Dataset Information

Combined proteomics and miRNomics of a glioblastoma immunotherapy cohort reveals resistance factor candidates and novel counterstrategy concepts

ABSTRACT: Glioblastoma is the most prevalent and aggressive form of brain cancer. With a median overall survival of ~20 months under current standard therapy (surgery, chemotherapy, radiotherapy), novel treatment approaches are desperately needed. Immunotherapies are being investigated in clinical trials but failed so far. This includes a recent phase II clinical trial with a personalized immunotherapy based on tumor lysate-charged dendritic cells (NCT01213407). Here, we investigated tumor tissue from patients from this trial to explore glioblastoma (immuno)resistance factors and strategies to overcome them. 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 (RBPP1), ferritin heavy chain (FTH1) and focal adhesion kinase 2 (FAK2) as resistance factor candidates. MicroRNA analysis identified miR-216b, miR-216a, miR-708 and let-7i as molecules potentially associated with overcoming resistance as they were enriched in patients with a comparably longer survival. In silico pathway prediction indicated they down-regulate the focal adhesion pathway – among others. FAK2 was enriched in short-term surviving immunotherapy patients. In vitro, FAK inhibitors prevented the formation of glioblastoma cell adhesion in the form of gliomaspheres. Taken together, we here mapped possible drivers of glioblastoma’s immuno)resistance 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 – e.g. targeting focal adhesion. Small-molecule inhibitors or microRNAs capable of altering multiple pathways at once are candidates to explore.

OTHER RELATED OMICS DATASETS IN: GSE132554

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Brain

DISEASE(S): Brain Cancer

SUBMITTER: Hannes Hahne

LAB HEAD: Hannes Hahne

PROVIDER: PXD012616 | Pride | 2020-01-27

REPOSITORIES: Pride

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Publications

Combined proteomics/miRNomics of dendritic cell immunotherapy-treated glioblastoma patients as a screening for survival-associated factors.

Erhart Friedrich F Hackl Matthias M Hahne Hannes H Buchroithner Johanna J Meng Chen C Klingenbrunner Simone S Reitermaier René R Fischhuber Katrin K Skalicky Susanna S Berger Walter W Spiegl-Kreinecker Sabine S Lötsch Daniela D Ricken Gerda G Kuster Bernhard B Wöhrer Adelheid A Widhalm Georg G Hainfellner Johannes J Felzmann Thomas T Dohnal Alexander M AM Marosi Christine C Visus Carmen C

NPJ vaccines 20200116 1

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 com ...[more]

PMID: 31969991

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Project description:Background: Safe and sensitive methods for glioblastoma diagnosis and disease monitoring are urgently needed. Exosomes are nano-sized extracellular vesicles that contain molecules characteristic of their cell-of-origin, including microRNA. Exosomes released by glioblastoma tumors cross the blood-brain-barrier ino the peripheral circulation. Methods: Serum exosomal-microRNA isolated from IDH-wildtype glioblastoma (n=12) and IDH-mutant glioma grades II-III (n=10) were analyzed using small-RNA next generation sequencing and compared to age- and gender-matched healthy controls. Differentially expressed miRNAs (|fold change|³2 and p-value ≤ 0.05 in three statistical tests, Fischer-exact, t-test, and Wilcoxon) were identified and the predictive power of individual and subsets of miRNAs were tested using univariate (logistic regression) and multivariate (Random Forest) analyses. Additional glioblastoma sera (n=4) and independent sets of healthy (n=9) and non-glioma (n=10) controls were used to further test the predictive power of our glioblastoma miRNA signature. Results: Twenty-six miRNAs were differentially expressed in glioblastoma relative to healthy controls. Seven miRNAs (miR-182-5p, miR-328-3p, miR-339-5p, miR-340-5p, miR-485-3p, miR-486-5p and miR-543) were the most stable for classifying glioblastoma, achieving a predictive power of 91.7%. Strikingly, the combined expressions of miR-182-5p, miR-328-3p miR-485-3p miR-486-5p distinguished glioblastoma patients from controls with perfect accuracy. This miRNA panel was able to correctly classify all specimens in validation cohorts (n=23). An analogous approach was used to identify 23 dysregulated miRNAs in IDH-mutant gliomas, including a distinct subset of stable miRNAs for classifying patients with lower-grade IDH-mutant gliomas. Conclusions: Our serum exosomal-miRNA signature can accurately diagnose glioblastoma preoperatively. These findings have significant scope to revolutionize glioblastoma tumor diagnosis and disease monitoring. IMPORTANCE OF STUDY: There is a real need for accurate biomarkers that can measure glioblastoma disease activity and treatment response in a safe and timely manner. This study demonstrates that exosome-associated microRNAs have exceptional utility as blood-based biomarkers in glioma patients. This work also shows the potential for exosomal microRNA profiles to be used for glioma subtyping, grading and determining mutational states. The development of specific, sensitive and non-invasive screening tests would have significant clinical benefit by reducing costs of treatment monitoring, improving accessibility and quality-of-life measures. Moreover, such tests have the potential to provide objectively measured surrogate endpoints to allow clinical trial protocols to be more dynamic and adaptive.

Dataset Information

Combined proteomics and miRNomics of a glioblastoma immunotherapy cohort reveals resistance factor candidates and novel counterstrategy concepts

Publications

Combined proteomics/miRNomics of dendritic cell immunotherapy-treated glioblastoma patients as a screening for survival-associated factors.

Similar Datasets

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