Simulating cell-free chromatin using preclinical models for cancer-specific biomarker discovery
Bibliographic record
Abstract
ABSTRACT Cell-free chromatin (cf-chromatin) is a rich source of biomarkers across various conditions, including cancer. Tumor-derived circulating cf-chromatin can be profiled for epigenetic features, including nucleosome positioning and histone modifications that govern cell type-specific chromatin conformations. However, the low fractional abundance of tumor-derived cf-chromatin in blood and constrained access to plasma samples pose challenges for epigenetic biomarker discovery. Conditioned media from preclinical tissue culture models could provide an unencumbered source of pure tumor-derived cf-chromatin, but large cf-chromatin complexes from such models do not resemble the nucleosomal structures found predominantly in plasma, thereby limiting the applicability of many analysis techniques. Here, we developed a robust and generalizable framework for simulating cf-chromatin with physiologic nucleosomal distributions using an optimized nuclease treatment. We profiled the resulting nucleosomes by whole genome sequencing and confirmed that inferred nucleosome positioning reflected gene expression and chromatin accessibility patterns specific to the cell type. Compared with plasma, simulated cf-chromatin displayed stronger nucleosome positioning patterns at genomic locations of accessible chromatin from patient tissue. We then utilized simulated cf-chromatin to develop methods for genome-wide profiling of histone post-translational modifications associated with heterochromatin states. Cell-free chromatin immunoprecipitation and sequencing (cf-ChIP-Seq) of H3K27me3 identified heterochromatin domains associated with repressed gene expression, and when combined with H3K4me3 cfChIP-Seq revealed bivalent domains consistent with an intermediate state of transcriptional activity. Combining cfChIP-Seq of both modifications provided more accurate predictions of transcriptional activity from the cell of origin. Altogether, our results demonstrate the broad applicability of preclinical simulated cf-chromatin for epigenetic liquid biopsy biomarker discovery.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".