Effects of anti-angiogenesis on glioblastoma growth and migration: model to clinical predictions

Mathematical modeling of brain metastases growth and response to therapies: A review

Brain metastases (BMs) are the most common intracranial tumor type and a significant health concern, affecting approximately 10% to 30% of all oncological patients. Although significant progress is being made, many aspects of the metastatic process to the brain and the growth of the resulting lesions are still not well understood. There is a need for an improved understanding of the growth dynamics and the response to treatment of these tumors. Mathematical models have been proven valuable for drawing inferences and making predictions in different fields of cancer research, but few mathematical works have considered BMs. This comprehensive review aims to establish a unified platform and contribute to fostering emerging efforts dedicated to enhancing our mathematical understanding of this intricate and challenging disease. We focus on the progress made in the initial stages of mathematical modeling research regarding BMs and the significant insights gained from such studies. We also explore the vital role of mathematical modeling in predicting treatment outcomes and enhancing the quality of clinical decision-making for patients facing BMs.

Carbon-ion radiotherapy boost with standard dose proton radiation for incomplete-resected high-grade glioma: a phase 1 study

Background

To investigate the maximal tolerated dose (MTD) of a carbon-ion radiotherapy (CIRT) boost prior to standard dose proton radiotherapy (PRT) for newly diagnosed glioblastoma (GBM) and anaplastic astrocytoma (AA) patients with residual lesion after resection.

Methods

In total, 18 patients with high-grade glioma (HGG) (16 with GBM and 2 with AA) were enrolled in a prospective 3×3 design phase 1 trial. We investigated four dose-levels of CIRT boost [9 (starting level), 12, 15, and 18 Gy relative biological effectiveness (RBE)] delivered in three equal fractions prior to the standard dose PRT (60 Gy RBE in 30 fractions). Concurrent temozolomide (TMZ) was not provided during the CIRT boost but was initiated on the first day of PRT. Acute and late toxicities were scored based on the Common Terminology Criteria for Adverse Events (CTCAE, v 4.03). Dose-limiting toxicities (DLTs) were defined as radiation-induced severe toxicities (≥ grade 3).

Results

With a median follow-up of 17.9 months, no severe (≥ grade 3) acute or late toxicities were observed in patients treated with the first three dose levels (CIRT boost doses of 9, 12, 15 Gy RBE). Severe late toxicity (grade 3 radiation necrosis) was observed in the first patient treated with the 18 Gy RBE CIRT boost level. Therefore, this trial was terminated and the MTD of the induction CIRT boost was determined at 15 Gy RBE in 3 fractions. At the time of this analysis, both patients with AA were alive without disease progression. The progression-free survival (PFS) and overall survival (OS) for GBM at 12 months were 50.6% and 78.6%, respectively.

Conclusions

Particle beam radiotherapy consisting of a CIRT boost of 15 Gy RBE (in 3 fractions) following standard dose PRT (60 Gy RBE in 30 fractions), and used in conjunction with TMZ, is safe and potentially effective for patients with HGG.

Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging

OBJECTIVE

The aim of this study was to evaluate various radiomics-based machine learning classification models using the apparent diffusion coefficient (ADC) and cerebral blood flow (CBF) maps for differentiating between low-grade gliomas (LGGs) and high-grade gliomas (HGGs).

METHODS

Fifty-two glioma patients, including 18 LGGs (grade II) and 34 HGGs (grade III/IV), were examined using a 3.0-T magnetic resonance scanner. The ADC and CBF maps were obtained from diffusion-weighted imaging and pseudo-continuous arterial spin labeling perfusion-weighted imaging, respectively. A total of 91 radiomic features were extracted from each of the tumor volume on the ADC and CBF maps. We constructed 4 types of machine learning classifiers based on (1) least absolute shrinkage and selection operator regularized logistic regression (LASSO-LR), (2) random forest (RF), (3) support vector machine (SVM) with the radial basis function kernel (SVM-RBF), and (4) SVM with the linear kernel (SVM-L). A training set with 36 gliomas (70%) was used to select the important radiomic features and train each model using 5-fold cross-validation. The remaining 16 gliomas (30%) were used as a test set. Receiver operating characteristic analysis was performed to evaluate the model performance.

