Data from Multiple germline events contribute to cancer development in patients with Li-Fraumeni syndrome
Bibliographic record
Abstract
Abstract Li-Fraumeni syndrome (LFS) is an autosomal dominant cancer-predisposition disorder. Approximately 70% of individuals who fit the clinical definition of LFS harbour a pathogenic germline variant in the TP53 tumour suppressor gene. However, the remaining 30% of patients lack a TP53 variant and even among variant TP53 carriers, approximately 20% remain cancer-free. Understanding the variable cancer penetrance and phenotypic variability in LFS is critical to developing rational approaches to accurate, early tumour detection and risk-reduction strategies. We leveraged family-based whole-genome sequencing and DNA methylation to evaluate the germline genomes of a large, multi-institutional cohort of LFS patients (n=396) with variant (n=374) or wildtype TP53 (n=22). We identified alternative cancer-associated genetic aberrations in 8/14 wildtype TP53 carriers who developed cancer. Among variant TP53 carriers, 19/49 who developed cancer harboured a pathogenic variant in another cancer gene. Modifier variants in the WNT signaling pathway were associated with decreased cancer incidence. Furthermore, we leveraged the non-coding genome and methylome to identify inherited epimutations in genes including ASXL1, ETV6 and LEF1 that confer increased cancer risk. Using these epimutations, we built a machine learning model that can predict cancer risk in LFS patients with an AUROC of 0.725 [0.633-0.810].
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".