Galectin-9 has emerged as a promising biological target for cancer immunotherapy due to its role as a regulator of macrophage and T-cell differentiation. In addition, its expression in tumor cells modulates tumor cell adhesion, metastasis, and apoptosis. Malignant mesothelioma (MM) is an aggressive neoplasm of the mesothelial cells lining the pleural and peritoneal cavities, and in this study, we found that both human MM tissues and mouse MM cells express high levels of galectin-9. Using a novel monoclonal antibody (mAb) (Clone P4D2) that binds the C-terminal carbohydrate recognition domain (CRD) of galectin-9, we demonstrate unique agonistic properties resulting in MM cell apoptosis. Furthermore, the P4D2 mAb reduced tumor-associated macrophages differentiation toward a protumor phenotype. Importantly, these effects exerted by the P4D2 mAb were observed in both human and mouse in vitro experiments and not observed with another antigalectin-9 specific mAb (clone P1D9) that engages the N-terminus CRD of galectin-9. In syngeneic murine models of MM, P4D2 mAb treatment inhibited tumor growth and improved survival, with tumors from P4D2-treated mice exhibited reduced infiltration of tumor-associated M2 macrophages. This was consistent with an increased production of inducible nitric oxide synthase, which is a major enzyme-regulating macrophage inflammatory response to cancer. These data suggest that using an antigalectin 9 mAb with agonistic properties similar to those exerted by galectin-9 may provide a novel multitargeted strategy for the treatment of mesothelioma and possibly other galectin-9 expressing tumors.
ARTICLE HISTORY
Matching the right people to the right job considering constraints such as qualifications, availability and cost is the cornerstone of IT projects delivery services. We present a study to improve data accuracy and completeness for resource matching by integrating unstructured data sources and introducing text mining techniques to dynamically adapt resource profile for resource planning decisions. Our approach discovers resource categories by extracting and learning new patterns from employee resumes; and incorporating resource experience for the job-matching optimization during the resource planning exercise.
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