RESULTS

A radiomic feature, ADC first-order-based skewness, was selected as an important variable in all classification models. According to the receiver operating characteristic analysis, the areas under the curve of the LASSO-LR, RF, SVM-RBF, and SVM-L models for the training set were 0.965, 1.000, 0.979, and 0.969, respectively. For the test set, the areas under the curve of the LASSO-LR, RF, SVM-RBF, and SVM-L models were 0.883, 0.917, 0.717, and 0.917, respectively. All classification models showed sufficient diagnostic performance on the test set.

CONCLUSIONS

Radiomics-based machine learning classifiers using the quantitative ADC and CBF maps are useful for differentiating HGGs from LGGs.

Non-local multiscale approach for the impact of go or grow hypothesis on tumour-viruses interactions

We propose and study computationally a novel non-local multiscale moving boundary mathematical model for tumour and oncolytic virus (OV) interactions when we consider the go or grow hypothesis for cancer dynamics. This spatio-temporal model focuses on two cancer cell phenotypes that can be infected with the OV or remain uninfected, and which can either move in response to the extracellular-matrix (ECM) density or proliferate. The interactions between cancer cells, those among cancer cells and ECM, and those among cells and OV occur at the macroscale. At the micro-scale, we focus on the interactions between cells and matrix degrading enzymes (MDEs) that impact the movement of tumour boundary. With the help of this multiscale model we explore the impact on tumour invasion patterns of two different assumptions that we consider in regard to cell-cell and cell-matrix interactions. In particular we investigate model dynamics when we assume that cancer cell fluxes are the result of local advection in response to the density of extracellular matrix (ECM), or of non-local advection in response to cell-ECM adhesion. We also investigate the role of the transition rates between mainly-moving and mainly-growing cancer cell sub-populations, as well as the role of virus infection rate and virus replication rate on the overall tumour dynamics.

Physiological MRI Biomarkers in the Differentiation Between Glioblastomas and Solitary Brain Metastases

PURPOSE

Glioblastomas (GB) and solitary brain metastases (BM) are the most common brain tumors in adults. GB and BM may appear similar in conventional magnetic resonance imaging (cMRI). Their management strategies, however, are quite different with significant consequences on clinical outcome. The aim of this study was to evaluate the usefulness of a previously presented physiological MRI approach scoping to obtain quantitative information about microvascular architecture and perfusion, neovascularization activity, and oxygen metabolism to differentiate GB from BM.

PROCEDURES

Thirty-three consecutive patients with newly diagnosed, untreated, and histopathologically confirmed GB or BM were preoperatively examined with our physiological MRI approach as part of the cMRI protocol.

RESULTS

Physiological MRI biomarker maps revealed several significant differences in the pathophysiology of GB and BM: Central necrosis was more hypoxic in GB than in BM (30 %; P = 0.036), which was associated with higher neovascularization activity (65 %; P = 0.043) and metabolic rate of oxygen (48 %; P = 0.004) in the adjacent contrast-enhancing viable tumor parts of GB. In peritumoral edema, GB infiltration caused neovascularization activity (93 %; P = 0.018) and higher microvascular perfusion (30 %; P = 0.022) associated with higher tissue oxygen tension (33 %; P = 0.020) and lower oxygen extraction from vasculature (32 %; P = 0.040).

CONCLUSION

Our physiological MRI approach, which requires only 7 min of extra data acquisition time, might be helpful to noninvasively distinguish GB and BM based on pathophysiological differences. However, further studies including more patients are required.

The role of extracellular vesicles in acquisition of resistance to therapy in glioblastomas

Glioblastoma (GBM) is the most aggressive primary brain tumor with a median survival of 15 months despite standard care therapy consisting of maximal surgical debulking, followed by radiation therapy with concurrent and adjuvant temozolomide treatment. The natural history of GBM is characterized by inevitable recurrence with patients dying from increasingly resistant tumor regrowth after therapy. Several mechanisms including inter- and intratumoral heterogeneity, the evolution of therapy-resistant clonal subpopulations, reacquisition of stemness in glioblastoma stem cells, multiple drug efflux mechanisms, the tumor-promoting microenvironment, metabolic adaptations, and enhanced repair of drug-induced DNA damage have been implicated in therapy failure. Extracellular vesicles (EVs) have emerged as crucial mediators in the maintenance and establishment of GBM. Multiple seminal studies have uncovered the multi-dynamic role of EVs in the acquisition of drug resistance. Mechanisms include EV-mediated cargo transfer and EVs functioning as drug efflux channels and decoys for antibody-based therapies. In this review, we discuss the various mechanisms of therapy resistance in GBM, highlighting the emerging role of EV-orchestrated drug resistance. Understanding the landscape of GBM resistance is critical in devising novel therapeutic approaches to fight this deadly disease.

