Characterization and Optimization of the Tumor Microenvironment in Patient-Derived Organotypic Slices and Organoid Models of Glioblastoma

While glioblastoma (GBM) is still challenging to treat, novel immunotherapeutic approaches have shown promising effects in preclinical settings. However, their clinical breakthrough is hampered by complex interactions of GBM with the tumor microenvironment (TME). Here, we present an analysis of TME composition in a patient-derived organoid model (PDO) as well as in organotypic slice cultures (OSC). To obtain a more realistic model for immunotherapeutic testing, we introduce an enhanced PDO model. We manufactured PDOs and OSCs from fresh tissue of GBM patients and analyzed the TME. Enhanced PDOs (ePDOs) were obtained via co-culture with PBMCs (peripheral blood mononuclear cells) and compared to normal PDOs (nPDOs) and PT (primary tissue). At first, we showed that TME was not sustained in PDOs after a short time of culture. In contrast, TME was largely maintained in OSCs. Unfortunately, OSCs can only be cultured for up to 9 days. Thus, we enhanced the TME in PDOs by co-culturing PDOs and PBMCs from healthy donors. These cellular TME patterns could be preserved until day 21. The ePDO approach could mirror the interaction of GBM, TME and immunotherapeutic agents and may consequently represent a realistic model for individual immunotherapeutic drug testing in the future.

A long non-coding RNA signature in glioblastoma multiforme predicts survival

Long non-coding RNAs (lncRNAs) represent the leading edge of cancer research, and have been implicated in cancer biogenesis and prognosis. We aimed to identify lncRNA signatures that have prognostic values in glioblastoma multiforme (GBM). Using a lncRNA-mining approach, we performed lncRNA expression profiling in 213 GBM tumors from The Cancer Genome Atlas (TCGA), randomly divided into a training (n=107) and a testing set (n=106). We analyzed the associations between lncRNA signatures and clinical outcome in the training set, and validated the findings in the testing set. We also validated the identified lncRNA signature in another two independent GBM data sets from Gene Expression Omnibus (GEO), which contained specimens from 68 and 101 patients, respectively. We identified a set of six lncRNAs that were significantly associated with the overall survival in the training set (P≤0.01). Based on this six-lncRNA signature, the training-set patients could be classified into high-risk and low-risk subgroups with significantly different survival (HR=2.13, 95% CI=1.38-3.29; P=0.001). The prognostic value of this six-lncRNA signature was confirmed in the testing set and the two independent data sets. Further analysis revealed that the prognostic value of this signature was independent of age and O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. The identification of the prognostic lncRNAs indicates the potential roles of lncRNAs in GBM pathogenesis. This six-lncRNA signature may have clinical implications in the subclassification of GBM.