Tumor-Naïve Circulating Tumor DNA as an Early Response Biomarker for Patients Treated With Immunotherapy in Early Phase Clinical Trials
Bibliographic record
Abstract
PURPOSE To evaluate early circulating tumor DNA (ctDNA) kinetics using a tumor-naïve assay and correlate it with clinical outcomes in early phase immunotherapy (IO) trials. METHODS Plasma samples were analyzed using a 425-gene next-generation sequencing panel at baseline and before cycle 2 (3-4 weeks) in patients with advanced solid tumors treated with investigational IO agents. Variant allele frequency (VAF) for mutations in each gene, mean VAF (mVAF) from all mutations, and change in mVAF between both time points were calculated. Hyperprogression (HyperPD) was measured using Matos and Caramella criteria. RESULTS A total of 162 plasma samples were collected from 81 patients with 27 different tumor types. Patients were treated in 37 different IO phase I/II trials, 72% of which involved a PD-1/PD-L1 inhibitor. ctDNA was detected in 122 plasma samples (75.3%). A decrease in mVAF from baseline to precycle 2 was observed in 24 patients (37.5%) and was associated with longer progression-free survival (hazard ratio [HR], 0.43; 95% CI, 0.24 to 0.77; P < .01) and overall survival (HR, 0.54; 95% CI, 0.3 to 0.96; P = .03) compared with an increase. These differences were more marked if there was a >50% decrease in mVAF for both progression-free survival (HR, 0.29; 95% CI, 0.13 to 0.62; P < .001) and overall survival (HR, 0.23; 95% CI, 0.09 to 0.6; P = .001). No differences in mVAF changes were observed between the HyperPD and progressive disease patients. CONCLUSION A decrease in ctDNA within 4 weeks of treatment was associated with treatment outcomes in patients in early phase IO trials. Tumor-naïve ctDNA assays may be useful for identifying early treatment benefits in phase I/II IO trials.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".