Plasma ctDNA Response Is an Early Marker of Treatment Effect in Advanced NSCLC
Bibliographic record
Abstract
PURPOSE Plasma circulating tumor DNA (ctDNA) analysis is routine for genotyping of advanced non–small-cell lung cancer (NSCLC); however, early response assessment using plasma ctDNA has yet to be well characterized. MATERIALS AND METHODS Patients with advanced EGFR-mutant NSCLC across three phase I NCI osimertinib combination trials were analyzed in this study, and an institutional cohort of patients with KRAS-, EGFR-, and BRAF-mutant advanced NSCLC receiving systemic treatment was used for validation. Plasma was collected before treatment initiation and serially before each cycle of therapy, and key driver mutations in ctDNA were characterized by droplet digital polymerase chain reaction. Timing of plasma versus imaging response was compared in a separate cohort of patients with EGFR-mutant NSCLC treated with osimertinib. Across cohorts, we also studied ctDNA variability before treatment start. RESULTS In the NCI cohort, 14/16 (87.5%) patients exhibited ≥ 90% decrease in mutation abundance by the first on-treatment timepoint (20-28 days from treatment start) with minimal subsequent change. Similarly, 47/56 (83.9%) patients with any decrease in the institutional cohort demonstrated ≥ 90% decrease in mutation abundance by the first follow-up draw (7-30 days from treatment start). All 16 patients in the imaging cohort with radiographic partial response showed best plasma response within one cycle, preceding best radiographic response by a median of 24 weeks (range: 3-147 weeks). Variability in ctDNA levels before treatment start was observed. CONCLUSION Plasma ctDNA response is an early phenomenon, with the majority of change detectable within the first cycle of therapy. These kinetics may offer an opportunity for early insight into treatment effect before standard imaging timepoints.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".