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Background: Epstein-Barr virus (EBV)+ and EBV- primary CNS lymphomas (PCNSL) carry distinct mutational landscapes, but their transcriptional and epigenetic profiles have not been integrated and compared. This precludes further insights into pathobiology and molecular differences, relevant for classification and targeted therapy., Methods: 23 EBV- and 15 EBV+ PCNSL, histologically classified as diffuse large B-cell lymphomas, were subjected to RNA-Sequencing and EPIC methylation arrays. Unsupervised clustering analyses were performed. Differentially expressed and differentially methylated genes were identified and integrated., Results: Two distinct transcriptional clusters were found, which separated EBV-and EBV+PCNSL (p < 0.0001). The EBV+ transcriptional signature contained genes (GPR15, FCER2/CD23, SLAMF1/CD150) closely regulated by EBV oncogenes in B-cells. Pathway enrichment analysis uncovered enhanced B-cell receptor (BCR) and WNT/beta-catenin signaling in EBV-lymphomas, whereas Interleukin-10, NOTCH, and viral life cycle pathways were upregulated in EBV+PCNSL. Correspondingly, BCR-associated SYK kinase activity was enriched in EBV-tumors while JAK2 was overrepresented in EBV+PCNSL. Epigenetic profiling revealed reduced global promoter methylation in EBV+PCNSL. Two methylation clusters were recognized, which separated EBV-and EBV+PCNSL (p < 0.0001). Epigenetic profiles were distinct from 2,788 other brain tumor and non-malignant reference samples. Promoter region hypermethylation of CD79B, a BCR subunit critical for sustained proliferation in EBV-disease, highly correlated (R = -0.7) with its transcriptional downregulation in EBV+PCNSL., Conclusions: EBV+ and EBV- PCNSL harbor distinct transcriptional and epigenetic profiles, corroborating them as distinctive biological subtypes. Uncovered differences provide novel insights into their pathobiology, may guide molecular diagnostics and targeted therapies., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Purpose: To propose a novel recursive partitioning analysis (RPA) classification model in patients with IDH-wildtype glioblastomas that incorporates the recently expanded conception of the extent of resection (EOR) in terms of both supramaximal and total resections., Experimental Design: This multicenter cohort study included a developmental cohort of 622 patients with IDH-wildtype glioblastomas from a single institution (Severance Hospital) and validation cohorts of 536 patients from three institutions (Seoul National University Hospital, Asan Medical Center, and Heidelberg University Hospital). All patients completed standard treatment including concurrent chemoradiotherapy and underwent testing to determine their IDH mutation and MGMTp methylation status. EORs were categorized into either supramaximal, total, or non-total resections. A novel RPA model was then developed and compared with a previous Radiation Therapy Oncology Group (RTOG) RPA model., Results: In the developmental cohort, the RPA model included age, MGMTp methylation status, Karnofsky performance status, and EOR. Younger patients with MGMTp methylation and supramaximal resections showed a more favorable prognosis [class I: median overall survival (OS) 57.3 months], whereas low-performing patients with non-total resections and without MGMTp methylation showed the worst prognosis (class IV: median OS 14.3 months). The prognostic significance of the RPA was subsequently confirmed in the validation cohorts, which revealed a greater separation between prognostic classes for all cohorts compared with the previous RTOG RPA model., Conclusions: The proposed RPA model highlights the impact of supramaximal versus total resections and incorporates clinical and molecular factors into survival stratification. The RPA model may improve the accuracy of assessing prognostic groups. See related commentary by Karschnia et al., p. 4811., (©2024 American Association for Cancer Research.)

