Parameters assessing neurological status in malignant glioma patients: prognostic value for survival and relapse-free time

Genomic alterations predictive of response to radiosurgery in recurrent IDH-WT glioblastoma

INTRODUCTION

Despite aggressive treatment, glioblastoma invariably recurs. The optimal treatment for recurrent glioblastoma (rGBM) is not well defined. Stereotactic radiosurgery (SRS) for rGBM has demonstrated favorable outcomes for selected patients; however, its efficacy in molecular GBM subtypes is unknown. We sought to identify genetic alterations that predict response/outcomes from SRS in rGBM-IDH-wild-type (IDH-WT).

METHODS

rGBM-IDH-WT patients undergoing SRS at first recurrence and tested by next-generation sequencing (NGS) were reviewed (2009-2018). Demographic, clinical, and molecular characteristics were evaluated. NGS interrogating 205-genes was performed. Primary outcome was survival from GK-SRS assessed by Kaplan-Meier method and multivariable Cox proportional-hazards.

RESULTS

Sixty-three lesions (43-patients) were treated at 1st recurrence. Median age was 61-years. All patients were treated with resection and chemoradiotherapy. Median time from diagnosis to 1st recurrence was 8.7-months. Median cumulative volume was 2.895 cm3 and SRS median marginal dose was 18 Gy (median isodose-54%). Bevacizumab was administered in 81.4% patients. PFS from SRS was 12.9-months. Survival from SRS was 18.2-months. PTEN-mutant patients had a longer PFS (p = 0.049) and survival from SRS (p = 0.013) in multivariable analysis. Although no statistically significant PTEN-mutants patients had higher frequency of radiation necrosis (21.4% vs. 3.4%) and lower in-field recurrence (28.6% vs. 37.9%) compared to PTEN-WT patients.

CONCLUSIONS

SRS is a safe and effective treatment option for selected rGBM-IDH-WT patients following first recurrence. rGBM-IDH-WT harboring PTEN-mutation have improved survival with salvage SRS compared to PTEN-WT patients. PTEN may be used as a molecular biomarker to identify a subset of rGBM patients who may benefit the most from SRS.

Perfusion, Diffusion, Or Brain Tumor Barrier Integrity: Which Represents The Glioma Features Best?

Purpose

This study aims to incorporate informative histogram indicator analyses and advanced multimodal MRI parameters to differentiate low-grade gliomas (LGGs) from high-grade gliomas (HGGs) and to explore the features associated with patients’ survival.

Patients and methods

A total of 120 patients with pathologically confirmed LGGs or HGGs receiving conventional and advanced MRI such as three-dimensional arterial spin labeling (3D-ASL), intravoxel incoherent motion-diffusion weighted imaging (IVIM-DWI), and dynamic contrast-enhanced MRI (DCE-MRI) were included. The mean and histogram indicators from advanced MRI were calculated from the entire tumor. The efficacies of a single indicator or multiple parameters were tested in distinguishing HGGs from LGGs and predicting patients’ survival. Receiver operating characteristic (ROC) curve and multivariable stepwise logistic regression were used to evaluate the diagnostic efficacies. Leave-one-out cross-validation was further used to validate the accuracy of the parameter sets in glioma grading. Log-rank test using the Kaplan–Meier curve was utilized to predict patients’ survival.

Results

Overall, parameters from DCE-MRI performed better than those from 3D-ASL or IVIM-DWI in both glioma grading and survival prediction. The histogram metrics of V_e were demonstrated to have higher accuracies (the accuracies for Extended Tofts_V_e^mean and Extended Tofts_V_e^median were 68.33% and 71.67%, respectively, while those for the Incremental_V_e^mean and Incremental_V_e^75th were 68.33% and 72.50%, respectively) in grading LGGs from HGGs. The combination of Tofts_V_e histogram metrics was the one with the highest accuracy (81.67%) and area under ROC curve (AUC = 0.840). On the other hand, Patlak_K_trans^95th (AUC = 0.9265) and Extended Tofts_V_e^95th (AUC = 0.9154) performed better than their corresponding means (Patlak_K_trans^mean: AUC = 0.9118 and Extended Tofts_V_e^mean: AUC = 0.9044) in predicting patients’ overall survival (OS) at 18-month follow-up.