Mathematical formulation and parametric analysis of in vitro cell models in microfluidic devices: application to different stages of glioblastoma evolution

In silico models and computer simulation are invaluable tools to better understand complex biological processes such as cancer evolution. However, the complexity of the biological environment, with many cell mechanisms in response to changing physical and chemical external stimuli, makes the associated mathematical models highly non-linear and multiparametric. One of the main problems of these models is the determination of the parameters' values, which are usually fitted for specific conditions, making the conclusions drawn difficult to generalise. We analyse here an important biological problem: the evolution of hypoxia-driven migratory structures in Glioblastoma Multiforme (GBM), the most aggressive and lethal primary brain tumour. We establish a mathematical model considering the interaction of the tumour cells with oxygen concentration in what is called the go or grow paradigm. We reproduce in this work three different experiments, showing the main GBM structures (pseudopalisade and necrotic core formation), only changing the initial and boundary conditions. We prove that it is possible to obtain versatile mathematical tools which, together with a sound parametric analysis, allow to explain complex biological phenomena. We show the utility of this hybrid "biomimetic in vitro-in silico" platform to help to elucidate the mechanisms involved in cancer processes, to better understand the role of the different phenomena, to test new scientific hypotheses and to design new data-driven experiments.

A Mechanistic Investigation into Ischemia-Driven Distal Recurrence of Glioblastoma

Glioblastoma (GBM) is the most aggressive primary brain tumor with a short median survival. Tumor recurrence is a clinical expectation of this disease and usually occurs along the resection cavity wall. However, previous clinical observations have suggested that in cases of ischemia following surgery, tumors are more likely to recur distally. Through the use of a previously established mechanistic model of GBM, the Proliferation Invasion Hypoxia Necrosis Angiogenesis (PIHNA) model, we explore the phenotypic drivers of this observed behavior. We have extended the PIHNA model to include a new nutrient-based vascular efficiency term that encodes the ability of local vasculature to provide nutrients to the simulated tumor. The extended model suggests sensitivity to a hypoxic microenvironment and the inherent migration and proliferation rates of the tumor cells are key factors that drive distal recurrence.

Diagnosing growth in low-grade gliomas with and without longitudinal volume measurements: A retrospective observational study

BACKGROUND

Low-grade gliomas cause significant neurological morbidity by brain invasion. There is no universally accepted objective technique available for detection of enlargement of low-grade gliomas in the clinical setting; subjective evaluation by clinicians using visual comparison of longitudinal radiological studies is the gold standard. The aim of this study is to determine whether a computer-assisted diagnosis (CAD) method helps physicians detect earlier growth of low-grade gliomas.

METHODS AND FINDINGS

We reviewed 165 patients diagnosed with grade 2 gliomas, seen at the University of Alabama at Birmingham clinics from 1 July 2017 to 14 May 2018. MRI scans were collected during the spring and summer of 2018. Fifty-six gliomas met the inclusion criteria, including 19 oligodendrogliomas, 26 astrocytomas, and 11 mixed gliomas in 30 males and 26 females with a mean age of 48 years and a range of follow-up of 150.2 months (difference between highest and lowest values). None received radiation therapy. We also studied 7 patients with an imaging abnormality without pathological diagnosis, who were clinically stable at the time of retrospective review (14 May 2018). This study compared growth detection by 7 physicians aided by the CAD method with retrospective clinical reports. The tumors of 63 patients (56 + 7) in 627 MRI scans were digitized, including 34 grade 2 gliomas with radiological progression and 22 radiologically stable grade 2 gliomas. The CAD method consisted of tumor segmentation, computing volumes, and pointing to growth by the online abrupt change-of-point method, which considers only past measurements. Independent scientists have evaluated the segmentation method. In 29 of the 34 patients with progression, the median time to growth detection was only 14 months for CAD compared to 44 months for current standard of care radiological evaluation (p < 0.001). Using CAD, accurate detection of tumor enlargement was possible with a median of only 57% change in the tumor volume as compared to a median of 174% change of volume necessary to diagnose tumor growth using standard of care clinical methods (p < 0.001). In the radiologically stable group, CAD facilitated growth detection in 13 out of 22 patients. CAD did not detect growth in the imaging abnormality group. The main limitation of this study was its retrospective design; nevertheless, the results depict the current state of a gold standard in clinical practice that allowed a significant increase in tumor volumes from baseline before detection. Such large increases in tumor volume would not be permitted in a prospective design. The number of glioma patients (n = 56) is a limitation; however, it is equivalent to the number of patients in phase II clinical trials.