Background and Objectives: Neurolymphomatosis (NL) refers to lymphomatous infiltration of the peripheral nervous system (PNS). NL diagnosis and treatment are challenging given the broad differential diagnosis of peripheral neuropathy, the lack of larger cohorts, and the subsequent unavailability of prognostic factors or consensus therapy. This study aimed to define characteristics and prognostic factors of NL., Methods: A systematic review of the literature (2004-2023) was performed using PubMed and Scopus databases and reported following PRISMA guidelines. Studies reporting individual patient data on cases with definitive NL diagnosis were included. Clinical, radiologic, pathologic, and outcome information were extracted. Univariable and multivariable survival analyses were performed using log-rank tests and Cox proportional hazard models., Results: A total of 459 NL cases from 264 studies were accumulated. NL was the first manifestation of malignancy (primary NL) in 197 patients. PNS relapse of known non-Hodgkin lymphoma (secondary NL) occurred in 262 cases after a median 12 months. NL predominantly presented with rapidly deteriorating, asymmetric painful polyneuropathy. Infiltrated structures included peripheral nerves (56%), nerve roots (52%), plexus (33%), and cranial nerves (32%). Diagnosis was established at a median of 3 months after symptom onset with substantial delays in primary NL. It mainly relied on PNS biopsy or FDG-PET, which carried high diagnostic yields (>90%). Postmortem diagnoses were rare (3%). Most cases were classified as B-cell (90%) lymphomas. Tumor-directed therapy was administered in 96% of patients and typically consisted of methotrexate or rituximab-based polychemotherapy. The median overall survival was 18 months. Primary NL without concurrent systemic disease outside the nervous system (hazard ratio [HR]: 0.44; 95% CI 0.25-0.78; p = 0.005), performance status (ECOG <2, HR: 0.30; 95% CI 0.18-0.52; p < 0.0001), and rituximab-based treatment (HR: 0.46; 95% CI 0.28-0.73; p = 0.001) were identified as favorable prognostic markers on multivariable analysis when adjusting for clinical and sociodemographic parameters., Discussion: Advances in neuroimaging modalities, particularly FDG-PET, facilitate NL diagnosis and offer a high diagnostic yield. Yet, diagnostic delays in primary NL remain common. Rituximab-based therapy improves NL outcome. Findings may assist clinicians in early recognition, prognostic stratification, and treatment of NL.

Background: The purpose of this study was to elucidate the relationship between distinct brain regions and molecular subtypes in glioblastoma (GB), focusing on integrating modern statistical tools and molecular profiling to better understand the heterogeneity of Isocitrate Dehydrogenase wild-type (IDH-wt) gliomas., Methods: This retrospective study comprised 441 patients diagnosed with new IDH-wt glioma between 2009 and 2020 at Heidelberg University Hospital. The diagnostic process included preoperative magnetic resonance imaging and molecular characterization, encompassing IDH-status determination and subclassification, through DNA-methylation profiling. To discern and map distinct brain regions associated with specific methylation subtypes, a support-vector regression-based lesion-symptom mapping (SVR-LSM) was employed. Lesion maps were adjusted to 2 mm³ resolution. Significance was assessed with beta maps, using a threshold of P  < .005, with 10 000 permutations and a cluster size minimum of 100 voxels., Results: Of 441 initially screened glioma patients, 423 (95.9%) met the inclusion criteria. Following DNA-methylation profiling, patients were classified into RTK II (40.7%), MES (33.8%), RTK I (18%), and other methylation subclasses (7.6%). Between molecular subtypes, there was no difference in tumor volume. Using SVR-LSM, distinct brain regions correlated with each subclass were identified: MES subtypes were associated with left-hemispheric regions involving the superior temporal gyrus and insula cortex, RTK I with right frontal regions, and RTK II with 3 clusters in the left hemisphere., Conclusions: This study linked molecular diversity and spatial features in glioblastomas using SVR-LSM. Future studies should validate these findings in larger, independent cohorts to confirm the observed patterns., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Background: This study investigates the influence of diffusion-weighted Magnetic Resonance Imaging (DWI-MRI) on radiomic-based prediction of glioma types according to molecular status and assesses the impact of DWI intensity normalization on model generalizability., Methods: Radiomic features, compliant with image biomarker standardization initiative standards, were extracted from preoperative MRI of 549 patients with diffuse glioma, known IDH, and 1p19q-status. Anatomical sequences (T1, T1c, T2, FLAIR) underwent N4-Bias Field Correction (N4) and WhiteStripe normalization (N4/WS). Apparent diffusion coefficient (ADC) maps were normalized using N4 or