The ability of diffusion tensor MRI to detect the preferential diffusion of water in cerebral white matter tracts enables neurosurgeons to noninvasively visualize the relationship of lesions to ...functional neural pathways. Although viewed as a research tool in its infancy, diffusion tractography has evolved into a neurosurgical tool with applications in glioma surgery that are enhanced by evolutions in crossing fiber visualization, edema correction, and automated tract identification. In this paper the current literature supporting the use of tractography in brain tumor surgery is summarized, highlighting important clinical studies on the application of diffusion tensor imaging (DTI) for preoperative planning of glioma resection, and risk assessment to analyze postoperative outcomes. The key methods of tractography in current practice and crucial white matter fiber bundles are summarized. After a review of the physical basis of DTI and post-DTI tractography, the authors discuss the methodologies with which to adapt DT image processing for surgical planning, as well as the potential of connectomic imaging to facilitate a network approach to oncofunctional optimization in glioma surgery.
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.
Immunologically-cold tumors including glioblastoma (GBM) are refractory to checkpoint blockade therapy, largely due to extensive infiltration of immunosuppressive macrophages (Mϕs). Consistent with a ...pro-tumor role of IL-6 in alternative Mϕs polarization, we here show that targeting IL-6 by genetic ablation or pharmacological inhibition moderately improves T-cell infiltration into GBM and enhances mouse survival; however, IL-6 inhibition does not synergize PD-1 and CTLA-4 checkpoint blockade. Interestingly, anti-IL-6 therapy reduces CD40 expression in GBM-associated Mϕs. We identify a Stat3/HIF-1α-mediated axis, through which IL-6 executes an anti-tumor role to induce CD40 expression in Mϕs. Combination of IL-6 inhibition with CD40 stimulation reverses Mϕ-mediated tumor immunosuppression, sensitizes tumors to checkpoint blockade, and extends animal survival in two syngeneic GBM models, particularly inducing complete regression of GL261 tumors after checkpoint blockade. Thus, antibody cocktail-based immunotherapy that combines checkpoint blockade with dual-targeting of IL-6 and CD40 may offer exciting opportunities for GBM and other solid tumors.
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.
Tumor types are classically distinguished based on biopsies of the tumor itself, as well as a radiological interpretation using diverse MRI modalities. In the current study, the overarching goal is ...to demonstrate that primary (glioblastomas) and secondary (brain metastases) malignancies can be differentiated based on the microstructure of the peritumoral region. This is achieved by exploiting the extracellular water differences between vasogenic edema and infiltrative tissue and training a convolutional neural network (CNN) on the Diffusion Tensor Imaging (DTI)-derived free water volume fraction. We obtained 85% accuracy in discriminating extracellular water differences between local patches in the peritumoral area of 66 glioblastomas and 40 metastatic patients in a cross-validation setting. On an independent test cohort consisting of 20 glioblastomas and 10 metastases, we got 93% accuracy in discriminating metastases from glioblastomas using majority voting on patches. This level of accuracy surpasses CNNs trained on other conventional DTI-based measures such as fractional anisotropy (FA) and mean diffusivity (MD), that have been used in other studies. Additionally, the CNN captures the peritumoral heterogeneity better than conventional texture features, including Gabor and radiomic features. Our results demonstrate that the extracellular water content of the peritumoral tissue, as captured by the free water volume fraction, is best able to characterize the differences between infiltrative and vasogenic peritumoral regions, paving the way for its use in classifying and benchmarking peritumoral tissue with varying degrees of infiltration.
