Andreas Mock

Reduced promoter methylation and increased expression of CSPG4 negatively influences survival of HNSCC patients.

Proteoglycans are often overexpressed in tumors and can be found on several normal and neoplastic stem cells. In this study, we analyzed in‐depth the role of CSPG4 in head and neck squamous cell carcinomas (HNSCC). Analysis of CSPG4 in a homogeneous study sample of HPV‐negative stage IVa HNSCCs revealed overexpression of protein and mRNA levels in a subgroup of HNSCC tumors and a significant association of high CSPG4 protein levels with poor survival. This could be validated in three publicly available microarray datasets. As a potential cause for upregulated CSPG4 expression, we identified DNA hypomethylation in a CpG‐island of the promoter region. Accordingly, we found an inverse correlation of methylation and patient outcome. Finally, CSPG4 re‐expression was achieved by demethylating treatment of highly methylated HNSCC cell lines establishing a direct link between methylation and CSPG4 expression. In conclusion, we identified CSPG4 as a novel biomarker in HNSCC on several biological levels and established a causative link between DNA methylation and CSPG4 protein and mRNA expression.

Transcriptomic analysis of aggressive meningiomas identifies PTTG1 and LEPR as prognostic biomarkers independent of WHO grade

Meningiomas are frequent central nervous system tumors. Although most meningiomas are benign (WHO grade I) and curable by surgery, WHO grade II and III tumors remain therapeutically challenging due to frequent recurrence. Interestingly, relapse also occurs in some WHO grade I meningiomas. Hence, we investigated the transcriptional features defining aggressive (recurrent, malignantly progressing or WHO grade III) meningiomas in 144 cases. Meningiomas were categorized into non-recurrent (NR), recurrent (R), and tumors undergoing malignant progression (M) in addition to their WHO grade. Unsupervised transcriptomic analysis in 62 meningiomas revealed transcriptional profiles lining up according to WHO grade and clinical subgroup. Notably aggressive subgroups (R+M tumors and WHO grade III) shared a large set of differentially expressed genes (n=332; p<0.01, FC>1.25). In an independent multicenter validation set (n=82), differential expression of 10 genes between WHO grades was confirmed. Additionally, among WHO grade I tumors differential expression between NR and aggressive R+M tumors was affirmed for PTTG1, AURKB, ECT2, UBE2C and PRC1, while MN1 and LEPR discriminated between NR and R+M WHO grade II tumors. Univariate survival analysis revealed a significant association with progression-free survival for PTTG1, LEPR, MN1, ECT2, PRC1, COX10, UBE2C expression, while multivariate analysis identified a prediction for PTTG1 and LEPR mRNA expression independent of gender, WHO grade and extent of resection. Finally, stainings of PTTG1 and LEPR confirmed malignancy-associated protein expression changes. In conclusion, based on the so far largest study sample of WHO grade III and recurrent meningiomas we report a comprehensive transcriptional landscape and two prognostic markers.

Prognostic value of the extent of resection in supratentorial WHO grade II astrocytomas stratified for IDH1 mutation status: a single-center volumetric analysis

