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BACKGROUND: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. METHODS: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (

PURPOSE: While immune checkpoint inhibitors (ICI) have had success with various malignancies, their efficacy in brain cancer is still unclear. Retrospective and prospective studies using PD-1 inhibitors for recurrent glioblastoma (GBM) have not established survival benefit. This study evaluated if ICI may be effective for select patients with recurrent GBM. METHODS: This was a single-center retrospective study of adult patients diagnosed with first recurrence GBM and received pembrolizumab or nivolumab with or without concurrent bevacizumab. Archival tissue was used for immunohistochemistry (IHC) and targeted DNA next-generation sequencing (NGS) analysis. RESULTS: Median overall survival (mOS) from initial diagnosis was 24.5 months (range 10-42). mOS from onset of ICI was 10 months (range 1-31) with 75% surviving > 6 months and 46% > 12 months. Additional IHC analysis on tumors from eight patients demonstrated a trend of longer survival after ICI for those with elevated PD-L1 expression. NGS of samples from 15 patients identified EGFR amplification at initial diagnosis and at any time point to be associated with worse survival after ICI (HR 12.2, 95% CI 1.37-108, p = 0.025 and HR 3.92, 95% CI 1.03-14.9, p = 0.045, respectively). This significance was corroborated with previously tested EGFR amplification via in situ hybridization. CONCLUSION: ICI did not extend overall survival for recurrent GBM. However, molecular sequencing identified EGFR amplification as associated with worse survival. Prospective studies can validate if EGFR amplification is a biomarker of ICI resistance and determine if its use can stratify responders from non-responders.

PurposeThe treatment of glioblastoma (GBM) poses challenges. The use of immune checkpoint inhibition (ICI) has been disappointing as GBM is characterized by low mutational burden and low T-cell infiltration. The combination of ICI with other treatment modalities may improve efficacy.Patient and methodsPatients with recurrent GBM were treated with avelumab, a human IgG1 antibody directed against PD-L1 (part A), or avelumab within a week after laser interstitial thermal therapy (LITT) and continuation of avelumab (part B). Bevacizumab was allowed to be combined with ICI to spare steroid use. The primary objective was to characterize the tolerability and safety of the regimens. The secondary objectives included overall survival, progression-free survival (PFS), signatures of plasma analytes, and immune cells.ResultsA total of 12 patients (median age 64; range, 37-73) enrolled, five in part A and seven in part B. Two serious adverse events occurred in the same patient, LITT treated, not leading to death. The median survival from enrollment was 13 months [95% confidence interval (CI), 4-16 months] with no differences for part A or B. The median PFS was 3 months (95% CI, 1.5-4.5 months). The decrease in MICA/MICB, γδT cells, and CD4+ T cell EMRA correlated with prolonged survival.ConclusionsAvelumab was generally well tolerated. Adding bevacizumab to ICI may be beneficial by lowering cytokine and immune cell expression. The development of this combinatorial treatment warrants further investigation. Exploring the modulation of adaptive and innate immune cells and plasma analytes as biomarker signatures may instruct future studies in this dismal refractory disease.SignificanceOur phase I of PD-L1 inhibition combined with LITT and using bevacizumab to spare steroids had a good safety profile for recurrent GBM. Developing combinatory treatment may help outcomes. In addition, we found significant immune modulation of cytokines and immune cells by bevacizumab, which may enhance the effect of ICI.

