Prediction of Stroke Outcome in Mice Based on Noninvasive MRI and Behavioral Testing.

BACKGROUND: Prediction of poststroke outcome using the degree of subacute deficit or magnetic resonance imaging is well studied in humans. While mice are the most commonly used animals in preclinical stroke research, systematic analysis of outcome predictors is lacking. METHODS: We intended to incorporate heterogeneity into our retrospective study to broaden the applicability of our findings and prediction tools. We therefore analyzed the effect of 30, 45, and 60 minutes of arterial occlusion on the variance of stroke volumes. Next, we built a heterogeneous cohort of 215 mice using data from 15 studies that included 45 minutes of middle cerebral artery occlusion and various genotypes. Motor function was measured using a modified protocol for the staircase test of skilled reaching. Phases of subacute and residual deficit were defined. Magnetic resonance images of stroke lesions were coregistered on the Allen Mouse Brain Atlas to characterize stroke topology. Different random forest prediction models that either used motor-functional deficit or imaging parameters were generated for the subacute and residual deficits. RESULTS: Variance of stroke volumes was increased by 45 minutes of arterial occlusion compared with 60 minutes. The inclusion of various genotypes enhanced heterogeneity further. We detected both a subacute and residual motor-functional deficit after stroke in mice and different recovery trajectories could be observed. In mice with small cortical lesions, lesion volume was the best predictor of the subacute deficit. The residual deficit could be predicted most accurately by the degree of the subacute deficit. When using imaging parameters for the prediction of the residual deficit, including information about the lesion topology increased prediction accuracy. A subset of anatomic regions within the ischemic lesion had particular impact on the prediction of long-term outcomes. Prediction accuracy depended on the degree of functional impairment. CONCLUSIONS: For the first time, we developed and validated a robust tool for the prediction of functional outcomes after experimental stroke in mice using a large and genetically heterogeneous cohort. These results are discussed in light of study design and imaging limitations. In the future, using outcome prediction can improve the design of preclinical studies and guide intervention decisions.

Using Extracellular miRNA Signatures to Identify Patients with LRRK2-Related Parkinson's Disease.

Background: Mutations in the Leucine Rich Repeat Kinase 2 gene are highly relevant in both sporadic and familial cases of Parkinson's disease. Specific therapies are entering clinical trials but patient stratification remains challenging. Dysregulated microRNA expression levels have been proposed as biomarker candidates in sporadic Parkinson's disease. Objective: In this proof-of concept study we evaluate the potential of extracellular miRNA signatures to identify LRRK2-driven molecular patterns in Parkinson's disease. Methods: We measured expression levels of 91 miRNAs via RT-qPCR in ten individuals with sporadic Parkinson's disease, ten LRRK2 mutation carriers and eleven healthy controls using both plasma and cerebrospinal fluid. We compared miRNA signatures using heatmaps and t-tests. Next, we applied group sorting algorithms and tested sensitivity and specificity of their group predictions. Results: miR-29c-3p was differentially expressed between LRRK2 mutation carriers and sporadic cases, with miR-425-5p trending towards significance. Individuals clustered in principal component analysis along mutation status. Group affiliation was predicted with high accuracy in the prediction models (sensitivity up to 89%, specificity up to 70%). miRs-128-3p, 29c-3p, 223-3p, and 424-5p were identified as promising discriminators among all analyses. Conclusions: LRRK2 mutation status impacts the extracellular miRNA signature measured in plasma and separates mutation carriers from sporadic Parkinson's disease patients. Monitoring LRRK2 miRNA signatures could be an interesting approach to test drug efficacy of LRRK2-targeting therapies. In light of small sample size, the suggested approach needs to be validated in larger cohorts.

We know that alterations in a gene called Leucine Rich Repeat Kinase 2 are important in both inherited and non-inherited cases of Parkinson's disease. We also know that treatments for Parkinson's disease specifically targeting LRRK2 are currently being developed. Challenges for developing such a treatment, however, are how to accurately identify patients who could benefit from these therapies and to observe whether the treatment interacts with its target on a molecular level. In this study, we tested whether individuals with an alteration in the LRRK2 gene display a distinct pattern of small RNA molecules, called microRNAs. We measured the amount of 91 microRNAs present in blood of individuals with an alteration in the LRRK2 gene and compared their pattern to patients with a non-inherited form of Parkinson's disease and healthy controls.We found that the amount of a specific microRNA called miR-29c-3p was different between individuals with or without an alteration of the LRRK2 gene. Additionally, we developed models that could predict whether someone had a LRRK2 mutation based on the microRNA pattern in the plasma. Of course, we have easier methods to find these gene alterations, but our findings suggest that changes of LRRK2 result in a shift of microRNA patterns in the blood. This could help us to observe the effects of a LRRK2 specific treatment which tries to revert these changes. As we know that LRRK2 also plays a role in some patients with a non-inherited form of Parkinson's disease, this microRNA pattern could maybe even help us to identify these individuals. It is important to note that our study involved a small number of individuals, so that further research with larger groups is needed to confirm our findings.

