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Audencel is a dendritic cell (DC)-based cellular cancer immunotherapy against glioblastoma multiforme (GBM). It is characterized by loading of DCs with autologous whole tumor lysate and in vitro maturation via “danger signals”. The recent phase II “GBM-Vax” trial showed no clinical efficacy for Audencel as assessed with progression-free and overall survival in all patients. Here we present immunological research accompanying the trial with a focus on immune system factors related to outcome and Audencel’s effect on the immune system. Methodologically, peripheral blood samples (from apheresis before Audencel or venipuncture during Audencel) were subjected to functional characterization via enzyme-linked immunospot (ELISPOT) assays connected with cytokine bead assays (CBAs) as well as phenotypical characterization via flow cytometry and mRNA quantification. GBM tissue samples (from surgery) were subjected to T cell receptor sequencing and immunohistochemistry. As results we found: Patients with favorable pre-existing anti-tumor characteristics lived longer under Audencel than Audencel patients without them. Pre-vaccination blood CD8+ T cell count and ELISPOT Granzyme B production capacity in vitro upon tumor antigen exposure were significantly correlated with overall survival. Despite Audencel’s general failure to induce a significant clinical response, it nevertheless seemed to have an effect on the immune system. For instance, Audencel led to a significant up-regulation of the Th1-related immunovariables ELISPOT IFNγ, the transcription factor T-bet in the blood and ELISPOT IL-2 in a dose-dependent manner upon vaccination. Post-vaccination levels of ELISPOT IFNγ and CD8+ cells in the blood were indicative of a significantly better survival. In summary, Audencel failed to reach an improvement of survival in the recent phase II clinical trial. No clinical efficacy was registered. Our concomitant immunological work presented here indicates that outcome under Audencel was influenced by the state of the immune system. On the other hand, Audencel also seemed to have stimulated the immune system. Overall, these immunological considerations suggest that DC immunotherapy against glioblastoma should be studied further – with the goal of translating an apparent immunological response into a clinical response. Future research should concentrate on investigating augmentation of immune reactions through combination therapies or on developing meaningful biomarkers.

Glioblastoma is the most prevalent and aggressive brain cancer. With a median overall survival of ~15–20 months under standard therapy, novel treatment approaches are desperately needed. A recent phase II clinical trial with a personalized immunotherapy based on tumor lysate-charged dendritic cell (DC) vaccination, however, failed to prolong survival. Here, we investigated tumor tissue from trial patients to explore glioblastoma survival-related factors. We followed an innovative approach of combining mass spectrometry-based quantitative proteomics ( n  = 36) with microRNA sequencing plus RT-qPCR ( n  = 38). Protein quantification identified, e.g., huntingtin interacting protein 1 (HIP1), retinol-binding protein 1 (RBP1), ferritin heavy chain (FTH1) and focal adhesion kinase 2 (FAK2) as factor candidates correlated with a dismal prognosis. MicroRNA analysis identified miR-216b, miR-216a, miR-708 and let-7i as molecules potentially associated with favorable tissue characteristics as they were enriched in patients with a comparably longer survival. To illustrate the utility of integrated miRNomics and proteomics findings, focal adhesion was studied further as one example for a pathway of potential general interest. Taken together, we here mapped possible drivers of glioblastoma outcome under immunotherapy in one of the largest DC vaccination tissue analysis cohorts so far—demonstrating usefulness and feasibility of combined proteomics/miRNomics approaches. Future research should investigate agents that sensitize glioblastoma to (immuno)therapy—potentially building on insights generated here. Glioblastoma: screening for survival-related factors Glioblastoma is an aggressive form of brain cancer and effective immunotherapeutics are limited, with treatment currently based on chemotherapy and radiotherapy. A recent phase II clinical trial tested a personalized, targeted dendritic cell-based immunotherapy but there was no observed improvement in patient survival or progression-free survival compared to standard-of-care therapy. Here, Carmen Visus and colleagues have used tumor tissue samples from glioblastoma patients involved in this trial and receiving immunotherapy. Using a combination of mass spectrometry-based proteomics, microRNA sequencing and RT-qPCR they identified factors associated with survival or poor prognosis. Proteomics associated poor prognosis with various proteins including focal adhesion kinase 2 (FAK2), whilst microRNAs, miR-216b, miR-216a, miR-708 and let-7i, were associated with longer survival. Focussing on one pathway, FAK2, they integrated the proteomic and microRNA datasets and saw a negative association with overall survival across all patients. To test this, they added an FAK inhibitor to glioblastoma cell lines, including cells isolated from trial patients, and observed inhibition of gliomaspheres in treated cells, providing insights into potential immunotherapy targets.

Glioblastoma is the most dangerous brain cancer. One reason for glioblastoma's aggressiveness are glioblastoma stem‐like cells. To target them, a number of markers have been proposed (CD133, CD44, CD15, A2B5, CD36, CXCR4, IL6R, L1CAM, and ITGA6). A comprehensive study of co‐expression patterns of them has, however, not been performed so far. Here, we mapped the multidimensional co‐expression profile of these stemness‐associated molecules. Gliomaspheres – an established model of glioblastoma stem‐like cells – were used. Seven different gliomasphere systems were subjected to multicolor flow cytometry measuring the nine markers CD133, CD44, CD15, A2B5, CD36, CXCR4, IL6R, L1CAM, and ITGA6 all simultaneously based on a novel 9‐marker multicolor panel developed for this study. The viSNE dimensionality reduction algorithm was applied for analysis. All gliomaspheres were found to express at least five different glioblastoma stem‐like cell markers. Multi‐dimensional analysis showed that all studied gliomaspheres consistently harbored a cell population positive for the molecular signature CD44+/CD133+/ITGA6+/CD36+. Glioblastoma patients with an enrichment of this combination had a significantly worse survival outcome when analyzing the two largest available The Cancer Genome Atlas datasets (MIT/Harvard Affymetrix: P = 0.0015, University of North Carolina Agilent: P = 0.0322). In sum, we detected a previously unknown marker combination – demonstrating feasibility, usefulness, and importance of high‐dimensional gliomasphere marker combinatorics.

