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PD-L1 expression combined with immune phenotype is a potential predictor of outcome with PD-1 inhibitor monotherapy in patients with recurrent or metastatic head and neck squamous cell carcinoma
10.1016/j.oooo.2025.11.018
2026-03-01
0
PRE
AI
Abstract
En 中文
Objective. Programmed cell death-ligand 1 (PD-L1) expression and immune phenotype (IP) are potential predictive biomarkers for immune checkpoint inhibitors (ICIs) in recurrent and/or metastatic head and neck squamous cell carcinoma (R/M HNSCC). This study evaluated the predictive value of combining PD-L1 expression and IP in R/M HNSCC. Study Design. Forty-one R/M HNSCC patients treated with ICI were included. PD-L1 expression was evaluated using the standardized 22C3 pharmDx assay. IPs were assessed using Lunit SCOPE IO, an artificial intelligence-powered tumor-infiltrating lymphocyte analyzer. Results. Thirty-nine patients (95.1%) were classified as PD-L1 positive (combined positive score >= 1). Overall, 27 (65.9%) had desert IP. PD-L1 expression and IP were combined to classify patients into 3 groups: group A, negative PD-L1; group B, positive PD-L1 with desert IP; group C, positive PD-L1 with non-desert IP. The median progression-free survival (PFS) was 1.2 months in group A, 2.1 months in group B, and 12.1 months in group C (P = .015). In multivariate Cox analysis, PD-L1 expression combined with IP was an independent factor for PFS, with a hazard ratio of 0.14 (P = .018) in group C and 0.37 (P = .186) in group B, relative to group A. Conclusions. In R/M HNSCC, integrating IP with PD-L1 expression may enhance prediction of ICI outcomes.
Keywords:
OPEN-LABEL
PEMBROLIZUMAB
NIVOLUMAB
SURVIVAL
CETUXIMAB
Journal
O
IF:
1.9
Papers: 75
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Citations: 0
Researchers
K
Kim, Dong Hyun
H-index:
0
Papers: 5
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Citations: 0
K
Koh, Jiwon
H-index:
22
Papers: 127
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Citations: 1.6K
A
Ahn, Jong Seok
H-index:
0
Papers: 0
・
Citations: 0
M
Moon, Jimin
H-index:
0
Papers: 0
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Citations: 0
K
Kang, Mingu
H-index:
0
Papers: 0
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Citations: 0
Organization
S
Seoul National University Hospital
Scholars:
1.1W
Papers: 9.2K
・
Citations: 9.3K


