Correlated MRI and Ultramicroscopy (MR-UM) of Brain Tumors Reveals Vast Heterogeneity of Tumor Infiltration and Neoangiogenesis in Preclinical Models and Human Disease

Assessment of Tumor Cell Invasion and Radiotherapy Response in Experimental Glioma by Magnetic Resonance Elastography

BACKGROUND

Gliomas are highly invasive brain neoplasms. MRI is the most important tool to diagnose and monitor glioma but has shortcomings. In particular, the assessment of tumor cell invasion is insufficient. This is a clinical dilemma, as recurrence can arise from MRI-occult glioma cell invasion.

HYPOTHESIS

Tumor cell invasion, tumor growth and radiotherapy alter the brain parenchymal microstructure and thus are assessable by diffusion tensor imaging (DTI) and MR elastography (MRE).

STUDY TYPE

Experimental, animal model.

ANIMAL MODEL

Twenty-three male NMRI nude mice orthotopically implanted with S24 patient-derived glioma cells (experimental mice) and 9 NMRI nude mice stereotactically injected with 1 μL PBS (sham-injected mice).

FIELD STRENGTH/SEQUENCE

2D and 3D T2-weighted rapid acquisition with refocused echoes (RARE), 2D echo planar imaging (EPI) DTI, 2D multi-slice multi-echo (MSME) T2 relaxometry, 3D MSME MRE at 900 Hz acquired at 9.4 T (675 mT/m gradient strength).

ASSESSMENT

Longitudinal 4-weekly imaging was performed for up to 4 months. Tumor volume was assessed in experimental mice (n = 10 treatment-control, n = 13 radiotherapy). The radiotherapy subgroup and 5 sham-injected mice underwent irradiation (3 × 6 Gy) 9 weeks post-implantation/sham injection. MRI-/MRE-parameters were assessed in the corpus callosum and tumor core/injection tract. Imaging data were correlated to light sheet microscopy (LSM) and histology.

STATISTICAL TESTS

Paired and unpaired t-tests, a P-value ≤0.05 was considered significant.

RESULTS

From week 4 to 8, a significant callosal stiffening (4.44 ± 0.22 vs. 5.31 ± 0.29 kPa) was detected correlating with LSM-proven tumor cell invasion. This was occult to all other imaging metrics. Histologically proven tissue destruction in the tumor core led to an increased T2 relaxation time (41.65 ± 0.34 vs. 44.83 ± 0.66 msec) and ADC (610.2 ± 12.27 vs. 711.2 ± 13.42 × 10-6 mm2/s) and a softening (5.51 ± 0.30 vs. 4.24 ± 0.29 kPa) from week 8 to 12. Radiotherapy slowed tumor progression.

DATA CONCLUSION

MRE is promising for the assessment of key glioma characteristics.

EVIDENCE LEVEL

NA TECHNICAL EFFICACY: Stage 2.

Models for evaluating glioblastoma invasion along white matter tracts

White matter tracts (WMs) are one of the main invasion paths of glioblastoma multiforme (GBM). The lack of ideal research models hinders our understanding of the details and mechanisms of GBM invasion along WMs. To date, many potential in vitro models have been reported; nerve fiber culture models and nanomaterial models are biocompatible, and the former have electrically active neurons. Brain slice culture models, organoid models, and microfluidic chip models can simulate the real brain and tumor microenvironment (TME), which contains a variety of cell types. These models are closer to the real in vivo environment and are helpful for further studying not only invasion along WMs by GBM, but also perineural invasion and brain metastasis by solid tumors.

A Cellular Ground Truth to Develop MRI Signatures in Glioma Models by Correlative Light Sheet Microscopy and Atlas-Based Coregistration

Glioblastoma is the most common malignant primary brain tumor with poor overall survival. Magnetic resonance imaging (MRI) is the main imaging modality for glioblastoma but has inherent shortcomings. The molecular and cellular basis of MR signals is incompletely understood. We established a ground truth-based image analysis platform to coregister MRI and light sheet microscopy (LSM) data to each other and to an anatomic reference atlas for quantification of 20 predefined anatomic subregions. Our pipeline also includes a segmentation and quantification approach for single myeloid cells in entire LSM datasets. This method was applied to three preclinical glioma models in male and female mice (GL261, U87MG, and S24), which exhibit different key features of the human glioma. Multiparametric MR data including T2-weighted sequences, diffusion tensor imaging, T2 and T2* relaxometry were acquired. Following tissue clearing, LSM focused on the analysis of tumor cell density, microvasculature, and innate immune cell infiltration. Correlated analysis revealed differences in quantitative MRI metrics between the tumor-bearing and the contralateral hemisphere. LSM identified tumor subregions that differed in their MRI characteristics, indicating tumor heterogeneity. Interestingly, MRI signatures, defined as unique combinations of different MRI parameters, differed greatly between the models. The direct correlation of MRI and LSM allows an in-depth characterization of preclinical glioma and can be used to decipher the structural, cellular, and, likely, molecular basis of tumoral MRI biomarkers. Our approach may be applied in other preclinical brain tumor or neurologic disease models, and the derived MRI signatures could ultimately inform image interpretation in a clinical setting.SIGNIFICANCE STATEMENT We established a histologic ground truth-based approach for MR image analyses and tested this method in three preclinical glioma models exhibiting different features of glioblastoma. Coregistration of light sheet microscopy to MRI allowed for an evaluation of quantitative MRI data in histologically distinct tumor subregions. Coregistration to a mouse brain atlas enabled a regional comparison of MRI parameters with a histologically informed interpretation of the results. Our approach is transferable to other preclinical models of brain tumors and further neurologic disorders. The method can be used to decipher the structural, cellular, and molecular basis of MRI signal characteristics. Ultimately, information derived from such analyses could strengthen the neuroradiological evaluation of glioblastoma as they enhance the interpretation of MRI data.