N4/z-score. Nine machine-learning algorithms were trained for multiclass prediction of glioma types (IDH-mutant 1p/19q codeleted, IDH-mutant 1p/19q non-codeleted, IDH-wild type). Four approaches were compared: Anatomical, anatomical + ADC naive, anatomical + ADC N4, and anatomical + ADC N4/z-score. The University of California San Francisco (UCSF)-glioma dataset ( n  = 409) was used for external validation., Results: Naïve-Bayes algorithms yielded overall the best performance on the internal test set. Adding ADC radiomics significantly improved AUC from 0.79 to 0.86 ( P  = .011) for the IDH-wild-type subgroup, but not for the other 2 glioma subgroups ( P  > .05). In the external UCSF dataset, the addition of ADC radiomics yielded a significantly higher AUC for the IDH-wild-type subgroup ( P  ≤ .001): 0.80 (N4/WS anatomical alone), 0.81 (anatomical + ADC naive), 0.81 (anatomical + ADC N4), and 0.88 (anatomical + ADC N4/z-score) as well as for the IDH-mutant 1p/19q non-codeleted subgroup ( P  < .012 each)., Conclusions: ADC radiomics can enhance the performance of conventional MRI-based radiomic models, particularly for IDH-wild-type glioma. The benefit of intensity normalization of ADC maps depends on the type and context of the used data., Competing Interests: The authors have no relevant financial or nonfinancial interests to disclose., (© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Background: Glioblastoma is the most common primary malignancy of the central nervous system with a dismal prognosis. Genomic signatures classify isocitrate dehydrogenase 1 (IDH)-wildtype glioblastoma into three subtypes: proneural, mesenchymal, and classical. Dasatinib, an inhibitor of proto-oncogene kinase Src (SRC), is one of many therapeutics which, despite promising preclinical results, have failed to improve overall survival in glioblastoma patients in clinical trials. We examined whether glioblastoma subtypes differ in their response to dasatinib and could hence be evaluated for patient enrichment strategies in clinical trials., Methods: We carried out in silico analyses on glioblastoma gene expression (TCGA) and single-cell RNA-Seq data. In addition, in vitro experiments using glioblastoma stem-like cells (GSCs) derived from primary patient tumors were performed, with complementary gene expression profiling and immunohistochemistry analysis of tumor samples., Results: Patients with the mesenchymal subtype of glioblastoma showed higher SRC pathway activation based on gene expression profiling. Accordingly, mesenchymal GSCs were more sensitive to SRC inhibition by dasatinib compared to proneural and classical GSCs. Notably, SRC phosphorylation status did not predict response to dasatinib treatment. Furthermore, serpin peptidase inhibitor clade H member 1 (SERPINH1), a collagen-related heat-shock protein associated with cancer progression, was shown to correlate with dasatinib response and with the mesenchymal subtype., Conclusion: This work highlights further molecular-based patient selection strategies in clinical trials and suggests the mesenchymal subtype as well as SERPINH1 to be associated with response to dasatinib. Our findings indicate that stratification based on gene expression subtyping should be considered in future dasatinib trials., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after repeated GBCA administration with yet unknown clinical significance. We aimed to assess the feasibility and diagnostic value of synthetic post-contrast T1-weighted MRI generated from pre-contrast MRI sequences through deep convolutional neural networks (dCNN) for tumour response assessment in neuro-oncology., Methods: In this multicentre, retrospective cohort study, we used MRI examinations to train and validate a dCNN for synthesising post-contrast T1-weighted sequences from pre-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery sequences. We used MRI scans with availability of these sequences from 775 patients with glioblastoma treated at Heidelberg University Hospital, Heidelberg, Germany (775 MRI examinations); 260 patients who participated in the phase 2 CORE trial (1083 MRI examinations, 59 institutions); and 505 patients who participated in the phase 3 CENTRIC trial (3147 MRI examinations, 149 institutions). Separate training runs to rank the importance of individual sequences and (for a subset) diffusion-weighted imaging were conducted. Independent testing was performed on MRI data from the phase 2 and phase 3 EORTC-26101 trial (521 patients, 1924 MRI examinations, 32 institutions). The similarity between synthetic and true contrast enhancement on post-contrast T1-weighted MRI was quantified using the structural similarity index measure (SSIM). Automated tumour segmentation and volumetric tumour response assessment based on synthetic versus true post-contrast T1-weighted sequences was performed in the EORTC-26101 trial and agreement was assessed with Kaplan-Meier plots., Findings: The median SSIM score for predicting contrast enhancement on synthetic post-contrast