CONCLUSIONS

The current practice of visual comparison of longitudinal MRI scans is associated with significant delays in detecting growth of low-grade gliomas. Our findings support the idea that physicians aided by CAD detect growth at significantly smaller volumes than physicians using visual comparison alone. This study does not answer the questions whether to treat or not and which treatment modality is optimal. Nonetheless, early growth detection sets the stage for future clinical studies that address these questions and whether early therapeutic interventions prolong survival and improve quality of life.

Carbon ion radiotherapy boost in the treatment of glioblastoma: a randomized phase I/III clinical trial

Background

Glioblastoma (GBM) is a highly virulent tumor of the central nervous system, with a median survival < 15 months. Clearly, an improvement in treatment outcomes is needed. However, the emergence of these malignancies within the delicate brain parenchyma and their infiltrative growth pattern severely limit the use of aggressive local therapies. The particle therapy represents a new promising therapeutic approach to circumvent these prohibitive conditions with improved treatment efficacy.

Methods and design

Patients with newly diagnosed malignant gliomas will have their tumor tissue samples submitted for the analysis of the status of O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation. In Phase I, the patients will undergo an induction carbon ion radiotherapy (CIRT) boost followed by 60 GyE of proton irradiation with concurrent temozolomide (TMZ) at 75 mg/m². To determine the maximal dose of safe induction boost, the tolerance, and acute toxicity rates in a dose-escalation manner from 9 to 18 GyE in three fractions will be used. In Phase III, GBM-only patients will be randomized to receive either 60 GyE (2 GyE per fraction) of proton irradiation with concurrent TMZ (control arm) or a CIRT boost (dose determined in Phase I of this trial) followed by 60 GyE of proton irradiation with concurrent TMZ. The primary endpoints are overall survival (OS) and toxicity rates (acute and long-term). Secondary endpoints are progression-free survival (PFS), and tumor response (based upon assessment with C-methionine/fluoro-ethyl-tyrosine positron emission tomography [MET/FET PET] or magnetic resonance imaging [MRI] and detection of serologic immune markers). We hypothesize that the induction CIRT boost will result in a greater initial tumor-killing ability and prime the tumor microenvironment for enhanced immunologic tumor clearance, resulting in an expected 33% improvement in OS rates.

Discussion

The prognosis of GBM remains grim. The mechanism underpinning the poor prognosis of this malignancy is its chronic state of tumor hypoxia, which promotes both immunosuppression/immunologic evasion and radio-resistance. The unique physical and biological properties of CIRT are expected to overcome these microenvironmental limitations to confer an improved tumor-killing ability and anti-tumor immune response, which could result in an improvement in OS with minimal toxicity.

Trial registration number This trial has been registered with the China Clinical Trials Registry, and was allocated the number ChiCTR-OID-17013702.

Radiologic progression of glioblastoma under therapy-an exploratory analysis of AVAglio

Background

In this exploratory analysis of AVAglio, a randomized phase III clinical study that investigated the addition of bevacizumab (Bev) to radiotherapy/temozolomide in newly diagnosed glioblastoma, we aim to radiologically characterize glioblastoma on therapy until progression and investigate whether the type of radiologic progression differs between treatment arms and is related to survival and molecular data.

Methods

Five progression types (PTs) were categorized using an adapted algorithm according to MRI contrast enhancement behavior in T1- and T2-weighted images in 621 patients (Bev, n = 299; placebo, n = 322). Frequencies of PTs (designated as classic T1, cT1 relapse, T2 diffuse, T2 circumscribed, and primary nonresponder), time to progression (PFS), and overall survival (OS) were assessed within each treatment arm and compared with molecular subtypes and O6-methylguanine DNA methyltransferase (MGMT) promoter methylation status.

Results

PT frequencies differed between the Bev and placebo arms, except for "T2 diffuse" (12.4% and 7.1%, respectively). PTs showed differences in PFS and OS; with "T2 diffuse" being associated with longest survival. Complete disappearance of contrast enhancement during treatment ("cT1 relapse") showed longer survival than only partial contrast enhancement decrease ("classic T1"). "T2 diffuse" was more commonly MGMT hypermethylated. Only weak correlations to molecular subtypes from primary tissue were detected.

