Mosaicism in Tumor Suppressor Gene Syndromes: Prevalence, Diagnostic Strategies, and Transmission Risk
Bibliographic record
Abstract
A mosaic state arises when pathogenic variants are acquired in certain cell lineages during postzygotic development, and mosaic individuals may present with a generalized or localized phenotype. Here, we review the current state of knowledge regarding mosaicism for eight common tumor suppressor genes— NF1, NF2, TSC1, TSC2, PTEN, VHL, RB1, and TP53—and their related genetic syndromes/entities. We compare and discuss approaches for comprehensive diagnostic genetic testing, the spectrum of variant allele frequency, and disease severity. We also review affected individuals who have no mutation identified after conventional genetic analysis, as well as genotype–phenotype correlations and transmission risk for each tumor suppressor gene in full heterozygous and mosaic patients. This review provides new insight into similarities as well as marked differences regarding the appreciation of mosaicism in these tumor suppressor syndromes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".