T1-weighted sequences in the EORTC-26101 test set was 0·818 (95% CI 0·817-0·820). Segmentation of the contrast-enhancing tumour from synthetic post-contrast T1-weighted sequences yielded a median tumour volume of 6·31 cm 3 (5·60 to 7·14), thereby underestimating the true tumour volume by a median of -0·48 cm 3 (-0·37 to -0·76) with the concordance correlation coefficient suggesting a strong linear association between tumour volumes derived from synthetic versus true post-contrast T1-weighted sequences (0·782, 0·751-0·807, p<0·0001). Volumetric tumour response assessment in the EORTC-26101 trial showed a median time to progression of 4·2 months (95% CI 4·1-5·2) with synthetic post-contrast T1-weighted and 4·3 months (4·1-5·5) with true post-contrast T1-weighted sequences (p=0·33). The strength of the association between the time to progression as a surrogate endpoint for predicting the patients' overall survival in the EORTC-26101 cohort was similar when derived from synthetic post-contrast T1-weighted sequences (hazard ratio of 1·749, 95% CI 1·282-2·387, p=0·0004) and model C-index (0·667, 0·622-0·708) versus true post-contrast T1-weighted MRI (1·799, 95% CI 1·314-2·464, p=0·0003) and model C-index (0·673, 95% CI 0·626-0·711)., Interpretation: Generating synthetic post-contrast T1-weighted MRI from pre-contrast MRI using dCNN is feasible and quantification of the contrast-enhancing tumour burden from synthetic post-contrast T1-weighted MRI allows assessment of the patient's response to treatment with no significant difference by comparison with true post-contrast T1-weighted sequences with administration of GBCAs. This finding could guide the application of dCNN in radiology to potentially reduce the necessity of GBCA administration., Funding: Deutsche Forschungsgemeinschaft., Competing Interests: Declaration of interests SH reports grants from the German Research Council and Dietmar-Hopp Foundation, outside of the submitted work. JD reports grants from ViewRay, the Clinical Research Institute, Accuray, RaySearch Laboratories, Vision RT, Merck, Astellas Pharma, AstraZeneca, Siemens Healthcare, Solution Akademie, Egomed, Quintiles, Pharmaceutical Research Association, Boehringer Ingelheim, PTW-Freiburg, and Nanobiotix, outside of the submitted work. MP reports non-financial support from Pfizer, and grants and personal fees from Bayer, outside of the submitted work. MP also has a licensed patent for IDH1 vaccines, a patent H3 vaccine pending, and a patent AHR inhibitor with royalties paid to Bayer. AI reports grants and travel funding from Carthera; research grants from Transgene, Sanofi, Air Liquide, and Nutritheragene; travel funding from Leo Pharma; and is on the advisory board for Novocure and Leo Pharma, outside the submitted work. MjvdB reports personal fees from Roche, Cellgene, Bristol Myers Squibb, Agios, Merck Sharpe & Dohme, and Boehringer Ingelheim; and grants and personal fees from AbbVie, outside of the submitted work. BN is on the scientific advisory board for Karyopharm and BTG Pharmaceuticals and is on the data safety and monitoring board for the University of Pennsylvania (Philadelphia, PA, USA), outside of the submitted work. J-CT reports personal fees from BrainLab and carThera, outside of the submitted work. WW reports grants from Apogenix, Boehringer Ingelheim, and Pfizer; grants and personal fees from Merck Sharp and Dohme and Roche; and personal fees from Bristol Myers Squibb and Celldex, outside of the submitted work. MB reports personal fees from Boehringer Ingelheim, Merck, Bayer, Teva, B Braun, Springer, and Vascular Dynamics; grants and personal fees from Novartis, Codman, and Guerbet; and grants from Siemens, Hopp Foundation, the German Research Council, the EU, Stryker, and Medtronic, outside of the submitted work. All other authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

The dynamics and phenotypes of intratumoral myeloid cells during tumor progression are poorly understood. Here we define myeloid cellular states in gliomas by longitudinal single-cell profiling and demonstrate their strict control by the tumor genotype: in isocitrate dehydrogenase (IDH)-mutant tumors, differentiation of infiltrating myeloid cells is blocked, resulting in an immature phenotype. In late-stage gliomas, monocyte-derived macrophages drive tolerogenic alignment of the microenvironment, thus preventing T cell response. We define the IDH-dependent tumor education of infiltrating macrophages to be causally related to a complex re-orchestration of tryptophan metabolism, resulting in activation of the aryl hydrocarbon receptor. We further show that the altered metabolism of IDH-mutant gliomas maintains this axis in bystander cells and that pharmacological inhibition of tryptophan metabolism can reverse immunosuppression. In conclusion, we provide evidence of a glioma genotype-dependent intratumoral network of resident and recruited myeloid cells and identify tryptophan metabolism as a target for immunotherapy of IDH-mutant tumors., (© 2021. The Author(s).)

