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-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 3

Molecular subtyping of HNSCC using WES data and its relevance to clinical outcomes after ICB treatment.

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Molecular subtyping of HNSCC using WES data and its relevance to clinica...
(A) Hierarchical clustering of 133 R/M HNSCC samples based on 13 genomic features that significantly associated with PFS in a univariable Cox model (listed right) and viral status. The dendrogram was cut at constant height, yielding 6 subtypes. Bottom tracks show the ICB response (printed as a percentage) and the tumor site. (B) ORR per molecular subtype and for grouped subtypes considered high risk (subtypes 1, 2, and 6) and low risk (subtypes 3, 4, and 5). Total n = 133. The P value was calculated using Fisher’s exact test. (C) PFS estimate for each molecular subtype. HRs and 95% CIs were calculated using Cox regression, with subtype 4 as a reference. The P value calculated using a log-rank test. (D) PFS estimate for tumors belonging to subtypes considered high risk (1, 2, and 6) and low risk (3, 4, and 5). HRs and 95% CIs were calculated using Cox regression, with low-risk tumors as a reference. The P value was calculated using a log-rank test. (E) Immunogenomic profiles of high-risk (yellow) and low-risk (blue) samples as well as each subtype individually, based on 7 parameters: high CD8-positive T cell infiltration, CPS of 1 or higher, high TMB, viral positivity, absence of 9p24.1 deletion (locus of CD274 [PD-L1], PDCD1LG2 [PD-L2], and JAK2), absence of a smoking signature, and the presence of an APOBEC signature. Radars extend from 0%–100%; the percentage of tumors positive per parameter is shown. For CD8-positive T cells, the cohort median was used as a cutoff. Thresholds for TMB (3.34 muts/Mbp), APOBEC signature, and smoking signature were chosen to obtain the best performance for predicting PFS in a univariable model. Genomic variables were available for 133 samples and IHC features for 62 samples. amp/wt, amplified or wild-type (diploid) copy number. (F) External validation of the subtypes relevance to the ICB response using KEYNOTE-012 data on patients with HNSCC (n = 102). Bars represent the ORR. The P value was calculated using Fisher’s exact test.