Multimodal 3D Mouse Brain Atlas Framework with the Skull-Derived Coordinate System

Magnetic resonance imaging (MRI) and light-sheet fluorescence microscopy (LSFM) are technologies that enable non-disruptive 3-dimensional imaging of whole mouse brains. A combination of complementary information from both modalities is desirable for studying neuroscience in general, disease progression and drug efficacy. Although both technologies rely on atlas mapping for quantitative analyses, the translation of LSFM recorded data to MRI templates has been complicated by the morphological changes inflicted by tissue clearing and the enormous size of the raw data sets. Consequently, there is an unmet need for tools that will facilitate fast and accurate translation of LSFM recorded brains to in vivo, non-distorted templates. In this study, we have developed a bidirectional multimodal atlas framework that includes brain templates based on both imaging modalities, region delineations from the Allen's Common Coordinate Framework, and a skull-derived stereotaxic coordinate system. The framework also provides algorithms for bidirectional transformation of results obtained using either MR or LSFM (iDISCO cleared) mouse brain imaging while the coordinate system enables users to easily assign in vivo coordinates across the different brain templates.

Advanced Computational Methods to Evaluate Vascular Heterogeneity in Tumor Tissue Based on Single Plane Illumination Microscopy

During tumor growth, the complex composition of vasculature is prone to dynamic changes due to mechanic and biochemical challenges. Perivascular invasion of tumor cells to co-opt existing vasculature, but also formation of de-novo vasculature and other effects on the vascular network, may lead to altered geometric vessel properties as well as changes in vascular network topology, which is defined by vascular multifurcations and connections between vessel segments. The intricate organization and heterogeneity of the vascular network can be analyzed with advanced computational methods to uncover vascular network signatures that may allow differentiating between pathological and physiological vessel regions. Herein, we present a protocol to evaluate vascular heterogeneity in whole vascular networks, using morphological and topological measures. The protocol was developed for single plane illumination microscopy images of mice brain vasculature but can be applied to any vascular network.

Brain tumor classification of virtual NMR voxels based on realistic blood vessel-induced spin dephasing using support vector machines

Remodeling of tissue microvasculature commonly promotes neoplastic growth; however, there is no imaging modality in oncology yet that noninvasively quantifies microvascular changes in clinical routine. Although blood capillaries cannot be resolved in typical magnetic resonance imaging (MRI) measurements, their geometry and distribution influence the integral nuclear magnetic resonance (NMR) signal from each macroscopic MRI voxel. We have numerically simulated the expected transverse relaxation in NMR voxels with different dimensions based on the realistic microvasculature in healthy and tumor-bearing mouse brains (U87 and GL261 glioblastoma). The 3D capillary structure in entire, undissected brains was acquired using light sheet fluorescence microscopy to produce large datasets of the highly resolved cerebrovasculature. Using this data, we trained support vector machines to classify virtual NMR voxels with different dimensions based on the simulated spin dephasing accountable to field inhomogeneities caused by the underlying vasculature. In prediction tests with previously blinded virtual voxels from healthy brain tissue and GL261 tumors, stable classification accuracies above 95% were reached. Our results indicate that high classification accuracies can be stably attained with achievable training set sizes and that larger MRI voxels facilitated increasingly successful classifications, even with small training datasets. We were able to prove that, theoretically, the transverse relaxation process can be harnessed to learn endogenous contrasts for single voxel tissue type classifications on tailored MRI acquisitions. If translatable to experimental MRI, this may augment diagnostic imaging in oncology with automated voxel-by-voxel signal interpretation to detect vascular pathologies.