Specific biological properties of those circulating cancer cells that are the origin of brain metastases (BM) are not well understood. Here, single circulating breast cancer cells were fate-tracked during all steps of the brain metastatic cascade in mice after intracardial injection over weeks. A novel in vivo two-photon microscopy methodology was developed that allowed to determine the specific cellular and molecular features of breast cancer cells that homed in the brain, extravasated, and successfully established a brain macrometastasis. Those BM-initiating breast cancer cells (BMIC) were mainly originating from a slow-cycling subpopulation that included only 16% to 20% of all circulating cancer cells. BMICs showed enrichment of various markers of cellular stemness. As a proof of principle for the principal usefulness of this approach, expression profiling of BMICs versus non-BMICs was performed, which revealed upregulation of NDRG1 in the slow-cycling BMIC subpopulation in one BM model. Here, BM development was completely suppressed when NDRG1 expression was downregulated. In accordance, in primary human breast cancer, NDRG1 expression was heterogeneous, and high NDRG1 expression was associated with shorter metastasis-free survival. In conclusion, our data identify temporary slow-cycling breast cancer cells as the dominant source of brain and other metastases and demonstrates that this can lead to better understanding of BMIC-relevant pathways, including potential new approaches to prevent BM in patients. IMPLICATIONS: Cancer cells responsible for successful brain metastasis outgrowth are slow cycling and harbor stemness features. The molecular characteristics of these metastasis-initiating cells can be studied using intravital microscopy technology., (©2020 American Association for Cancer Research.)

Both the perivascular niche (PVN) and the integration into multicellular networks by tumor microtubes (TMs) have been associated with progression and resistance to therapies in glioblastoma, but their specific contribution remained unknown. By long-term tracking of tumor cell fate and dynamics in the live mouse brain, differential therapeutic responses in both niches are determined. Both the PVN, a preferential location of long-term quiescent glioma cells, and network integration facilitate resistance against cytotoxic effects of radiotherapy and chemotherapy-independently of each other, but with additive effects. Perivascular glioblastoma cells are particularly able to actively repair damage to tumor regions. Population of the PVN and resistance in it depend on proficient NOTCH1 expression. In turn, NOTCH1 downregulation induces resistant multicellular networks by TM extension. Our findings identify NOTCH1 as a central switch between the PVN and network niche in glioma, and demonstrate robust cross-compensation when only one niche is targeted.

Background: Anaplastic lymphoma kinase (ALK) is expressed in ~ 60% of glioblastomas and conveys tumorigenic functions. Therefore, ALK inhibitory strategies with alectinib are conceivable for patients with glioblastoma. The aims of this preclinical study were to investigate efficacy as well as to understand and potentially overcome primary and acquired resistance mechanisms of alectinib in glioblastoma., Methods: Efficacy of alectinib was analyzed dependent on ALK expression in different glioblastoma initiating cells and after lentiviral knockdown of ALK. Alectinib resistant cells were generated by continuous treatment with increasing alectinib doses over 3 months. M-RNA, phospho-protein and protein regulation were analyzed to decipher relevant pathways associated to treatment or resistance and specifically inhibited to evaluate rational salvage therapies., Results: Alectinib reduced clonogenicity and proliferation and induced apoptosis in ALK expressing glioblastoma initiating cells, whereas cells without ALK expression or after ALK depletion via knockdown showed primary resistance against alectinib. High expression of cMyc and activation of the ERK1/2 pathway conferred resistance against alectinib in ALK expressing glioblastoma cells. Pharmacological inhibition of these pathways by cMyc inhibitor or MEK inhibitor, trametinib, overcame alectinib resistance and re-sensitized resistant cells to continued alectinib treatment. The combination of alectinib with radiotherapy demonstrated synergistic effects in inhibition of clonogenicity in non-resistant and alectinib resistant glioblastoma cells., Conclusion: The data offer rationales for alectinib treatment in ALK expressing glioblastoma and for the use of ALK expression status as potential biomarker for alectinib treatment. In addition, the results propose MEK inhibition or radiotherapy as reasonable salvage treatments after acquired alectinib resistance.

