Glioblastoma (GBM) is a devastating and incurable brain tumour, with a median overall survival of fifteen months. Identifying the cell of origin that harbours mutations that drive GBM could provide a fundamental basis for understanding disease progression and developing new treatments. Given that the accumulation of somatic mutations has been implicated in gliomagenesis, studies have suggested that neural stem cells (NSCs), with their self-renewal and proliferative capacities, in the subventricular zone (SVZ) of the adult human brain may be the cells from which GBM originates. However, there is a lack of direct genetic evidence from human patients with GBM. Here we describe direct molecular genetic evidence from patient brain tissue and genome-edited mouse models that show astrocyte-like NSCs in the SVZ to be the cell of origin that contains the driver mutations of human GBM. First, we performed deep sequencing of triple-matched tissues, consisting of (i) normal SVZ tissue away from the tumour mass, (ii) tumour tissue, and (iii) normal cortical tissue (or blood), from 28 patients with isocitrate dehydrogenase (IDH) wild-type GBM or other types of brain tumour. We found that normal SVZ tissue away from the tumour in 56.3% of patients with wild-type IDH GBM contained low-level GBM driver mutations (down to approximately 1% of the mutational burden) that were observed at high levels in their matching tumours. Moreover, by single-cell sequencing and laser microdissection analysis of patient brain tissue and genome editing of a mouse model, we found that astrocyte-like NSCs that carry driver mutations migrate from the SVZ and lead to the development of high-grade malignant gliomas in distant brain regions. Together, our results show that NSCs in human SVZ tissue are the cells of origin that contain the driver mutations of GBM.

Although mesenchymal stem cells (MSCs) have been implicated as stromal components of several cancers, their ultimate contribution to tumorigenesis and their potential to drive cancer stem cells, particularly in the unique microenvironment of human brain tumors, remains largely undefined. Consequently, using established criteria, we isolated glioma-associated-human MSCs (GA-hMSCs) from fresh human glioma surgical specimens for the first time. We show that these GA-hMSCs are nontumorigenic stromal cells that are phenotypically similar to prototypical bone marrow-MSCs. Low-passage genomic sequencing analyses comparing GA-hMSCs with matched tumor-initiating glioma stem cells (GSCs) suggest that most GA-hMSCs (60%) are normal cells recruited to the tumor (Group 1 GA-hMSCs), although, rarely (10%), GA-hMSCs may differentiate directly from GSCs (Group 2 GA-hMSCs) or display genetic patterns intermediate between these groups (Group 3 GA-hMSCs). Importantly, GA-hMSCs increase proliferation and self-renewal of GSCs in vitro, and enhance GSC tumorigenicity and mesenchymal features in vivo, confirming their functional significance within the GSC niche. These effects are mediated by GA-hMSC-secreted interleukin-6, which activates STAT3 in GSCs. Our results establish GA-hMSCs as a potentially new stromal component of gliomas that drives the aggressiveness of GSCs, and point to GA-hMSCs as a novel therapeutic target within gliomas.

To investigate whether radiomic features at MRI improve survival prediction in patients with glioblastoma multiforme (GBM) when they are integrated with clinical and genetic profiles. Materials and Methods: Data in patients with a diagnosis of GBM between December 2009 and January 2017 (217 patients) were retrospectively reviewed up to May 2017 and allocated to training and test sets (3:1 ratio). Radiomic features (n = 796) were extracted from multiparametric MRI. A random survival forest (RSF) model was trained with the radiomic features along with clinical and genetic profiles (O-6-methylguanine-DNA-methyltransferase promoter methylation and isocitrate dehydrogenase 1 mutation statuses) to predict overall survival (OS) and progression-free survival (PFS). The RSF models were validated on the test set. The incremental values of radiomic features were evaluated by using the integrated area under the receiver operating characteristic curve (iAUC). Results: The 217 patients had a mean age of 57.9 years, and there were 87 female patients (age range, 22-81 years) and 130 male patients (age range, 17-85 years). The median OS and PFS of patients were 352 days (range, 20-1809 days) and 264 days (range, 21-1809 days), respectively. The RSF radiomics models were successfully validated on the test set (iAUC, 0.652 [95% confidence interval {CI}, 0.524, 0.769] and 0.590 [95% CI: 0.502, 0.689] for OS and PFS, respectively). The addition of a radiomics model to clinical and genetic profiles improved survival prediction when compared with models containing clinical and genetic profiles alone (P = .04 and .03 for OS and PFS, respectively). Conclusion: Radiomic MRI phenotyping can improve survival prediction when integrated with clinical and genetic profiles and thus has potential as a practical imaging biomarker.

We demonstrated that tumors in freshly excised whole brain tissue could be differentiated clearly from normal brain tissue using a reflection-type terahertz (THz) imaging system. THz binary images of brain tissues with tumors indicated that the tumor boundaries in the THz images corresponded well to those in visible images. Grey and white-matter regions were distinguishable owing to the different distribution of myelin in the brain tissue. THz images corresponded closely with magnetic resonance imaging (MRI) results. The MRI and hematoxylin and eosin-stained microscopic images were investigated to account for the intensity differences in the THz images for fresh and paraffin-embedded brain tissue. Our results indicated that the THz signals corresponded to the cell density when water was removed. Thus, THz imaging could be used as a tool for label-free and real-time imaging of brain tumors, which would be helpful for physicians to determine tumor margins during brain surgery.

• Machine-learning algorithm radiomics can help to differentiate primary central PCNSL from GBM. • This approach yields a higher diagnostic accuracy than visual analysis by radiologists. • Radiomics can strengthen radiologists' diagnostic decisions whenever conventional MRI sequences are available.

