The introduction of immune checkpoint inhibition for recurrent and metastatic head and neck cancer has brought a new treatment option for patients suffering from advanced oral cancers without a chance for curation using surgery or radiotherapy. The application of immune checkpoint inhibitors in most cases is based on the expression levels of PD-L1 in the tumor tissue. To date, there is a lack of data on the dynamic regulation of PD-L1 during disease progression. Therefore, this study aimed to evaluate the expression levels of PD-L1 in a large cohort of patients (n = 222) with oral squamous cell carcinoma including primary and recurrent tumors. Semiautomatic digital pathology scoring was used for the assessment of PD-L1 expression levels in primary and recurrent oral squamous cell carcinoma. Survival analysis was performed to evaluate the prognostic significance of the protein expression at different stages of the disease. We found a significant up-regulation of PD-L1 expression from primary disease to recurrent tumors (mean PD-L1 H-scores: primary tumors: 47.1 ± 31.4; recurrent tumors: 103.5 ± 62.8, p < 0.001). In several cases, a shift from low PD-L1 expression in primary tumors to high PD-L1 expression in recurrent tumors was identified. Multivariate Cox regression analysis did not reveal a significantly higher risk of death (p = 0.078) or recurrence (p = 0.926) in patients with higher PD-L1 expression. Our findings indicate that the exclusive analysis of primary tumor tissue prior to the application of checkpoint blockade may lead to the misjudgment of PD-L1 expression in recurrent tumors.

Purpose: Identification of molecularly-defined cancer subgroups and targeting tumor-specific vulnerabilities have a strong potential to improve treatment response and patient outcomes but remain an unmet challenge of high clinical relevance, especially in head and neck squamous cell carcinoma (HNSC). Experimental design: We established a UCHL1-related gene set to identify and molecularly characterize a UCHL1-related subgroup within TCGA-HNSC by integrative analysis of multi-omics data. An extreme gradient boosting model was trained on TCGA-HNSC based on GSVA scores for gene sets of the MSigDB to robustly predict UCHL1-related cancers in other solid tumors and cancer cell lines derived thereof. Potential vulnerabilities of UCHL1-related cancer cells were elucidated by an in-silico drug screening approach. Results: We established a 497-gene set, which stratified the TCGA-HNSC cohort into distinct subgroups with a UCHL1-related or other phenotype. UCHL1-related HNSC were characterized by higher frequencies of genomic alterations, which was also evident for UCHL1-related cancers of other solid tumors predicted by the classification model. These data indicated an impaired maintenance of genomic integrity and vulnerability for DNA-damaging treatment, which was supported by a favorable prognosis of UCHL1-related tumors after radiotherapy, and a higher sensitivity of UCHL1-related cancer cells to irradiation or DNA-damaging compounds (e.g., Oxaliplatin). Conclusion: Our study established UCHL1-related cancers as a novel subgroup across most solid tumor entities with a unique molecular phenotype and DNA-damaging treatment as a specific vulnerability, which requires further proof-of-concept in pre-clinical models and future clinical trials.