Molecular markers have become pivotal in brain tumor diagnostics. Mutational analyses by targeted next-generation sequencing of DNA and array-based DNA methylation assessment with copy number analyses are increasingly being used in routine diagnostics. However, the broad variety of gene fusions occurring in brain tumors is marginally covered by these technologies and often only assessed by targeted assays. Here, we assessed the feasibility and clinical value of investigating gene fusions in formalin-fixed paraffin-embedded (FFPE) tumor tissues by next-generation mRNA sequencing in a routine diagnostic setting. After establishment and optimization of a workflow applicable in a routine setting, prospective diagnostic application in a neuropathology department for 26 months yielded relevant fusions in 66 out of 101 (65%) analyzed cases. In 43 (43%) cases, the fusions were of decisive diagnostic relevance and in 40 (40%) cases the fusion genes rendered a druggable target. A major strength of this approach was its ability to detect fusions beyond the canonical alterations for a given entity, and the unbiased search for any fusion event in cases with uncertain diagnosis and, thus, uncertain spectrum of expected fusions. This included both rare variants of established fusions which had evaded prior targeted analyses as well as the detection of previously unreported fusion events. While the impact of fusion detection on diagnostics is highly relevant, it is especially the detection of "druggable" fusions which will most likely provide direct benefit to the patients. The wider application of this approach for unbiased fusion identification therefore promises to be a major advance in identifying alterations with immediate impact on patient care.

A network of communicating tumour cells that is connected by tumour microtubes mediates the progression of incurable gliomas. Moreover, neuronal activity can foster malignant behaviour of glioma cells by non-synaptic paracrine and autocrine mechanisms. Here we report a direct communication channel between neurons and glioma cells in different disease models and human tumours: functional bona fide chemical synapses between presynaptic neurons and postsynaptic glioma cells. These neurogliomal synapses show a typical synaptic ultrastructure, are located on tumour microtubes, and produce postsynaptic currents that are mediated by glutamate receptors of the AMPA subtype. Neuronal activity including epileptic conditions generates synchronised calcium transients in tumour-microtube-connected glioma networks. Glioma-cell-specific genetic perturbation of AMPA receptors reduces calcium-related invasiveness of tumour-microtube-positive tumour cells and glioma growth. Invasion and growth are also reduced by anaesthesia and the AMPA receptor antagonist perampanel, respectively. These findings reveal a biologically relevant direct synaptic communication between neurons and glioma cells with potential clinical implications.