Objectives To evaluate the added value of amide proton transfer (APT) imaging to the apparent diffusion coefficient (ADC) from diffusion tensor imaging (DTI) and the relative cerebral blood volume (rCBV) from perfusion magnetic resonance imaging (MRI) for discriminating between high- and low-grade gliomas. Methods Forty-six consecutive adult patients with diffuse gliomas who underwent preoperative APT imaging, DTI and perfusion MRI were enrolled. APT signals were compared according to the World Health Organization grade. The diagnostic ability and added value of the APT signal to the ADC and rCBV for discriminating between low- and high-grade gliomas were evaluated using receiver operating characteristic (ROC) analyses and integrated discrimination improvement. Results The APT signal increased as the glioma grade increased. The discrimination abilities of the APT, ADC and rCBV values were not significantly different. Using both the APT signal and ADC significantly improved discrimination vs. the ADC alone (area under the ROC curve [AUC], 0.888−0.910; P = 0.007), whereas using both the APT signal and rCBV did not improve discrimination vs. the rCBV alone (AUC, 0.927−0.923; P = 0.222). Conclusions APT imaging may be a useful imaging biomarker that adds value to the ADC for discriminating between low- and high-grade gliomas.

Background Glioma prognosis depends on the isocitrate dehydrogenase (IDH) mutation status. We aimed to predict the IDH status of gliomas from preoperative MR images using a fully automated hybrid approach with convolutional neural networks (CNNs) and radiomics. Methods We reviewed 1,166 preoperative MR images of gliomas (grades II-IV) from Severance Hospital (n=856, Severance Set), Seoul National University Hospital (n=107, SNUH set), and The Cancer Imaging Archive (n=203, TCIA set). The Severance set was subdivided into the development (n=727) and internal test (n=129) sets. Based on T1 postcontrast, T2, and fluid-attenuated inversion-recovery images, a fully automated model was developed that comprised a CNN for tumor segmentation (Model 1) and CNN-based classifier for IDH status prediction (Model 2) that uses a hybrid approach based on 2-dimensional tumor images and radiomic features from 3-dimensional tumor shape and loci guided by Model 1. The trained model was tested on internal (a subset of the Severance set) and external (SNUH and TCIA) test sets. Results The CNN for tumor segmentation (Model 1) achieved a dice coefficient of 0.86–0.92 across datasets. Our hybrid model achieved accuracies of 93.8%, 87.9%, and 78.8%; with areas under the receiver operating characteristic curves of 0.96, 0.94, and 0.86; and areas under the precision-recall curves of 0.88, 0.82, and 0.81 in the internal test, SNUH, and TCIA sets, respectively. Conclusions Our fully automated hybrid model demonstrated the potential to be a highly reproducible and generalizable tool across different datasets for the noninvasive prediction of the IDH status of gliomas.

Glioblastoma multiforme (GBM) is the most common brain tumor with very aggressive and infiltrative. Extracellular matrix (ECM) plays pivotal roles in the infiltrative characteristics of GBM.To understand the invasive characteristic of GBM, it is necessary to study cell-ECM interaction in the physiologically relevant biomimetic model that recapitulates the GBM-specific ECM microenvironment. Here, we propose biomimetic GBM-specific ECM microenvironment for studying mode and dynamics of glioblastoma cell invasion. Using tissue decellularization process, we constructed a patient tissuederived ECM (pdECM)-based three-dimensional in vitro model. In our model, GBM cells exhibited heterogeneous morphology and altered the invasion routes in a microenvironment-adaptive manner. We further elucidate the effects of inhibition of ECM remodeling-related enzymatic activity (Matrix metalloproteinase (MMP) 2/9, hyaluronan synthase (HAS)) on GBM cell invasion. Interestingly, after blocking both enzyme activity, GBM cells underwent morphological transition and switch the invasion mode. Such adaptability could render cell invasion resistant to anti-cancer target therapy. There results provide insight of how organ-specific matrix differentially regulates cancer cell phenotype, and have significant implications for the design of matrix with appropriate physiologically relevant properties for in vitro tumor model.Invasion and dissemination of cancer cells cause migration of neoplastic cells into surrounding tissues, resulting in mortality in tumor patients 1-3 . In particular, cancer cell invasion is of enormous clinical importance, since it involves both distant metastasis and local spreading, whereby cancer cells degrade and migrate through the tissue. The significance of cancer cell invasion is evident in the glioblastoma multiforme (GBM), which shows infiltrative and rapid growth into the surrounding brain tissue. The invasive and migratory characteristics of GBM cells provide a wealth of information about tumors within a patient 3 .Three-dimensional (3D) in vitro culture systems are increasingly employed to assess cell-ECM interactions and invasion of tumor cells. Indeed, cell behavior and responsiveness are dramatically different in a 3D physiological environment versus two-dimensional (2D) Petri dish conditions 4,5 . Importantly, in 3D cultures, ECM is an essential determinant of the cellular response to invasion and migration processes 6 . Brain ECM has a distinct composition from ECM in other tissues and organs, with a low stiffness and loosely connected cellular network. The ECM component of brain tissues contains high amounts of hyaluronic acid (HA), glycosaminoglycans (GAGs), and proteoglycans, but lacks fibrous materials such as collagen, fibronectin, etc 7,8 .In fact, the interaction between GBM cells and unique extracellular environment of the brain could impact on the invasive characteristics of GBM cells. Accumulated experimental and clinical data demonstrate that invasion