Abstract 53: Zebrafish <i>tp53</i> mutants recapitulate Li-Fraumeni syndrome phenotypes and provide a preclinical platform for evaluating tumor preventive compounds
Bibliographic record
Abstract
Abstract Background: Li-Fraumeni syndrome (LFS) is a hereditary cancer predisposition disorder associated with a highly penetrant and diverse tumor spectrum characterized by germline mutations in the TP53 tumor suppressor gene. Individuals with LFS experience early tumor onset, often during childhood, with a cumulative lifetime cancer risk of 68% in males and 93% in females. Being prone to multiple cancer diagnoses, individuals with LFS endure frequent long-term multi-modal cancer screening protocols, which have improved survival through early detection but are non-specific and can be very burdensome. Preclinical animal models recapitulating the genomic landscape observed in LFS offer the potential to define cancer-driving mechanisms in LFS to personalize tumor surveillance, while also providing a preclinical platform for testing molecularly targeted therapies. Zebrafish are a powerful cancer research tool due to their high degree of genetic conservation; histological/molecular similarities in tumor development with their human counterparts; optical clarity facilitating high-resolution imaging and non-invasive tumor screening. Experimental Design: We have generated two tp53 zebrafish point mutants, R217H and R242H, (representing commonly mutated human LFS and sporadic residues, R248 and R273H) and obtained tp53 null mutants (Langenau lab). The point mutants recapitulate LFS phenotypes, including partial-to-no expression of p53 target genes; resistance to p53-mediated apoptosis; dominant negative activity; and tp53 loss-of-heterozygosity in tumor tissue. These mutants develop tumors beginning at 6-9 months post-fertilization that histologically resemble human sarcomas. R242H mutants exhibit earlier tumor onset and higher life-time incidence than both R217H and null mutants, suggesting that this mutation is more aggressive. Additionally, R217H and R242H mutants each present with a distinctive anatomic tumor distribution and skewed sex ratios, indicating different tumor-driving mechanisms. Whole larvae RNA sequencing and DNA methylation analyses are in progress to reveal mutation-specific mechanisms and pathways, which may reveal novel molecular targets for therapeutic intervention. tp53 R217H and R242H mutants are currently being subjected to treatment with pCAPs (p53 conformation activating peptides) that restore p53 tumor-suppressive hallmarks and/or re-establish native p53 folding to potentially prevent tumor initiation. Significance: In sum, these zebrafish LFS models hold tremendous potential to unravel the pathogenesis of tumor formation in this cancer predisposition syndrome and facilitate a precision-medicine approach to cancer surveillance and prevention to increase survival and enhance quality of life for this vulnerable population. Citation Format: Kim Kobar, Erin Burnley, Lissandra Tuzi, Craig Midgen, Adam Shlien, David Malkin, Sergey V. Prykhozhij, Jason N. Berman. Zebrafish tp53 mutants recapitulate Li-Fraumeni syndrome phenotypes and provide a preclinical platform for evaluating tumor preventive compounds [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 53.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".