Current evidence supports a maximized extent of resection (EOR) in low-grade gliomas (LGG), regardless of different histological subtypes and molecular markers. We therefore evaluated the prognostic impact of extensive, mainly intraoperative (i)MRI-guided surgery in low-grade astrocytomas stratified for IDH1 mutation status. Retrospective assessment of 46 consecutive cases of newly diagnosed supratentorial WHO grade II astrocytomas treated during the last decade was performed. IDH1 mutation status was obtained for all patients. Volumetric analysis of tumor volumes was performed pre-, intra-, early postoperatively and at first follow-up. Survival analysis was conducted with uni-and multivariate regression models implementing clinical parameters and continuous volumetric variables. Median EOR was 90.4 % (range 17.5–100 %) and was increased to 94.9 % (range 34.8–100 %) in iMRI-guided resections (n = 33). A greater EOR was prognostic for increased progression-free survival (HR 0.23, p = 0.031) and time to re-intervention (TTR) (HR 0.23, p = 0.03). In IDH1 mutant patients, smaller residual tumor volumes were associated with increased TTR (HR 1.01, p = 0.03). IDH1 mutation (38/46 cases) was an independent positive prognosticator for overall survival (OS) in multivariate analysis (HR 0.09, p = 0.002), while extensive surgery had limited impact upon OS. In a subgroup of patients with ≥40 % EOR (n = 39), however, initial and residual tumor volumes were prognostic for OS (HR 1.03, p = 0.005 and HR 1.08, p = 0.007, respectively), persistent to adjustment for IDH1. No association between EOR and neurologic morbidity was found. In this analysis of low-grade astrocytomas stratified for IDH1, extensive tumor resections were prognostic for progression and TTR and, in patients with ≥40 % EOR, for OS.

LOC283731 promoter hypermethylation prognosticates survival after radiochemotherapy in IDH1 wild-type glioblastoma patients

MGMT promoter methylation status is currently the only established molecular prognosticator in IDH wild‐type glioblastoma multiforme (GBM). Therefore, we aimed to discover novel therapy‐associated epigenetic biomarkers. After enrichment for hypermethylated fractions using methyl‐CpG‐immunoprecipitation (MCIp), we performed global DNA methylation profiling for 14 long‐term (LTS; >36 months) and 15 short‐term (STS; 6–10 months) surviving GBM patients. Even after exclusion of the G‐CIMP phenotype, we observed marked differences between the LTS and STS methylome. A total of 1,247 probes in 706 genes were hypermethylated in LTS and 463 probes in 305 genes were found to be hypermethylated in STS patients (p values < 0.05, log2 fold change ± 0.5). We identified 13 differentially methylated regions (DMRs) with a minimum of four differentially methylated probes per gene. Indeed, we were able to validate a subset of these DMRs through a second, independent method (MassARRAY) in our LTS/STS training set (ADCY1, GPC3, LOC283731/ISLR2). These DMRs were further assessed for their prognostic capability in an independent validation cohort (n = 62) of non‐G‐CIMP GBMs from the TCGA. Hypermethylation of multiple CpGs mapping to the promoter region of LOC283731 correlated with improved patient outcome (p = 0.03). The prognostic performance of LOC283731 promoter hypermethylation was confirmed in a third independent study cohort (n = 89), and was independent of gender, performance (KPS) and MGMT status (p = 0.0485, HR = 0.63). Intriguingly, the prediction was most pronounced in younger GBM patients (<60 years). In conclusion, we provide compelling evidence that promoter methylation status of this novel gene is a prognostic biomarker in IDH1 wild‐type/non‐G‐CIMP GBMs.

Spatial transcriptome analysis reveals Notch pathway-associated prognostic markers in IDH1 wild-type glioblastoma involving the subventricular zone

BackgroundThe spatial relationship of glioblastoma (GBM) to the subventricular zone (SVZ) is associated with inferior patient survival. However, the underlying molecular phenotype is largely unknown. We interrogated an SVZ-dependent transcriptome and potential location-specific prognostic markers.MethodsmRNA microarray data of a discovery set (n = 36 GBMs) were analyzed for SVZ-dependent gene expression and process networks using the MetaCore™ workflow. Differential gene expression was confirmed by qPCR in a validation set of 142 IDH1 wild-type GBMs that was also used for survival analysis.ResultsMicroarray analysis revealed a transcriptome distinctive of SVZ+ GBM that was enriched for genes associated with Notch signaling. No overlap was found to The Cancer Genome Atlas’s molecular subtypes. Independent validation of SVZ-dependent expression confirmed four genes with simultaneous prognostic impact: overexpression of HES4 (p = 0.034; HR 1.55) and DLL3 (p = 0.017; HR 1.61) predicted inferior, and overexpression of NTRK2 (p = 0.049; HR 0.66) and PIR (p = 0.025; HR 0.62) superior overall survival (OS). Additionally, overexpression of DLL3 was predictive of shorter progression-free survival (PFS) (p = 0.043; HR 1.64). Multivariate analysis revealed overexpression of HES4 to be independently associated with inferior OS (p = 0.033; HR 2.03), and overexpression of DLL3 with inferior PFS (p = 0.046; HR 1.65).ConclusionsWe identified four genes with SVZ-dependent expression and prognostic significance, among those HES4 and DLL3 as part of Notch signaling, suggesting further evaluation of location-tailored targeted therapies.

