Changes in circulating tumor DNA and outcomes in solid tumors treated with immune checkpoint inhibitors: a systematic review
Bibliographic record
Abstract
BACKGROUND: Quantification of circulating tumor DNA (ctDNA) levels is a reliable prognostic tool in several malignancies. Dynamic changes in ctDNA levels in response to treatment may also provide prognostic information. Here, we explore the value of changes in ctDNA levels in response to immune checkpoint inhibitors (ICIs). METHODS: We searched MEDLINE (host: PubMed) for trials of ICIs in advanced solid tumors in which outcomes were reported based on change in ctDNA levels. ctDNA reduction was defined as reported in individual trials. Typically, this was either >50% reduction or a reduction to undetectable levels. We extracted HRs and related 95% CIs and/or p values comparing ctDNA reduction versus no reduction for progression-free survival (PFS) and/or overall survival (OS). Data were then pooled in a meta-analysis. Variation in effect size was examined using subgroup analyses. RESULTS: Eighteen trials were included in the meta-analysis. ctDNA levels were detectable in all participants in all studies prior to initiation of ICIs. A reduction in ctDNA measured 6-16 weeks after starting treatment was associated with significantly better PFS (HR 0.20; 95% CI, 0.14 to 0.28; p<0.001). Similarly, OS was superior in patients with reduced ctDNA levels (HR 0.18; 95% CI, 0.12 to 0.26; p<0.001). The results were consistent across all disease sites, lines of treatment, magnitude of change (to undetectable vs >50% reduction) and whether treatment exposure comprised single or combination ICIs. CONCLUSIONS: In advanced solid tumors, a reduction in ctDNA levels in response to ICIs is associated with substantial improvements in outcome. ctDNA change is an early response biomarker which may allow for de-escalation of cross-sectional imaging in patients receiving ICIs or support treatment de-escalation strategies.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".