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

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

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 6

Validation of the clinical-genomic model associated with PFS upon ICB treatment in an independent cohort of 30 patients and model simplification using RPA.

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Validation of the clinical-genomic model associated with PFS upon ICB tr...
Patient characteristics for the independent cohort are provided in Supplemental Table 2, and model simplification using RPA is shown in Supplemental Figure 5. (A) C-index illustrating the performance of the PFS-RF14 model applied in the independent validation cohort (n = 30), compared with the model based on TMB (PFS-TMB). (B) ROC analysis illustrating the performance of the PFS-RF14 and TMB model in predicting 6-month PFS, 12-month OS, and objective response in the validation cohort (n = 30). (C) PFS in the validation cohort for the PFS-RF14 and PFS-TMB models. The median predicted PFS for each model was used to divide patients into predicted high-survival (blue) and low-survival (yellow) groups. HRs and 95% CIs were calculated using Cox regression, with predicted low-survival tumors as a reference. P values were calculated using a log-rank test. (D) OS in the validation cohort for the PFS-RF14 and PFS-TMB models. The median predicted PFS for each model was used as a threshold to divide patients into predicted high-survival and low-survival groups. HRs and 95% CIs were calculated using Cox regression, with predicted low-survival tumors (yellow) as a reference. (E) RPA classifier created in the main cohort (n = 131) using PFS as a dependent variable. Variables selected for the model were the top 3 features from the PFS-RF23 model: SIRI, TMB, and smoking signature. Patients were classified into high-risk, intermediate-risk, and low-risk groups. (F) PFS (left) and OS (right) of the high- (red), intermediate- (orange), and low-risk (blue) groups obtained using the RPA classifier in the main cohort (n = 131). HRs and 95% CIs were calculated using Cox regression, with low-risk tumors as a reference. P values were calculated using a log-rank test. (G) ORR in the high- (red), intermediate- (orange), and low-risk (blue) groups obtained using the RPA classifier in the main cohort (n = 131). The P value was calculated using a Fisher’s exact test with Freeman-Halton extension. Intermed., intermediate.