Plasma ctDNA kinetics as a predictor of systemic therapy response for advanced non-small cell lung cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Predicting early treatment response in advanced non-small cell lung cancer (NSCLC) is challenging. Longitudinal monitoring of circulating tumor DNA (ctDNA) can track tumor response to treatments like immune checkpoint blockade (ICB) and correlate with outcomes. This meta-analysis evaluated whether ctDNA clearance or decrease is associated with improved survival across various settings in NSCLC. METHODS: A systematic review of MEDLINE, EMBASE, and Cochrane databases (up to April 2024) identified studies evaluating the impact of ctDNA kinetics on survival outcomes in non-curative NSCLC settings. Pooled hazard ratios (HR) for progression-free survival (PFS) and overall survival (OS) were calculated using a random effects model. RESULTS: We included 32 studies with 3047 NSCLC patients receiving systemic therapies such as targeted therapy (TT), ICB, and chemotherapy. Meta-analysis of 31 studies showed that ctDNA decrease/clearance was linked to improved PFS (HR: 0.32 [0.26, 0.40], I² = 63%, P < .01). Subgroup analysis indicated strong PFS benefits from ctDNA clearance (HR: 0.27 [0.20, 0.36]). Similar improvements were seen across patients undergoing targeted therapy (HR: 0.34) and ICB (HR: 0.33). Analysis of 25 studies revealed a significant association between ctDNA reduction and better OS (HR: 0.31 [0.23, 0.42], I² = 47%, P < .01). Subgroup findings were consistent for both TT (HR: 0.41) and ICB (HR: 0.32). Sensitivity analysis demonstrated that ctDNA clearance/decrease was consistently associated with improved PFS across study designs and ctDNA analysis methods. There was no significant variation in hazard ratios for PFS based on NSCLC subtypes, smoking status, or sex. CONCLUSION: Plasma ctDNA kinetics was associated with improved survival outcomes in patients diagnosed with advanced NSCLC undergoing treatment with TT and ICB.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.043 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".