Abstract 6198: <i>TP53</i> mutational clusters stratify the Li-Fraumeni syndrome spectrum
Bibliographic record
Abstract
Abstract Li-Fraumeni syndrome (LFS) is a highly penetrant cancer predisposition disorder caused by germline variants in the TP53 tumor suppressor gene. LFS has recently been redefined as a ‘spectrum’ disorder to reflect the highly variable cancer types with largely unpredictable ages-of-onset and disease severity. The broad 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 to improve variant interpretation and patient care. Here, we performed an unsupervised cluster analysis leveraging p53 mutagenesis datasets (Kato et al., 2003 & Giacomelli et al., 2018) that revealed five TP53 mutational clusters with unique mutation patterns, structural features, and functional consequences. Stratifying germline carriers based on the five clusters exposed important clinical characteristics to consider for patient management, such as ages-of-onset, tumor type development, and survival outcomes. In particular, we discovered an osteosarcoma-prone subgroup comprised of monomeric mutant p53 carriers with aggressive cancer phenotypes. We also identified a cluster of TP53 variants found more often in non-cancer and healthy older populations, and carriers of these variants that developed cancer had less “LFS-like” phenotypes including an older age-of-onset and a significantly higher frequency of colorectal cancers. Remarkably, our TP53 variant clustering strategy could also stratify breast cancer survival among germline carriers. This work provides a new framework to delineate the LFS spectrum toward the development of machine learning-based approaches for personalized cancer surveillance plans. Citation Format: Nicholas W. Fischer, Brianne Laverty, Ran Kafri, Kara N. Maxwell, Emma R. Woodward, Christian Kratz, David Malkin. TP53 mutational clusters stratify the Li-Fraumeni syndrome spectrum [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6198.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".