The kinetics and fragmentation of cell-free DNA from cultured cancer cells are influenced by anticancer treatments
Bibliographic record
Abstract
Abstract Liquid biopsy-based detection of circulating free DNA (cfDNA) is a promising tool to monitor tumor progression and treatment response. cfDNA release is thought to result from a combination of cell death (apoptosis and necrosis), and active cellular secretion. As such, cytotoxic anti-cancer therapies can impact cfDNA kinetics. This makes the interpretation of cfDNA analyses pivotal for its applicability as a biomarker. In this study, we assessed the kinetics and fragmentation of cfDNA in cancer cells of various origins following standard anti-cancer treatments with different cytotoxic effects and mechanisms of action.Human colorectal carcinoma, lung adenocarcinoma, and uveal melanoma cancer cells were subjected to different forms of cytotoxic stress, including induction of apoptosis (tumor necrosis factor (TNF)-related apoptosis-inducing ligand (Apo2L/TRAIL)), cell cycle arrest (Roscovitine, Valproic acid), necrosis (radiotherapy), and senescence. Following treatments, cells were analyzed for their cell cycle progression, level of senescence, and mechanism of cell death (apoptosis vs. necrosis). Heat treatment was used as a control for necrosis-related cell death. Total cfDNA and mutant cfDNA (based on the mutations of each parental cell line) were isolated from cultured media and quantified using the Qubit assay and digital droplet PCR targeting, respectively. Fragment length of the isolated cfDNA was visualized using the Bioanalyzer 2100.Total and mutant cfDNA levels increased during all cytotoxic treatments in a concentration-dependent manner. Notably, cells undergoing apoptosis shed higher levels of cfDNA compared to necrotic and senescent cells. In addition, electropherogram images showed that cytotoxic conditions alter fragment size distribution, with smaller fragments of <200bp associated with apoptosis and >1000 bp with necrosis.The kinetics and fragmentation of released cfDNA are influenced by cytotoxic insults. Determining the characteristics of cfDNA can facilitate and improve its use as a clinical biomarker during anti-cancer therapy. Our data pave the way for the establishment of criteria to apply when monitoring cfDNA for cancer management based on the anticancer therapeutic strategy.
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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 | 0.000 | 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".