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Recent work has highlighted the tumor microenvironment as a central player in cancer. In particular, interactions between tumor and immune cells may help drive the development of brain tumors such as glioblastoma multiforme (GBM). Despite significant research into the molecular classification of glioblastoma, few studies have characterized in a comprehensive manner the immune infiltrate in situ and within different GBM subtypes.In this study, we use an unbiased, automated immunohistochemistry-based approach to determine the immune phenotype of the four GBM subtypes (classical, mesenchymal, neural and proneural) in a cohort of 98 patients. Tissue Micro Arrays (TMA) were stained for CD20 (B lymphocytes), CD5, CD3, CD4, CD8 (T lymphocytes), CD68 (microglia), and CD163 (bone marrow derived macrophages) antibodies. Using automated image analysis, the percentage of each immune population was calculated with respect to the total tumor cells. Mesenchymal GBMs displayed the highest percentage of microglia, macrophage, and lymphocyte infiltration. CD68+ and CD163+ cells were the most abundant cell populations in all four GBM subtypes, and a higher percentage of CD163+ cells was associated with a worse prognosis. We also compared our results to the relative composition of immune cell type infiltration (using RNA-seq data) across TCGA GBM tumors and validated our results obtained with immunohistochemistry with an external cohort and a different method. The results of this study offer a comprehensive analysis of the distribution and the infiltration of the immune components across the four commonly described GBM subgroups, setting the basis for a more detailed patient classification and new insights that may be used to better apply or design immunotherapies for GBM.

Glioblastoma treatment options remain limited due to its aggressive and invasive nature. It is increasingly appreciated that molecular heterogeneity between tumors and within tumors likely contributes to the lack of therapeutic advances. To maintain the inherent heterogeneity of glioblastoma, we employed a novel method to rapidly culture glioblastoma organoids (GBOs) directly from neurosurgical resection. GBOs are routinely generated around two weeks following initial resection. Comprehensive histologic and sequencing analyses demonstrated similarity to primary tumors. Leveraging clinical molecular and sequencing data, selected GBOs were treated with radiation/temozolamide and targeted inhibitor therapies. The effect on proliferation was measured by the percentage of KI67+ cells and gene set enrichment (GSEA) analysis was performed to compare the pre-treated expression signature amongst responsive and non-responsive tumors. Treatment of organoids with radiation/temozolamide led to a decrease in the percentage of KI67+ cells in four of eight patient-derived organoid lines with some evidence of correlative radiographic response Gene sets associated with radiation response and TNF signaling were enriched in radiation/temozolamide sensitive GBOs. GBO response to EGFR inhibition via gefitinib treatment was specific to EGFR altered tumors, whose expression also enriched for EGF signaling pathway expression. Two GBOs had downstream NF1 mutations that responded to the MEK inhibitor trametinib. On GSEA, gene expression of NF1 mutated GBOs enriched for RAS signaling. One GBO line was found to have a PI3K mutation and responded dramatically to mTOR inhibition via everolimus. Dichotomous efficacy of MEK and mTOR inhibition was also noted by tumor-specific changes in GBO diameter following treatment. This novel culturing method of GBOs maintains intertumoral and intratumoral heterogeneity and allows for therapeutic testing within two weeks of neurosurgical resection. As clinical sequencing because increasingly prevalent, GBOs may become a valuable tool to functionally test mutation-specific treatment strategies in a patient-specific manner within a clinically relevant time frame.

BACKGROUND This study builds on the results of the University of Pennsylvania sponsored phase I study of a single peripheral infusion of chimeric antigen receptor (CAR) T cells targeting epidermal growth factor receptor variant III (EGFRvIII) in recurrent glioblastoma (GBM) (NCT02209376). A dose of 5x108 CART-EGFRvIII cells was safe, and the cells were able to expand in the host and reach the GBM tumor in the brain. In addition, there was no cross-reactivity of CART-EGFRvIII cells with wild-type EGFR normally expressed by human tissues. Some patients required tumor resection after CAR T cell infusion. In situ evaluation of the tumor microenvironment demonstrated increased and robust expression of inhibitory molecules, such as programmed death-ligand 1 (PD-L1), compared to pre–CART-EGFRvIII tumor specimens. Therefore, we hypothesized that using a combination of CART-EGFRvIII cells and a PD-1 inhibitor would improve the outcome of the treatment. METHODS This single-center study (NCT03726515) has a single-arm, open-label, phase 1 design and will enroll 7 patients with newly diagnosed, O6-methylguanine-methyltransferase (MGMT)-unmethylated, EGFRvIII+ GBM. Following maximal safe tumor resection, patients receive a short course of adjuvant radiation with a total dose of 40 Gy administered in 15 fractions. Peripheral IV infusions of 2x108 CART-EGFRvIII cells and 200mg pembrolizumab begin 2–3 weeks after completing radiation therapy. Thereafter, subjects receive CART-EGFRvIII cells + pembrolizumab in 3-week cycles for up to 3 infusions of CART-EGFRvIII cells and 4 infusions of pembrolizumab. The primary endpoint of the study is the safety and tolerability of administering multiple infusions of CART-EGFRvIII cells in combination with pembrolizumab, as measured by the occurrence of study-related adverse events. Secondary endpoints include overall survival, progression-free survival, and objective response rate. PROGRESS: At 5 June 2019, 2 patients have been enrolled and treated on study.

High grade glioma is the most common malignant primary brain tumor. Despite the best available therapy, the median survival is limited. Chimeric antigen receptor (CAR)-T cells effectively attack target positive tumor cells, demonstrating a promising possible treatment. Based on our previous clinical and pre-clinical studies, we utilized epidermal growth factor receptor variant III (EGFRvIII) and interleukin 13 receptor subunit alpha 2 (IL13Rα2) targeting single-chain variable fragments (scFvs) to generate bi-specific tandem CAR T cells and bi-specific parallel CAR T cells to decrease the potential of antigen escape and tumor recurrence. 5, 10, 15 or 20 flexible amino acids were utilized to link scFvs in tandem CARs. Ribosomal “skipping” 2A peptides were utilized to generate parallel CAR T cells. Flow-based IL13Rα2 staining on glioma stem cells (GSCs) showed 7 out 11 cases positive (64%) heterogeneous intratumoral expression. Tandem CAR T cells and parallel CAR T cells upregulated CD69 expression (P< 0.0001), generated IFNγ, interleukin 2 and TNFα (P< 0.0001), as well as exhibiting cytotoxic activity (P< 0.0001) when compared against un-transduced T cells, in co-culture with either single- or double-target positive GSCs. There was no significant difference between different lengths of linker in tandem CAR T cells. Parallel CAR T cells generated a larger cytokine response (P< 0.0001) and more effectively killed target positive cells (P< 0.05) than tandem CAR T cells. In summary, glioma associated target expression is heterogeneous in GSCs. Multivalent tandem CAR T cells and parallel CAR T cells effectively respond to target expressing tumor cells. The function of tandem CAR T cells is not relative with the length of linker. Parallel CAR T cells are a more promising strategy in generating gene modified multivalent targeting T cells.

BACKGROUND Accurate differentiation of brain infections from necrotic glioblastomas (GBMs) may not always be possible on morphologic MRI or on diffusion tensor imaging (DTI) and dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) if these techniques are used independently. PURPOSE To investigate the combined analysis of DTI and DSC-PWI in distinguishing brain injections from necrotic GBMs. STUDY TYPE Retrospective. POPULATION Fourteen patients with brain infections and 21 patients with necrotic GBMs. FIELD STRENGTH/SEQUENCE 3T MRI, DTI, and DSC-PWI. ASSESSMENT Parametric maps of mean diffusivity (MD), fractional anisotropy (FA), coefficient of linear (CL), and planar anisotropy (CP) and leakage corrected cerebral blood volume (CBV) were computed and coregistered with postcontrast T1 -weighted and FLAIR images. All lesions were segmented into the central core and enhancing region. For each region, median values of MD, FA, CL, CP, relative CBV (rCBV), and top 90th percentile of rCBV (rCBVmax ) were measured. STATISTICAL TESTS All parameters from both regions were compared between brain infections and necrotic GBMs using Mann-Whitney tests. Logistic regression analyses were performed to obtain the best model in distinguishing these two conditions. RESULTS From the central core, significantly lower MD (0.90 × 10-3 ± 0.44 × 10-3 mm2 /s vs. 1.66 × 10-3 ± 0.62 × 10-3 mm2 /s, P = 0.001), significantly higher FA (0.15 ± 0.06 vs. 0.09 ± 0.03, P

In patients with certain hematologic malignancies, the use of autologous T cells genetically modified to express chimeric antigen receptors (CARs) has led to unprecedented clinical responses. Although progress in solid tumors has been elusive, recent clinical studies have demonstrated the feasibility and safety of CAR T-cell therapy for glioblastoma. In addition, despite formidable barriers to T-cell localization and effector function in glioblastoma, signs of efficacy have been observed in select patients. In this review, we begin with a discussion of established obstacles to systemic therapy in glioblastoma and how these may be overcome by CAR T cells. We continue with a summary of previously published CAR T-cell trials in GBM, and end by outlining the key therapeutic challenges associated with the use of CAR T cells in this disease.

BACKGROUND: Brain metastases are the most common central nervous system (CNS) tumors, occurring in 300,000 people per year in the US. The benefit of surgical resection, over radiosurgery, for dominant lesions remains unclear. METHODS: The University of Pennsylvania Health System database was retrospectively reviewed for patients presenting with multiple brain metastases from 1/1/16 to 8/31/18 with one dominant lesion > 2 cm in diameter, who underwent initial treatment with either resection of the dominant lesion or Gamma Knife radiosurgery (GKS). Inclusion criteria were age > 18, >1 brain metastasis, and presence of a dominant lesion (>2 cm). We analyzed factors associated with mortality. RESULTS: 129 patients were identified (surgery=84, GKS=45). The median number of intracranial metastases was 3 (IQR: 2-5). The median diameter of the largest lesion was 31 mm (IQR: 25-38) in the surgery group vs 21 mm (IQR: 20-24) in the GKS group (pCONCLUSION: In our institution, patients with multiple brain metastases and one symptomatic dominant lesion demonstrated similar survival after GKS when compared with up-front surgical resection of the dominant lesion.

