Personalized circulating tumor DNA (ctDNA) analysis in patients with recurrent/metastatic head and neck squamous cell cancer (R/M HNSCC).
Bibliographic record
Abstract
6052 Background: Immuno-oncology agents (IO) have become standard-of-care in the treatment of R/M HNSCC, but only a subset of patients (pts) benefit. Highly sensitive quantification of plasma circulating tumor DNA (ctDNA) may permit real time assessment of disease under selective pressures of treatment. Methods: R/M HNSCC pts treated with platinum-based chemotherapy (CT) or IO (anti-PD1/L1 +/- second IO) underwent serial ctDNA collection pre-cycles 1/2/3 and at disease progression, corresponding to timepoints (T) 1-4. T1 was considered baseline. Whole exome sequencing of pt tumor tissue identified patient specific somatic variants which were used as targets for RaDaR, a personalized multiplexed PCR-based NGS assay. Matched buffy coat DNA was sequenced to filter germline mutations and identify confounding CHIP. RaDaR was applied at each available T, an estimated variant allele frequency (eVAF) was calculated and correlated with progression free- (PFS) and overall- survival (OS). Findings were compared against prior (ESMO 2021) data generated using a fixed 580 gene CAPP-seq (CAncer Personalized Profiling by deep Sequencing) panel designed specifically against squamous cell carcinoma. Results: A total 114 plasma samples from 38 pts were analyzed. Of 35 pts with ctDNA detected at T1 and/or T2, 26 received IO and 9 CT. Median age was 62 (20-84), 77% were male, 69% prior smokers and 26% HPV positive. Median PFS and OS, for all 35 pts, was 2.57 mo (95% CI 0.48-4.66) and 8.37 mo (95% CI 5.42-11.32) respectively. For IO treated pts, median PFS was 2.45 mo (95% CI 0-5.18) and median OS 7.38 mo (95% CI 3.84-10.93). RaDaR panels targeted a median 48 variants (17-50). ctDNA was detected in 35/38 (92%) patients at baseline, with median eVAF 0.345% (range 0.0004% - 43.37%). ctDNA abundance at baseline did not correlate with PFS or OS. A decrease in Δ eVAF from T1 to T2, by > 30%, or > 50% identified pts with improved PFS, with HR 0.45 (0.21, 0.96) p = 0.04, 0.31 (0.14, 0.70) p < 0.01, and 0.23 (0.10, 0.56) p < 0.01, respectively. Similar results were observed for the 26 IO pts, with HR 0.40 (0.16, 1.03) p = 0.06, 0.19 (0.05, 0.66) p < 0.01, and 0.06 (0.01, 0.47) p < 0.01, respectively. A similar, but non-significant, trend was seen in median OS for pts with a decrease vs. increase in Δ eVAF (T1 to 2), 8.8 mo vs 7.3 mo (HR = 0.87 (0.42, 1.79)). For 31 pts, a comparison of Δ ctDNA levels, based on personalized RaDaR vs. CAPP-seq assays, from T1 to T2 demonstrated a correlation coefficient of R = 0.57, P < 0.01. Conclusions: In pts with R/M HNSCC, a decrease in ctDNA eVAF after first treatment correlated with improved PFS. There was a significant correlation between fixed CAPP-seq and personalized RaDaR assays when comparing Δ in ctDNA levels. Clinical Trial: NCT03712566.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".