PurposeThe T2-FLAIR mismatch sign has shown promise in determining IDH mutant 1p/19q non-co-deleted gliomas with a high specificity and modest sensitivity. To develop a multi-parametric radiomic model using MRI to predict 1p/19q co-deletion status in patients with newly diagnosed IDH1 mutant glioma and to perform a comparative analysis to T2-FLAIR mismatch sign+.MethodsIn this retrospective study, patients with diagnosis of IDH1 mutant gliomas with known 1p/19q status who had preoperative MRI were included. T2-FLAIR mismatch was evaluated independently by two board-certified neuroradiologists. Texture features were extracted from glioma segmentation of FLAIR images. eXtremeGradient Boosting (XGboost) classifiers were used for model development. Leave-one-out-cross-validation (LOOCV) and external validation performances were reported for both the training and external validation sets.ResultsA total of 103 patients were included for model development and 18 patients for external testing validation. The diagnostic performance (sensitivity/specificity/accuracy) in the determination of the 1p/19q co-deletion status was 59%/83%/67% (training) and 62.5%/70.0%/66.3% (testing) for the T2-FLAIR mismatch sign. This was significantly improved (p = 0.04) using the radiomics model to 77.9%/82.8%/80.3% (training) and 87.5%/89.9%/88.8% (testing), respectively. The addition of radiomics as a computer-assisted tool resulted in significant (p = 0.02) improvement in the performance of the neuroradiologist with 13 additional corrected cases in comparison to just using the T2-FLAIR mismatch sign.ConclusionThe proposed radiomic model provides much needed sensitivity to the highly specific T2-FLAIR mismatch sign in the determination of the 1p/19q non-co-deletion status and improves the overall diagnostic performance of neuroradiologists when used as an assistive tool.

(1) Background: Gliomas are the most common primary brain neoplasms accounting for roughly 40−50% of all malignant primary central nervous system tumors. We aim to develop a deep learning-based framework for automated segmentation and prediction of biomarkers and prognosis in patients with gliomas. (2) Methods: In this retrospective two center study, patients were included if they (1) had a diagnosis of glioma with known surgical histopathology and (2) had preoperative MRI with FLAIR sequence. The entire tumor volume including FLAIR hyperintense infiltrative component and necrotic and cystic components was segmented. Deep learning-based U-Net framework was developed based on symmetric architecture from the 512 × 512 segmented maps from FLAIR as the ground truth mask. (3) Results: The final cohort consisted of 208 patients with mean ± standard deviation of age (years) of 56 ± 15 with M/F of 130/78. DSC of the generated mask was 0.93. Prediction for IDH-1 and MGMT status had a performance of AUC 0.88 and 0.62, respectively. Survival prediction of

BackgroundEarly identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma.MethodsIn this retrospective study, patients were included if they (1) had diagnosis of gliomas with known IDH1, EGFR, MGMT, ATRX, TP53, and PTEN status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed.ResultsFrom a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for IDH1, 0.99/80% for ATRX, 0.79/67% for MGMT, and 0.77/66% for EGFR. The addition of diffusion data to conventional MRI features significantly (P < .05) increased predictive performance for IDH1, MGMT, and ATRX. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (IDH1), 70% (ATRX), 70% (MGMT), and 75% (EGFR).ConclusionAddition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.

Monocytes and monocyte-derived macrophages (MDMs) from blood circulation infiltrate glioblastoma (GBM) and promote growth. Here, we show that PDGFS-driven GBM cells induce the expression of the potent proinflammatory cytokine IL- 1[beta] in MDM, which engages IL-1R1 in tumor cells, activates the NF-[kappa]B pathway, and subsequently leads to induction of monocyte chemoattractant proteins (MCPs). Thus, a feedforward paracrine circuit of IL-1[beta]/IL-1R1 between tumors and MDM creates an interdependence driving PDGFS-driven GBM progression. Genetic loss or locally antagonizing IL-1[beta]/IL-1R1 leads to reduced MDM infiltration, diminished tumor growth, and reduced exhausted [CD8.sup.+] T cells and thereby extends the survival of tumor-bearing mice. In contrast to IL-1[beta], IL-1[alpha] exhibits antitumor effects. Genetic deletion of ll1a/b is associated with decreased recruitment of lymphoid cells and loss-of-interferon signaling in various immune populations and subsets of malignant cells and is associated with decreased survival time of PDGFS-driven tumor-bearing mice. In contrast to PDGFB-driven GBM, Nf1-silenced tumors have a constitutively active NF-[kappa]B pathway, which drives the expression of MCPs to recruit monocytes into tumors. These results indicate local antagonism of IL-1[beta] could be considered as an effective therapy specifically for proneural GBM., Introduction Glioblastoma (GBM) is the most prevalent and aggressive primary brain tumor in children and adults and has poor overall survival rates. Numerous therapies have entered clinical trials, with only [...]