Large-scale characterization of the microvascular geometry in development and disease by tissue clearing and quantitative ultramicroscopy

Three-dimensional assessment of optically cleared, entire organs and organisms has recently become possible by tissue clearing and selective plane illumination microscopy ("ultramicroscopy"). Resulting datasets can be highly complex, encompass over a thousand images with millions of objects and data of several gigabytes per acquisition. This constitutes a major challenge for quantitative analysis. We have developed post-processing tools to quantify millions of microvessels and their distribution in three-dimensional datasets from ultramicroscopy and demonstrate the capabilities of our pipeline within entire mouse brains and embryos. Using our developed acquisition, segmentation, and analysis platform, we quantify physiological vascular networks in development and the healthy brain. We compare various geometric vessel parameters (e.g. vessel density, radius, tortuosity) in the embryonic spinal cord and brain as well as in different brain regions (basal ganglia, corpus callosum, cortex). White matter tract structures (corpus callosum, spinal cord) showed lower microvascular branch densities and longer vessel branch length compared to grey matter (cortex, basal ganglia). Furthermore, we assess tumor neoangiogenesis in a mouse glioma model to compare tumor core and tumor border. The developed methodology allows rapid quantification of three-dimensional datasets by semi-automated segmentation of fluorescently labeled objects with conventional computer hardware. Our approach can aid preclinical investigations and paves the way towards "quantitative ultramicroscopy".

The Management of Brain Metastases-Systematic Review of Neurosurgical Aspects

The multidisciplinary management of patients with brain metastases (BM) consists of surgical resection, different radiation treatment modalities, cytotoxic chemotherapy, and targeted molecular treatment. This review presents the current state of neurosurgical technology applied to achieve maximal resection with minimal morbidity as a treatment paradigm in patients with BM. In addition, we discuss the contribution of neurosurgical resection on functional outcome, advanced systemic treatment strategies, and enhanced understanding of the tumor biology.

Addressing BBB Heterogeneity: A New Paradigm for Drug Delivery to Brain Tumors

Effective treatments for brain tumors remain one of the most urgent and unmet needs in modern oncology. This is due not only to the presence of the neurovascular unit/blood-brain barrier (NVU/BBB) but also to the heterogeneity of barrier alteration in the case of brain tumors, which results in what is referred to as the blood-tumor barrier (BTB). Herein, we discuss this heterogeneity, how it contributes to the failure of novel pharmaceutical treatment strategies, and why a "whole brain" approach to the treatment of brain tumors might be beneficial. We discuss various methods by which these obstacles might be overcome and assess how these strategies are progressing in the clinic. We believe that by approaching brain tumor treatment from this perspective, a new paradigm for drug delivery to brain tumors might be established.

A practical method for multimodal registration and assessment of whole-brain disease burden using PET, MRI, and optical imaging

Many neurological diseases present with substantial genetic and phenotypic heterogeneity, making assessment of these diseases challenging. This has led to ineffective treatments, significant morbidity, and high mortality rates for patients with neurological diseases, including brain cancers and neurodegenerative disorders. Improved understanding of this heterogeneity is necessary if more effective treatments are to be developed. We describe a new method to measure phenotypic heterogeneity across the whole rodent brain at multiple spatial scales. The method involves co-registration and localized comparison of in vivo radiologic images (e.g. MRI, PET) with ex vivo optical reporter images (e.g. labeled cells, molecular targets, microvasculature) of optically cleared tissue slices. Ex vivo fluorescent images of optically cleared pathology slices are acquired with a preclinical in vivo optical imaging system across the entire rodent brain in under five minutes, making this methodology practical and feasible for most preclinical imaging labs. The methodology is applied in various examples demonstrating how it might be used to cross-validate and compare in vivo radiologic imaging with ex vivo optical imaging techniques for assessing hypoxia, microvasculature, and tumor growth.

Gibbs point field model quantifies disorder in microvasculature of U87-glioblastoma

Microvascular proliferation in glioblastoma multiforme is a biological key mechanism to facilitate tumor growth and infiltration and a main target for treatment interventions. The vascular architecture can be obtained by Single Plane Illumination Microscopy (SPIM) to evaluate vascular heterogeneity in tumorous tissue. We make use of the Gibbs point field model to quantify the order of regularity in capillary distributions found in the U87 glioblastoma model in a murine model and to compare tumorous and healthy brain tissue. A single model parameter Γ was assigned that is linked to tissue-specific vascular topology through Monte-Carlo simulations. Distributions of the model parameter Γ differ significantly between glioblastoma tissue with mean 〈Γ_G〉=2.1±0.4, as compared to healthy brain tissue with mean 〈Γ_H〉=4.9±0.4, suggesting that the average Γ-value allows for tissue differentiation. These results may be used for diagnostic magnetic resonance imaging, where it has been shown recently that Γ is linked to tissue-inherent relaxation parameters.