In malignant primary brain tumors, cancer cells infiltrate into the peritumoral brain structures which results in inevitable recurrence. Quantitative assessment of infiltrative heterogeneity in the ...peritumoral region, the area where biopsy or resection can be hazardous, is important for clinical decision making. Here, we derive a novel set of Artificial intelligence (AI)-based markers capturing the heterogeneity of tumor infiltration, by characterizing free water movement restriction in the peritumoral region using Diffusion Tensor Imaging (DTI)-based free water volume fraction maps. We leverage the differences in the peritumoral region of metastasis and glioblastomas, the former consisting of vasogenic versus the latter containing infiltrative edema, to extract a voxel-wise deep learning-based peritumoral microenvironment index (PMI). Descriptive characteristics of locoregional hubs of uniformly high PMI values are then extracted as AI-based markers to capture distinct aspects of infiltrative heterogeneity. The proposed markers are utilized to stratify patients' survival and IDH1 mutation status on a population of 275 adult-type diffuse gliomas (CNS WHO grade 4). Our results show significant differences in the proposed markers between patients with different overall survival and IDH1 mutation status (t test, Wilcoxon rank sum test, linear regression; p < 0.01). Clustering of patients using the proposed markers reveals distinct survival groups (logrank; p < 10
, Cox hazard ratio = 1.82; p < 0.005). Our findings provide a panel of markers as surrogates of infiltration that might capture novel insight about underlying biology of peritumoral microstructural heterogeneity, providing potential biomarkers of prognosis pertaining to survival and molecular stratification, with applicability in clinical decision making.
Characterization of healthy versus pathological tissue in the peritumoral area is confounded by the presence of edema, making free water estimation the key concern in modeling tissue microstructure. ...Most methods that model tissue microstructure are either based on advanced acquisition schemes not readily available in the clinic or are not designed to address the challenge of edema. This underscores the need for a robust free water elimination (FWE) method that estimates free water in pathological tissue but can be used with clinically prevalent single-shell diffusion tensor imaging data. FWE in single-shell data requires the fitting of a bi-compartment model, which is an ill-posed problem. Its solution requires optimization, which relies on an initialization step. We propose a novel initialization approach for FWE, FERNET, which improves the estimation of free water in edematous and infiltrated peritumoral regions, using single-shell diffusion MRI data. The method has been extensively investigated on simulated data and healthy dataset. Additionally, it has been applied to clinically acquired data from brain tumor patients to characterize the peritumoral region and improve tractography in it.
Brain tumors are inherently difficult to treat in large part due to the cellular blood-brain barriers (BBBs) that limit the delivery of therapeutics to the tumor tissue from the systemic circulation. ...Virtually no large molecules, including antibody-based proteins, can penetrate the BBB. With antibodies fast becoming attractive ligands for highly specific molecular targeting to tumor antigens, a variety of methods are being investigated to enhance the access of these agents to intracranial tumors for imaging or therapeutic applications.
This review describes the characteristics of the BBB and the vasculature in brain tumors, described as the blood-brain tumor barrier (BBTB). Antibodies targeted to molecular markers of central nervous system (CNS) tumors will be highlighted, and current strategies for enhancing the delivery of antibodies across these cellular barriers into the brain parenchyma to the tumor will be discussed. Noninvasive imaging approaches to assess BBB/BBTB permeability and/or antibody targeting will be presented as a means of guiding the optimal delivery of targeted agents to brain tumors.
Preclinical and clinical studies highlight the potential of several approaches in increasing brain tumor delivery across the BBB divide. However, each carries its own risks and challenges. There is tremendous potential in using neuroimaging strategies to assist in understanding and defining the challenges to translating and optimizing molecularly targeted antibody delivery to CNS tumors to improve clinical outcomes.
Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the ...additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS < 6 months) vs all, and low-risk (OS ≥ 18 months) vs all. The trained radiomic model was independently tested in the replication cohort (n = 112) and a patient-wise survival prediction index was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed area under the ROC curves (AUCs) of 0.78 (95% CI 0.70-0.85)/0.75 (95% CI 0.64-0.79) and 0.75 (95% CI 0.65-0.84)/0.63 (95% CI 0.52-0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95% CI 0.6-0.7) for clinical data improving to 0.75 (95% CI 0.72-0.79) for the combination of all omics. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM.
Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public ...datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.
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