Conclusion

DCE-MRI-derived histogram features from the entire tumor were promising metrics for glioma grading and OS prediction. Combining single modal histogram features improved glioma grading.

Trial registration

NCT 02622620.

An Inflammatory Landscape for Preoperative Neurologic Deficits in Glioblastoma

Introduction: Patients with glioblastoma (GBM), one of the most aggressive forms of primary brain tumors, exhibit a wide range of neurologic signs, ranging from headaches to neurologic deficits and cognitive impairment, at first clinical presentation. While such variability is attributed to inter-individual differences in increased intracranial pressure, tumor infiltration, and vascular compromise, a direct association with disease stage, tumor size and location, edema, and necrotic cell death has yet to be established. The lack of specificity of neurologic symptoms often confounds the diagnosis of GBM. It also limits clinicians' ability to elect treatment regimens that not only prolong survival but also promote symptom management and high quality of life. Methods: To decipher the heterogeneous presentation of neurologic symptoms in GBM, we investigated differences in the molecular makeup of tumors from patients with and without preoperative neurologic deficits. We used the Ivy GAP (Ivy Glioblastoma Atlas Project) database to integrate RNA sequencing data from histologically defined GBM tumor compartments and neurologic examination records for 41 patients. We investigated the association of neurologic deficits with various tumor and patient attributes. We then performed differential gene expression and co-expression network analysis to identify a transcriptional signature specific to neurologic deficits in GBM. Using functional enrichment analysis, we finally provided a comprehensive and detailed characterization of involved pathways and gene interactions. Results: An exploratory investigation of the association of tumor and patient variables with the early development of neurologic deficits in GBM revealed a lack of robust and consistent clinicopathologic prognostic factors. We detected significant differences in the expression of 728 genes (FDR-adjusted p-value ≤ 0.05 and relative fold-change ≥ 1.5), unique to the cellular tumor (CT) anatomical compartment, between neurologic deficit groups. Upregulated differentially expressed genes in CT were enriched for mesenchymal subtype-predictive genes. Applying a systems approach, we then identified co-expressed gene sets that correlated with neurological deficit manifestation (FDR-adjusted p-value < 0.1). Collectively, these findings uncovered significantly enriched immune activation, oxidative stress response, and cytokine-mediated proinflammatory processes. Conclusion: Our study posits that inflammatory processes, as well as a mesenchymal tumor subtype, are implicated in the pathophysiology of preoperative neurologic deficits in GBM.

Downregulation of PCDH9 predicts prognosis for patients with glioma

Recent evidence has indicated that biological markers are essential in estimating the prognosis of patients with gliomas. The aim of this study was to determine the status and clinical significance of a novel tumor suppressor, PCDH9 (protocadherin 9) in glioma using tissue microarrays and immunohistochemistry. Normal brain tissue showed strong positive immunostaining for PCDH9, but this was downregulated in the primary cerebral glial tumor samples (51.7%). Loss of PCDH9 expression was associated significantly with a higher histological grade. Survival analysis demonstrated that patients with PCDH9-negative tumors had significantly shorter survival times than those with PCDH9-positive tumors and that PCDH9 was an independent prognostic factor. Our results suggest that PCDH9 might function as a tumor suppressor during cancer development and progression and could be regarded as a useful biomarker for predicting the outcome of patients with cerebral glial tumors.

Support vector machines in DSC-based glioma imaging: suggestions for optimal characterization

Dynamic susceptibility contrast magnetic resonance perfusion imaging (DSC-MRI) is a useful method to characterize gliomas. Recently, support vector machines (SVMs) have been introduced as means to prospectively characterize new patients based on information from previous patients. Based on features derived from automatically segmented tumor volumes from 101 DSC-MR examinations, four different SVM models were compared. All SVM models achieved high prediction accuracies (>82%) after rebalancing the training data sets to equal amounts of samples per class. Best discrimination was obtained using a SVM model with a radial basis function kernel. A correct prediction of low-grade glioma was obtained at 83% (true positive rate) and for high-grade glioma at 91% (true negative rate) on the independent test data set. In conclusion, the combination of automated tumor segmentation followed by SVM classification is feasible. Thereby, a powerful tool is available to characterize glioma presurgically in patients.