The genesis of peritumoral vasogenic brain edema and tumor cysts: a hypothetical role for tumor-derived vascular permeability factor.

Cerebral edema and fluid-filled cysts are common accompaniments of brain tumors. They contribute to the mass effect imposed by the primary tumor and are often responsible for a patient''s signs and symptoms. Cerebral edema significantly increases the morbidity associated with tumor biopsy, excision, radiation therapy, and chemotherapy. Both edema and cyst formation are thought to result from a deficiency in the blood-brain barrier, with consequent extravasation of water, electrolytes, and plasma proteins from altered tumor microvessels. The resultant expansion of the cerebral interstitial space contributes to the elevated intracranial pressure observed with brain tumors. Departure from the typical blood-brain barrier microvascular architecture may only partially explain the occurrence of edema and tumor cyst formation. Biochemical mediators have also been implicated in vascular extravasation. Vascular permeability factor or vascular endothelial growth factor (VPF/VEGF) is a protein that has recently been isolated from a variety of tumors including human brain tumors. VPFb is an extraordinarily potent inducer of both microvascular extravasation (edemagenesis) and the formation of new blood vessels (angiogenesis). Its role in tumor growth and progression would therefore appear pivotal. Herein, the author presents an updated account of the investigation of VPF. Historical and clinical perspectives of the study and treatment of tumor associated edema are provided. The efficacy of high-dose dexamethasone in the treatment of neoplastic brain edema is discussed. A hypothetical role for VPF in edemagenesis is presented and discussed. It is hoped that an expanded understanding of the mechanisms responsible for the genesis of edema will ultimately facilitate therapeutic intervention.     

兔脑种植VX2肿瘤动物模型的建立

目的建立兔VX2肿瘤脑内种植动物模型,观察其生长特性。方法采用兔脑内VX2肿瘤组织块种植法将VX2肿瘤组织块种植入24只成年New Zealand大白兔右侧大脑皮质内,用B超检测肿瘤的生长情况,在实验兔在肿瘤种植后第13、171、9、21、232、5天取材,进行组织学观察。并观察实验兔在种植VX2肿瘤后的生存期及出现厌食、偏瘫等神经系统体征和死亡的时间。结果VX2肿瘤组织块种植入脑内后荷瘤兔的中位生存期为24.5 d,平均生存期为24.8 d,肿瘤体积随种植后的时间在对数坐标中接近一条直线。VX2肿瘤种植入兔脑17 d后血供较丰富、呈鱼肉状生长,与正常脑组织边缘界限不清楚,第17~19天肿瘤中心出现坏死并出现腹腔内转移。光镜下从VX2肿瘤种植后第17天开始肿瘤细胞向正常脑组织浸润,并形成瘤巢,瘤周脑组织形成水肿带。结论在缺乏兔源性脑肿瘤的情况下,采用兔VX2肿瘤组织块颅内种植能较好模拟颅内肿瘤生长,为对脑肿瘤的某些实验研究提供条件。     

颅内压动力学仿真

颅内压(Intracranial Pressure,ICP)研究在临床上有十分重要的意义。生理上由于内外多种原因会引起颅内压变化,而同时心搏、呼吸、以及神经调节等的影响也会使颅内压出现波动。本研究在动物(犬)实验结果的基础上,建立了反映颅内压变化的集中参数数学模型。模型中包括了脑血管床、脑脊液的生成和吸收、颅脑顺应性等模块以及这些模块之间的相互作用。脑血管顺应性是表征脑血管弹性程度的重要参量,仿真采用指数拟合的脑血管容积压力实验关系来表示脑血管顺应性。模型较好地模拟了颅内压动力学以及颅内压的波动,模型参数的变化和动物实验状况的变化相吻合,可以为临床颅内压监护及诊疗提供一定的参考。     

Extracellular Vesicles in Glioma: From Diagnosis to Therapy

Increasing evidence indicates that extracellular vesicles (EVs) secreted from tumor cells play a key role in the overall progression of the disease state. EVs such as exosomes are secreted by a wide variety of cells and transport a varied population of proteins, lipids, DNA, and RNA species within the body. Gliomas constitute a significant proportion of all primary brain tumors and majority of brain malignancies. Glioblastoma multiforme (GBM) represents grade IV glioma and is associated with very poor prognosis despite the cumulative advances in diagnostic procedures and treatment strategies. Here, the authors describe the progress in understanding the role of EVs, especially exosomes, in overall glioma progression, and how new research is unraveling the utilities of exosomes in glioma diagnostics and development of next‐generation therapeutic systems. Finally, based on an understanding of the latest scientific literature, a model for the possible working of therapeutic exosomes in glioma treatment is proposed.     