PURPOSE Multi-omics data integration captures tumor characteristics at multiple scales [i.e., microscopic (genomics and epigenetics), macroscopic (radiomics), clinical manifestation], provides a more comprehensive assessment of patient’s risk, and facilitates personalized therapies. In this work, we investigated the synergistic value of such multiple data sources for risk stratification and prediction of overall survival in IDH-wildtype glioblastoma tumors. METHODS Quantitative conventional and deep radiomics were extracted from pre-operative multi-parametric structural MRI (T1, T1Gd, T2, T2-FLAIR) of 501 patients with newly diagnosed glioblastoma. 389/501 and 112/501 patients formed our discovery and replication cohorts, respectively. Conventional radiomics were extracted from CaPTk, and deep radiomics from a pre-trained VGG-19 model. Multivariate SVM classification was performed on the discovery cohort to stratify patients in high, medium, and low-risk groups, using recursive feature elimination and 5-fold cross-validation. This model was independently tested on the replication cohort, and a radiomic-based survival prediction index (SPIradiomics) was calculated for each patient. Multi-stage integration of omics data, i.e., clinical (age, gender, extent of resection (EOR)), SPIradiomics, epigenetics (MGMT promoter methylation), and genomics (27 clinically relevant gene mutations via next-generation sequencing (NGS)), was performed using multivariate Cox proportional hazards (Cox-PH) model for stratification of the risk in the replication cohort. RESULTS Cox-PH modeling resulted in a concordance index (c-index) of 0.65 (95% CI:0.6–0.7) for clinical data, 0.67 (95% CI:0.62–0.72) for clinical and epigenetics, 0.70 (95% CI:0.65–0.75) for clinical and radiomics, 0.72 (95% CI:0.68–0.77) for clinical, epigenetics, and radiomics, and 0.75 (95% CI:0.71 – 0.78) for the multi-omics combination of all data; highlighting the added value of each layer of information in prediction of the patient’s risk. CONCLUSION Our results reinforce the synergistic value of integrated diagnostic methods for improving risk assessment of patients with glioblastoma that may pave the path towards a more personalized treatment planning.

PURPOSE Intratumor heterogeneity is frequent in glioblastoma (GB), giving rise to the tumor’s resistance to standard therapies and, ultimately, poorer clinical outcomes. Yet heterogeneity is often not quantified when assessing the genomic or methylomic profile of a tumor, when a single tissue sample is analyzed. This study proposes a novel approach to non-invasively characterize heterogeneity across glioblastoma using deep learning analysis MRI scans, using MGMT promoter methylation (MGMTpm) as a test-case, and validates the imaging-derived heterogeneity maps with MGMTpm heterogeneity measured via multiple tissue samples. METHODS Multi-parametric MRI (mpMRI) scans (T1, T1-Gd, T2, T2-FLAIR) of 181 patients with newly diagnosed glioblastoma, who underwent surgical tumor resection and had MGMT methylation assessment results, were retrospectively collected. We trained a 5-fold cross-validated deep convolutional neural network with six convolutional layers for a discovery cohort of 137 patients by placing overlapping regional patches over the whole tumor on mpMRI scans to capture spatial heterogeneity of MGMTpm status in different regions within the tumor. Our approach effectively hypothesized that despite heterogeneity in the training examples, dominant imaging patterns would be captured by deep learning. Trained model was independently applied to an unseen replication cohort of 44 patients, with multiple tissue specimens chosen from different spatial regions within the tumor, allowing us to compare imaging- and tissue-based MGMTpm estimates. RESULTS Our model yielded AUC of 0.75 (95% CI: 0.65–0.79) for global MGMT status prediction, which reflected the heterogeneity in MGMTpm, but also that a dominant imaging pattern of MGMT methylation seemed to emerge. In methylated patients with multiple tissue samples, a significant Pearson's correlation coefficient of 0.64 (p< 0.05) was found between imaging-based heterogeneity maps and MGMTpm heterogeneity. CONCLUSION A novel method based on mpMRI and deep neural networks yielded imaging-based heterogeneity maps that strongly associated with intratumor molecular heterogeneity in MGMT promoter methylated tumors.

BACKGROUND A therapeutic approach for chimeric antigen receptor T (CART) cell therapy in glioblastoma is targeting the epidermal growth factor receptor variant III (EGFRvIII), present in approximately 30% of glioblastomas. While earlier research demonstrated that phenotypic and genotypic characteristics in patient T cells and CART product may predict therapeutic success for hematologic malignancies, no study has investigated such determinants for clinical response in glioblastoma. METHODS We analyzed apheresis and infusion products for patients in the first-in-human trial of EGFRvIII-directed CART for recurrent glioblastoma (NCT02209376, n=9). Inherent T cell phenotypes were characterized by flow cytometry assay using a panel of 27 markers. Clinical response was quantified via engraftment in peripheral circulation and time-on-trial (ToT). RESULTS Mean area under the curve (AUC) peripheral CART engraftment for the study period was 213 log10 copies/μg (SD=173) and mean ToT was 134 days (SD=173). For the CAR+/CD4+ population in the infusion product, PD1 positivity was positively associated with AUC engraftment (r=0.753, P=0.012) and ToT (r=0.800, P=0.010). On immune checkpoint inhibitor analysis, CTLA-4, TIM3, and LAG3 did not exhibit significant associations with AUC engraftment or ToT. For activation markers, PD1/GRZB (r=0.794, P=0.011) and PD1/HLA-DR (r=0.769, P=0.016) were directly associated with ToT. PD1/GRZB was also predictive of AUC engraftment (r=0.794, P=0.011). For the CAR+/CD8+ population, PD1 positivity correlated with elevated AUC engraftment (r=0.639, P=0.047). In the CAR-/CD4+ population, PD1 remained positively correlated with AUC engraftment (r=0.690, P=0.027) and ToT (r=0.726, P=0.027). No significant associations were observed for apheresis products. CONCLUSION PD1 in CART infusion products predicted peripheral engraftment and ToT in recurrent glioblastoma. Double-positive cells for PD1 and activation markers also displayed a positive correlation, suggesting PD1 highlights a population of activated CAR T cells. Further characterization of predictors of EGFRvIII-directed CART treatment efficacy may improve selection of patients and starting T cell populations for clinical expansion.

PURPOSE Glioblastomas display significant heterogeneity on the molecular level, typically harboring several co-occurring mutations, which likely contributes to failure of molecularly targeted therapeutic approaches. Radiogenomics has emerged as a promising tool for in vivo characterization of this heterogeneity. We derive radiogenomic signatures of four mutations via machine learning (ML) analysis of multiparametric MRI (mpMRI) and evaluate them in the presence and absence of other co-occurring mutations. METHODS We identified a retrospective cohort of 359 IDH-wildtype glioblastoma patients, with available pre-operative mpMRI (T1, T1Gd, T2, T2-FLAIR) scans and targeted next generation sequencing (NGS) data. Radiomic features, including morphologic, histogram, texture, and Gabor wavelet descriptors, were extracted from the mpMRI. Multivariate predictive models were trained using cross-validated SVM with LASSO feature selection to predict mutation status in key driver genes, EGFR, PTEN, TP53, and NF1. ML models and spatial population atlases of genetic mutations were generated for stratification of the tumors (1) with co-occurring mutations versus wildtypes, (2) with exclusive mutations in each driver gene versus the tumors without any mutations in the pathways associated with these genes. RESULTS ML models yielded AUCs of 0.75 (95%CI:0.62-0.88) / 0.87 (95%CI:0.70-1) for co-occurring / exclusive EGFR mutations, 0.69 (95%CI:0.58-0.80) / 0.80 (95%CI:0.61-0.99) for co-occurring / exclusive PTEN mutations, and 0.77 (95%CI:0.65-0.88) / 0.86 (95%CI:0.69-1) for co-occurring / exclusive TP53 cases. Spatial atlases revealed a predisposition of left temporal lobe for NF1 and right frontotemporal region for TP53 in mutually exclusive tumors, which was not observed in the co-occurring mutation atlases. CONCLUSION Our results suggest the presence of distinct radiogenomic signatures of several glioblastoma mutations, which become even more pronounced when respective mutations do not co-occur with other mutations. These in vivo signatures can contribute to pre-operative stratification of patients for molecular targeted therapies, and potentially longitudinal monitoring of mutational changes during treatment.

PURPOSE Understanding the molecular underpinnings of imaging signatures of glioblastoma can provide insights into the biologic basis of tumor formation and progression as well as in vivo surrogate markers of molecular events driving the tumor’s phenotype. Through machine learning (ML), this study demonstrates that distinct imaging subtypes of glioblastoma are related to specific molecular alterations. METHODS Pre-operative multi-parametric MRI (T1, T2, T1CE, T2-FLAIR, DSC-MRI, DTI-MRI) of 669 IDH-wildtype subjects with glioblastoma were retrospectively collected and radiomic features, including descriptors of morphology, intensity, histogram, and texture, were extracted. Imaging subtypes were identified by a feature selection and clustering approach. Genomic markers, obtained using next generation sequencing (NGS) panel of 27 key glioblastoma genes, were available in 358/669 patients. Canonical correlation analysis (CCA) was conducted within each imaging subtype between the selected imaging features and genetic variables to seek maximum correlations between combinations of variables in imaging and genomic sets, and hence elucidate the molecular drivers of respective subtypes. RESULTS Three distinct imaging subtypes were identified by clustering on 50 selected features, representing characteristics of morphology, tumor neo-angiogenesis (DSC-derived features), and cellular density (DTI-derived features). These subtypes yielded differentiable overall survival based on Kaplan-Meier analysis. The canonical coefficients of each subtype revealed the distinction of the underlying genomic characteristics: one exhibited frequently mutated [ARID2, NTRK1], another subtype showed increased frequency of mutation in [ATRX, EGFR, PIK3R1], while the third was associated with all these genes and [NF1, PIK3CA, RB1], additionally. CONCLUSION We derived three distinct radiomic MRI subtypes for glioblastoma that highly correlate with the patients' survival and molecular genetic characteristics. Investigating the relationship between imaging and genomic information may enable identification of molecularly- and phenotypically-consistent tumor subtypes, which would offer non-invasive approaches for characterizing heterogeneity of glioblastoma, further facilitating patient stratification and treatment planning.

