Early-stage cancer results in a multiplicative increase in cell-free DNA originating from healthy tissue
Bibliographic record
Abstract
Cell-free DNA (cfDNA) is a promising biomarker for cancer detection. However, the sources of elevated cfDNA in patients with early-stage cancer, and the mechanisms by which cfDNA is shed into, and subsequently cleared from the circulation are still poorly understood. Using a rich dataset of cfDNA in healthy individuals and early-stage cancer patients, we find a multiplicative increase in cfDNA concentration in the presence of cancer. This increase is cancer type-specific, ranging from an approximately 1.3-fold increase in lung cancer to an approximately 12-fold increase in liver cancer, and does not originate from the tumour, but from healthy tissue. Employing an additional dataset reporting the tissue of origin of cfDNA, we observe a significant increase in the correlation between cfDNA originating from leukocytes and from non-leukocyte sources in cancer patients. Introducing a mathematical model for cfDNA dynamics, we find that the observed correlation can be explained by a saturation mechanism in cfDNA clearance. Saturation in clearance implies that smaller increases in cfDNA shedding may lead to proportionally larger increases in cfDNA levels. Our findings quantify cfDNA dynamics in patients with cancer, with implications for improving the accuracy of liquid biopsies for early cancer detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".