Background: The Response Assessment in Neuro-Oncology (RANO) criteria and requirements for a uniform protocol have been introduced to standardise assessment of MRI scans in both clinical trials and clinical practice. However, these criteria mainly rely on manual two-dimensional measurements of contrast-enhancing (CE) target lesions and thus restrict both reliability and accurate assessment of tumour burden and treatment response. We aimed to develop a framework relying on artificial neural networks (ANNs) for fully automated quantitative analysis of MRI in neuro-oncology to overcome the inherent limitations of manual assessment of tumour burden., Methods: In this retrospective study, we compiled a single-institution dataset of MRI data from patients with brain tumours being treated at Heidelberg University Hospital (Heidelberg, Germany; Heidelberg training dataset) to develop and train an ANN for automated identification and volumetric segmentation of CE tumours and non-enhancing T2-signal abnormalities (NEs) on MRI. Independent testing and large-scale application of the ANN for tumour segmentation was done in a single-institution longitudinal testing dataset from the Heidelberg University Hospital and in a multi-institutional longitudinal testing dataset from the prospective randomised phase 2 and 3 European Organisation for Research and Treatment of Cancer (EORTC)-26101 trial (NCT01290939), acquired at 38 institutions across Europe. In both longitudinal datasets, spatial and temporal tumour volume dynamics were automatically quantified to calculate time to progression, which was compared with time to progression determined by RANO, both in terms of reliability and as a surrogate endpoint for predicting overall survival. We integrated this approach for fully automated quantitative analysis of MRI in neuro-oncology within an application-ready software infrastructure and applied it in a simulated clinical environment of patients with brain tumours from the Heidelberg University Hospital (Heidelberg simulation dataset)., Findings: For training of the ANN, MRI data were collected from 455 patients with brain tumours (one MRI per patient) being treated at Heidelberg hospital between July 29, 2009, and March 17, 2017 (Heidelberg training dataset). For independent testing of the ANN, an independent longitudinal dataset of 40 patients, with data from 239 MRI scans, was collected at Heidelberg University Hospital in parallel with the training dataset (Heidelberg test dataset), and 2034 MRI scans from 532 patients at 34 institutions collected between Oct 26, 2011, and Dec 3, 2015, in the EORTC-26101 study were of sufficient quality to be included in the EORTC-26101 test dataset. The ANN yielded excellent performance for accurate detection and segmentation of CE tumours and NE volumes in both longitudinal test datasets (median DICE coefficient for CE tumours 0·89 [95% CI 0·86-0·90], and for NEs 0·93 [0·92-0·94] in the Heidelberg test dataset; CE tumours 0·91 [0·90-0·92], NEs 0·93 [0·93-0·94] in the EORTC-26101 test dataset). Time to progression from quantitative ANN-based assessment of tumour response was a significantly better surrogate endpoint than central RANO assessment for predicting overall survival in the EORTC-26101 test dataset (hazard ratios ANN 2·59 [95% CI 1·86-3·60] vs central RANO 2·07 [1·46-2·92]; p<0·0001) and also yielded a 36% margin over RANO (p<0·0001) when comparing reliability values (ie, agreement in the quantitative volumetrically defined time to progression [based on radiologist ground truth vs automated assessment with ANN] of 87% [266 of 306 with sufficient data] compared with 51% [155 of 306] with local vs independent central RANO assessment). In the Heidelberg simulation dataset, which comprised 466 patients with brain tumours, with 595 MRI scans obtained between April 27, and Sept 17, 2018, automated on-demand processing of MRI scans and quantitative tumour response assessment within the simulated clinical environment required 10 min of computation time (average per scan)., Interpretation: Overall, we found that ANN enabled objective and automated assessment of tumour response in neuro-oncology at high throughput and could ultimately serve as a blueprint for the application of ANN in radiology to improve clinical decision making. Future research should focus on prospective validation within clinical trials and application for automated high-throughput imaging biomarker discovery and extension to other diseases., Funding: Medical Faculty Heidelberg Postdoc-Program, Else Kröner-Fresenius Foundation., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The oncometabolite (R)-2-hydroxyglutarate (R-2-HG) produced by isocitrate dehydrogenase (IDH) mutations promotes gliomagenesis via DNA and histone methylation. Here, we identify an additional activity of R-2-HG: tumor cell-derived R-2-HG is taken up by T cells where it induces a perturbation of nuclear factor of activated T cells transcriptional activity and polyamine biosynthesis, resulting in suppression of T cell activity. IDH1-mutant gliomas display reduced T cell abundance and altered calcium signaling. Antitumor immunity to experimental syngeneic IDH1-mutant tumors induced by IDH1-specific vaccine or checkpoint inhibition is improved by inhibition of the neomorphic enzymatic function of mutant IDH1. These data attribute a novel, non-tumor cell-autonomous role to an oncometabolite in shaping the tumor immune microenvironment.

