Higher YAP1 Levels Are Associated With Shorter Survival of Patients With Low Grade Astrocytoma.

BACKGROUND/AIM: Glioblastoma multiforme (GBM) is one of the most lethal types of brain cancer with a median survival of only 12 months due to its aggressiveness and lack of effective treatment options. Astrocytomas and oligodendrogliomas are classified as low-grade gliomas (LGG) and have the potential to progress into secondary GBM. YAP1 and TAZ are transcriptional co-activators of the hippo pathway and play an important role in tumorigenesis by controlling cell proliferation and differentiation. The aim of this study was to analyze whether YAP1 and TAZ influence the survival in patients with astrocytoma and oligodendroglioma. PATIENTS AND METHODS: A total of 22 patient samples of astrocytoma and 11 samples of oligodendroglioma were analyzed using real-time PCR. We utilized open-access data from The Cancer Genome Atlas (TCGA) focusing on "brain lower grade glioma". mRNA expression rates were used to validate our findings on survival analysis. RESULTS: Expression of YAP1 was twice as high in astrocytoma than in oligodendroglioma, whereas there was no difference in TAZ. In oligodendrogliomas, the expression of TAZ was higher in relapsed than in primary tumors. Patients with astrocytoma having a high YAP1 expression had a significantly shorter overall survival than patients with lower expression (median survival 161 vs. 86 months, p=0.0248). These findings were validated with survival analysis of TCGA data. CONCLUSION: High YAP1 expression shows a high correlation with poorer overall survival in LGG. YAP1 has higher levels of expression in astrocytomas than in oligodendrogliomas.

Influence of helical pitch and gantry rotation time on image quality and file size in ultrahigh-resolution photon-counting detector CT.

The goal of this experimental study was to quantify the influence of helical pitch and gantry rotation time on image quality and file size in ultrahigh-resolution photon-counting CT (UHR-PCCT). Cervical and lumbar spine, pelvis, and upper legs of two fresh-frozen cadaveric specimens were subjected to nine dose-matched UHR-PCCT scan protocols employing a collimation of 120 × 0.2 mm with varying pitch (0.3/1.0/1.2) and rotation time (0.25/0.5/1.0 s). Image quality was analyzed independently by five radiologists and further substantiated by placing normed regions of interest to record mean signal attenuation and noise. Effective mAs, CT dose index (CTDIvol), size-specific dose estimate (SSDE), scan duration, and raw data file size were compared. Regardless of anatomical region, no significant difference was ascertained for CTDIvol (p ≥ 0.204) and SSDE (p ≥ 0.240) among protocols. While exam duration differed substantially (all p ≤ 0.016), the lowest scan time was recorded for high-pitch protocols (4.3 ± 1.0 s) and the highest for low-pitch protocols (43.6 ± 15.4 s). The combination of high helical pitch and short gantry rotation times produced the lowest perceived image quality (intraclass correlation coefficient 0.866; 95% confidence interval 0.807-0.910; p < 0.001) and highest noise. Raw data size increased with acquisition time (15.4 ± 5.0 to 235.0 ± 83.5 GByte; p ≤ 0.013). Rotation time and pitch factor have considerable influence on image quality in UHR-PCCT and must therefore be chosen deliberately for different musculoskeletal imaging tasks. In examinations with long acquisition times, raw data size increases considerably, consequently limiting clinical applicability for larger scan volumes.

Expression of ENO1 Is Up-regulated in Low-grade Glioma and Positively Correlated With Meningioma Grade.