Delineating the longitudinal tumor evolution using organoid models

Cancer is an evolutionary process fueled by genetic or epigenetic alterations in the genome. Understanding the evolutionary dynamics that are operative at different stages of tumor progression might inform effective strategies in early detection, diagnosis, and treatment of cancer. However, our understanding on the dynamics of tumor evolution through time is very limited since it is usually impossible to sample patient tumors repeatedly. The recent advances in in vitro 3D organoid culture technologies have opened new avenues for the development of more realistic human cancer models that mimic many in vivo biological characteristics in human tumors. Here, we review recent progresses and challenges in cancer genomic evolution studies and advantages of using tumor organoids to study cancer evolution. We propose to establish an experimental evolution model based on continuous passages of patient-derived organoids and longitudinal sampling to study clonal dynamics and evolutionary patterns over time. Development and integration of population genetic theories and computational models into time-course genomic data in tumor organoids will help to pinpoint the key cellular mechanisms underlying cancer evolutionary dynamics, thus providing novel insights on therapeutic strategies for highly dynamic and heterogeneous tumors.     

Prevention of ammonia-induced brain edema in dogs infused with a branched-chain amino acid solution

Infusion of an ammonium acetate solution into dogs during mannitol-induced reversible opening of the blood-brain barrier resulted in a marked rise in intracranial pressure (ICP). The preventive effect of a branched-chain amino acid (BCAA) solution on ammonia-induced brain edema was tested by measuring ICP and brain water content. The BCAA solution apparently prevented ammonia-induced brain edema, indicating that BCAA accelerated ammonia detoxification in the brain.     

A mathematical model of intracranial pressure dynamics for brain hypothermia treatment

Brain hypothermia treatment is used as a neuroprotectant to decompress the elevated intracranial pressure (ICP) in acute neuropatients. However, a quantitative relationship between decompression and brain hypothermia is still unclear, this makes medical treatment difficult and ineffective. The objective of this paper is to develop a general mathematical model integrating hemodynamics and biothermal dynamics to enable a quantitative prediction of transient responses of elevated ICP to ambient cooling temperature. The model consists of a lumped-parameter compartmental representation of the body, and is based on two mechanisms of temperature dependence encountered in hypothermia, i.e. the van't Hoff's effect of metabolism and the Arrhenius' effect of capillary filtration. Model parameters are taken from the literature. The model is verified by comparing the simulation results to population-averaged data and clinical evidence of brain hypothermia treatment. It is possible to assign special model inputs to mimic clinical maneuvers, and to adjust model parameters to simulate pathophysiological states of intracranial hypertension. Characteristics of elevated ICP are quantitatively estimated by using linear approximation of step response with respect to ambient cooling temperature. Gain of about 4.9 mmHg degrees C(-1), dead time of about 1.0 h and a time constant of about 9.8h are estimated for the hypothermic decompression. Based on the estimated characteristics, a feedback control of elevated ICP is introduced in a simulated intracranial hypertension of vasogenic brain edema. Simulation results suggest the possibility of an automatic control of the elevated ICP in brain hypothermia treatment.     

Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition

Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI.     

Deciphering intratumor heterogeneity and temporal acquisition of driver events to refine precision medicine

The presence of multiple subclones within tumors mandates understanding of longitudinal and spatial subclonal dynamics. Resolving the spatial and temporal heterogeneity of subclones with cancer driver events may offer insight into therapy response, tumor evolutionary histories and clinical trial design.     