PURPOSE Multi-parametric MRI based radiomic signatures have highlighted the promise of artificial intelligence (AI) in neuro-oncology. However, inter-institution heterogeneity hinders generalization to data from unseen clinical institutions. To this end, we formulated the ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium for glioblastoma. Here, we seek non-invasive generalizable radiomic signatures from routine clinically-acquired MRI for prognostic stratification of glioblastoma patients. METHODS We identified a retrospective cohort of 606 patients with near/gross total tumor resection ( >90%), from 13 geographically-diverse institutions. All pre-operative structural MRI scans (T1,T1-Gd,T2,T2-FLAIR) were aligned to a common anatomical atlas. An automatic algorithm segmented the whole tumors (WTs) into 3 sub-compartments, i.e., enhancing (ET), necrotic core (NC), and peritumoral T2-FLAIR signal abnormality (ED). The combination of ET+NC defines the tumor core (TC). Quantitative radiomic features were extracted to generate our AI model to stratify patients into short- (< 14mts) and long-survivors ( >14mts). The model trained on 276 patients from a single institution was independently validated on 330 unseen patients from 12 left-out institutions, using the area-under-the-receiver-operating-characteristic-curve (AUC). RESULTS Each feature individually offered certain (limited but reproducible) value for identifying short-survivors: 1) TC closer to lateral ventricles (AUC=0.62); 2) larger ET/brain (AUC=0.61); 3) larger TC/brain (AUC=0.59); 4) larger WT/brain (AUC=0.55); 5) larger ET/WT (AUC=0.59); 6) smaller ED/WT (AUC=0.57); 7) larger ventricle deformations (AUC=0.6). Integrating all features and age, through a multivariate AI model, resulted in higher accuracy (AUC=0.7; 95% C.I.,0.64-0.77). CONCLUSION Prognostic stratification using basic radiomic features is highly reproducible across diverse institutions and patient populations. Multivariate integration yields relatively more accurate and generalizable radiomic signatures, across institutions. Our results offer promise for generalizable non-invasive in vivo signatures of survival prediction in patients with glioblastoma. Extracted features from clinically-acquired imaging, renders these signatures easier for clinical translation. Large-scale evaluation could contribute to improving patient management and treatment planning. *Indicates equal authorship.

Summary: A number of mitochondrial diseases arise from single-nucleotide variant (SNV) accumulation in multiple mitochondria. Here, we present a method for identification of variants present at the single-mitochondrion level in individual mouse and human neuronal cells, allowing for extremely high-resolution study of mitochondrial mutation dynamics. We identified extensive heteroplasmy between individual mitochondrion, along with three high-confidence variants in mouse and one in human that were present in multiple mitochondria across cells. The pattern of variation revealed by single-mitochondrion data shows surprisingly pervasive levels of heteroplasmy in inbred mice. Distribution of SNV loci suggests inheritance of variants across generations, resulting in Poisson jackpot lines with large SNV load. Comparison of human and mouse variants suggests that the two species might employ distinct modes of somatic segregation. Single-mitochondrion resolution revealed mitochondria mutational dynamics that we hypothesize to affect risk probabilities for mutations reaching disease thresholds. : Morris et al. use independent sequencing of multiple individual mitochondria from mouse and human brain cells to show high pervasiveness of mutations. The mutations are heteroplasmic within single mitochondria and within and between cells. These findings suggest mechanisms by which mutations accumulate over time, resulting in mitochondrial dysfunction and disease. Keywords: single mitochondrion, single cell, human neuron, mouse neuron, single-nucleotide variation

Glioblastoma (GBM) is the most prevalent and lethal primary brain tumor, which carries a poor prognosis due to a high level of therapeutic resistance and rate of tumor recurrence. Underlying this aggressive behavior is extraordinary genomic and cellular heterogeneity, conferring numerous avenues for treatment escape and refractory growth. This work leverages single cell genomic analyses to link somatic mutations to transcriptomic signatures within the native tumor tissue. We implemented a three-stage pipeline to perform the following tasks: variant calling, machine learning (ML) classification, and biological pathway analysis. Somatic variants were called from smart-seq based scRNA-seq data and filtered based on predicted lethality, reference in cancer mutation databases e.g. COSMIC, and population prevalence. In the second stage, the genotyped cells and normalized gene expression profiles were pre-processed using differential gene expression, and input into ML classifiers trained on the task of predicting mutation status. The utilized ML models included a Support Vector Machine and Ridge Classifier and were selected based on performance and interpretability. The efficacy of the models was evaluated using the AUC-ROC metric across multiple dataset partitions. The output of this stage was a set of “important genes” identified using the learned ML model weights. In the third stage, the sets of identified genes were subsampled and analyzed using the g:Profiler API to identify significant biological pathways. The analysis was performed on 2 datasets containing 677 samples from 5 patients and 1091 samples from 4 patients. The EGFRvIII mutation was used to validate the performance of the methodology due to its prevalence in primary GBM tumors. The mutation was detected in 17 single-cell samples. The ML models both produced a mean AUC-ROC of 0.93 [0.92, 094], and the classifiers identified NPM1, TCEA1, PPIA, SUMO2, among other genes, to be of significance. The individual genes have functions involving cell cycle regulation and protein folding, while the identified biological pathways associated with gene subsets were linked to ribonuclease activity and transcription factor post-transcriptional modifications. The ML component was applied to an additional dataset containing cell samples from 4 patients and correctly identified the 2 patients positive for EGFRvIII. The larger analysis identified point mutations in EGFR, TP53, and ATRX, and the classifiers exhibited AUC-ROC metrics ranging from 0.94 [0.93, 0.94] to 0.99 [0.99, 1.0]. Overall, we developed and employed a pipeline to link somatic variants with their gene expression signatures at the single cell level, identified biological pathways for further examination and established a ML classifier that can be applied to high-throughput scRNA-seq methods that lack the transcript coverage of the variant loci. Citation Format: Christopher Streiffer, Daniel Zhang, Ryan Salinas, Steven Brem, Donald O'Rourke, Zev Binder, Han-Chiao Isaac Chen, Hongjun Song. Identifying the transcriptomic signatures of mutational heterogeneity in GBM using single cell genomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2203.

Investigation of human CNS disease and drug effects has been hampered by the lack of a system that enables single-cell analysis of live adult patient brain cells. We developed a culturing system, based on a papain-aided procedure, for resected adult human brain tissue removed during neurosurgery. We performed single-cell transcriptomics on over 300 cells, permitting identification of oligodendrocytes, microglia, neurons, endothelial cells, and astrocytes after 3 weeks in culture. Using deep sequencing, we detected over 12,000 expressed genes, including hundreds of cell-type-enriched mRNAs, lncRNAs and pri-miRNAs. We describe cell-type- and patient-specific transcriptional hierarchies. Single-cell transcriptomics on cultured live adult patient derived cells is a prime example of the promise of personalized precision medicine. Because these cells derive from subjects ranging in age into their sixties, this system permits human aging studies previously possible only in rodent systems.

BACKGROUND High expression of O6-methylguanine-DNA methyltransferase (MGMT) in glioblastoma is associated with resistance to temozolomide, as tumor cells lacking MGMT activity are significantly more sensitive to the cytotoxic effects of temozolomide. The MGMT promoter methylation status (MGMTpms) is typically determined as MGMT-methylated or MGMT-unmethylated. Some single-center studies have reported results ranging from 70–95% detection rates using MRI. We aim to further validate these findings using a multi-institutional data set. We hypothesize that transfer learning based features when integrated via machine learning may lead to non-invasive determination of MGMTpms. METHODS A total of 270 patients were included across the 3 institutions (Hospital of the University of Pennsylvania (HUP), Jefferson University Hospital (JUH); the TCIA). JUH and TCIA datasets comprised conventional modalities (T1,T2,T2-FLAIR,T1-Gd), whereas HUP dataset had additional modalities (DSC,DTI) as well. We used transfer learning and adapted a convolutional neural network (CNN) model pre-trained on 1.2 million 3-channel images of the ImageNet to extract deep learning features from the given images. A support vector machine multivariately integrated these features towards a non-invasive marker of MGMTpms. RESULTS The cross-validated accuracy of our MGMT marker in classifying the mutation status in individual patients was 86.95%, 81.56%, and 82.43%, respectively, in HUP, JUH, and TCIA. Our marker revealed MGMT-methylated tumors with lower neovascularization and cell density, when compared with MGMT-unmethylated tumors. MGMT-unmethylated tumors were found to be more lateralized to the right hemisphere, when compared with MGMT-methylated tumors. CONCLUSION Our findings suggest that transfer learning features when integrated via machine learning allow robust prediction of MGMTpms on mpMRI acquired within multiple institutions. The proposed non-invasive MGMT marker may contribute to (i) MGMTpms determination for patients with inadequate tissue/inoperable tumors, (ii) stratification of patients into clinical trials, (iii) patient selection for targeted therapy, and (iv) personalized treatment planning.