Quantitative T1 mapping indicates tumor infiltration beyond the enhancing part of glioblastomas

The aim of this study was to evaluate whether maps of quantitative T1 (qT1) differences induced by a gadolinium-based contrast agent (CA) are better suited than conventional T1-weighted (T1w) MR images for detecting infiltration inside and beyond the peritumoral edema of glioblastomas. Conventional T1w images and qT1 maps were obtained before and after gadolinium-based CA administration in 33 patients with glioblastoma before therapy. The following data were calculated: (i) absolute qT1-difference maps (qT1 pre-CA - qT1 post-CA), (ii) relative qT1-difference maps, (iii) absolute and (iv) relative differences of conventional T1w images acquired pre- and post-CA. The values of these four datasets were compared in four different regions: (a) the enhancing tumor, (b) the peritumoral edema, (c) a 5 mm zone around the pathology (defined as the sum of regions a and b), and (d) the contralateral normal appearing brain tissue. Additionally, absolute qT1-difference maps (displayed with linear gray scaling) were visually compared with respective conventional difference images. The enhancing tumor was visible both in the difference of conventional pre- and post-CA T1w images and in the absolute qT1-difference maps, whereas only the latter showed elevated values in the peritumoral edema and in some cases even beyond. Mean absolute qT1-difference values were significantly higher (P < 0.01) in the enhancing tumor (838 ± 210 ms), the peritumoral edema (123 ± 74 ms) and in the 5 mm zone around the pathology (81 ± 31 ms) than in normal appearing tissue (32 ± 35 ms). In summary, absolute qT1-difference maps-in contrast to the difference of T1w images-of untreated glioblastomas appear to be able to visualize CA leakage, and thus might indicate tumor cell infiltration in the edema region and beyond. Therefore, the absolute qT1-difference maps are potentially useful for treatment planning.

Glioma Cell Migration Dynamics in Brain Tissue Assessed by Multimodal Optical Imaging

Glioblastoma is a primary malignant brain tumor characterized by highly infiltrative glioma cells. Vasculature and white matter tracts are considered to be the preferred and fastest routes for glioma invasion through brain tissue. In this study, we systematically quantified the routes and motility of the U251 human glioblastoma cell line in mouse brain slices by multimodal imaging. Specifically, we used polarization-sensitive optical coherence tomography to delineate nerve fiber tracts while confocal fluorescence microscopy was used to image cell migration and brain vasculature. Somewhat surprisingly, we found that in mouse brain slices, U251 glioma cells do not follow white matter tracts but rather preferentially migrate along vasculature in both gray and white matter. In addition, U251 cell motility is ∼2-fold higher in gray matter than in white matter (91 vs. 43 μm2/h), with a substantial fraction (44%) of cells in both regions invading without close association with vasculature. Interestingly, within both regions, the rates of migration for the perivascular and televascular routes of invasion were indistinguishable. Furthermore, by imaging of local vasculature deformation dynamics during cell migration, we found that U251 cells are capable of exerting traction forces that locally pull on their environment, suggesting the applicability of a "motor-clutch"-based model for migration in vivo. Overall, by quantitatively analyzing the migration dynamics along the diverse pathways followed by invading U251 glioma cells as observed by our multimodal imaging approach, our studies suggest that effective antiinvasive strategies will need to simultaneously limit parallel routes of both perivascular and televascular invasion through both gray and white matter.

Glioblastoma multiforme restructures the topological connectivity of cerebrovascular networks

Glioblastoma multiforme alters healthy tissue vasculature by inducing angiogenesis and vascular remodeling. To fully comprehend the structural and functional properties of the resulting vascular network, it needs to be studied collectively by considering both geometric and topological properties. Utilizing Single Plane Illumination Microscopy (SPIM), the detailed capillary structure in entire healthy and tumor-bearing mouse brains could be resolved in three dimensions. At the scale of the smallest capillaries, the entire vascular systems of bulk U87- and GL261-glioblastoma xenografts, their respective cores, and healthy brain hemispheres were modeled as complex networks and quantified with fundamental topological measures. All individual vessel segments were further quantified geometrically and modular clusters were uncovered and characterized as meta-networks, facilitating an analysis of large-scale connectivity. An inclusive comparison of large tissue sections revealed that geometric properties of individual vessels were altered in glioblastoma in a relatively subtle way, with high intra- and inter-tumor heterogeneity, compared to the impact on the vessel connectivity. A network topology analysis revealed a clear decomposition of large modular structures and hierarchical network organization, while preserving most fundamental topological classifications, in both tumor models with distinct growth patterns. These results augment our understanding of cerebrovascular networks and offer a topological assessment of glioma-induced vascular remodeling. The findings may help understand the emergence of hypoxia and necrosis, and prove valuable for therapeutic interventions such as radiation or antiangiogenic therapy.