Association of PD-L1 Expression on Tumor and Immune Cells with Survival in Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma and Assay Validation
Bibliographic record
Abstract
Programmed cell death ligand-1 (PD-L1), expressed on both tumor cells (TC) and tumor-associated immune cells (IC), has been shown to be a useful biomarker and predictive of response to anti-PD-L1 agents in certain tumor types. In recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC), there is a growing interest in the role of PD-L1 expression on ICs, as well as TCs, for predicting response to immune checkpoint inhibitors. Using pooled data from the phase II HAWK and CONDOR studies, we investigated the association of baseline PD-L1 expression with durvalumab efficacy in patients with R/M HNSCC. To determine an optimal PD-L1 cut-off point for predicting survival, we assessed PD-L1 expression levels at different TC and IC cut-off points in patients treated with durvalumab. Longer survival was associated with higher TC membrane PD-L1 expression and IC staining. When the combined TC/IC algorithm was applied, a cut-off point for PD-L1 expression of ≥50% on TCs or ≥25% on ICs (TC ≥ 50%/IC ≥ 25%) showed a higher objective response rate (17.2% vs. 8.8%), longer median progression-free survival (2.8 vs. 1.9 months), and longer median overall survival (8.4 vs. 5.4 months) in the PD-L1-high versus PD-L1-low/negative patient populations, respectively. A scoring algorithm combining PD-L1 expression on TCs and ICs using the cut-off point TC ≥ 50%/IC ≥ 25% was optimal for identifying patients with HNSCC most likely to benefit from durvalumab treatment. The new algorithm is robust and can be reproducibly scored by trained pathologists. Significance: A novel algorithm for PD-L1 expression using the cut-off point TC ≥ 50%/IC ≥ 25% is robust for identifying patients with HNSCC most likely to benefit from durvalumab treatment and can be reproducibly scored by trained pathologists.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".