Mutational Landscape of Recurrent/Metastatic Head and Neck Squamous Cell Carcinoma and Association with Immune Checkpoint Inhibitor Outcomes
Bibliographic record
Abstract
PURPOSE: Understanding the mutational landscape of recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) is important in identifying biomarkers to determine which patients may benefit from immune checkpoint inhibitors (ICI). EXPERIMENTAL DESIGN: The HAWK (NCT02207530), CONDOR (NCT02319044), and EAGLE (NCT02369874) studies evaluated R/M HNSCC treatment with durvalumab or durvalumab-tremelimumab. Tumor tissue samples pooled from HAWK/CONDOR (n = 153) and plasma cell-free DNA samples from EAGLE (n = 285) were analyzed to identify somatic alterations and association with survival. RESULTS: The mutational landscape was similar in tissue and plasma. Compared with the wild type, TP53 mutations were associated with significantly shorter overall survival (OS; HR; 95% confidence interval) with standard of care (SoC; EAGLE: 2.12; 1.20-3.78) and ICIs (HAWK/CONDOR: 1.49; 1.05-2.12 and EAGLE: 1.44; 0.99-2.10). In EAGLE, patients with TP53 mutations had significantly longer OS with durvalumab-tremelimumab versus SoC (P = 0.045). KMT2D mutations were associated with a trend toward longer OS (HR; 95% confidence interval) versus the wild type in HAWK/CONDOR (0.81; 0.56-1.19) and a trend toward longer OS with ICIs versus SoC in EAGLE. For both mutations, a European Cooperative Oncology Group performance status of 1 was associated with worsened OS, and PD-L1 positivity was associated with improved OS. CONCLUSIONS: This is the first large-scale study to show the mutational landscape of R/M HNSCC and its association with clinical outcomes in patients treated with ICIs or SoC. The TP53 mutation was a negative prognostic marker; however, treatment with durvalumab-tremelimumab significantly improved survival over SoC. Further investigation of KMT2D as a predictive biomarker for immunotherapy in R/M HNSCC is warranted.
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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".