Glioblastoma is the most dangerous brain cancer. One reason for glioblastoma's aggressiveness are glioblastoma stem‐like cells. To target them, a number of markers have been proposed ( CD 133, CD 44, CD 15, A2B5, CD 36, CXCR 4, IL 6R, L1 CAM , and ITGA 6). A comprehensive study of co‐expression patterns of them has, however, not been performed so far. Here, we mapped the multidimensional co‐expression profile of these stemness‐associated molecules. Gliomaspheres – an established model of glioblastoma stem‐like cells – were used. Seven different gliomasphere systems were subjected to multicolor flow cytometry measuring the nine markers CD 133, CD 44, CD 15, A2B5, CD 36, CXCR 4, IL 6R, L1 CAM , and ITGA 6 all simultaneously based on a novel 9‐marker multicolor panel developed for this study. The vi SNE dimensionality reduction algorithm was applied for analysis. All gliomaspheres were found to express at least five different glioblastoma stem‐like cell markers. Multi‐dimensional analysis showed that all studied gliomaspheres consistently harbored a cell population positive for the molecular signature CD 44+/ CD 133+/ ITGA 6+/ CD 36+. Glioblastoma patients with an enrichment of this combination had a significantly worse survival outcome when analyzing the two largest available The Cancer Genome Atlas datasets ( MIT /Harvard Affymetrix: P  = 0.0015, University of North Carolina Agilent: P  = 0.0322). In sum, we detected a previously unknown marker combination – demonstrating feasibility, usefulness, and importance of high‐dimensional gliomasphere marker combinatorics.

Background The transcription factor Ecotropic Virus Integration site 1 (EVI1) regulates cellular proliferation, differentiation, and apoptosis, and its overexpression contributes to an aggressive course of disease in myeloid leukemias and other malignancies. Notwithstanding, knowledge about the target genes mediating its biological and pathological functions remains limited. We therefore aimed to identify and characterize novel EVI1 target genes in human myeloid cells. Methods U937T_EVI1, a human myeloid cell line expressing EVI1 in a tetracycline regulable manner, was subjected to gene expression profiling. qRT-PCR was used to confirm the regulation of membrane-spanning-4-domains subfamily-A member-3 ( MS4A3 ) by EVI1. Reporter constructs containing various parts of the MS4A3 upstream region were employed in luciferase assays, and binding of EVI1 to the MS4A3 promoter was investigated by chromatin immunoprecipitation. U937 derivative cell lines experimentally expressing EVI1 and/or MS4A3 were generated by retroviral transduction, and tested for their tumorigenicity by subcutaneous injection into severe combined immunodeficient mice. Results Gene expression microarray analysis identified 27 unique genes that were up-regulated, and 29 unique genes that were down-regulated, in response to EVI1 induction in the human myeloid cell line U937T. The most strongly repressed gene was MS4A3 , and its down-regulation by EVI1 was confirmed by qRT-PCR in additional, independent experimental model systems. MS4A3 mRNA levels were also negatively correlated with those of EVI1 in several published AML data sets. Reporter gene assays and chromatin immunoprecipitation showed that EVI1 regulated MS4A3 via direct binding to a promoter proximal region. Experimental re-expression of MS4A3 in an EVI1 overexpressing cell line counteracted the tumor promoting effect of EVI1 in a murine xenograft model by increasing the rate of apoptosis. Conclusions Our data reveal MS4A3 as a novel direct target of EVI1 in human myeloid cells, and show that its repression plays a role in EVI1 mediated tumor aggressiveness.

Glioblastoma is the most prevalent and aggressive brain cancer. With a median overall survival of ~15-20 months under standard therapy, novel treatment approaches are desperately needed. A recent phase II clinical trial with a personalized immunotherapy based on tumor lysate-charged dendritic cell (DC) vaccination, however, failed to prolong survival. Here, we investigated tumor tissue from trial patients to explore glioblastoma survival-related factors. We followed an innovative approach of combining mass spectrometry-based quantitative proteomics (n = 36) with microRNA sequencing plus RT-qPCR (n = 38). Protein quantification identified, e.g., huntingtin interacting protein 1 (HIP1), retinol-binding protein 1 (RBP1), ferritin heavy chain (FTH1) and focal adhesion kinase 2 (FAK2) as factor candidates correlated with a dismal prognosis. MicroRNA analysis identified miR-216b, miR-216a, miR-708 and let-7i as molecules potentially associated with favorable tissue characteristics as they were enriched in patients with a comparably longer survival. To illustrate the utility of integrated miRNomics and proteomics findings, focal adhesion was studied further as one example for a pathway of potential general interest.Taken together, we here mapped possible drivers of glioblastoma outcome under immunotherapy in one of the largest DC vaccination tissue analysis cohorts so far-demonstrating usefulness and feasibility of combined proteomics/miRNomics approaches. Future research should investigate agents that sensitize glioblastoma to (immuno)therapy-potentially building on insights generated here.