BACKGROUND: α-Enolase (ENO1) is a glycolytic enzyme involved in the Warburg effect which cancer cells utilize to satisfy their higher need for nutrients. Up-regulation of ENO1 has been detected in several tumor types, including melanoma and endometrial, gastric and colorectal cancer. In these tumors, ENO1 may function as prognostic marker. Therefore, it was our interest to determine the expression of ENO1 in glioma and meningioma and whether chemotherapy of glioma alters ENO1 expression. MATERIAL AND METHODS: Tumor samples and control tissues were obtained during neurosurgery. All tumor samples were grouped according to WHO classification. Quantitative polymerase chain reaction and western blot were used to detect the expression of ENO1 in glioma and meningioma. All assays were carried out in triplicates; ß-actin was used as a housekeeping gene. For western blots, all samples were incubated with mouse monoclonal anti-ENO1 followed by secondary horseradish peroxidase-linked anti-mouse antibody, with ß-actin as a loading control. Immunofluorescence (n=33) was performed to determine the presence of ENO1 in tumor and control tissues using primary antibody to ENO1 and anti-Cy3 as secondary antibody. RESULTS: The expression of ENO1 mRNA was significantly higher in the control group compared to glioma (p<0.0001) and its protein was also significantly up-regulated in low-grade glioma in comparison to high-grade (p<0.0001). ENO1 expression in grade II and III meningiomas was increased compared to grade I (p=0.016 and p=0.0010, respectively) and in grade III compared to grade II (p=0.0363). CONCLUSION: Our findings suggest that ENO1 might be a marker for meningioma progression and that ENO1 is up-regulated in low-grade glioma.

TERT and its binding protein: overexpression of GABPA/B in high grade gliomas.

Enhanced expression of TERT in gliomas is a result of two hotspot mutations, C228T and C250T, at the promoter region. GA-binding proteins selectively bind at these positions, respectively, causing an activation of the promoter and overexpression of TERT. GABP is a multimeric protein consisting of GABPA and GABPB with its isoforms GABPB1, GABPB1-L, GABPB1-S, GABPB2. In this study, we investigated the mRNA expression and association between TERT and GABPA/B isoforms in tumor samples of different glioma grades. The expression was determined by quantitative real-time PCR and the results were statistically analyzed. We present that TERT is mainly expressed in primary glioblastomas. All GA-binding proteins progress through the glioma grades and have the highest expression levels in secondary glioblastomas. In secondary glioblastomas after chemotherapy, GABPB1 and GABPB1-L are expressed on a lower level than without treatment. In high grades, TERT and GABPA, GAPB1, GABPB1-L, GABPB1-S are upregulated compared to low grades. Between primary and secondary glioblastomas with and without chemotherapy, TERT is elevated in the former while GABPB1 is increased in the secondary glioblastomas. GABPA and GABPB1, GABPB1-L and GABPB1-S positive correlate in primary glioblastomas. The present study confirms the upregulation of TERT in primary glioblastomas while all GABP proteins rise with the malignancy of the gliomas. Further investigations must be made to elucidate the relation between TERT and all GABP proteins as it may play a key role in the gliomagenesis.

Automated Meningioma Segmentation in Multiparametric MRI : Comparable Effectiveness of a Deep Learning Model and Manual Segmentation.

PURPOSE: Volumetric assessment of meningiomas represents a valuable tool for treatment planning and evaluation of tumor growth as it enables a more precise assessment of tumor size than conventional diameter methods. This study established a dedicated meningioma deep learning model based on routine magnetic resonance imaging (MRI) data and evaluated its performance for automated tumor segmentation. METHODS: The MRI datasets included T1-weighted/T2-weighted, T1-weighted contrast-enhanced (T1CE) and FLAIR of 126 patients with intracranial meningiomas (grade I: 97, grade II: 29). For automated segmentation, an established deep learning model architecture (3D deep convolutional neural network, DeepMedic, BioMedIA) operating on all four MR sequences was used. Segmentation included the following two components: (i) contrast-enhancing tumor volume in T1CE and (ii) total lesion volume (union of lesion volume in T1CE and FLAIR, including solid tumor parts and surrounding edema). Preprocessing of imaging data included registration, skull stripping, resampling, and normalization. After training of the deep learning model using manual segmentations by 2 independent readers from 70 patients (training group), the algorithm was evaluated on 56 patients (validation group) by comparing automated to ground truth manual segmentations, which were performed by 2 experienced readers in consensus. RESULTS: Of the 56 meningiomas in the validation group 55 were detected by the deep learning model. In these patients the comparison of the deep learning model and manual segmentations revealed average dice coefficients of 0.91 ± 0.08 for contrast-enhancing tumor volume and 0.82 ± 0.12 for total lesion volume. In the training group, interreader variabilities of the 2 manual readers were 0.92 ± 0.07 for contrast-enhancing tumor and 0.88 ± 0.05 for total lesion volume. CONCLUSION: Deep learning-based automated segmentation yielded high segmentation accuracy, comparable to manual interreader variability.