Targeting TGF beta receptor 1 in head and neck squamous cell carcinoma.

OBJECTIVES: The transforming growth factor-Beta (TGF-ß) pathway may be involved in the radioresistance of head and neck squamous cell carcinoma (HNSCC). This study analyzed TGF-ß receptor 1 (TGFBR1) expression in HNSCC patients and evaluated the antineoplastic and radiosensitizing effects of vactosertib, a novel TGFBR1 inhibitor, in vitro. MATERIALS AND METHODS: TGFBR1 expression was examined in HNSCC patients at the mRNA level in silico and the protein level by immunohistochemistry, including surgical specimens of primary tumors, matched lymph node metastasis, and recurrent disease. Furthermore, a novel small molecule TGFBR1 inhibitor was evaluated in HNSCC cell lines. Finally, an indirect coculture model using patient-derived cancer-associated fibroblasts was applied to mimic the tumor microenvironment. RESULTS: Patients with high TGFBR1 mRNA levels showed significantly worse overall survival in silico (OS, p = 0.024). At the protein level, an association between TGFBR1+ tumor and OS was observed for the subgroup with TGFBR1-stroma (p = 0.001). Those results prevailed in multivariable analysis. Inhibition of TGFBR1 showed antineoplastic effects in vitro. In combination with radiation, vactosertib showed synergistic effects. CONCLUSION: Our results indicate a high risk of death in tumorTGFBR1+ |stromaTGFBR1- expressing patients. In vitro data suggest a potential radiosensitizing effect of TGFBR1 inhibition by vactosertib.

Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells.

Chemical modulation of proteins enables a mechanistic understanding of biology and represents the foundation of most therapeutics. However, despite decades of research, 80% of the human proteome lacks functional ligands. Chemical proteomics has advanced fragment-based ligand discovery toward cellular systems, but throughput limitations have stymied the scalable identification of fragment-protein interactions. We report proteome-wide maps of protein-binding propensity for 407 structurally diverse small-molecule fragments. We verified that identified interactions can be advanced to active chemical probes of E3 ubiquitin ligases, transporters, and kinases. Integrating machine learning binary classifiers further enabled interpretable predictions of fragment behavior in cells. The resulting resource of fragment-protein interactions and predictive models will help to elucidate principles of molecular recognition and expedite ligand discovery efforts for hitherto undrugged proteins.