Conclusions

Progression of glioblastoma under therapy can be characterized radiologically. These radiologic phenotypes are influenced by treatment and develop differently over time with differential outcomes. Complete resolution of contrast enhancement during treatment is a favorable factor for outcome.

The Anti-Angiogenic Effect of Atorvastatin in Glioblastoma Spheroids Tumor Cultured in Fibrin Gel: in 3D in Vitro Model

Purpose:

Glioblastoma multiform (GBM) is the most aggressive glial neoplasm. Researchers have exploited the fact that GBMs are highly vascularized tumors. Anti-angiogenic strategies including those targeting VEGF pathway have been emerged for treatment of GBM. Previously, we reported the anti-inflammatory effect of atorvastatin on GBM cells. In this study, we investigated the anti-angiogenesis and apoptotic activity of atorvastatin on GBM cells.

Methods:

Different concentrations of atorvastatin (1, 5, 10µM) were used on engineered three-dimensional (3D) human tumor models using glioma spheroids and Human Umbilical Vein Endothelial cells (HUVECs) in fibrin gel as tumor models. To reach for these aims, angiogenesis as tube-like structures sprouting of HUVECs were observed after 24 hour treatment with different concentrations of atorvastatin into the 3-D fibrin matrix and we focused on it by angiogenesis antibody array. After 48 hours exposing with different concentrations of atorvastatin, cell migration of HUVECs were investigated. After 24 and 48 hours exposing with different concentrations of atorvastatin VEGF, CD31, caspase-3 and Bcl-2 genes expression by real time PCR were assayed.

Results:

The results showed that atorvastatin has potent anti-angiogenic effect and apoptosis inducing effect against glioma spheroids. Atorvastatin down-regulated the expression of VEGF, CD31 and Bcl-2, and induced the expression of caspase-3 especially at 10µM concentration. These effects are dose dependent.

Conclusion:

These results suggest that this biomimetic model with fibrin may provide a vastly applicable 3D culture system to study the effect of anti-cancer drugs such as atorvastatin on tumor malignancy in vitro and in vivo and atorvastatin could be used as agent for glioblastoma treatment.

Angiogenesis and radiological tumor growth in patients with glioblastoma

BACKGROUND

The preoperative growth of human glioblastomas (GBMs) has been shown to vary among patients. In animal studies, angiogenesis has been linked to hypoxia and faster growth of GBM, however, its relation to the growth of human GBMs is sparsely studied. We have therefore aimed to look for associations between radiological speed of growth and microvessel density (MVD) counts of the endothelial markers vWF (Factor VIII related antigen) and CD105 (endoglin).

METHODS

Preoperative growth was estimated from segmented tumor volumes of two preoperative T1-weighted postcontrast magnetic resonance imaging scans taken ≥14 days apart in patients with newly diagnosed GBMs. A Gompertzian growth curve was computed from the volume data and separated the patients into two groups of either faster or slower tumor growth than expected. MVD counts of the immunohistochemical markers von Willebrand factor (vWF) (a pan-endothelial marker) and CD105 (a marker of proliferating endothelial cells) were assessed for associations with fast-growing tumors using Mann-Whitney U tests and a multivariable binary logistic regression analysis.

RESULTS

We found that only CD105-MVD was significantly associated with faster growth in a univariable analysis (p = 0.049). However, CD105-MVD was no longer significant when corrected for the presence of thromboses and high cellular density in a multivariable model, where the latter features were significant independent predictors of faster growth with respective odds ratios 4.2 (95% confidence interval, 1.2, 14.3), p = 0.021 and 2.6 (95% confidence interval, 1.0, 6.5), p = 0.048.

CONCLUSIONS

MVDs of neither endothelial marker were independently associated with faster growth, suggesting angiogenesis-independent processes contribute to faster glioblastoma growth.

Blood-brain barrier-on-a-chip: Microphysiological systems that capture the complexity of the blood-central nervous system interface

