Multiparameter imaging reveals clinically relevant cancer cell-stroma interaction dynamics in head and neck cancer.

Epithelial tumors are characterized by abundant inter- and intra-tumor heterogeneity, which complicates diagnostics and treatment. The contribution of cancer-stroma interactions to this heterogeneity is poorly understood. Here, we report a paradigm to quantify phenotypic diversity in head and neck squamous cell carcinoma (HNSCC) with single-cell resolution.

Identification of stemness-related glycosylation changes in head and neck squamous cell carcinoma.

Background: Altered glycosylation is a hallmark of cancer associated with therapy resistance and tumor behavior. In this study, we investigated the glycosylation profile of stemness-related proteins OCT4, CIP2A, MET, and LIMA1 in HNSCC tumors.

Methods: Tumor, adjacent normal tissue, and blood samples of 25 patients were collected together with clinical details.

Assessment of targeted therapy opportunities in sinonasal cancers using patient-derived functional tumor models.

Unlabelled: Malignant tumors derived from the epithelium lining the nasal cavity region are termed sinonasal cancers, a highly heterogeneous group of rare tumors accounting for 3 - 5 % of all head and neck cancers. Progress with next-generation molecular profiling has improved our understanding of the complexity of sinonasal cancers and resulted in the identification of an increasing number of distinct tumor entities. Despite these significant developments, the treatment of sinonasal cancers has hardly evolved since the 1980s, and an advanced sinonasal cancer presents a poor prognosis as targeted therapies are usually not available.

TSPO is a potential independent prognostic factor associated with cellular respiration and p16 in head and neck squamous cell carcinoma.

Background: Treatment resistance and relapse are common problems in head and neck squamous cell carcinoma (HNSCC). Except for p16, no clinically accepted prognostic biomarkers are available for HNSCC. New biomarkers predictive of recurrence and survival are crucial for optimal treatment planning and patient outcome.