Atlas registration for edema-corrected MRI lesion volume in mouse stroke models.

Lesion volume measurements with magnetic resonance imaging are widely used to assess outcome in rodent models of stroke. In this study, we improved a mathematical framework to correct lesion size for edema which is based on manual delineation of the lesion and hemispheres. Furthermore, a novel MATLAB toolbox to register mouse brain MR images to the Allen brain atlas is presented. Its capability to calculate edema-corrected lesion size was compared to the manual approach. Automated image registration performed equally well in in a mouse middle cerebral artery occlusion model (Pearson r = 0.976, p = 2.265e-11). Information encapsulated in the registration was used to generate maps of edema induced tissue volume changes. These showed discrepancies to simplified tissue models underlying the manual approach. The presented techniques provide biologically more meaningful, voxel-wise biomarkers of vasogenic edema after stroke.

Prognostic Relevance of Tumor Purity and Interaction with MGMT Methylation in Glioblastoma.

Promoter methylation status of O-6-methylguanine-DNA methyltransferase (MGMT), a DNA repair enzyme, is a critical biomarker in glioblastoma (GBM), as treatment decisions and clinical trial inclusion rely on its accurate assessment. However, interpretation of results is complicated by poor interassay reproducibility as well as a weak correlation between methylation status and expression levels of MGMT. This study systematically investigates the influence of tumor purity on tissue subjected to MGMT analysis. A quantitative, allele-specific real-time PCR (qAS-PCR) assay was developed to determine genotype and mutant allele frequency of telomerase promoter (pTERT) mutations as a direct measure of tumor purity. We studied tumor purity, pTERT mutation by Sanger sequencing, MGMT methylation by pyrosequencing, IDH1 mutation status, and clinical parameters in a cohort of high-grade gliomas (n = 97). The qAS-PCR reliably predicted pTERT genotype and tumor purity compared with independent methods. Tumor purity positively and significantly correlated with the extent of methylation in MGMT methylated GBMs. Extent of MGMT methylation differed significantly with respect to pTERT mutation hotspot (C228T vs. C250T). Interestingly, frontal lobe tumors showed greater tumor purity than those in other locations. Above all, tumor purity was identified as an independent prognostic factor in GBM. In conclusion, we determined mutual associations of tumor purity with MGMT methylation and pTERT mutations and found that the extent of MGMT methylation reflects tumor purity. In turn, tumor purity is prognostic in IDH1 wild-type GBM.Implications: Tumor purity is an independent prognostic marker in glioblastoma and is associated with the extent of MGMT methylation. Mol Cancer Res; 15(5); 532-40. ©2017 AACR.

Cellular heterogeneity contributes to subtype-specific expression of ZEB1 in human glioblastoma.

The transcription factor ZEB1 has gained attention in tumor biology of epithelial cancers because of its function in epithelial-mesenchymal transition, DNA repair, stem cell biology and tumor-induced immunosuppression, but its role in gliomas with respect to invasion and prognostic value is controversial. We characterized ZEB1 expression at single cell level in 266 primary brain tumors and present a comprehensive dataset of high grade gliomas with Ki67, p53, IDH1, and EGFR immunohistochemistry, as well as EGFR FISH. ZEB1 protein expression in glioma stem cell lines was compared to their parental tumors with respect to gene expression subtypes based on RNA-seq transcriptomic profiles. ZEB1 is widely expressed in glial tumors, but in a highly variable fraction of cells. In glioblastoma, ZEB1 labeling index is higher in tumors with EGFR amplification or IDH1 mutation. Co-labeling studies showed that tumor cells and reactive astroglia, but not immune cells contribute to the ZEB1 positive population. In contrast, glioma cell lines constitutively express ZEB1 irrespective of gene expression subtype. In conclusion, our data indicate that immune infiltration likely contributes to differential labelling of ZEB1 and confounds interpretation of bulk ZEB1 expression data.