Abstract IA10: Li-Fraumeni syndrome: p53 and beyond
Bibliographic record
Abstract
Abstract Germline TP53 mutations are observed in ∼80% of patients with Li-Fraumeni syndrome (LFS) and a lesser frequency of patients meeting the revised Chompret criteria. A variety of studies are being conducted exploring the role of genetic modifiers on p53 function and their impact in refining the genotype:phenotype correlation of the TP53:LFS relationship. In this presentation, four classical pediatric-onset LFS tumors (choroid plexus carcinoma, medulloblastoma, adrenocortical carcinoma and rhabdomyosarcoma) will be used as examples to demonstrate the molecular nuances of this complex relationship. A spectrum of findings will be outlined that support the notion that presence of a germline TP53 mutation in the context of different modifying effects of intragenic TP53 polymorphism as well as variants in genes and proteins that play important roles in the p53 network confers certain biological features to the presentation of these tumors. Biological mechanisms will be discussed and strategies for early cancer detection will be presented. Citation Format: Jonathan Wasserman, Anita Villani, Nardin Samuel, Diana Merino, Ana Novokmet, Margaret Pienkowska, Badr IdSaid, David Malkin. Li-Fraumeni syndrome: p53 and beyond. [abstract]. In: Proceedings of the AACR Special Conference: Cancer Susceptibility and Cancer Susceptibility Syndromes; Jan 29-Feb 1, 2014; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(23 Suppl):Abstract nr IA10. doi:10.1158/1538-7445.CANSUSC14-IA10
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".