DICER1 in pediatric and adult cancer predisposition populations: Prevalence, phenotypes, and mosaicism
Bibliographic record
Abstract
PURPOSE: DICER1 tumor predisposition syndrome (DTPS) is a hereditary condition affecting children and young adults. Identification of DICER1 carriers is key for prevention and actionability in families. However, DTPS diagnosis is hindered by its incomplete penetrance and broad phenotypic spectrum. METHODS: We performed an analysis of DICER1 sequencing data from 92 children and 6108 adults with suspected cancer predisposition syndrome. Clinical and DICER1 somatic data from selected carriers and public data sets were studied. RESULTS: The prevalence of germline DICER1 pathogenic variants was 1:30 in children and 1:3054 in adults. No adult referral phenotype was a known DTPS-associated tumor, although 3 of 5 carriers developed thyroid alterations. We provide functional evidence supporting the pathogenicity of a novel in-frame deletion. A 56-year-old woman with ovarian carcinoma and toxic diffuse thyroid hyperplasia was found to have a postzygotic hotspot missense variant. CONCLUSION: The prevalence of DICER1 pathogenic variants in cancer predisposition populations was 5 to 6 times that reported in the general population. Pediatric-onset DTPS is well characterized, whereas adult carriers mainly present with thyroid abnormalities in the absence of DICER1-related family history, thus requiring accurate criteria for its identification when in constellation with other tumor types. Postzygotic hotspot missense variants may exist without the expected severe phenotype.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".