Purpose: The clinical utility of plasma cell-free DNA (cfDNA) has not been assessed prospectively in patients with glioblastoma (GBM). We aimed to determine the prognostic impact of plasma cfDNA in GBM, as well as its role as a surrogate of tumor burden and substrate for next-generation sequencing (NGS). Experimental Design: We conducted a prospective cohort study of 42 patients with newly diagnosed GBM. Plasma cfDNA was quantified at baseline prior to initial tumor resection and longitudinally during chemoradiotherapy. Plasma cfDNA was assessed for its association with progression-free survival (PFS) and overall survival (OS), correlated with radiographic tumor burden, and subjected to a targeted NGS panel. Results: Prior to initial surgery, GBM patients had higher plasma cfDNA concentration than age-matched healthy controls (mean 13.4 vs. 6.7 ng/mL, P < 0.001). Plasma cfDNA concentration was correlated with radiographic tumor burden on patients' first post-radiation magnetic resonance imaging scan (ρ = 0.77, P = 0.003) and tended to rise prior to or concurrently with radiographic tumor progression. Preoperative plasma cfDNA concentration above the mean (>13.4 ng/mL) was associated with inferior PFS (median 4.9 vs. 9.5 months, P = 0.038). Detection of ≥1 somatic mutation in plasma cfDNA occurred in 55% of patients and was associated with nonstatistically significant decreases in PFS (median 6.0 vs. 8.7 months, P = 0.093) and OS (median 5.5 vs. 9.2 months, P = 0.053). Conclusions: Plasma cfDNA may be an effective prognostic tool and surrogate of tumor burden in newly diagnosed GBM. Detection of somatic alterations in plasma is feasible when samples are obtained prior to initial surgical resection.

Introduction: Gliosarcoma (GS) is a rare, malignant mixed tumor of the central nervous system with a median survival of approximately 13 months across multiple studies. Although the value of the extent of resection (EOR) has been confirmed as a prognostic survival factor in glioblastoma, no such association has been defined for GS. The goal of this study was to establish an association between EOR and survival and to determine if a threshold of resection exists for which a survival benefit is conferred in GS. Methods: The authors identified 11 patients with histologically confirmed GS between January 2005 and January 2015, treated at the Hospital of the University of Pennsylvania. Clinical, radiographic, and outcome data were retrospectively reviewed. Volumetric analysis was completed using semi-automated segmentation to measure the change in contrast-enhancing material based on preoperative T1-contrast (T1c) and postoperative T1 & T1c magnetic resonance imaging (MRI) scans. A log-rank test was completed to confirm an association between EOR and survival, and a series of Kaplan-Meier curves were constructed to determine an EOR threshold. Univariate Cox proportional hazards model (CPHM) followed by multivariate CPHM was also completed to evaluate associations between the prognostic clinical and immunohistochemistry variables under consideration. Results: Extent of resection categories were defined as gross total resection (GTR >95%), subtotal resection (STR 90%-95%), and partial resection (PR

BACKGROUND Imaging of glioblastoma patients after maximal safe resection and chemoradiation commonly demonstrates new enhancements that raise concerns about tumor progression. However, in 30% to 50% of patients, these enhancements primarily represent the effects of treatment, or pseudo-progression (PsP). We hypothesize that quantitative machine learning analysis of clinically acquired multiparametric magnetic resonance imaging (mpMRI) can identify subvisual imaging characteristics to provide robust, noninvasive imaging signatures that can distinguish true progression (TP) from PsP. METHODS We evaluated independent discovery (n = 40) and replication (n = 23) cohorts of glioblastoma patients who underwent second resection due to progressive radiographic changes suspicious for recurrence. Deep learning and conventional feature extraction methods were used to extract quantitative characteristics from the mpMRI scans. Multivariate analysis of these features revealed radiophenotypic signatures distinguishing among TP, PsP, and mixed response that compared with similar categories blindly defined by board-certified neuropathologists. Additionally, interinstitutional validation was performed on 20 new patients. RESULTS Patients who demonstrate TP on neuropathology are significantly different (P

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults and carries a dismal prognosis. Significant challenges in the care of patients with GBM include marked vascular heterogeneity and arteriovenous (AV) shunting, which results in tumor hypoxia and inadequate delivery of systemic treatments to reach tumor cells. In this study, we investigated the utility of different MR perfusion techniques to detect and quantify arteriovenous (AV) shunting and tumor hypoxia in patients with GBM. Macrovascular shunting was present in 33% of subjects, with the degree of shunting ranging from (37–60%) using arterial spin labeling perfusion. Among the dynamic susceptibility contrast-enhanced perfusion curve features, there were a strong negative correlation between hypoxia score, DSC perfusion curve recovery slope (r = −0.72, P = 0.018) and angle (r = −0.73, P = 0.015). The results of this study support the possibility of using arterial spin labeling and pattern analysis of dynamic susceptibility contrast-enhanced MR Imaging for evaluation of arteriovenous shunting and tumor hypoxia in glioblastoma.

Molecular profiling of glioblastoma has revealed complex cytogenetic, epigenetic, and molecular abnormalities that are necessary for diagnosis, prognosis, and treatment. Our neuro-oncology group has developed a data-driven, institutional consensus guideline for efficient and optimal workup of glioblastomas based on our routine performance of molecular testing. We describe our institution’s testing algorithm, assay development, and genetic findings in glioblastoma, to illustrate current practices and challenges in neuropathology related to molecular and genetic testing. We have found that coordination of test requisition, tissue handling, and incorporation of results into the final pathologic diagnosis by the neuropathologist improve patient care. Here, we present analysis of O 6 -methylguanine-DNA-methyltransferase promoter methylation and next-generation sequencing results of 189 patients, obtained utilizing our internal processes led by the neuropathology team. Our institutional pathway for neuropathologist-driven molecular testing has streamlined the management of glioblastoma samples for efficient return of results for incorporation of genomic data into the pathological diagnosis and optimal patient care.

Glioblastoma, the most common primary malignant brain tumor, is genetically diverse and classified into four transcriptomic subtypes, i.e., classical, mesenchymal, proneural, neural. We sought a noninvasive robust quantitative imaging phenomic (QIP) signature associated with this transcriptomic classification of glioblastoma patients, derived from clinically-acquired imaging protocols and without the need for advanced genetic testing. This QIP signature was discovered and evaluated in a retrospective cohort of 112 pathology-proven de novo glioblastoma patients for whom basic preoperative multi-parametric MRI (mpMRI) data (T1, T1-Gd, T2, T2-FLAIR) were available, and compared with the tumor subtype as obtained through an RNA isoform-based classifier. Comprehensive and diverse QIP features capturing intensity distributions, volume, morphology, statistics, tumors’ anatomical location, and texture for each tumor sub-region, were multivariately integrated via support vector machines to construct our QIP signature. The performance/generalizability of the model was evaluated using 5-fold cross-validation. The overall accuracy of the proposed method was estimated equal to 71% for identifying the transcriptomic tumor subtype; 82.14% [AUC:0.82], 75.89% [AUC:0.78], 75.89% [AUC:0.81], and 88.39% [AUC:0.84] for predicting proneural, neural, mesenchymal and classical subtypes, respectively. The obtained QIP signature revealed a macroscopic biological insight of the complex tumor subtypes, including more pronounced presence of tissue with higher water content in neural subtype, larger enhancement component of the tumor in mesenchymal subtype, and overall smaller tumors in classical subtype. Our results indicate that quantitative analysis of imaging features extracted from clinically-acquired mpMRI yields prompt non-invasive biomarkers of the molecular profile of glioblastoma patients, important in influencing surgical decision-making, treatment planning, and assessment of inoperable tumors.

© 2019 Elsevier B.V. Objective: Corpus callosum (CC) involvement is a poor prognostic factor in patients with glioblastoma (GBM). The purpose of this study was to determine whether diffusion tensor imaging (DTI) can quantify occult tumor infiltration in the CC and predict the overall survival in GBM patients. Methods: Forty-eight patients with pathologically proven GBM and 17 normal subjects were included in this retrospective study. Patients were divided into four groups based on CC invasion and overall survival: long survivors without CC invasion; short survivors without CC invasion; long survivors with CC invasion; short survivors with CC invasion. All patients underwent DTI at 3T MRI scanner. Fractional anisotropy (FA) and mean diffusivity (MD) values were measured from genu, mid-body, and splenium of the CC. The mean values of these parameters were compared between different groups and Kaplan Meier curves were used for prediction of overall survival. Results: Patients with short survival and CC invasion had the lowest FA values (0.64 ± 0.05) from the CC compared with other groups (p < 0.05). Receiver operator characteristic curve (ROC) analysis indicated that a FA cutoff value of 0.70 was the best predictor for overall survival with an area under the curve (AUC) of 0.77, sensitivity 1, specificity 0.59. Kaplan-Meier survival curves demonstrated that the mean survival time was significantly longer for patients with high FA (>0.70) compared with those with low FA (

Meningiomas are the most common primary brain tumor of adults. The majority are benign (WHO grade I), with a mostly indolent course; 20% of them (WHO grade II and III) are, however, considered aggressive and require a more complex management. WHO grade II and III tumors are heterogeneous and, in some cases, can develop from a prior lower grade meningioma, although most arise de novo. Mechanisms leading to progression or implicated in de novo grade II and III tumorigenesis are poorly understood. RNA-seq was used to profile the transcriptome of grade I, II, and III meningiomas and to identify genes that may be involved in progression. Bioinformatic analyses showed that grade I meningiomas that progress to a higher grade are molecularly different from those that do not. As such, we identify GREM2, a regulator of the BMP pathway, and the snoRNAs SNORA46 and SNORA48, as being significantly reduced in meningioma progression. Additionally, our study has identified several novel fusion transcripts that are differentially present in meningiomas, with grade I tumors that did not progress presenting more fusion transcripts than all other tumors. Interestingly, our study also points to a difference in the tumor immune microenvironment that correlates with histopathological grade. Electronic supplementary material The online version of this article (10.1186/s40478-019-0690-x) contains supplementary material, which is available to authorized users.

