Anti-ERBB3 monoclonal antibody, clone SGP.1

This study was aimed to explore the prognosis-related biomarkers in glioblastoma and guide the therapy. The gene expression profile of glioblastoma samples with different prognosis outcomes was downloaded from National Center of Biotechnology Information Gene Expression Omnibus. The differently expressed genes (DEGs) among different samples were identified through pairwise comparison via Limma package of R. The DEGs were clustered using the Mfuzz package of R. The clusters with gene expression increasing or decreasing with the prognosis were selected, and functional enrichment of the selected genes was analyzed via the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the selected genes was constructed through the Search Tool for Retrieval of Interacting Proteins and visualized by Cytoscape. The Cancer Genome Atlas database and IVY-GAP database were used to verify the DEGs. We analyzed the correlation between subtypes and the DEGs. Totally, 2649 DEGs were identified and divided into 10 clusters. Expression value of the genes in clusters 2 and 9 kept increasing and decreasing, respectively, with the improved prognosis. The DEGs of cluster 2/9 were enriched in 23/24 Gene Ontology terms and 6/4 Kyoto Encyclopedia of Genes and Genomes pathways. Annotation of transcription factor binding sites of DEGs revealed that most genes were regulated by transcription factors. In the PPI network, CACNA1D, GNAO1, STAT3 and ERBB3 had 11, 11, 11 and 10 node degree, respectively. Bioinformatics methods could help to identify significant genes and pathways in glioblastoma. CACNA1D, GNAO1, STAT3 and ERBB3 might serve as the prognostic biomarkers in glioblastoma.

Kinase-targeted cancer therapy (KTCT) with ATP-competitive inhibitors has been widely applied in clinics. However, a number of kinase missense mutations were observed to confer acquired drug resistance during therapy, largely limiting the clinical application of kinase inhibitors in KTCT. Instead of directly influencing inhibitor binding, kinase mutations can also cause generic resistance to ATP-competitive inhibitors by increasing ATP affinity. Herein, the intermolecular interaction of the ATP molecule with clinically observed drug-resistant EGFR family kinase mutations involved in human cancer are systematically characterize. Rigorous quantum mechanics/molecular mechanics (QM/MM) calculation and empirical Poisson-Boltzmann/surface area (PB/SA) analysis as well as in vitro kinase assay and surface plasmon resonance analysis were integrated to explore the binding capability of ATP to mutant residues in the structural context of the kinase domain, which resulted in a comprehensive profile of ATP response to acquired drug-resistant mutations of four EGFR family kinases (EGFR/ErbB1, ErbB2, ErbB3 and ErbB4). From the profile, it was possible to identify those potent mutations that may influence ATP binding significantly; such mutations are potential candidates to cause generic resistance for ATP-competitive inhibitors. Consequently, the well documented generic drug-resistant mutation EGFR T790M and its counterpart ErbB2 T798M are found to increase ATP affinity by establishing an additional S-p interaction between the side-chain thioether group of the mutant Met residue and the aromatic adenine moiety of the ATP molecule, while EGFR D761Y is identified as a new generic drug-resistant mutation that can increase ATP affinity by eliminating unfavorable electrostatic repulsion. In contrast, ErbB2 K753E and T768I are considered to be two generic drug-sensitive mutations that can decrease ATP affinity by unfavorable charge reversal and by impairing favorable polar interaction, respectively. In addition, the EGFR L858R mutation is located at the kinase activation loop and nearby the kinase active site, thus largely complicating the multiply dependent relation-ship of kinase, ATP and inhibitor, which therefore exhibits divergent effects on different tested inhibitors.