record
Abstract
BACKGROUND: Genetic predisposition is has been identified as a cause of cancer, yet little is known about the role of adult cancer predisposition syndromes in childhood cancer. We examined the extent to which heterozygous pathogenic germline variants in BRCA1, BRCA2, PALB2, ATM, CHEK2, MSH2, MSH6, MLH1, and PMS2 contribute to cancer risk in children and adolescents. METHODS: We conducted a meta-analysis of 11 studies that incorporated comprehensive germline testing for children and adolescents with cancer. ClinVar pathogenic or likely pathogenic variants (PVs) in genes of interest were compared with 2 control groups. Results were validated in a cohort of mainly European patients and controls. We employed the Proxy External Controls Association Test to account for different pipelines. RESULTS: Among 3975 children and adolescents with cancer, statistically significant associations with cancer risk were observed for PVs in BRCA1 and 2 (26 PVs vs 63 PVs among 27 501 controls, odds ratio = 2.78, 95% confidence interval = 1.69 to 4.45; P < .001) and mismatch repair genes (19 PVs vs 14 PVs among 27 501 controls, odds ratio = 7.33, 95% confidence interval = 3.64 to 14.82; P <.001). Associations were seen in brain and other solid tumors but not in hematologic neoplasms. We confirmed similar findings in 1664 pediatric cancer patients primarily of European descent. CONCLUSION: These data suggest that heterozygous PVs in BRCA1 and 2 and mismatch repair genes contribute with reduced penetrance to cancer risk in children and adolescents. No changes to predictive genetic testing and surveillance recommendations are required.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".