Peripheral blood DNA methylation predicts the early onset of primary tumor in TP53 mutation carriers
Bibliographic record
Abstract
Li-Fraumeni syndrome (LFS) confers high lifetime cancer risk due to germline TP53 pathogenic variants (PV). A comprehensive surveillance regimen termed the ‘Toronto Protocol’, has been adopted for early tumor detection, demonstrating improved survival among TP53 PV carriers. However, the protocol’s “one-size-fits-all” approach fails to consider individual cancer risk. To personalize screening, we developed a support vector machine model to predict early onset of primary tumors (age < 6) using peripheral blood methylation data of TP53 PV carriers (n = 237). Validation (n = 64) and external testing (n = 79) showed AUROC = 0.928 [0.835–1.000], F1-score = 0.692 [0.435–0.867], and NPV = 0.984 [0.946–1.000]. The model achieved 91% accuracy, correctly classifying 90% of patients with cancer before the age of six and 87% of cancer-free individuals in the external test set. Our tool enables risk stratification for early-onset malignancies, to optimize clinical surveillance and improve patient outcomes. Li-Fraumeni syndrome leads to an increased predisposition to tumour development. Here, the authors develop a support vector machine model to predict early cancer risk in individuals using peripheral blood DNA methylation profiles.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".