The LACE+ index (Length of stay, Acuity of admission, Charlson Comorbidity Index score, and Emergency department [ED] visits in the past 6 months) is a tool used to predict 30-day readmissions. We sought to examine this predictive tool in patients undergoing brain tumor surgery.Admissions and readmissions for patients undergoing craniotomy for supratentorial neoplasm at a single multihospital academic medical center were analyzed. All brain tumor cases for which the patient was alive at 30 days after surgery were included (n = 352). Simple logistic regression analyses were used to assess the ability of the LACE+ index and subsequent single variables to accurately predict the outcome measures of 30-day readmission, reoperation, and ED visit. Analysis of the model's or variable's discrimination was determined by the receiver operating characteristic curve as represented by the C-statistic.The sample included admissions for craniotomy for supratentorial neoplasm (n = 352). Assessment of the LACE+ index demonstrates a 1.02× increased odds of 30-day readmission for every 1-unit increase in LACE+ score (P = 0.031, CI = 1.00-1.03). Despite this, analysis of the receiver operating characteristic curve indicates that LACE+ index has poor specificity in predicting 30-day readmission (C-statistic = 0.58). A 1-unit increase in LACE+ score also predicts a 0.98× reduction in odds of home discharge (P0.001, CI = 0.97-0.99, C-statistic = 0.70). But LACE+ index does not predict 30-day reoperation (P = 0.945) or 30-day ED visits (P = 0.218).The results of this study demonstrate that the LACE+ index is not yet suitable as a prediction model for 30-day readmission in a brain tumor population.

EGFRvIII targeted chimeric antigen receptor T (CAR-T) cell therapy has recently been reported for treating glioblastomas (GBMs); however, physiology-based MRI parameters have not been evaluated in this setting. Ten patients underwent multiparametric MRI at baseline, 1, 2 and 3 months after CAR-T therapy. Logistic regression model derived progression probabilities (PP) using imaging parameters were used to assess treatment response. Four lesions from “early surgery” group demonstrated high PP at baseline suggestive of progression, which was confirmed histologically. Out of eight lesions from remaining six patients, three lesions with low PP at baseline remained stable. Two lesions with high PP at baseline were associated with large decreases in PP reflecting treatment response, whereas other two lesions with high PP at baseline continued to demonstrate progression. One patient didn’t have baseline data but demonstrated progression on follow-up. Our findings indicate that multiparametric MRI may be helpful in monitoring CAR-T related early therapeutic changes in GBM patients.

PURPOSE: Mutational status of isocitrate dehydrogenase (IDH1) is a defining feature of the World Health Organization classification scheme for high grade gliomas (HGGs). IDH-mutant HGGs confer significantly improved prognoses when compared with IDH-wildtype, which typically describe the most common malignant primary HGGs in adults, namely glioblastoma. HGGs are densely cellular, pleomorphic tumors with high mitotic activity, with glioblastoma having either microvascular proliferation, or necrosis, or both. We hypothesize that integrative analysis of multi-parametric magnetic resonance imaging (mpMRI) via multivariate machine learning (ML), will enhance subtle yet important radiographic HGG characteristics, and reveal imaging signatures determinant of IDH1 mutational status. METHODS: 86 HGG patients were retrospectively identified with available pre-operative clinically-acquired mpMRI data (T1, T1-Gd, T2, T2-FLAIR, DTI, DSC-MRI). Each HGG was delineated into sub-regions of enhancement, non-enhancement, and peritumoral edema/invasion. 342 quantitative imaging phenomic (QIP) features extracted across sub-regions from all mpMRI, comprising descriptors of size, morphology, texture, intensity, and biophysical growth modeling. Cross-validated sequential feature selection determined the most discriminative QIP features for our integrative ML predictor of IDH1 status. The predicted classifications, following a 10-fold cross-validation, were compared with the IDH1 status obtained by next generation sequencing, or immunohistochemistry. RESULTS: 61 QIP features, primarily descriptive of tumor texture, were determined as most important for an IDH1 imaging signature. Using this signature, our predictor classified IDH1 mutational status with an accuracy of 88.4% (sensitivity=66.7%, specificity=92.9%). CONCLUSION: Quantitative analysis of clinically-acquired mpMRI reveals subtle/visually-imperceptible, yet informative features, which integrated via ML yield a non-invasive in vivo IDH1 imaging signature in HGG. Knowledge of IDH1 mutational status at initial presentation can influence therapeutic decision-making, which will have a significant impact on patient care. Particularly in this precision medicine era, as mutant-IDH enzyme inhibitors and immunotherapy targeting IDH-mutant tumor cells are developed, imaging to diagnose and follow IDH-mutant tumors can be invaluable. *equal contribution

BACKGROUND: The promoter methylation status of the gene encoding for the repair enzyme O6-methylguanine-DNA methyltransferase (MGMT) indicates increased efficacy of current standard of care therapy, which is concomitant adjuvant chemoradiotherapy with temozolomide. The MGMT promoter methylation status (MGMTpms) is typically determined as MGMT-methylated or MGMT-unmethylated by tissue-based polymerase chain reaction assays, which can be limited by inadequate specimen or assay failures. Thus, we investigate the hypothesis that integration of subtle, yet distinctive, quantitative imaging phenomic (QIP) features using machine learning may lead to non-invasive determination of MGMTpms. METHODS: We identified a retrospective cohort of 122 (46 MGMT-methylated) pathology-proven de novo glioblastoma patients with available baseline pre-operative multi-parametric magnetic resonance imaging (mpMRI) data (T1, T1-Gd, T2, T2-FLAIR, DSC, DTI). MGMTpms was obtained through MGMT methylation testing (pyrosequencing across 4 CpG sites in the MGMT promoter). Following delineation of distinct abnormal sub-regions (enhancing, non-enhancing, edematous), comprehensive and diverse QIP features were extracted using the Cancer Imaging Phenomics Toolkit (CaPTk, www.cbica.upenn.edu/captk), capturing intensity, volume, morphology, statistics, and texture of each sub-region. A support vector machine multivariately integrated these features towards a non-invasive marker of MGMTpms. RESULTS: The cross-validated accuracy of our MGMT marker in classifying the mutation status in individual patients was 84.43% (sensitivity=80.43%, specificity=86.84%, area under the curve [AUC]=0.85). Our marker revealed MGMT-methylated tumors with lower neovascularization and cell density, when compared with MGMT-unmethylated tumors, and a distinct spatial distribution pattern between MGMT-methylated and MGMT-unmethylated tumors, with the latter being more lateralized to the right hemisphere. CONCLUSION: Multivariate integrative analysis of QIP features extracted from mpMRI yields an accurate, non-invasive marker of MGMTpms in glioblastoma. The proposed non-invasive MGMT marker may contribute to (i) MGMTpms determination for patients with inadequate tissue/inoperable tumors, (ii) stratification of patients into clinical trials, (iii) patient selection for targeted therapy, and (iv) personalized treatment planning. *equal contribution

PURPOSE Differentiation of true tumor progression from pseudoprogression (PsP) is a major unmet need in patients with glioblastoma (GBM). [18F]Fluciclovine is a synthetic amino acid PET radiotracer that is FDA approved in the setting of biochemical recurrence in prostate cancer. The aim of this study was to assess the value of [18F]Fluciclovine PET in differentiation of true tumor progression and PsP in post-treatment of glioblastoma. METHODS 15 patients with GBM with new contrast-enhancing lesions or lesions showing increased enhancement (>25% increase) on standard MRI after completion of radiation underwent 60-minutes dynamic [18F]Fluciclovine PET imaging. Patients subsequently (within 1 week) underwent resection of the enhancing lesion and the tumor percentage vs treatment-related changes were quantified on histopathology. Patients were considered true tumor progression if tumor represented ≥ 50% of the resected specimen and considered PsP if treatment-related changes represented ≥70% of the resected specimen. Summed 30- to 40-minute post-injection PET images were used to measure SUVpeak, SUVmax, and 50% threshold SUVmean. RESULTS 10 patients with true tumor progression and 5 patients with PsP were included. Patients who demonstrated true tumor progression had significantly higher SUVpeak compared to patients with PsP (5.3±1.4 vs 3.1± 0.9, p=0.002, AUC=0.92, p DISCUSSION Our preliminary results indicated that [18F]Fluciclovine PET imaging can accurately differentiate true tumor progression from PsP. Further studies are required to confirm these promising early results and determine the optimal criteria for interpreting [18F]Fluciclovine PET to distinguish PsP from true tumor progression.

Immunotherapy, including chimeric antigen receptor (CAR) T cell therapy, has yielded major advancements for a number of hard-to-treat cancers; however, its transformative potential has yet to be realized in glioblastoma (GBM). Clinical studies of EGFRvIII targeted CAR T cell therapy have indicated that tumor heterogeneity, immune microenvironment, and adaptive responses to treatment may play important roles in limiting overall efficacy. We sought to examine comprehensively the dynamic molecular landscape of CAR T cell therapy in GBM using patient-derived GBM organoids (GBOs), a newly established laboratory model of inter- and intra- tumoral heterogeneity. Especially advantageous in these studies, GBOs preserve the intrinsic composition of somatic mutations, transcriptomic states, and non-neoplastic cells that contribute to the tumor microenvironment. Complementing the complexity of this biological system, we constructed an integrated single-cell multi-omics platform to interrogate gene expression, cell surface protein expression, somatic variants, and TCR sequences all from the same cell. Co-culture of GBOs and EGFRvIII targeted CAR T cells led to rapid T cell activation with concomitant upregulation of cytokine response gene programs in both antigen-positive and antigen-negative tumor cells. The adaptive tumor response also included expression of immune checkpoint receptor (ICR) ligands, such as PD-L1. At later time points, T cells transitioned into a dysfunctional or exhausted state, as characterized by increased cell surface expression of inhibitory receptors, such as PD-1, and decreased markers of effector activity despite the presence of antigen. Interestingly, CAR T cell therapy not only led to changes in immune-related pathways and tumor microenvironment, but it also induced shifts in tumor cell states with the depletion of an oligodendrocyte precursor cell-like state and corresponding enrichment in an astrocyte-like state. This finding suggests that EGFRvIII targeted CAR T cell therapy may leverage intrinsic cellular plasticity to induce differentiation-like effects in the surviving tumor cells.