The blood-brain barrier is a dynamic and highly organized structure that strictly regulates the molecules allowed to cross the brain vasculature into the central nervous system. The blood-brain barrier pathology has been associated with a number of central nervous system diseases, including vascular malformations, stroke/vascular dementia, Alzheimer's disease, multiple sclerosis, and various neurological tumors including glioblastoma multiforme. There is a compelling need for representative models of this critical interface. Current research relies heavily on animal models (mostly mice) or on two-dimensional (2D) in vitro models, neither of which fully capture the complexities of the human blood-brain barrier. Physiological differences between humans and mice make translation to the clinic problematic, while monolayer cultures cannot capture the inherently three-dimensional (3D) nature of the blood-brain barrier, which includes close association of the abluminal side of the endothelium with astrocyte foot-processes and pericytes. Here we discuss the central nervous system diseases associated with blood-brain barrier pathology, recent advances in the development of novel 3D blood-brain barrier -on-a-chip systems that better mimic the physiological complexity and structure of human blood-brain barrier, and provide an outlook on how these blood-brain barrier-on-a-chip systems can be used for central nervous system disease modeling. Impact statement The field of microphysiological systems is rapidly evolving as new technologies are introduced and our understanding of organ physiology develops. In this review, we focus on Blood-Brain Barrier (BBB) models, with a particular emphasis on how they relate to neurological disorders such as Alzheimer's disease, multiple sclerosis, stroke, cancer, and vascular malformations. We emphasize the importance of capturing the three-dimensional nature of the brain and the unique architecture of the BBB - something that until recently had not been well modeled by in vitro systems. Our hope is that this review will provide a launch pad for new ideas and methodologies that can provide us with truly physiological BBB models capable of yielding new insights into the function of this critical interface.

Key rates for the grades and transformation ability of glioma: model simulations and clinical cases

Tumor progression to higher grade is a fundamental property of cancer. The malignant advancement of the pathological features may either develop during the later stages of cancer growth (natural evolution) or it may necessitate new mutations or molecular events that alter the rates of growth, dispersion, or neovascularization (transformation). Here, we model the pathological and radiological features of grades 2-4 gliomas at the times of diagnosis and death and study grade development and the progression to higher grades. We perform a retrospective review of clinical cases based on model predictions. Simulations uncover two unusual patterns of glioma progression, which are supported by clinical cases: (1) some grades 2 and 3 gliomas lack the ability of progression to higher grades, and (2) grade 3 glioma may evolve to GBM in a few weeks. All 13 gliomas that recurred at the same grade carry either the IDH1-R132H or the ATRX mutation. All (five of five) grade 3 tumors are 1p/19q co-deleted, IDH1-R132H mutated and ATRX wt. Furthermore, three of seven grade 2 gliomas are both IDH1-R132H mutated and ATRX mutated. Simulations replicate the good prognosis of secondary GBM. The results support the hypothesis that constant rates of dispersion, proliferation, and angiogenesis prescribe either a natural evolution or the inability to progress to higher grades. Furthermore, the accrual of molecular events that change a tumor's ability to infiltrate, proliferate or neovascularize may transform the glioma either into a more aggressive tumor at the same grade or elevate its grade.

Personalized Radiotherapy Planning Based on a Computational Tumor Growth Model

In this article, we propose a proof of concept for the automatic planning of personalized radiotherapy for brain tumors. A computational model of glioblastoma growth is combined with an exponential cell survival model to describe the effect of radiotherapy. The model is personalized to the magnetic resonance images (MRIs) of a given patient. It takes into account the uncertainty in the model parameters, together with the uncertainty in the MRI segmentations. The computed probability distribution over tumor cell densities, together with the cell survival model, is used to define the prescription dose distribution, which is the basis for subsequent Intensity Modulated Radiation Therapy (IMRT) planning. Depending on the clinical data available, we compare three different scenarios to personalize the model. First, we consider a single MRI acquisition before therapy, as it would usually be the case in clinical routine. Second, we use two MRI acquisitions at two distinct time points in order to personalize the model and plan radiotherapy. Third, we include the uncertainty in the segmentation process. We present the application of our approach on two patients diagnosed with high grade glioma. We introduce two methods to derive the radiotherapy prescription dose distribution, which are based on minimizing integral tumor cell survival using the maximum a posteriori or the expected tumor cell density. We show how our method allows the user to compute a patient specific radiotherapy planning conformal to the tumor infiltration. We further present extensions of the method in order to spare adjacent organs at risk by re-distributing the dose. The presented approach and its proof of concept may help in the future to better target the tumor and spare organs at risk.

The role of antiangiogenic agents in the treatment of gastric cancer: A systematic review and meta-analysis

BACKGROUND

The survival of advanced gastric cancer (GC) is dismal, and effects of antiangiogenic agents remain inconclusive. The purpose of this study is to assess combination of chemotherapy with antiangiogenic therapy versus traditional chemotherapy.

