<i>TP53</i> Variant Clusters Stratify the Li-Fraumeni Spectrum and Reveal an Osteosarcoma-Prone Subgroup
Bibliographic record
Abstract
ABSTRACT Li-Fraumeni syndrome (LFS) has recently been redefined as a ‘spectrum’ cancer predisposition disorder to reflect its broad phenotypic heterogeneity. The wide functional gradient associated with different TP53 variants is thought to contribute to LFS heterogeneity, although it is still poorly understood and there is an unmet clinical need for risk stratification strategies. Leveraging p53 mutagenesis dataset, we performed an unsupervised cluster analysis that revealed five TP53 variant clusters with unique structural and functional consequences. Classifying variant carriers according to these clusters stratified cancer onset and survival using discovery and validation cohorts, and exposed important clinical characteristics to consider for patient management. In particular, we identified a subgroup of monomeric TP53 variant carriers prone to osteosarcoma, along with a cluster associated with less “LFS-like” phenotypes enriched in carriers with no history of cancer. Our classification of TP53 variants demonstrates the existence of a wide TP53- heritable cancer susceptibility spectrum and provides a new framework to delineate carriers toward personalized patient care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".