Mechanical role of a growing solid tumor on cortical folding

Cortical folding, or convolution of the brain, is a vital process in mammals that causes the brain to have a wrinkled appearance. The existence of different types of prenatal solid tumors may alter this complex phenomenon and cause severe brain disorders. Here we interpret the effects of a growing solid tumor on the cortical folding in the fetal brain by virtue of theoretical analyses and computational modeling. The developing fetal brain is modeled as a simple, double-layered, and soft structure with an outer cortex and an inner core, in combination with a circular tumor model imbedded in the structure to investigate the developmental mechanism of cortical convolution. Analytical approaches offer introductory insight into the deformation field and stress distribution of a developing brain. After the onset of instability, analytical approaches fail to capture complex secondary evolution patterns, therefore a series of non-linear finite element simulations are carried out to study the crease formation and the influence from a growing solid tumor inside the structure. Parametric studies show the dependency of the cortical folding pattern on the size, location, and growth speed of a solid tumor in fetal brain. It is noteworthy to mention that there is a critical distance from the cortex/core interface where the growing tumor shows its pronounced effect on the cortical convolution, and that a growing tumor decreases the gyrification index of cortical convolution while its stiffness does not have a profound effect on the gyrification process.     

Cerebral inflammation contributes to encephalopathy and brain edema in acute liver failure: protective effect of minocycline

Encephalopathy and brain edema are serious complications of acute liver failure (ALF). The precise pathophysiologic mechanisms responsible have not been fully elucidated but it has been recently proposed that microglia‐derived proinflammatory cytokines are involved. In the present study we evaluated the role of microglial activation and the protective effect of the anti‐inflammatory drug minocycline in the pathogenesis of hepatic encephalopathy and brain edema in rats with ALF resulting from hepatic devascularisation. ALF rats were killed 6 h after hepatic artery ligation before the onset of neurological symptoms and at coma stages of encephalopathy along with their appropriate sham‐operated controls and in parallel with minocycline‐treated ALF rats. Increased OX‐42 and OX‐6 immunoreactivities confirming microglial activation were accompanied by increased expression of interleukins (IL‐1β, IL‐6) and tumor necrosis factor‐alpha (TNF‐α) in the frontal cortex at coma stage of encephalopathy in ALF rats compared with sham‐operated controls. Minocycline treatment prevented both microglial activation as well as the up‐regulation of IL‐1β, ΙL‐6 and TNF‐α mRNA and protein expression with a concomitant attenuation of the progression of encephalopathy and brain edema. These results offer the first direct evidence for central proinflammatory mechanisms in the pathogenesis of brain edema and its complications in ALF and suggest that anti‐inflammatory agents may be beneficial in these patients.     

A Two-Stage Model for In Vivo Assessment of Brain Tumor Perfusion and Abnormal Vascular Structure Using Arterial Spin Labeling

The ability to assess brain tumor perfusion and abnormalities in the vascular structure in vivo could provide significant benefits in terms of lesion diagnosis and assessment of treatment response. Arterial spin labeling (ASL) has emerged as an increasingly viable methodology for non-invasive assessment of perfusion. Although kinetic models have been developed to describe perfusion in healthy tissue, the dynamic behaviour of the ASL signal in the brain tumor environment has not been extensively studied. We show here that dynamic ASL data acquired in brain tumors displays an increased level of ‘biphasic’ behaviour, compared to that seen in healthy tissue. A new two-stage model is presented which more accurately describes this behaviour, and provides measurements of perfusion, pre-capillary blood volume fraction and transit time, and capillary bolus arrival time. These biomarkers offer a novel contrast in the tumor and surrounding tissue, and provide a means for measuring tumor perfusion and vascular structural abnormalities in a fully non-invasive manner.     

Nuclear magnetic resonance in the investigation of cerebral tumors and cerebral edema: a clue to the cellular alterations that may affect the distribution of water

The understanding of tumor-associated cerebral edema involves an elucidation of the mechanisms involved in the altered distribution of water in the vicinity of cells. Changes in cellular macromolecules such as intracellular proteins, extracellular matrix components, and cell-membrane proteins may alter the water interactions in and around cells. The technique of pulsed nuclear magnetic resonance (NMR) gives a measure of the relaxation properties of protons in water molecules in such systems. The T1 and T2 relaxation times are increased in cerebral tumors and peritumoral tissue compared with normal brain. The in vitro study of cerebral tumors requires a tumor model that possesses the properties of the actual tumor under study. The C6 astrocytoma cell line has many of the properties of glioblastoma multiforme. An NMR study of C6 astrocytoma cells grown in monolayer, as spheroids of varying sizes and when implanted into rat hosts, has been undertaken. Results show that T1 and T2 relaxation times are not a static feature of the tumor cells but may reflect changing microenvironments that result from the contribution of a number of interacting factors present in the growing tumor.     