Aberrant vascularization is a hallmark of cancer progression and treatment resistance. Here, we have shown that endothelial cell (EC) plasticity drives aberrant vascularization and chemoresistance in glioblastoma multiforme (GBM). By utilizing human patient specimens, as well as allograft and genetic murine GBM models, we revealed that a robust endothelial plasticity in GBM allows acquisition of fibroblast transformation (also known as endothelial mesenchymal transition [Endo-MT]), which is characterized by EC expression of fibroblast markers, and determined that a prominent population of GBM-associated fibroblast-like cells have EC origin. Tumor ECs acquired the mesenchymal gene signature without the loss of EC functions, leading to enhanced cell proliferation and migration, as well as vessel permeability. Furthermore, we identified a c-Met/ETS-1/matrix metalloproteinase–14 (MMP-14) axis that controls VE-cadherin degradation, Endo-MT, and vascular abnormality. Pharmacological c-Met inhibition induced vessel normalization in patient tumor–derived ECs. Finally, EC-specific KO of Met inhibited vascular transformation, normalized blood vessels, and reduced intratumoral hypoxia, culminating in suppressed tumor growth and prolonged survival in GBM-bearing mice after temozolomide treatment. Together, these findings illustrate a mechanism that controls aberrant tumor vascularization and suggest that targeting Endo-MT may offer selective and efficient strategies for antivascular and vessel normalization therapies in GBM, and possibly other malignant tumors.

Introduction: Glioblastoma (GBM) is malignant and aggressive brain tumor with a median overall survival of 14.6 months, despite the development of new treatments. Chimeric Antigen Receptor (CAR) modified T cells are powerful new class of immune based therapies that have shown a high rate of long term durable response in CD19 expressing B-cell malignancies. However, this success has yet to be translated to solid tumors, in part due to a lack of surface antigens specifically expressed on tumor cells while not present on normal tissue and an immunosuppressive tumor microenvironment. Approximately 60% of GBMs contain a mutation, rearrangement, splicing alteration, and/or amplification of EGFR. Investigation of 411 GBM patient samples from University of Pennsylvania identified EGFR amplification in 38% of cases and EGFRvIII in 25% of cases and most common oncogenic missense mutations in extracellular domain (ECD) A289D/T/V, R108G/K, and G598V in 12-13% of cases. ECD missense mutations are associated with increased invasiveness aided by ligand independent activation of receptor and decreased patient survival compared to patients that are wild type EGFR at R108, A289 and G59V locus. Methods: We generated two structurally different CARs by fusing the scFv of mAb806 to 4-1BB and Killer immunoglobulin like receptor (KIR) co-stimulatory domains. mAb806 recognizes a conformationally exposed epitope of wild-type EGFR when it is overexpressed on tumor cells or in the presence of oncogenic mutations such as EGFRvIII. We tested in vitro cytolytic activity of 806-EGFR-CART cells against U87-MG (expressing low levels of EGFR) and U87-MG EGFR (U87-MG parental cell line transduced with EGFR wild type for amplified background) cell lines. These lines were engineered to ectopically express targeted EGFR-ECD missense mutations and EGFRvIII. Results: Both 4-1BB and KIR based EGFR806 CAR T cells specifically lysed tumor cells, proliferated, and secreted cytokines when co-cultured with target-expressing cell lines. To test exclusive specificity of EGFR806 CART to targeted mutations, K562 cells were transduced with EGFR-ECD mutations and co-cultured with 4-1BB and KIR CART cells. While the EGFR806 lysed K562 cells expressing wild type EGFR, EGFR-ECD Mutants, and EGFRvIII, it did not demonstrate any activity against K562-Parental cells. Unlike EGFR-specific cetuximab based CAR, EGFR-806CART cells did not kill EGFR wild type expressing fetal brain astrocytes and keratinocytes in vitro. We further evaluated the in vivo efficacy of CARs in NSG mice by injecting U87-MG, U87-MG EGFR and U87-MG EGFR expressing EGFRvIII subcutaneously and assessed tumor burden and mice survival. Though both 4-1BB and KIR based EGFR806 CART cells control tumor growth, KIR based CAR outperformed 4-1BB based CAR in terms of tumor burden and improved mice survival. Conclusion: Though our CART clinical trial for EGFRvIII positive recurrent GBM patients demonstrated CART therapy is safe and CART cells are able to successfully traffic to regions of active GBM, antigen loss and tumor heterogeneity resulted in limited therapeutic success. Broad specificity of EGFR806 CART cells to Amplified EGFR, EGFRvIII and EGFR-ECD mutants gives us the potential to clear various forms of EGFR. The enhanced anti-tumor efficacy by KIR based CAR in vivo setting provides us with additional therapeutic options for GBM. Citation Format: Radhika Thokala, Zev Binder, Yibo Yin, Michael Milone, Donald M. Orourke. High affinity chimeric antigen receptors with cross reactivity to clinically relevant EGFR mutated proteins [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6596.

To understand potential immune system alterations in glioblastoma patients and provide a foundation for immunotherapy, we profiled intra tumoral leucocytes from newly diagnosed de novo patients as well as patients with recurrent tumor using high dimensional CyTOF. All recurrent patients underwent standard-of-care therapy, including surgical resection, concurrent temozolomide and radiotherapy, and adjuvant temozolomide. We used two immune profiling panels: a broad profiling panel that includes 45 phenotypic markers that together permit the identification and enumeration of the main innate and adaptive immune cell subsets in humans, and a deep profiling panel that includes 45 features focused on T cell phenotype and biology. We report in both de novo and recurrent tumors a predominant infiltration by CD11b+CD14+ monocytes (43.6±13%. of CD45+ cells). These monocytes displayed an immunomodulatory M2 profile (CD163+HLA-DR-/+CD80-CD86-/+). The adaptive compartment was sparse and dominated by alpha beta T cells, although some mucosal associated invariant T cells and gamma delta T cells were also present. T cells were both conventional T cells and regulatory T cells. The latter tended to be more abundant in the de novo as compared to recurrent tumors (22.8±17.9% of CD4+ T cells in de novo vs 12.9±12.5% in recurrent tumors, P=0.09). Conventional T cells were mostly CD27-CD45RA- effectors, with high expression of PD1 (69.9±17.6% of CD8+ T cells). Using high dimensional approaches, we confirmed that this high expression of PD1 reflects T cell exhaustion, as defined by co-expression of other inhibitory receptors such as Tim3, CTLA-4 and LAG-3. Exhausted T cells typically co-expressed PD1 and CD39 in the de novo tumors, and this pattern was less often observed in recurrent tumors (63.4±22.84% of CD8+ T cells in de novo vs 46.3±14.5% in recurrent tumors, P=0.03). Together, our results highlight the potential issue of a substandard T cell compartment at the time of GBM diagnosis. We are now investigating the mechanisms underlying these observations, as well as their impact on T cell immunity of the patients, as well as current immunotherapy strategies. Our studies should provide a foundation for improving therapy in glioblastoma patients. Citation Format: Cécile Alanio, Zev A. Binder, Donald M. O'Rourke, E. John Wherry. Deep immune profiling of de novo and recurrent glioblastoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-356.

e22015 Background: We evaluated spatially-resolved protein profiling of longitudinal tumor specimens derived from two patients with metastatic melanoma who progressed on sequential therapies including targeted therapy (TT) and immune checkpoint blockade (ICB) therapy targeting T cell-surface antigens (CTLA-4 and PD-1). The purpose of this study was to identify molecular determinants of resistance to sequential TT and ICB therapies. Methods: We performed multiplexed and multidimensional spatial protein profiling using NanoString’s GeoMx Digital Spatial Profiling (DSP) platform and single-cell level imaging analysis with Cyclic Immunofluorescence (CycIF) to simultaneously determine dynamic changes in tumor intrinsic signaling pathways and immune response in the tumor microenvironment (TME). Results: The first patient presented with a BRAFV600E-positive brain metastasis. This patient was sequentially treated with ipilimumab (Ipi), the combination of dabrafenib and trametinib, and pembrolizumab (Pembro) and progressed despite of high expression of CD56 NK cell after all treatments. In addition, CycIF analysis revealed drug resistance in a subpopulation of cells that had continued activation of mTOR (pS6) and EGFR pathways. The second patient presented with dermal metastases on the flank with NRASQ61K mutation. This patient was sequentially treated with Pembo, Talimogene Laherparepvec, the combination of Ipi plus nivolumab, and two different investigational agents combined with Pembro. This patient displayed stable disease (SD) on Pembro but eventually progressed on the subsequent therapies. DSP analysis demonstrated CD68/CD40 myeloid cell infiltrates as well as HLA-DR and CD44 in the TME after the last treatment. CycIF analysis revealed dynamic changes in tumoral characteristics including DNA damage and proliferation during treatment. Furthermore, the analysis suggested that there might be a resistant subpopulation in the last tumor biopsy, which is in line with progression of the disease. Conclusions: In this study, we conducted detailed analyses on serial specimens from two patients to precisely define the spatial distribution of immune responses and cancer signaling pathways. The findings propose that concurrent proteomics analysis and immune monitoring of longitudinal tumor biopsies can be informative in clinical evaluation in order to identify salvage therapies to overcome drug resistance.