METHODS

To achieve the goal of scientific rigor, statistics from both referenced works and experiments were analyzed. We carefully searched for the referenced works by retrieving, as well as analyzing, literature databases for information on antiangiogenic therapy compared to other therapeutic approaches used to treat GC patients. Two groups were defined in the experiment: experimental and control groups. The experimental group was treated with antiangiogenic drug, and the control group was treated with standard chemotherapy or placebo.

RESULTS

The study included a total of 3240 participants. Overall, there was significant improvement in overall survival (hazard ratio [HR] = 0.78, 95% confidence interval [CI]: 0.67-0.91, P = 0.002), progression-free survival (HR 0.65, 95% CI: 0.52-0.81, P = 0.0002), objective response rate (risk ratio [RR] = 1.58, 95% CI: 1.33-1.88, P < 0.00001), and disease control rate (RR 2.44, 95% CI: 1.57-3.78, P < 0.0001) in the group with antiangiogenic drug versus the group with standard chemotherapy or placebo. Moreover, this new treatment approach showed tolerable toxicity.

CONCLUSION

This study confirms the superior efficacy of combination therapy with antiangiogenic agents in comparison to traditional chemotherapy regimens for patients with GC. Moreover, this new treatment approach showed tolerable toxicity. This meta-analysis provides important information for clinicians who are interested in using antiangiogenic therapies to treat GC patients.

Single Cell Mathematical Model Successfully Replicates Key Features of GBM: Go-Or-Grow Is Not Necessary

Glioblastoma (GBM) is a malignant brain tumor that continues to be associated with neurological morbidity and poor survival times. Brain invasion is a fundamental property of malignant glioma cells. The Go-or-Grow (GoG) phenotype proposes that cancer cell motility and proliferation are mutually exclusive. Here, we construct and apply a single glioma cell mathematical model that includes motility and angiogenesis and lacks the GoG phenotype. Simulations replicate key features of GBM including its multilayer structure (i.e.edema, enhancement, and necrosis), its progression patterns associated with bevacizumab treatment, and replicate the survival times of GBM treated or untreated with bevacizumab. These results suggest that the GoG phenotype is not a necessary property for the formation of the multilayer structure, recurrence patterns, and the poor survival times of patients diagnosed with GBM.

Fast and high temperature hyperthermia coupled with radiotherapy as a possible new treatment for glioblastoma

Background

A new transcranial focused ultrasound device has been developed that can induce hyperthermia in a large tissue volume. The purpose of this work is to investigate theoretically how glioblastoma multiforme (GBM) can be effectively treated by combining the fast hyperthermia generated by this focused ultrasound device with external beam radiotherapy.

Methods/Design

To investigate the effect of tumor growth, we have developed a mathematical description of GBM proliferation and diffusion in the context of reaction–diffusion theory. In addition, we have formulated equations describing the impact of radiotherapy and heat on GBM in the reaction–diffusion equation, including tumor regrowth by stem cells. This formulation has been used to predict the effectiveness of the combination treatment for a realistic focused ultrasound heating scenario.

Our results show that patient survival could be significantly improved by this combined treatment modality.

Discussion

High priority should be given to experiments to validate the therapeutic benefit predicted by our model.

Electronic supplementary material

The online version of this article (doi:10.1186/s40349-016-0078-3) contains supplementary material, which is available to authorized users.

In silico analysis suggests differential response to bevacizumab and radiation combination therapy in newly diagnosed glioblastoma

Recently, two phase III studies of bevacizumab, an anti-angiogenic, for newly diagnosed glioblastoma (GBM) patients were released. While they were unable to statistically significantly demonstrate that bevacizumab in combination with other therapies increases the overall survival of GBM patients, there remains a question of potential benefits for subpopulations of patients. We use a mathematical model of GBM growth to investigate differential benefits of combining surgical resection, radiation and bevacizumab across observed tumour growth kinetics. The differential hypoxic burden after gross total resection (GTR) was assessed along with the change in radiation cell kill from bevacizumab-induced tissue re-normalization when starting therapy for tumours at different diagnostic sizes. Depending on the tumour size at the time of treatment, our model predicted that GTR would remove a variable portion of the hypoxic burden ranging from 11% to 99.99%. Further, our model predicted that the combination of bevacizumab with radiation resulted in an additional cell kill ranging from 2.6×10⁷ to 1.1×10¹⁰ cells. By considering the outcomes given individual tumour kinetics, our results indicate that the subpopulation of patients who would receive the greatest benefit from bevacizumab and radiation combination therapy are those with large, aggressive tumours and who are not eligible for GTR.