Histological examination on edema formation in the rabbit brain exposed to head-down tilt

Previous studies demonstrated that exposure to simulated microgravity, head-down tilt (HDT), caused cephalad fluid shift, increased capillary pressure in the head, and produced facial edema and nasal congestion. It is also known that exposure to HDT affects hemodynamics in the brain. Cerebral blood flow (CBF) velocity increases for at least 6 hours after the onset of 6 degrees HDT in humans. Intracranial pressure (ICP) elevates during 6 degrees HDT in humans and monkeys. However, there is little information regarding edema formation in the brain due to HDT except a morphological study reported by Kaplansky and colleagues who showed that perivascular edema occurred in the monkey brain after 7 days of 6 degrees HDT. Thus, it is interesting to examine whether edema formation occurs in the other animal model for simulation of microgravity, since several factors such as the duration of HDT, angle of HDT, and species difference may affect the result. In the present study, formation of brain edema was investigated by histological examinations in rabbits exposed to 45 degrees HDT for 2 days or 8 days. We hypothesized that HDT causes brain edema which can be demonstrated as extravasation of plasma constituents and histological changes.     

Can We Negotiate with a Tumor?

Recent progress in deciphering the molecular portraits of tumors promises an era of more personalized drug choices. However, current protocols still follow standard fixed-time schedules, which is not entirely coherent with the common observation that most tumors do not grow continuously. This unpredictability of the increases in tumor mass is not necessarily an obstacle to therapeutic efficiency, particularly if tumor dynamics could be exploited. We propose a model of tumor mass evolution as the integrated result of the dynamics of two linked complex systems, tumor cell population and tumor microenvironment, and show the practical relevance of this nonlinear approach.     

Emergent behaviors from a cellular automaton model for invasive tumor growth in heterogeneous microenvironments

Understanding tumor invasion and metastasis is of crucial importance for both fundamental cancer research and clinical practice. In vitro experiments have established that the invasive growth of malignant tumors is characterized by the dendritic invasive branches composed of chains of tumor cells emanating from the primary tumor mass. The preponderance of previous tumor simulations focused on non-invasive (or proliferative) growth. The formation of the invasive cell chains and their interactions with the primary tumor mass and host microenvironment are not well understood. Here, we present a novel cellular automaton (CA) model that enables one to efficiently simulate invasive tumor growth in a heterogeneous host microenvironment. By taking into account a variety of microscopic-scale tumor-host interactions, including the short-range mechanical interactions between tumor cells and tumor stroma, degradation of the extracellular matrix by the invasive cells and oxygen/nutrient gradient driven cell motions, our CA model predicts a rich spectrum of growth dynamics and emergent behaviors of invasive tumors. Besides robustly reproducing the salient features of dendritic invasive growth, such as least-resistance paths of cells and intrabranch homotype attraction, we also predict nontrivial coupling between the growth dynamics of the primary tumor mass and the invasive cells. In addition, we show that the properties of the host microenvironment can significantly affect tumor morphology and growth dynamics, emphasizing the importance of understanding the tumor-host interaction. The capability of our CA model suggests that sophisticated in silico tools could eventually be utilized in clinical situations to predict neoplastic progression and propose individualized optimal treatment strategies.     

Diffusion Tensor Imaging in Patients with Glioblastoma Multiforme Using the Supertoroidal Model

Purpose

Diffusion Tensor Imaging (DTI) is a powerful imaging technique that has led to improvements in the diagnosis and prognosis of cerebral lesions and neurosurgical guidance for tumor resection. Traditional tensor modeling, however, has difficulties in differentiating tumor-infiltrated regions and peritumoral edema. Here, we describe the supertoroidal model, which incorporates an increase in surface genus and a continuum of toroidal shapes to improve upon the characterization of Glioblastoma multiforme (GBM).

Materials and Methods

DTI brain datasets of 18 individuals with GBM and 18 normal subjects were acquired using a 3T scanner. A supertoroidal model of the diffusion tensor and two new diffusion tensor invariants, one to evaluate diffusivity, the toroidal volume (TV), and one to evaluate anisotropy, the toroidal curvature (TC), were applied and evaluated in the characterization of GBM brain tumors. TV and TC were compared with the mean diffusivity (MD) and fractional anisotropy (FA) indices inside the tumor, surrounding edema, as well as contralateral to the lesions, in the white matter (WM) and gray matter (GM).

