Circulating DNA and Survival in Solid Tumors
Bibliographic record
Abstract
BACKGROUND: The ability to undertake molecular analysis to inform on prognosis and predictors of response to therapy is limited by accessibility of tissue. Measurement of total circulating free DNA (cfDNA) or circulating tumor DNA (ctDNA) in peripheral blood may allow easier access to tumor material and help to predict clinical outcomes. METHODS: A systematic review of electronic databases identified publications exploring the association between cfDNA or ctDNA and overall survival (OS) in solid tumors. HRs for OS were extracted from multivariable analyses and included in a meta-analysis. Pooled HRs were computed and weighted using generic inverse variance and random-effect modeling. For studies not reporting multivariable analyses, univariable ORs were estimated from Kaplan-Meier curves for OS at 1 and 3 years. RESULTS: Thirty-nine studies comprising 4,052 patients were included in the analysis. Detection of ctDNA was associated with a significantly worse OS in multivariable analyses [HR, 2.70; 95% confidence interval (CI), 2.02-3.61; P < 0.001). Similar results were observed in the univariable analyses at 3 and 1 year (OR, 4.83; 95% CI, 3.20-7.28; P < 0.001).There was also a statistically significant association between high total cfDNA and worse OS for studies reporting multivariable and univariate data at 3 years (HR, 1.91; 95% CI, 1.59-2.29; P < 0.001 and OR, 2.82; 95% CI, 1.93-4.13; P < 0.001, respectively). CONCLUSIONS: High levels of total cfDNA and presence of ctDNA are associated with worse survival in solid tumors. IMPACT: Circulating DNA is associated with worse outcome in solid tumors.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".