Loss of Diacylglycerol Kinase α Enhances Macrophage Responsiveness.

The diacylglycerol kinases (DGKs) are a family of enzymes responsible for the conversion of diacylglycerol (DAG) to phosphatidic acid (PA). In addition to their primary function in lipid metabolism, DGKs have recently been identified as potential therapeutic targets in multiple cancers, including glioblastoma (GBM) and melanoma. Aside from its tumorigenic properties, DGKα is also a known promoter of T-cell anergy, supporting a role as a recently-recognized T cell checkpoint. In fact, the only significant phenotype previously observed in Dgka knockout (KO) mice is the enhancement of T-cell activity. Herein we reveal a novel, macrophage-specific, immune-regulatory function of DGKα. In bone marrow-derived macrophages (BMDMs) cultured from wild-type (WT) and KO mice, we observed increased responsiveness of KO macrophages to diverse stimuli that yield different phenotypes, including LPS, IL-4, and the chemoattractant MCP-1. Knockdown (KD) of Dgka in a murine macrophage cell line resulted in similar increased responsiveness. Demonstrating in vivo relevance, we observed significantly smaller wounds in Dgka-/- mice with full-thickness cutaneous burns, a complex wound healing process in which macrophages play a key role. The burned area also demonstrated increased numbers of macrophages. In a cortical stab wound model, Dgka-/- brains show increased Iba1+ cell numbers at the needle track versus that in WT brains. Taken together, these findings identify a novel immune-regulatory checkpoint function of DGKα in macrophages with potential implications for wound healing, cancer therapy, and other settings.

A Serum Proteomic Signature Predicting Survival in Patients with Glioblastoma.

PURPOSE: Glioblastoma (GBM) is the most common form of brain tumor and has a uniformly poor prognosis. Development of prognostic biomarkers in easily accessible serum samples have the potential to improve the outcomes of patients with GBM through personalized therapy planning. MATERIAL/METHODS: In this study pre-treatment serum samples from 30 patients newly diagnosed with GBM were evaluated using a 40-protein multiplex ELISA platform. Analysis of potentially relevant gene targets using The Cancer Genome Atlas database was done using the Glioblastoma Bio Discovery Portal (GBM-BioDP). A ten-biomarker subgroup of clinically relevant molecules was selected using a functional grouping analysis of the 40 plex genes with two genes selected from each group on the basis of degree of variance, lack of co-linearity with other biomarkers and clinical interest. A Multivariate Cox proportional hazard approach was used to analyze the relationship between overall survival (OS), gene expression, and resection status as covariates. RESULTS: Thirty of 40 of the MSD molecules mapped to known genes within TCGA and separated the patient cohort into two main clusters centered predominantly around a grouping of classical and proneural versus the mesenchymal subtype as classified by Verhaak. Using the values for the 30 proteins in a prognostic index (PI) demonstrated that patients in the entire cohort with a PI below the median lived longer than those patients with a PI above the median (HR 1.8, p=0.001) even when stratified by both age and MGMT status. This finding was also consistent within each Verhaak subclass and highly significant (range p=0.0001-0.011). Additionally, a subset of ten proteins including, CRP, SAA, VCAM1, VEGF, MDC, TNFA, IL7, IL8, IL10, IL16 were found to have prognostic value within the TCGA database and a positive correlation with overall survival in GBM patients who had received gross tumor resection followed by conventional radiation therapy and temozolomide treatment concurrent with the addition of valproic acid. CONCLUSION: These findings demonstrate that proteomic approaches to the development of prognostic assays for treatment of GBM may hold potential clinical value.