PRMT5 Inhibition Promotes PD-L1 Expression and Immuno-Resistance in Lung Cancer

Protein arginine transferase 5 (PRMT5) has been implicated as an important modulator of tumorigenesis as it promotes tumor cell proliferation, invasion, and metastasis. Studies have largely focused on PRMT5 regulating intrinsic changes in tumors; however, the effects of PRMT5 on the tumor microenvironment and particularly immune cells are largely unknown. Here we found that targeting PRMT5 by genetic or pharmacological inhibition reduced lung tumor progression in immunocompromised mice; however, the effects were weakened in immunocompetent mice. PRMT5 inhibition not only decreased tumor cell survival but also increased the tumor cell expression of CD274 in vitro and in vivo, which activated the PD1/PD-L1 axis and eliminated CD8+T cell antitumor immunity. Mechanistically, PRMT5 regulated CD274 gene expression through symmetric dimethylation of histone H4R3, increased deposition of H3R4me2s on CD274 promoter loci, and inhibition of CD274 gene expression. Targeting PRMT5 reduced this inhibitory effect and promoted CD274 expression in lung cancer. However, PRMT5 inhibitors represent a double-edged sword as they may selectively kill cancer cells but may also disrupt the antitumor immune response. The combination of PRMT5 inhibition and ani-PD-L1 therapy resulted in an increase in the number and enhanced the function of tumor-infiltrating T cells. Our findings address an unmet clinical need in which combining PRMT5 inhibition with anti-PD-L1 therapy could be a promising strategy for lung cancer treatment.

Therapeutic properties of Isoliquiritigenin with molecular modeling studies: Investigation of anti-pancreatic acinar cell tumor and as HMG-CoA reductase inhibitor for treatment of hypercholesterolemia

IntroductionIsoliquiritigenin, one of the components in the root of Glycyrrhiza glabra L., is a member of the flavonoids, which are known to have an anti-tumor activity in vitro and in vivo. HMG-CoA reductase inhibitors, called statins, are used to reduce the risk of heart disease by lowering blood cholesterol levels.Material and methodsHMG-CoA Reductase activity according to the method described by Takahashi S. et al. The structure of human HMG-COA reductase in the resolution of 2.22 Å with X-RAY diffraction method (PDB ID: 1HWK) was obtained from the PDB database.ResultsIn our study, inhibition result of Isoliquiritigenin on HMG-CoA reductase showed lower value IC50 = 193.77±14.85 µg / mL. For a better understanding of biological activities and interactions, the molecular docking study was accomplished. The results of molecular docking revealed that isoliquiritigenin with a docking score of -6.740 has a strong binding affinity to the HMG-COA reductase. Therefore, this compound could be considered as a potential inhibitor for the enzyme. Also, the properties of Isoliquiritigenin against common human pancreatic acinar cell tumor cell lines i.e. 266-6, TGP49, and TGP47 were evaluated.ConclusionsThe treated cells with Isoliquiritigenin were assessed by MTT assay for 48h about the cytotoxicity and anti-human pancreatic acinar cell tumor properties on normal (HUVEC) and human pancreatic acinar cell tumor cell lines i.e. 266-6, TGP49, and TGP47. The IC50 of Isoliquiritigenin were 262, 389, and 211 µg/mL against 266-6, TGP49, and TGP47 cell lines, respectively.

Matching anticancer compounds and tumor cell lines by neural networks with ranking loss

ABSTRACT Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug’s inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model’s capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data.