Investigating Early Kinetics in Plasma ctDNA and Peripheral T-cell Receptor Repertoire to Predict Treatment Outcomes to PD-1 Inhibitors in Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
PURPOSE: Immune checkpoint blockade (ICB) therapies targeting the PD-1 axis have significantly improved survival in patients with recurrent/metastatic (R/M) head and neck squamous cell carcinoma (HNSCC). Circulating tumor DNA (ctDNA) and peripheral T-cell receptor (TCR) repertoires are emerging as promising biomarkers for predicting ICB response. Early characterization of ctDNA and TCR dynamics may enable timely treatment adjustments before clinical or radiologic progression. EXPERIMENTAL DESIGN: The IO-KIN study (NCT04606940) is a single-center, prospective trial involving 15 patients with R/M HNSCC treated with nivolumab or pembrolizumab. Blood samples (n = 104) were collected across seven time points from baseline to day 29. ctDNA was analyzed using a personalized assay (Signatera), and peripheral TCR repertoires were profiled using CapTCR-seq in eight patients. RESULTS: A decline in ctDNA after day 8 was associated with radiologic response, longer progression-free survival, and a trend toward improved overall survival. TCR repertoires transiently diversified between days 8 and 22, with longer diversification windows in patients showing sustained ctDNA decline. Using the GLIPHII algorithm, an Epstein-Barr virus-specific TCR signature was identified and persisted in patients with clinical benefit. Additional TCR signatures, potentially recognizing tumor-associated antigens, emerged as early as day 3 and were linked to positive outcomes. CONCLUSIONS: Simultaneous early monitoring of ctDNA and TCR dynamics reveals key determinants of ICB outcomes in R/M HNSCC. The transient nature of TCR diversification emphasizes the importance of precise sample timing to guide early therapeutic decisions and improve patient outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".