Clinical benefit of immune checkpoint blockade in glioblastoma (GBM) is rare, and we hypothesize that tumor clonal evolution and the immune microenvironment are key determinants of response. Here, we present a detailed molecular characterization of the intratumoral and immune heterogeneity in an IDH wild-type, MGMT-negative GBM patient who plausibly benefited from anti-PD-1 therapy with an unusually long 25-mo overall survival time. We leveraged multiplex immunohistochemistry, RNA-seq, and whole-exome data from the primary tumor and three resected regions of recurrent disease to survey regional tumor-immune interactions, genomic instability, mutation burden, and expression profiles. We found significant regional heterogeneity in the neoantigenic and immune landscape, with a differential T-cell signature among recurrent sectors, a uniform loss of focal amplifications in EGFR, and a novel subclonal EGFR mutation. Comparisons with recently reported correlates of checkpoint blockade in GBM and with TCGA-GBM revealed appreciable intratumoral heterogeneity that may have contributed to a differential PD-1 blockade response.

Patients with gliomas, isocitrate dehydrogenase 1 (IDH1) mutation status have been studied as a prognostic indicator. Recent advances in machine learning (ML) have demonstrated promise in utilizing radiomic features to study disease processes in the brain. We investigate whether ML analysis of multiparametric radiomic features from preoperative Magnetic Resonance Imaging (MRI) can predict IDH1 mutation status in patients with glioma. This retrospective study included patients with glioma with known IDH1 status and preoperative MRI. Radiomic features were extracted from Fluid-Attenuated Inversion Recovery (FLAIR) and Diffused Weighted Imaging (DWI). The dataset was split into training, validation, and testing sets by stratified sampling. Synthetic Minority Oversampling Technique (SMOTE) was applied to the training sets. eXtreme Gradient Boosting (XGBoost) classifiers were trained, and the hyperparameters were tuned. Receiver operating characteristic curve (ROC), accuracy, and f1-scores were collected. A total of 100 patients (age: 55 ± 15, M/F 60/40); with IDH1 mutant (n = 22) and IDH1 wildtype (n = 78) were included. The best performance was seen with a DWI-trained XGBoost model, which achieved ROC with Area Under the Curve (AUC) of 0.97, accuracy of 0.90, and f1-score of 0.75 on the test set. The FLAIR-trained XGBoost model achieved ROC with AUC of 0.95, accuracy of 0.90, f1-score of 0.75 on the test set. A model that was trained on combined FLAIR-DWI radiomic features did not provide incremental accuracy. The results show that a XGBoost classifier using multiparametric radiomic features derived from preoperative MRI can predict IDH1 mutation status with > 90% accuracy.

Immediate-early response genes (IEGs) are rapidly and transiently induced following an extracellular signal. Elucidating the IEG response patterns in single cells (SCs) requires assaying large numbers of timed samples at high accuracy while minimizing handling effects. To achieve this, we developed and validated RNA stabilization Buffer for Examination of Single-cell Transcriptomes (RNA-Best), a versatile single-step cell and tissue preservation protocol that stabilizes RNA in intact SCs without perturbing transcription patterns. We characterize for the first time SC heterogeneity in IEG responses to pulsatile gonadotropin-releasing hormone (GnRH) stimuli in pituitary gonadotrope cells. Our study identifies a gene-specific hierarchical pattern of all-or-none transcript induction elicited by increasing concentrations of GnRH. This quantal pattern of gene activation raises the possibility that IEG activation, when accurately resolved at the SC level, may be mediated by gene bits that behave as pure binary switches.