Tumor Mutational Burden as a Predictor of Survival with Durvalumab and/or Tremelimumab Treatment in Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
PURPOSE: Biomarkers that predict response to immune checkpoint inhibitors (ICI) in recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC) are needed. This retrospective study assessed tumor mutational burden (TMB) and outcomes in the phase II HAWK and CONDOR and phase III EAGLE studies of durvalumab with or without tremelimumab in platinum-resistant R/M HNSCC. PATIENTS AND METHODS: Tumor samples from HAWK/CONDOR (N = 153) and blood samples from EAGLE (N = 247) were analyzed for TMB. Associations with survival were evaluated for tissue TMB (tTMB) at cutoffs from 10 to 20 mutations/megabase (mut/Mb) and for blood plasma TMB (bTMB) at cutoffs from 8 to 24 mut/Mb. RESULTS: In HAWK/CONDOR, overall survival (OS) with durvalumab with or without tremelimumab was longer for high versus low tTMB: statistically significant differences were observed with durvalumab plus tremelimumab at tTMB ≥ 10 mut/Mb [HR, 0.52 (95% confidence interval, CI, 0.28-0.98)] and tTMB ≥ 12 mut/Mb [HR, 0.46 (95% CI, 0.24-0.86)]. In EAGLE, a significant OS benefit versus chemotherapy was observed with durvalumab and durvalumab plus tremelimumab at bTMB≥16 mut/Mb [HR, 0.39 (95% CI, 0.20-0.76) and 0.38 (95% CI, 0.19-0.78), respectively] but not bTMB < 16 mut/Mb [HR, 0.92 (0.61-1.37) and 0.92 (95% CI, 0.62-1.36), respectively]. A significant progression-free survival benefit was also observed in the ICI arms versus chemotherapy at bTMB ≥ 16 mut/Mb. CONCLUSIONS: Findings support TMB as a biomarker for predicting survival in patients with platinum-resistant R/M HNSCC treated with ICIs. The analysis of EAGLE demonstrated that bTMB was predictive of survival with ICI treatment versus chemotherapy in a large, randomized controlled study population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".