Simulating cell-free chromatin using preclinical cancer models for liquid biopsy applications
Bibliographic record
Abstract
Cell-free DNA circulates in blood bound to nucleosomes, forming cell-free chromatin (cfChromatin) that retains epigenetic features, including nucleosome positioning and histone modifications. cfChromatin provides a rich source of cancer biomarkers; however, low abundance of tumor-derived cfChromatin and limited availability of clinical samples pose challenges for liquid biopsy research. To address this, we developed a framework to simulate cfChromatin nucleosomal distributions using nuclease-treated conditioned media from tissue cultures. Whole-genome sequencing confirmed that inferred nucleosome positioning reflected cell-type-specific gene expression and chromatin accessibility patterns, and comparisons with plasma cfChromatin from xenografted mice revealed concordant nucleosome profiles. Notably, simulated cfChromatin displayed stronger tumor-specific nucleosome profiles than patient plasma, where hematopoietic-derived cfChromatin dilutes signal. We further leveraged simulated cfChromatin to advance cell-free chromatin immunoprecipitation and sequencing methods, identifying repressive and bivalent chromatin domains predictive of transcriptional activity. Altogether, our results demonstrate the utility of simulated cfChromatin as a scalable preclinical tool for liquid biopsy research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".