Background: O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is a predictive biomarker in glioblastoma patients. Glioblastoma without hypermethylated MGMT promoter is largely resistant to treatment with temozolomide. These patients are in particular need of new treatment approaches, which are offered by biomarker-driven clinical trials with targeted drugs based on molecular characterization of individual tumors., Methods: In preparation for an upcoming clinical study, a comprehensive molecular profiling approach was undertaken on tissues from 43 glioblastoma patients harboring an unmethylated MGMT promoter at diagnosis. The diagnostic pipeline covered various levels of molecular characteristics, including whole-exome sequencing, low-coverage whole-genome sequencing, RNA sequencing, as well as microarray-based gene expression profiling and DNA methylation arrays., Results: Complex multilayer molecular diagnostics were feasible in this setting with a median turnaround time of 4-5 weeks from surgery to the molecular tumor board. In 35% of cases, potentially relevant therapeutic decisions were derived from the data. Alterations were most frequently found in receptor tyrosine kinases, members of the phosphoinositide 3-kinase/Akt/mechanistic target of rapamycin and mitogen-activated protein kinase pathway as well as cell cycle control and p53 regulation cascades. Individual tumors harbored clonal alterations such as oncogenic fusions of tyrosine kinases which constitute promising targets for targeted therapies. A prioritization algorithm is proposed to allocate patients with multiple targets to the potentially best treatment option., Conclusion: With this feasibility study, a comprehensive molecular profiling approach for patients with newly diagnosed glioblastoma harboring an unmethylated MGMT promoter is presented. Analyses in this pilot cohort serve as a basis for trials based on targetable alterations and on the question of allocation of patients to the best treatment arm.

Background: O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status is a predictive biomarker in glioblastoma. We investigated whether this marker furthermore defines a molecularly distinct tumor subtype with clinically different outcome., Methods: We analyzed copy number variation (CNV) and methylation profiles of 1095 primary and 92 progressive isocitrate dehydrogenase wildtype glioblastomas, including paired samples from 49 patients. DNA mutation data from 182 glioblastoma samples of The Cancer Genome Atlas (TCGA) and RNA expression from 107 TCGA and 55 Chinese Glioma Genome Atlas samples were analyzed., Results: Among untreated glioblastomas, MGMT promoter methylated (mMGMT) and unmethylated (uMGMT) tumors did not show different CNV or specific gene mutations, but a higher mutation count in mMGMT tumors. We identified 3 methylation clusters. Cluster 1 showed the highest average methylation and was enriched for mMGMT tumors. Seventeen genes including gastrulation brain homeobox 2 (GBX2) were found to be hypermethylated and downregulated on the mRNA level in mMGMT tumors. In progressive glioblastomas, platelet derived growth factor receptor alpha (PDGFRA) and GLI2 amplifications were enriched in mMGMT tumors. Methylated MGMT tumors gain PDGFRA amplification of PDGFRA, whereas uMGMT tumors with amplified PDGFRA frequently lose this amplification upon progression. Glioblastoma patients surviving <6 months and with mMGMT harbored less frequent epidermal growth factor receptor (EGFR) amplifications, more frequent TP53 mutations, and a higher tumor necrosis factor-nuclear factor-kappaB (TNF-NFκB) pathway activation compared with patients surviving >12 months., Conclusions: MGMT promoter methylation status does not define a molecularly distinct glioblastoma subpopulation among untreated tumors. Progressive mMGMT glioblastomas and mMGMT tumors of patients with short survival tend to have more unfavorable molecular profiles., (© The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com)

Adenovirus infection after stem cell transplantation is a significant cause of morbidity and mortality, especially in children. A robust T-cell response induced by dendritic cells (DC) is crucial for clearing the virus, suggesting their pivotal role for the response to human adenoviruses (HAdV). Despite the widespread use of adenoviral vectors, the properties and kinetics of HAdV infection of DC have not been addressed yet. We show that a recent clinical HAdV, subgenus C/serotype 2 (strain BB2000-61), infects cells of the myeloid lineage. Infected DC produce early and late viral antigens and show an altered expression of surface markers. Infection of monocytes renders them refractory to differentiation into DC. Additionally, HAdV-infected DC are strong stimulators of CD8+ T cells. In summary, HAdV seems to manipulate the immune response by infection of DC and possibly uses the infection of monocytes as a means to escape recognition by T cells.