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Clinical-genomic determinants of immune checkpoint blockade response in head and neck squamous cell carcinoma
Cristina Valero, … , Timothy A. Chan, Luc G.T. Morris
Cristina Valero, … , Timothy A. Chan, Luc G.T. Morris
Published August 10, 2023
Citation Information: J Clin Invest. 2023;133(19):e169823. https://doi.org/10.1172/JCI169823.
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Clinical Research and Public Health Immunology Oncology Article has an altmetric score of 4

Clinical-genomic determinants of immune checkpoint blockade response in head and neck squamous cell carcinoma

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Abstract

BACKGROUND Recurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) is generally an incurable disease, with patients experiencing median survival of under 10 months and significant morbidity. While immune checkpoint blockade (ICB) drugs are effective in approximately 20% of patients, the remaining experience limited clinical benefit and are exposed to potential adverse effects and financial costs. Clinically approved biomarkers, such as tumor mutational burden (TMB), have a modest predictive value in HNSCC.METHODS We analyzed clinical and genomic features, generated using whole-exome sequencing, in 133 ICB-treated patients with R/M HNSCC, of whom 69 had virus-associated and 64 had non-virus-associated tumors.RESULTS Hierarchical clustering of genomic data revealed 6 molecular subtypes characterized by a wide range of objective response rates and survival after ICB therapy. The prognostic importance of these 6 subtypes was validated in an external cohort. A random forest-based predictive model, using several clinical and genomic features, predicted progression-free survival (PFS), overall survival (OS), and response with greater accuracy than did a model based on TMB alone. Recursive partitioning analysis identified 3 features (systemic inflammatory response index, TMB, and smoking signature) that classified patients into risk groups with accurate discrimination of PFS and OS.CONCLUSION These findings shed light on the immunogenomic characteristics of HNSCC tumors that drive differential responses to ICB and identify a clinical-genomic classifier that outperformed the current clinically approved biomarker of TMB. This validated predictive tool may help with clinical risk stratification in patients with R/M HNSCC for whom ICB is being considered.FUNDING Fundación Alfonso Martín Escudero, NIH R01 DE027738, US Department of Defense CA210784, The Geoffrey Beene Cancer Research Center, The MSKCC Population Science Research Program, the Jayme Flowers Fund, the Sebastian Nativo Fund, and the NIH/NCI Cancer Center Support Grant P30 CA008748.

Authors

Cristina Valero, Mahdi Golkaram, Joris L. Vos, Bin Xu, Conall Fitzgerald, Mark Lee, Shannon Kaplan, Catherine Y. Han, Xin Pei, Reith Sarkar, Lillian A. Boe, Abhinav Pandey, Elizabeth S. Koh, Charlotte L. Zuur, David B. Solit, Traci Pawlowski, Li Liu, Alan L. Ho, Diego Chowell, Nadeem Riaz, Timothy A. Chan, Luc G.T. Morris

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Figure 4

Association of previously described predictors of ICB treatment response in HNSCC.

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Association of previously described predictors of ICB treatment response...
P values were calculated by comparing patients with a complete or partial response with patients who had stable or progressive disease using a Wilcoxon rank-sum test. Note that some y axes have been log10- or log1p-transformed for visualization purposes. (A) TMB, clonal mutational (mut.) load per exome, and indel load per exome in 64 V-negative samples, per objective response category. The clonal mutational load was available for 59 samples. (B) TMB, clonal mutational load per exome, and indel load per exome in 69 V-positive samples, per objective response category. The clonal mutational load was available for 65 samples. (C) CPS, intratumoral CD3-positive T cell count, and intratumoral CD8-positive T cell count in 36 V-negative samples, per objective response category. The CD3-positive T cell count was available in 35 samples. (D) CPS, intratumoral CD3-positive T cell count, and intratumoral CD8-positive T cell count in 26 V-positive samples, per objective response category. (E) ROC analysis illustrating the performance of the TMB, CPS, CD3-positive infiltration, and CD8-positive infiltration in predicting objective responses, 6-month PFS, and 12-month OS in the patients (V-negative and V-positive) for whom these data were available (n = 62). The AUROC curve (AUC) is printed in each plot.

Copyright © 2025 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

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