Olink

Olink®
Part of Thermo Fisher Scientific

The responses of HNSCC patients to immunotherapy are shown by two novel co-expression patterns

npj Precision Oncology, 2025

Daher-Ghanem N., Sharon S., Tao D., Sun Z., Rosales W., Rajamanickam V., Meng R., Kaur L., Bernard B., Piening B., Couey M., Gough M., Kravchenko-Balasha N.

Disease areaApplication areaSample typeProducts
Oncology
Immunotherapy
Patient Stratification
Tissue Supernatant
Olink Target 96

Olink Target 96

Abstract

Treatment of head and neck squamous cell carcinoma (HNSCC) is complex, with immunotherapy demonstrating potential yet facing challenges due to the tumor’s unique immune microenvironment. Biomarker expression has been employed to predict immune responses, albeit with limited efficacy. We predicted that due to the complexity of the immune response, no singular biomarker could consistently forecast the efficacy of immunotherapy. Consequently, we implemented a multi-index strategy that encompassed a comprehensive study of the networks associated with HNSCC. Secretomes from 72 explants obtained from six HNSCC patients (comprising 72 secretome profiles: 18 untreated and 54 treated) were subjected to an information-theoretic analysis. The resultant phenotypes were corroborated in two external cohorts (TCGA, n = 518; GEO GSE159067, n = 102). This methodology revealed two reproducible co-expression phenotypes—Activation (Act) and Infiltration (Inf)—that were significantly correlated with T-cell functionality. Only tissues exhibiting both Act and Inf phenotypes demonstrated a favorable response to anti-PD-1 and anti-GITR ex vivo, and displayed an increased presence of CD8+ T-cells in proximity to cancer cells. External validation in two different RNA-seq cohorts reproduced the two phenotypes and verified that patients possessing both signatures had significantly prolonged overall survival following PD-1/PD-L1 therapy. This study emphasizes the importance of multiple-index characterization of HNSCC tissues in enhancing patient classification and predicting immunotherapy efficacy.

Read publication ↗