Microenvironment and brain tumor stem cell maintenance: impact of the niche.

There is a lot of experimental evidence that brain tumors might be sustained by a subpopulation of immature cells, so-called brain tumor stem cells (BTSCs), which do not only drive tumor formation but are highly resistant to conventional therapies. Recent findings suggest a critical role of the molecular and cellular tumor microenvironment in which these cells reside for the maintenance of stem cell properties and therapy resistance. However, detection of different BTSC phenotypes even in the same patient tumor and the observation of a marked plasticity due to instability of the BTSC phenotype caused by the environmental niche have led to a controversial discussion on the validity of the cancer stem cell concept. What complicates the situation even more is that there are different types of niches and little is known about the interplay of the niche components with one another and with different types of BTSCs in the context of stem cell maintenance. In this article we review our current knowledge on different BTSC phenotypes and the cellular components and physiology of the niche in which these cells reside. In addition, we will summarize the molecular and functional interaction of niche cells and niche conditions and how this impacts on BTSC maintenance.

Non-invasive glioblastoma immunoprofiling by printed peptide arrays.

ABSTRACT Immune monitoring assays for patient stratification and treatment efficacy in clinical trials are in demand. We have recently described a cost-effective non-invasive assay to determine the immune status of glioblastoma patients. Profiling antitumor serum antibodies by customized printed peptide arrays identified response against a tenascin-C (TNC) peptide as a robust prognostic biomarker.

Lessons we Learned from High-Throughput and Top-Down Systems Biology Analyses about Glioma Stem Cells

A growing body of evidence suggests that glioma stem cells (GSCs) account for tumor initiation, therapy resistance, and the subsequent regrowth of gliomas. Thus, continuous efforts have been undertaken to further characterize this subpopulation of less differentiated tumor cells. Although we are able to enrich GSCs, we still lack a comprehensive understanding of GSC phenotypes and behavior. The advent of high-throughput technologies raised hope that incorporation of these newly developed platforms would help to tackle such questions. Since then a couple of comparative genome-, transcriptome- and proteome-wide studies on GSCs have been conducted giving new insights in GSC biology. However, lessons had to be learned in designing high-throughput experiments and some of the resulting conclusions fell short of expectations because they were performed on only a few GSC lines or at one molecular level instead of an integrative poly-omics approach. Despite these shortcomings, our knowledge of GSC biology has markedly expanded due to a number of survival-associated biomarkers as well as glioma-relevant signaling pathways and therapeutic targets being identified. In this article we review recent findings obtained by comparative high-throughput analyses of GSCs. We further summarize fundamental concepts of systems biology as well as its applications for glioma stem cell research.

MetaboDiff: an R package for differential metabolomic analysis

Abstract Summary Comparative metabolomics comes of age through commercial vendors offering metabolomics for translational researchers outside the mass spectrometry field. The MetaboDiff packages aims to provide a low-level entry to differential metabolomic analysis with R by starting off with the table of metabolite measurements. As a key functionality, MetaboDiffs offers the exploration of sample traits in a data-derived metabolic correlation network. Availability and implementation The MetaboDiff R package is platform-independent, available at http://github.com/andreasmock/MetaboDiff/ and released under the MIT licence. The package documentation comprises a step-by-step markdown tutorial. Supplementary information Supplementary data are available at Bioinformatics online.