Next-Generation Sequencing Analysis of Mutations in Circulating Tumor DNA from the Plasma of Patients with Head–Neck Cancer Undergoing Chemo-Radiotherapy Using a Pan-Cancer Cell-Free Assay
Bibliographic record
Abstract
Using next-generation sequencing (NGS), we investigated DNA mutations in the plasma tumor cell-free circulating DNA (ctDNA) of 38 patients with inoperable squamous cell head neck cancer (SCHNC) before and after the completion of chemoradiotherapy (CRT). Baseline mutations of the TP53 were recorded in 10/38 (26.3%) and persisted in 4/10 patients after CRT. ΤP53 mutations were further detected post CRT in 7/38 additional patients with undetectable mutations at baseline (overall rate 44.7%). Furthermore, 4/38 patients exhibited baseline mutations of the EGFR, AR, FGFR3, and FBXW3, and four new gene mutations were detected after CRT (MTOR, EGFR3, ALK, and SF3B1). Τ4 stage was related with a significantly higher rate of mutations (TP53 and overall). Mutations were observed in 8/30 (26.6%) responders (complete/partial response) vs. in 6/8 (75%) of the rest of the patients (p = 0.03). Significant poorer LRFS was noted for patients with mutations detected before and after CRT (p = 0.02). Patients who had detectable mutations either before or after CRT had significantly worse DMFS (p = 0.04 overall, and p = 0.02 for TP53 mutations). It was concluded that assessment of mutations before and after the end of CRT is essential to characterize patients with a high risk of locoregional recurrence or metastatic progression.
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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 | 0.001 |
| 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".