GraphMHC: Neoantigen prediction model applying the graph neural network to molecular structure.

Neoantigens are tumor-derived peptides and are biomarkers that can predict prognosis related to immune checkpoint inhibition by estimating their binding to major histocompatibility complex (MHC) proteins. Although deep neural networks have been primarily used for these prediction models, it is difficult to interpret the models reported thus far as accurately representing the interactions between biomolecules. In this study, we propose the GraphMHC model, which utilizes a graph neural network model applied to molecular structure to simulate the binding between MHC proteins and peptide sequences.

Antitumor activity of HM781-36B, a pan-HER tyrosine kinase inhibitor, in HER2-amplified breast cancer cells.

HM781-36B is an orally administered pan-human epidermal growth factor receptor (HER) inhibitor. To explore the role of pan-HER inhibitor in breast cancer, we investigated the antitumor effect and mechanisms of HM781-36B in breast cancer cell lines. Six breast cancer cell lines (BT474, MDA-MB-453, SK-BR-3, T47D, MCF-7, and MDA-MB-231) were tested.

Antitumor activity of HM781-36B, an irreversible Pan-HER inhibitor, alone or in combination with cytotoxic chemotherapeutic agents in gastric cancer.

Trastuzumab, a HER2 directed treatment has shown clinical benefit in HER2 amplified gastric cancer. This study demonstrated the potent antitumor activity of HM781-36B, a quinazoline-based irreversible pan-HER inhibitor, in HER2 amplified gastric cancer cells (SNU216 and N87) in vitro and in vivo. HM781-36B inhibited phosphorylation of HER family and downstream signaling molecules, and induced apoptosis and G1 arrest.

cDNA microarray gene expression analysis and toxicological phenotype for anticancer drug.

Toxicogenomics, the subdiscipline that merges genomics with toxicology, hold the promise to contributing toward the goal of elucidating mechanism by studying genomic profiling related with various drugs. The application of gene expression profiling technology to examine multiple genes and signaling pathways promises a significant advance in understanding the toxic mechanisms of various drugs and prediction of new drug candidate. Toxicogenomics is emerging field combining genomics and bioinformatics to identify and characterize mechanisms of toxicity of drug and various compounds.