2508 Background: Due to significant interpatient heterogeneity, survival outcomes vary widely in patients with GBM. Novel prognostic biomarkers are needed. We aimed to determine the prognostic impact of baseline plasma cfDNA concentration in patients with GBM. Methods: We analyzed 84 patients with newly diagnosed GBM and at least 7 months of follow-up time. The first 41 patients comprised a previously published derivation cohort (Bagley, Clin Cancer Res 2020). The subsequent 43 patients served as an independent validation cohort. cfDNA was extracted from plasma collected prior to initial surgical resection and quantified by qPCR for a 115 bp amplicon of the human ALU repeat element. Receiver operating characteristic (ROC) curve analysis was used in the derivation cohort to (1) assess the accuracy of plasma cfDNA concentration for predicting progression-free survival status at 7 months (PFS-7), a landmark based on the median PFS for newly diagnosed GBM (Stupp, N Engl J Med 2005), and (2) derive the optimal cutoff for dichotomizing patients into high- and low-cfDNA groups. In the validation cohort, logistic regression was used to measure the association of plasma cfDNA concentration (high vs. low) with PFS-7, adjusted for age, isocitrate dehydrogenase ( IDH) 1/2 mutational status, 0-6-methylguanine-methyltransferase ( MGMT) methylation, extent of resection, and performance status. Multivariate Cox regression was used for overall survival (OS) analysis. Results: In the derivation cohort, the optimal cutoff for plasma cfDNA was 25.0 ng/mL (area under the curve [AUC] = 0.663), with inferior PFS and OS in patients with cfDNA above this cutoff (PFS, median 4.9 vs. 9.5 months, log-rank p = 0.001; OS, median 8.5 vs. 15.5 months, log-rank p = 0.03). In the validation cohort, baseline plasma cfDNA concentration over the cutoff was independently associated with a lower likelihood of being alive and progression-free at 7 months (adjusted OR 0.13, 95% CI 0.02 – 0.75, p = 0.02). OS was also worse in in the validation cohort in patients with high plasma cfDNA (adjusted HR 3.0, 95% CI 1.1 – 8.0, p = 0.03). Conclusions: In patients with newly diagnosed GBM, high baseline plasma cfDNA concentration is associated with worse survival outcomes independent of other prognostic factors. Further validation in a larger, multicenter study is warranted.

Summary Glioblastomas exhibit vast inter- and intra-tumoral heterogeneity, complicating the development of effective therapeutic strategies. Current in vitro models are limited in preserving the cellular and mutational diversity of parental tumors and require a prolonged generation time. Here, we report methods for generating and biobanking patient-derived glioblastoma organoids (GBOs) that recapitulate the histological features, cellular diversity, gene expression, and mutational profiles of their corresponding parental tumors. GBOs can be generated quickly with high reliability and exhibit rapid, aggressive infiltration when transplanted into adult rodent brains. We further demonstrate the utility of GBOs to test personalized therapies by correlating GBO mutational profiles with responses to specific drugs and by modeling chimeric antigen receptor T cell immunotherapy. Our studies show that GBOs maintain many key features of glioblastomas and can be rapidly deployed to investigate patient-specific treatment strategies. Additionally, our live biobank establishes a rich resource for basic and translational glioblastoma research.

Chimeric antigen receptor (CAR) T cells are a form of autologous immunotherapy that has changed the therapeutic landscape of hematologic malignancies. CAR T cell therapy involves collection of a patient's T cells by apheresis, ex vivo genetic modification of the T cells to encode a synthetic receptor that binds a specific tumor antigen, and infusion of the T cells back into the patient. With the unprecedented success of CAR T cells in leukemia and lymphoma, a growing number of preclinical studies and clinical trials have focused on translating this treatment to solid tumors. In non-hematologic malignancies, however, response rates have been much less favorable. Some of the most significant challenges for CAR T cell immunotherapy in solid cancers include a paucity of unique tumor target antigens, limited CAR T cell trafficking to tumor sites, tumor heterogeneity and antigen loss, and the severely immunosuppressive tumor microenvironment. This review article provides an update on completed and ongoing clinical trials of CAR T cells for solid tumors, as well as an overview of strategies to improve CAR T cell efficacy in non-hematologic malignancies.

The prognostic impact of the histopathologic features of recurrent glioblastoma surgical specimens is unknown. We sought to determine whether key histopathologic characteristics in glioblastoma tumors resected after chemoradiotherapy are associated with overall survival (OS).The following characteristics were quantified in recurrent glioblastoma specimens at our institution: extent of viable tumor (accounting for % of specimen comprised of tumor and tumor cellularity), mitoses per 10 high-power fields (0, 1-10, 10), Ki-67 proliferative index (0-100%), hyalinization (0-6; none to extensive), rarefaction (0-6), hemosiderin (0-6), and % of specimen comprised of geographic necrosis (0-100%; converted to 0-6 scale). Variables associated with OS in univariate analysis, as well as age, eastern cooperative oncology group performance status (ECOG PS), extent of repeat resection, time from initial diagnosis to repeat surgery, and O37 specimens were assessed. In a multivariate model, high Ki-67 proliferative index was the only histopathologic characteristic associated with worse OS following repeat surgery for glioblastoma (hazard ratio (HR) 1.3, 95% CI 1.1-1.5, p = 0.003). Shorter time interval from initial diagnosis to repeat surgery (HR 1.11, 95% CI 1.02-1.21, p = 0.016) and ECOG PS ≥ 2 (HR 4.19, 95% CI 1.72-10.21, p = 0.002) were also independently associated with inferior OS.In patients with glioblastoma undergoing repeat resection following chemoradiotherapy, high Ki-67 index in the recurrent specimen, short time to recurrence, and poor PS are independently associated with worse OS. Histopathologic quantification of viable tumor versus therapy-related changes has limited prognostic influence.

Angiogenesis is a hallmark of cancer. However, most malignant solid tumors exhibit robust resistance to current anti-angiogenic therapies that primarily target VEGF pathways. Here we report that endothelial-mesenchymal transformation induces glioblastoma (GBM) resistance to anti-angiogenic therapy by downregulating VEGFR-2 expression in tumor-associated endothelial cells (ECs). We show that VEGFR-2 expression is markedly reduced in human and mouse GBM ECs. Transcriptome analysis verifies reduced VEGFR-2 expression in ECs under GBM conditions and shows increased mesenchymal gene expression in these cells. Furthermore, we identify a PDGF/NF-κB/Snail axis that induces mesenchymal transformation and reduces VEGFR-2 expression in ECs. Finally, dual inhibition of VEGFR and PDGFR eliminates tumor-associated ECs and improves animal survival in GBM-bearing mice. Notably, EC-specific knockout of PDGFR-β sensitizes tumors to VEGF-neutralizing treatment. These findings reveal an endothelial plasticity-mediated mechanism that controls anti-angiogenic therapy resistance, and suggest that vascular de-transformation may offer promising opportunities for anti-vascular therapy in cancer., Resistance to anti-angiogenic therapies often occurs in patients. Here, the authors demonstrate the role of PDGF signaling in GBM resistance to anti-VEGF treatment via a mechanism that involves endothelial-mesenchymal transformation and transcriptional regulation of VEGFR-2.

BACKGROUND Liquid biopsy has been not been widely utilized in patients with glioblastoma (GBM) compared to other solid tumors. However, the clinical utility of plasma cell-free DNA (cfDNA) in GBM has not been assessed prospectively or at the time of initial diagnosis. METHODS We conducted a prospective cohort study of patients with newly diagnosed GBM. Whole blood was collected in Streck® tubes at baseline prior to initial surgical resection and longitudinally during the course of adjuvant chemoradiotherapy. Plasma cfDNA concentration (ng/mL) was quantified by qPCR for a 115 bp amplicon of the human ALU repeat element, correlated with radiographic tumor burden by volumetry at multiple time points using Spearman rank correlation, and assessed for its impact on progression-free (PFS) and overall survival (OS) by Cox regression. RESULTS Prior to initial resection, GBM patients (N=42) had higher plasma cfDNA concentration compared to age-matched healthy controls (N=42) (mean 13.43 vs. 6.70 ng/mL, p< 0.001). Plasma cfDNA concentration was correlated with radiographic tumor burden on subjects’ first post-radiation MRI scan (r=0.77, p=0.003) and tended to rise prior to or concurrently with radiographic tumor progression. Pre-operative plasma cfDNA concentration above the mean (>13.4 ng/mL) was associated with inferior PFS (median 4.9 vs. 9.5 months, p=0.038) and OS (median 8.9 vs. 14.8 months, p=0.078). The impact on PFS persisted after adjusting for age, extent of resection, performance status, MGMT promoter methylation, and IDH1/2 mutational status (HR 2.48, 95% CI 1.1–6.1, p=0.046). CONCLUSIONS Plasma cfDNA may be an effective prognostic tool and noninvasive surrogate of tumor burden in newly diagnosed GBM. Tumor tissue samples from our cohort have been subjected to targeted next generation sequencing (NGS), and baseline plasma samples have been sent to Guardant Health for NGS. Plasma NGS results and concordance with matched tissue NGS will be included at time of presentation.

BACKGROUND Oligodendroglioma is a rare primary brain tumor characterized by mutation in the IDH1/IDH2 genes and codeletion of chromosomes 1p and 19q. Although median overall survival extends beyond 10 years for patients with oligodendroglioma who receive adjuvant radiation and alkylating chemotherapy, the disease uniformly relapses and has no other effective treatments. The CIC gene, which encodes a high-mobility group box transcriptional repressor, is mutated in about 70% of oligodendrogliomas and is associated with worse prognosis and overexpression of cyclin D1 (CCND1). Cyclin D1 assembles with the cyclin-dependent kinases CDK4 and CDK6 to cause phosphorylation of Rb protein and subsequent cell cycle progression from the G1 to S phase. Therapeutic inhibition of CDK4/6 activity, which leads to Rb dephosphorylation and cell cycle arrest, has revolutionized the care of breast cancer. The current phase 2 study will investigate the efficacy and safety of abemaciclib, a selective and potent small molecule inhibitor of CDK4 and CDK6 with significant brain-brain barrier penetration, in patients with 1p/19q codeleted oligodendroglioma that has recurred after standard radiation and alkylating chemotherapy. METHODS This protocol (NCT03969706) has a single-arm, open-label, single-stage phase 2 design. Subjects will be treated with abemaciclib 200mg tablet twice daily on 28-day cycles until disease progression or unacceptable toxicity. Tumor response will be assessed according to modified Response Assessment in Neuro-Oncology (mRANO) criteria. The primary objective of the study is to determine the efficacy of abemaciclib for recurrent oligodendroglioma. The primary endpoint is the progression-free survival status of the subject at 6 months after enrollment (PFS-6). The alternative hypothesis is that the proportion of subjects without an event at 6 months will be ³85%. We will test this hypothesis against a null hypothesis of PFS-6 = 50%. With enrollment of 10 planned subjects, the power to detect this difference is 82% (one-sided alpha = 0.05).

