tp53 R217H and R242H mutant zebrafish exhibit dysfunctional p53 hallmarks and recapitulate Li-Fraumeni syndrome phenotypes
Bibliographic record
Abstract
Li-Fraumeni syndrome (LFS) is a hereditary cancer predisposition syndrome associated with a highly penetrant cancer spectrum characterized by germline TP53 mutations. We characterized the first LFS zebrafish hotspot mutants, tp53 R217H and R242H (human R248H and R273H), and found these mutants exhibit partial-to-no activation of p53 target genes, have defective cell-cycle checkpoints, and display partial-to-full resistance to apoptosis, although the R217H mutation has hypomorphic characteristics. Spontaneous tumor development histologically resembling human sarcomas was observed as early as 6 months. tp53 R242H mutants had a higher lifetime tumor incidence compared to tp53 null and R217H mutants, suggesting it is a more aggressive mutation. We observed mutation-specific tumor phenotypes across tp53 mutants with associated diverse transcriptomic and DNA methylome profiles in tp53 mutant larvae, impacting metabolism, cell signalling, and biomacromolecule synthesis and degradation. These tp53 zebrafish mutants demonstrate fidelity to their human counterparts and provide new insights into underlying tumorigenesis mechanisms and kinetics that suggest metabolic rewiring and cellular signalling changes occur prior to tumor initiation, which will guide targeted therapeutics for LFS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".