Results

The supertoroidal model enhanced the borders between tumors and surrounding structures, refined the boundaries between WM and GM, and revealed the heterogeneity inherent to tumor-infiltrated tissue. Both MD and TV demonstrated high intensities in the tumor, with lower values in the surrounding edema, which in turn were higher than those of unaffected brain parenchyma. Both TC and FA were effective in revealing the structural degradation of WM tracts.

Conclusions

Our findings indicate that the supertoroidal model enables effective tensor visualization as well as quantitative scalar maps that improve the understanding of the underlying tissue structure properties. Hence, this approach has the potential to enhance diagnosis, preoperative planning, and intraoperative image guidance during surgical management of brain lesions.     

Mathematical modeling of human glioma growth based on brain topological structures: study of two clinical cases

Gliomas are the most common primary brain tumors and yet almost incurable due mainly to their great invasion capability. This represents a challenge to present clinical oncology. Here, we introduce a mathematical model aiming to improve tumor spreading capability definition. The model consists in a time dependent reaction-diffusion equation in a three-dimensional spatial domain that distinguishes between different brain topological structures. The model uses a series of digitized images from brain slices covering the whole human brain. The Talairach atlas included in the model describes brain structures at different levels. Also, the inclusion of the Brodmann areas allows prediction of the brain functions affected during tumor evolution and the estimation of correlated symptoms. The model is solved numerically using patient-specific parametrization and finite differences. Simulations consider an initial state with cellular proliferation alone (benign tumor), and an advanced state when infiltration starts (malign tumor). Survival time is estimated on the basis of tumor size and location. The model is used to predict tumor evolution in two clinical cases. In the first case, predictions show that real infiltrative areas are underestimated by current diagnostic imaging. In the second case, tumor spreading predictions were shown to be more accurate than those derived from previous models in the literature. Our results suggest that the inclusion of differential migration in glioma growth models constitutes another step towards a better prediction of tumor infiltration at the moment of surgical or radiosurgical target definition. Also, the addition of physiological/psychological considerations to classical anatomical models will provide a better and integral understanding of the patient disease at the moment of deciding therapeutic options, taking into account not only survival but also life quality.     

Simulating PDGF-Driven Glioma Growth and Invasion in an Anatomically Accurate Brain Domain

Gliomas are the most common of all primary brain tumors. They are characterized by their diffuse infiltration of the brain tissue and are uniformly fatal, with glioblastoma being the most aggressive form of the disease. In recent years, the over-expression of platelet-derived growth factor (PDGF) has been shown to produce tumors in experimental rodent models that closely resemble this human disease, specifically the proneural subtype of glioblastoma. We have previously modeled this system, focusing on the key attribute of these experimental tumors—the “recruitment” of oligodendroglial progenitor cells (OPCs) to participate in tumor formation by PDGF-expressing retrovirally transduced cells—in one dimension, with spherical symmetry. However, it has been observed that these recruitable progenitor cells are not uniformly distributed throughout the brain and that tumor cells migrate at different rates depending on the material properties in different regions of the brain. Here we model the differential diffusion of PDGF-expressing and recruited cell populations via a system of partial differential equations with spatially variable diffusion coefficients and solve the equations in two spatial dimensions on a mouse brain atlas using a flux-differencing numerical approach. Simulations of our in silico model demonstrate qualitative agreement with the observed tumor distribution in the experimental animal system. Additionally, we show that while there are higher concentrations of OPCs in white matter, the level of recruitment of these plays little role in the appearance of “white matter disease,” where the tumor shows a preponderance for white matter. Instead, simulations show that this is largely driven by the ratio of the diffusion rate in white matter as compared to gray. However, this ratio has less effect on the speed of tumor growth than does the degree of OPC recruitment in the tumor. It was observed that tumor simulations with greater degrees of recruitment grow faster and develop more nodular tumors than if there is no recruitment at all, similar to our prior results from implementing our model in one dimension. Combined, these results show that recruitment remains an important consideration in understanding and slowing glioma growth.