INTRODUCTION: We sought to determine which therapeutically targetable immune checkpoints, costimulatory signals, and other tumor microenvironment (TME) factors are independently associated with immune cytolytic activity (CYT), a gene expression signature of activated effector T cells, in human glioblastoma (GBM). METHODS: GlioVis was accessed for RNA-seq data from The Cancer Genome Atlas (TCGA). For subjects with treatment-naïve, primary GBM, we quantified mRNA expression of 28 therapeutically targetable TME factors. CYT (geometric mean of GZMA and PRF1 expression) was calculated for each tumor. Multiple linear regression was performed to determine the relationship between the dependent variable (CYT) and mRNA expression of each of the 28 factors. Variables associated with CYT in multivariate analysis were subsequently evaluated for this association in an independent cohort of newly diagnosed GBMs from the Chinese Glioma Cooperative Group (CGCG). RESULTS: 109 TCGA tumors were analyzed. The final multiple linear regression model included the following variables, each positively associated with CYT except VEGF-A (negative association): CSF-1 (p=0.003), CD137 (p=0.042), VEGF-A (p

Background Epidermal growth factor receptor variant III (EGFRvIII) is a driver mutation and potential therapeutic target in glioblastoma. Non-invasive in vivo EGFRvIII determination, using clinically acquired multiparametric MRI sequences, could assist in assessing spatial heterogeneity related to EGFRvIII, currently not captured via single-specimen analyses. We hypothesize that integration of subtle, yet distinctive, quantitative imaging/radiomic patterns using machine learning may lead to non-invasively determining molecular characteristics, and particularly the EGFRvIII mutation. Methods We integrated diverse imaging features, including the tumor's spatial distribution pattern, via support vector machines, to construct an imaging signature of EGFRvIII. This signature was evaluated in independent discovery (n = 75) and replication (n = 54) cohorts of de novo glioblastoma, and compared with the EGFRvIII status obtained through an assay based on next-generation sequencing. Results The cross-validated accuracy of the EGFRvIII signature in classifying the mutation status in individual patients of the independent discovery and replication cohorts was 85.3% (specificity = 86.3%, sensitivity = 83.3%, area under the curve [AUC] = 0.85) and 87% (specificity = 90%, sensitivity = 78.6%, AUC = 0.86), respectively. The signature was consistent with EGFRvIII+ tumors having increased neovascularization and cell density, as well as a distinctive spatial pattern involving relatively more frontal and parietal regions compared with EGFRvIII- tumors. Conclusions An imaging signature of EGFRvIII was found, revealing a complex, yet distinct macroscopic glioblastoma phenotype. By non-invasively capturing the tumor in its entirety, the proposed methodology can assist in evaluating the tumor's spatial heterogeneity, hence overcoming common spatial sampling limitations of tissue-based analyses. This signature can preoperatively stratify patients for EGFRvIII-targeted therapies, and potentially monitor dynamic mutational changes during treatment.

The remarkable heterogeneity of glioblastoma, across patients and over time, is one of the main challenges in precision diagnostics and treatment planning. Non-invasive in vivo characterization of this heterogeneity using imaging could assist in understanding disease subtypes, as well as in risk-stratification and treatment planning of glioblastoma. The current study leveraged advanced imaging analytics and radiomic approaches applied to multi-parametric MRI of de novo glioblastoma patients (n = 208 discovery, n = 53 replication), and discovered three distinct and reproducible imaging subtypes of glioblastoma, with differential clinical outcome and underlying molecular characteristics, including isocitrate dehydrogenase-1 (IDH1), O6-methylguanine–DNA methyltransferase, epidermal growth factor receptor variant III (EGFRvIII), and transcriptomic subtype composition. The subtypes provided risk-stratification substantially beyond that provided by WHO classifications. Within IDH1-wildtype tumors, our subtypes revealed different survival (p EGFRvIII-mutated tumors. The identified subtypes and their clinical and molecular correlates provide an in vivo portrait of phenotypic heterogeneity in glioblastoma, which points to the need for precision diagnostics and personalized treatment.

Spatiotemporal regulation of tumor immunity remains largely unexplored. Here we identify a vascular niche that controls alternative macrophage activation in glioblastoma (GBM). We show that tumor-promoting macrophages are spatially proximate to GBM-associated endothelial cells (ECs), permissive for angiocrine-induced macrophage polarization. We identify ECs as one of the major sources for interleukin-6 (IL-6) expression in GBM microenvironment. Furthermore, we reveal that colony-stimulating factor-1 and angiocrine IL-6 induce robust arginase-1 expression and macrophage alternative activation, mediated through peroxisome proliferator-activated receptor-γ-dependent transcriptional activation of hypoxia-inducible factor-2α. Finally, utilizing a genetic murine GBM model, we show that EC-specific knockout of IL-6 inhibits macrophage alternative activation and improves survival in the GBM-bearing mice. These findings illustrate a vascular niche-dependent mechanism for alternative macrophage activation and cancer progression, and suggest that targeting endothelial IL-6 may offer a selective and efficient therapeutic strategy for GBM, and possibly other solid malignant tumors., Macrophages in the tumour microenvironment (TME) acquire tumour-promoting functions upon M2 polarization. Here the authors show, in a mouse model of glioblastoma, that endothelial cells in the TME induce macrophage M2 polarization via IL-6 and that depletion of endothelial IL-6 improves survival.

Accurate differentiation of brain infections from necrotic glioblastomas (GBMs) may not always be possible on morphologic MRI or on diffusion tensor imaging (DTI) and dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) if these techniques are used independently.To investigate the combined analysis of DTI and DSC-PWI in distinguishing brain injections from necrotic GBMs.Retrospective.Fourteen patients with brain infections and 21 patients with necrotic GBMs.3T MRI, DTI, and DSC-PWI.Parametric maps of mean diffusivity (MD), fractional anisotropy (FA), coefficient of linear (CL), and planar anisotropy (CP) and leakage corrected cerebral blood volume (CBV) were computed and coregistered with postcontrast TAll parameters from both regions were compared between brain infections and necrotic GBMs using Mann-Whitney tests. Logistic regression analyses were performed to obtain the best model in distinguishing these two conditions.From the central core, significantly lower MD (0.90 × 10Combined analysis of DTI and DSC-PWI may provide better performance in differentiating brain infections from necrotic GBMs.1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:184-194.

Background: The EGF-receptor variant III (EGFRvIII) is expressed in approximately 30% of all primary glioblastoma (GBM). Although several anti-EGFRvIII strategies have already been tested, the importance of EGFRvIII expression on treatment response is still under debate. Our aim was to clarify the relevance of EGFRvIII for GBM treatment using uni- and multivariate analysis of clinical data and preclinical studies. Methods: We performed retrospective analysis of 338 IDH1 wild type GBM patients treated either at the University of Pennsylvania or North Bristol NHS Trust and included data from the TCGA database. Survival was stratified according to MGMT promoter methylation status, EGFRvIII expression and EGFR gene amplification. The effect of EGFRvIII expression was also analyzed at the cellular and tumor level using isogenic pairs of EGFRvIII-negative and -positive GBM cells and xenograft tumors. Findings: EGFRvIII expression was associated with a significantly prolonged median overall survival but only for GBM patients with MGMT promoter methylated tumors. Importantly, this effect was independent of EGFR gene amplification. We also observed increased temozolomide (TMZ) sensitivity of EGFRvIII positive cells, which translated into improved survival in xenograft experiments. This increase in TMZ sensitivity was associated with an elevated DNA damage induction accompanied by an increased expression of DNA mismatch repair (MMR) proteins. In line with this finding, knock down of MMR proteins or EGFRvIII restored TMZ resistance. Interpretation: EGFRvIII expression is of high relevance for the prediction of GBM therapy outcome. Due to EGFRvIII-dependent upregulation of MMR standard of care treatment is most efficient for GBM displaying both MGMT promoter methylation and EGFRvIII expression. Funding Statement: This work was supported by the Brigitte and Dr. Konstanze Wegener-Stiftung (M.K.), Wilhelm-Sander-Stiftung (M.K.), Forschungsforderungsfonds des Fachbereichs Medizin des Universitatsklinikums Hamburg-Eppendorf (N.S.) and the Federal Ministry of Education and Research (BMBF grant 02NUK032; M.K. K.R.), Cancer Research UK (CRUK/21600; S.C.S, T.B.), The Brain Tumour Charity (TBTC13/192; S.C.S., T.B.), Medical Research Council (MR/K015214/1; S.C.S., T.B.), University of Leeds Academic Fellowship (L.F.S), The Templeton Family Research Fund (Z.A.B) and the National Institutes of Health (NIH 2R01-NS042645-11A1; D.O.R.). U.S. is supported by the Fordergemeinschaft Kinderkrebs-Zentrum Hamburg Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: Animal Use: All work was performed in accordance with the United Kingdom Animals (Scientific Procedures) Act (1986), under the authority of project licence 70/7340.

Immunotherapy has emerged as a promising strategy for glioblastoma (GBM), a disease that remains universally fatal despite currently available standard-of-care. Adoptive T cell therapy has been shown to produce potent antitumor immunity while obviating the need for traditional antigen presentation and primary immune responses. Chimeric antigen receptors (CARs) are specialized molecules that can be expressed on the surface of T cells allowing for redirected cytotoxicity against tumor antigens of interest. To date, the application of CAR T cells for GBM has been relatively limited, in large part due to a dearth of well-described tumor specific antigens that are both homogenously and frequently expressed. A mutated version of the epidermal growth factor receptor, EGFRvIII, is a constitutively activated tyrosine kinase that is expressed on the surface of GBM and other common neoplasms, but completely absent from all normal tissues. We have recently generated CAR T cells directed against EGFRvIII and reported results from a Phase I clinical trial investigating this platform in patients with EGFRvIII-expressing GBM. Our study showed that despite conventional notions of central nervous system “immune-privilege,” EGFRvIII CAR T cells trafficked to intracerebral tumors, leading to successful targeting and eradication of this antigen in the brain. Here, we review our experience with EGFRvIII CAR T cells and highlight important considerations for the clinical translation of this therapy in patients with GBM.