Cell-free DNA topology depends on its subcellular and cellular origins in cancer
Bibliographic record
Abstract
Cancer cells release large quantities of cell-free DNA (cfDNA) into the surrounding tissue and circulation. As cfDNA is a common source of biomarkers for liquid biopsy and has been implicated as a functional mediator for intercellular communication, fundamental characterization of cfDNA topology has widespread biological and clinical ramifications. Whether the topology of cfDNA is such that it exists predominantly in membrane-bound extracellular vesicles (EVs) or in nonvesicular DNA-protein complexes remains poorly understood. Here, we employed a DNA-targeted approach to comprehensively assess total cfDNA topology in cancer. Using preclinical models and patient samples, we demonstrate that nuclear cfDNA is predominantly associated with nucleosomal particles and not EVs, while a substantial subset of mitochondrial cfDNA is membrane protected and disproportionately derived from nontumor cells. In addition, discrimination between membrane-protected and accessible mitochondrial cfDNA added diagnostic and prognostic value in a cohort of head and neck cancer patients. Our results support a revised model for cfDNA topology in cancer. Due to its abundance, nuclear cfDNA within nucleosomal particles is the most compelling liquid biopsy substrate, while EV-bound and accessible mitochondrial cfDNA represent distinct reservoirs of potential cancer biomarkers whose structural conformations may also influence their extracellular stability and propensity for uptake by recipient cells.
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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 | 0.001 |
| 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".