Adult-Onset Cancer Predisposition Syndromes in Children and Adolescents—To Test or not to Test?
Bibliographic record
Abstract
With the increasing use of comprehensive germline genetic testing of children and adolescents with cancer, it has become evident that pathogenic variants (PV) in adult-onset cancer predisposition genes (aoCPG) underlying adult-onset cancer predisposition syndromes, such as Lynch syndrome or hereditary breast and ovarian cancer, are enriched and reported in 1% to 2% of children and adolescents with cancer. However, the causal relationship between PVs in aoCPGs and childhood cancer is still under investigation. The best-studied examples include heterozygous PVs in mismatch repair genes associated with Lynch syndrome in children with mismatch repair deficient high-grade glioma, heterozygous PVs in BARD1 in childhood neuroblastoma, and heterozygous PVs in BRCA2 in children with rhabdomyosarcoma. The low penetrance for pediatric cancers is considered to result from a combination of the low baseline risk of cancer in childhood and the report of only a modest relative risk of disease in childhood. Therefore, we do not advise that healthy children empirically be tested for PVs in an aoCPG before adulthood outside a research study. However, germline panel testing is increasingly being performed in children and adolescents with cancer, and exome and genome sequencing may be offered more commonly in this population in the future. The precise pediatric cancer risks and spectra associated with PVs in aoCPGs, underlying cellular mechanisms and somatic mutational signatures, as well as treatment response, second neoplasm risks, and psycho-oncological aspects require further research.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".