Integrated analysis of cell-free DNA for the early detection of cancer in people with Li-Fraumeni Syndrome
Bibliographic record
Abstract
Summary Despite advances in cancer therapeutics, early detection is often the best prognostic indicator for survival ( 1 ). People with Li-Fraumeni syndrome harbor a germline pathogenic variant in the tumor suppressor gene TP53 ( 2 ) and face a near 100% lifetime risk of developing a wide spectrum of, often multiple, cancers ( 3 ). TP53 mutation carriers routinely undergo intensive surveillance protocols which, although associated with significantly improved survival, are burdensome to both the patient and the health care system ( 4 ). Liquid biopsy, the analysis of cell-free DNA fragments in bodily fluids, has become an attractive tool for a range of clinical applications, including early cancer detection, because of its ability to provide real-time holistic insight into the cellular milieu ( 5 ). Here, we assess the efficacy of a multi-modal liquid biopsy assay that integrates a targeted gene panel, shallow whole genome, and cell-free methylated DNA immunoprecipitation sequencing for the early detection of cancer in a cohort of Li-Fraumeni syndrome patients: 196 blood samples from 89 patients, of which 26 were pediatric and 63 were adults. Our integrated analysis was able to detect a cancer-associated signal in 79.4% of samples from patients with active cancer, a 37.5% – 58.8% improvement over each individual analysis. Through analysis of patient plasma at cancer negative timepoints, we were able to detect cancer-associated signals up to 16 months prior to occurrence of cancer as detected by conventional clinical modalities in 17.6% of TP53 mutation carriers. This study provides a framework for the integration of liquid biopsy into current surveillance methods for patients with Li-Fraumeni syndrome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".