Pattern of relapse of glioblastoma multiforme treated with radical radio-chemotherapy: Could a margin reduction be proposed?

To analyse the pattern of recurrence of patients treated with Stupp protocol in relation to technique, to compare in silico plans with reduced margin (1 cm) with the original ones and to analyse toxicity. 105 patients were treated: 85 had local recurrence and 68 of them were analysed. Recurrence was considered in field, marginal and distant if >80 %, 20-80 % or <20 % of the relapse volume was included in the 95 %-isodose. In silico plans were retrospectively recalculated using the same technique, fields angles and treatment planning system of the original ones. The pattern of recurrence was in field, marginal and distant in 88, 10 and 2 % respectively and was similar in in silico plans. The margin reduction appears to spare 100 cc of healthy brain by 57 Gy-volume (p = 0.02). The target coverage was worse in standard plans (pt student < 0.001), especially if the tumour was near to organs at risk (pχ2 < 0.001). PTV coverage was better with IMRT and helical-IMRT, than conformal-3D (pAnova test = 0.038). This difference was no more significant with in silico planning. A higher incidence of asthenia and leuko-encephalopathy was observed in patients with greater percentage of healthy brain included in 57 Gy-volume. No differences in the pattern of recurrence according to margins were found. The margin reduction determines sparing of healthy brain and could possibly reduce the incidence of late toxicity. Margin reduction could allow to use less sophisticated techniques, ensuring appropriate target coverage, and the choice of more costly techniques could be reserved to selected cases.

Computational Trials: Unraveling Motility Phenotypes, Progression Patterns, and Treatment Options for Glioblastoma Multiforme

Glioblastoma multiforme is a malignant brain tumor with poor prognosis and high morbidity due to its invasiveness. Hypoxia-driven motility and concentration-driven motility are two mechanisms of glioblastoma multiforme invasion in the brain. The use of anti-angiogenic drugs has uncovered new progression patterns of glioblastoma multiforme associated with significant differences in overall survival. Here, we apply a mathematical model of glioblastoma multiforme growth and invasion in humans and design computational trials using agents that target angiogenesis, tumor replication rates, or motility. The findings link highly-dispersive, moderately-dispersive, and hypoxia-driven tumors to the patterns observed in glioblastoma multiforme treated by anti-angiogenesis, consisting of progression by Expanding FLAIR, Expanding FLAIR + Necrosis, and Expanding Necrosis, respectively. Furthermore, replication rate-reducing strategies (e.g. Tumor Treating Fields) appear to be effective in highly-dispersive and moderately-dispersive tumors but not in hypoxia-driven tumors. The latter may respond to motility-reducing agents. In a population computational trial, with all three phenotypes, a correlation was observed between the efficacy of the rate-reducing agent and the prolongation of overall survival times. This research highlights the potential applications of computational trials and supports new hypotheses on glioblastoma multiforme phenotypes and treatment options.

MicroRNA-181c inhibits glioblastoma cell invasion, migration and mesenchymal transition by targeting TGF-β pathway

MicroRNAs (miRNAs) are small non-coding RNAs frequently dysregulated in human malignancies. In this study, we found that miR-181c was down-regulated both in glioblastoma tissues and cell lines. We also annotated 566 TCGA miRNA expression profiles and found that patients with high microRNA-181c (miR-181c)-expressing tumors had significantly longer OS and PFS. Overexpression of miR-181c evidently inhibited glioblastoma cell line T98G migration and invasion. Further, the expression of E-cadherin was significantly upregulated and that of N-cadherin and vimentin was significantly down-regulated. We also found that miR-181c overexpression inhibited TGF-β signaling by down-regulating TGFBR1, TGFBR2 and TGFBRAP1 expression. Overall, our study found that miR-181c plays a key role in glioblastoma cell invasion, migration and mesenchymal transition suggesting potential therapeutic applications.

Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence

Glioblastoma (GB) is the most common primary malignant brain tumor, and despite the availability of chemotherapy and radiotherapy to combat the disease, overall survival remains low with a high incidence of tumor recurrence. Technological advances are continually improving our understanding of the disease, and in particular, our knowledge of clonal evolution, intratumor heterogeneity, and possible reservoirs of residual disease. These may inform how we approach clinical treatment and recurrence in GB. Mathematical modeling (including neural networks) and strategies such as multiple sampling during tumor resection and genetic analysis of circulating cancer cells, may be of great future benefit to help predict the nature of residual disease and resistance to standard and molecular therapies in GB.