Background: We set out to investigate how the ongoing coronavirus pandemic affected the size of tumors and the duration of treatment delay in patients with surgically treated oral squamous cell carcinoma.Methods: Patients with surgically treated oral cavity squamous cell carcinoma were assessed retrospectively and divided into two groups depending on when they had first presented at our clinic. Patients presenting from 2010 to 2019, that is, before COVID‐19 onset (n = 566) were compared to patients presenting in 2020 (n = 58).Results: A total of 624 patients were included. Treatment delay was significantly longer in 2020 (median = 45 days) versus 2010–2019 (median = 35 days) (p = 0.004). We observed a higher pathological T classification in 2020 (p = 0.046), whereas pathological N classification was unchanged between groups (p = 0.843).Conclusions: While extraordinary efforts continue to be made in the context of the pandemic, it is imperative that this does not lead to significant disadvantages for many people with oral cancer.

Perineural invasion is a prevalent pathological finding in head and neck squamous cell carcinoma and a risk factor for unfavorable survival. An adequate diagnosis of perineural invasion by pathologic examination is limited due to the availability of tumor samples from surgical resection, which can arise in cases of definitive nonsurgical treatment. To address this medical need, we established a random forest prediction model for the risk assessment of perineural invasion, including occult perineural invasion, and characterized distinct cellular and molecular features based on our new and extended classification. RNA sequencing data of head and neck squamous cell carcinoma from The Cancer Genome Atlas were used as a training cohort to identify differentially expressed genes that are associated with perineural invasion. A random forest classification model was established based on these differentially expressed genes and was validated by inspection of H&E-stained whole image slides. Differences in epigenetic regulation and the mutational landscape were detected by an integrative analysis of multiomics data and single-cell RNA-sequencing data were analyzed. We identified a 44-gene expression signature related to perineural invasion and enriched for genes mainly expressed in cancer cells according to single-cell RNA-sequencing data. A machine learning model was trained based on the expression pattern of the 44-gene set with the unique feature to predict occult perineural invasion. This extended classification model enabled a more accurate analysis of alterations in the mutational landscape and epigenetic regulation by DNA methylation as well as quantitative and qualitative differences in the cellular composition in the tumor microenvironment between head and neck squamous cell carcinoma with or without perineural invasion. In conclusion, the newly established model could not only complement histopathologic examination as an additional diagnostic tool but also guide the identification of new drug targets for therapeutic intervention in future clinical trials with head and neck squamous cell carcinoma patients at a higher risk for treatment failure due to perineural invasion.

Purpose: Angiolymphatic invasion serves as a histopathological risk factor for unfavorable survival in head and neck squamous cell carinoma. The aim of the study was to explore the molecular mechanisms characterizing angiolymphatic invasion and therefore identify a gene expression signature related to angiolymphatic invasion. Methods: Gene expression analysis of head and neck squamous cell carcinoma was carried out based on clinical and whole genome expression data provided by The Cancer Genome Atlas. Results were validated in an independent cohort of laryngeal squamous cell carcinoma and confirmed by immunohistochemistry staining. Results: A gene expression signature consisting of six genes (SHH, SLC18A3, LCE3E, LCE2B, LCE3D and DSG-1) related to angiolymphatic invasion was identified. The gene expression profile identified a subset of patients with decreased overall survival (p = 0.02, log rank test), which was most prominent for patients with laryngeal squamous cell carcinoma (p = 0.004, log rank test). Furthermore, these patients showed a significant shorter progression-free survival (p = 0.002, log rank test). By use of this gene expression signature, patients at high risk of recurrence could be identified even if morphological changes were not yet recognizable. Conclusion: Angiolymphatic invasion is characterized by a distinct histopathological phenotype and specific gene expression signature. The newly identified signature might serve as a reliable predictor of outcome in laryngeal cancer and add additional benefit to histopathological evaluation.