Abstract IA02: Modeling sarcoma susceptibility: The Li-Fraumeni syndrome paradigm
Bibliographic record
Abstract
Abstract The original description of Li-Fraumeni syndrome (LFS) 1969 was generated from the ascertainment of medical and death records of over 600 children with rhabdomyosarcoma. Although the clinical definition of LFS has evolved over the decades, the occurrence of sarcoma in at least one affected TP53 gene mutation carrier is common to many families. However, the genotype:cancer phenotype association of sarcomas in mutation carriers remains poorly understood. This lecture will review some of the phenotypic characterizations of sarcomas in LFS and explore recent exciting advances that suggest a role for various genetic/genomic and epigenetic modifiers of the underlying germline TP53 mutation in defining these clinical phenotypes. The challenges and opportunities to enhance early tumor detection and modify disease risk will also be discussed. Citation Format: David Malkin. Modeling sarcoma susceptibility: The Li-Fraumeni syndrome paradigm [abstract]. In: Proceedings of the AACR Conference on Advances in Sarcomas: From Basic Science to Clinical Translation; May 16-19, 2017; Philadelphia, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(2_Suppl):Abstract nr IA02.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".