TP53 variant clusters stratify phenotypic diversity in germline carriers and reveal an osteosarcoma-prone subgroup
Bibliographic record
Abstract
Li-Fraumeni syndrome (LFS) has recently been redefined as a ‘spectrum’ cancer predisposition disorder to reflect its broad phenotypic heterogeneity. This variability is thought to stem in part from the diverse functional impacts of TP53 variants, although the underlying mechanisms remain poorly understood and there is an unmet clinical need for effective risk stratification. Here, we apply unsupervised clustering to functional datasets and identify distinct TP53 variant groups with clinical relevance, including a monomeric subgroup enriched in osteosarcoma cases. In cellular validation assays, dermal fibroblasts from carriers of more functionally impaired variants exhibit increased metabolic growth rates, mirroring trends observed in cluster-stratified clinical outcomes. These findings demonstrate the feasibility of developing diagnostic assays to guide personalized cancer risk assessment. More broadly, our results show that nuances in TP53 dysfunction shape the germline TP53-related cancer susceptibility spectrum and provide a framework for functionally delineating variant carriers. Li-Fraumeni syndrome is a cancer predisposition disorder caused by TP53 variants, but the way different TP53 variants contribute remains unclear. Here, the authors analyse TP53 mutagenesis datasets and identify five TP53 variant clusters that show associations with specific cancer patterns as well as potential clinical strategies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".