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Mosaicism in Tumor Suppressor Gene Syndromes: Prevalence, Diagnostic Strategies, and Transmission Risk

2022· review· en· W4294127795 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnual Review of Genomics and Human Genetics · 2022
Typereview
Languageen
FieldMedicine
TopicNeurofibromatosis and Schwannoma Cases
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersNational Cancer Institute
KeywordsBiologyGeneticsSuppressorPhenotypeAlleleGenePTENDiseaseGenotypeGenetic testingTumor suppressor geneMutationMedicineCarcinogenesisPathology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.927
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.317
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it