Quantifying efficacy of chemotherapy of brain tumors with homogeneous and heterogeneous drug delivery

Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model

Glioblastomas are the most aggressive primary brain tumors, characterized by their rapid proliferation and diffuse infiltration of the brain tissue. Survival patterns in patients with glioblastoma have been associated with a number of clinicopathologic factors including age and neurologic status, yet a significant quantitative link to in vivo growth kinetics of each glioma has remained elusive. Exploiting a recently developed tool for quantifying glioma net proliferation and invasion rates in individual patients using routinely available magnetic resonance images (MRI), we propose to link these patient-specific kinetic rates of biological aggressiveness to prognostic significance. Using our biologically based mathematical model for glioma growth and invasion, examination of serial pretreatment MRIs of 32 glioblastoma patients allowed quantification of these rates for each patient's tumor. Survival analyses revealed that even when controlling for standard clinical parameters (e.g., age and Karnofsky performance status), these model-defined parameters quantifying biological aggressiveness (net proliferation and invasion rates) were significantly associated with prognosis. One hypothesis generated was that the ratio of the actual survival time after whatever therapies were used to the duration of survival predicted (by the model) without any therapy would provide a therapeutic response index (TRI) of the overall effectiveness of the therapies. The TRI may provide important information, not otherwise available, about the effectiveness of the treatments in individual patients. To our knowledge, this is the first report indicating that dynamic insight from routinely obtained pretreatment imaging may be quantitatively useful in characterizing the survival of individual patients with glioblastoma. Such a hybrid tool bridging mathematical modeling and clinical imaging may allow for stratifying patients for clinical studies relative to their pretreatment biological aggressiveness.

Multi-scale, multi-resolution brain cancer modeling

In advancing discrete-based computational cancer models towards clinical applications, one faces the dilemma of how to deal with an ever growing amount of biomedical data that ought to be incorporated eventually in one form or another. Model scalability becomes of paramount interest. In an effort to start addressing this critical issue, here, we present a novel multi-scale and multi-resolution agent-based in silico glioma model. While 'multi-scale' refers to employing an epidermal growth factor receptor (EGFR)-driven molecular network to process cellular phenotypic decisions within the micro-macroscopic environment, 'multi-resolution' is achieved through algorithms that classify cells to either active or inactive spatial clusters, which determine the resolution they are simulated at. The aim is to assign computational resources where and when they matter most for maintaining or improving the predictive power of the algorithm, onto specific tumor areas and at particular times. Using a previously described 2D brain tumor model, we have developed four different computational methods for achieving the multi-resolution scheme, three of which are designed to dynamically train on the high-resolution simulation that serves as control. To quantify the algorithms' performance, we rank them by weighing the distinct computational time savings of the simulation runs versus the methods' ability to accurately reproduce the high-resolution results of the control. Finally, to demonstrate the flexibility of the underlying concept, we show the added value of combining the two highest-ranked methods. The main finding of this work is that by pursuing a multi-resolution approach, one can reduce the computation time of a discrete-based model substantially while still maintaining a comparably high predictive power. This hints at even more computational savings in the more realistic 3D setting over time, and thus appears to outline a possible path to achieve scalability for the all-important clinical translation.

In silico cancer modeling: is it ready for prime time?

At the dawn of the era of personalized, systems-driven medicine, computational or in silico modeling and the simulation of disease processes is becoming increasingly important for hypothesis generation and data integration in both experiments and clinics alike. Arguably, the use of these techniques is nowhere more visible than in oncology. To illustrate the field's vast potential, as well as its current limitations, we briefly review selected studies on modeling malignant brain tumors. Implications for clinical practice, and for clinical trial design and outcome prediction, are also discussed.

Multiscale agent-based cancer modeling

Agent-based modeling (ABM) is an in silico technique that is being used in a variety of research areas such as in social sciences, economics and increasingly in biomedicine as an interdisciplinary tool to study the dynamics of complex systems. Here, we describe its applicability to integrative tumor biology research by introducing a multi-scale tumor modeling platform that understands brain cancer as a complex dynamic biosystem. We summarize significant findings of this work, and discuss both challenges and future directions for ABM in the field of cancer research.

Three-dimensional Gaussian model to define brain metastasis limits on 11C-methionine PET

PURPOSE

Since 11C-methionine (MET) heavily accumulates in brain tumors, PET with MET (MET-PET) is proposed for the image-guided planning of their targeted therapy. Determination of bulk tumor limits is therefore a crucial component of MET-PET image analysis. We aimed at validating a Gaussian model of tumor delineation on MET-PET. We choose MET-PET and MRI data obtained in brain metastases to adjust the model. Indeed, MRI limits of these non-infiltrative hypermetabolic brain lesions are efficiently used for their curative treatment.

METHODS AND MATERIALS

We developed a three-dimensional (3D) Gaussian model that relates the tumor-limit-defining threshold to maximum and mean count values in the defined tumor volume and to mean count values in a reference region. To adjust the model to experimental data, we selected 25 brain metastases following these criteria: (i) no surgery or classical radiotherapy within 6 months, (ii) no previous radiosurgery, (iii) MET-PET and MRI acquired within a 48-h interval, (vi) necrosis representing less than 25% of tumor volume on MRI. We applied a progressive thresholding procedure on MET-PET so as to match tumor limits on contrast-enhanced co-registered MRI.

RESULTS

In 22 tumors, a match could be reached between tumor margins on MET-PET and MRI. The relation between mean, maximum and threshold values closely fits the 3D-Gaussian model function. We found a quadratic relation between the mean-to-threshold ratio and the maximum-to-cerebellum activity ratio.

CONCLUSIONS

A 3D-Gaussian model may describe the limits of MET uptake distribution within brain metastases, providing a simple method for metabolic tumor delineation.

Biocomputing: numerical simulation of glioblastoma growth using diffusion tensor imaging

Glioblastoma multiforma (GBM) is one of the most aggressive tumors of the central nervous system. It can be represented by two components: a proliferative component with a mass effect on brain structures and an invasive component. GBM has a distinct pattern of spread showing a preferential growth in the white fiber direction for the invasive component. By using the architecture of white matter fibers, we propose a new model to simulate the growth of GBM. This architecture is estimated by diffusion tensor imaging in order to determine the preferred direction for the diffusion component. It is then coupled with a mechanical component. To set up our growth model, we make a brain atlas including brain structures with a distinct response to tumor aggressiveness, white fiber diffusion tensor information and elasticity. In this atlas, we introduce a virtual GBM with a mechanical component coupled with a diffusion component. These two components are complementary, and can be tuned independently. Then, we tune the parameter set of our model with an MRI patient. We have compared simulated growth (initialized with the MRI patient) with observed growth six months later. The average and the odd ratio of image difference between observed and simulated images are computed. Displacements of reference points are compared to those simulated by the model. The results of our simulation have shown a good correlation with tumor growth, as observed on an MRI patient. Different tumor aggressiveness can also be simulated by tuning additional parameters. This work has demonstrated that modeling the complex behavior of brain tumors is feasible and will account for further validation of this new conceptual approach.

Understanding the role of the tumour vasculature in the transport of drugs to solid cancer tumours

OBJECTIVES

The vasculature of tumours imposes certain barriers that transport of anti-cancer drugs must overcome. Here follows an account of development of a general computational model that describes the mechanisms of drug transport to a solid tumour, with an emphasis on modelling the vasculature using solute transport concepts.

MATERIALS AND METHODS

Investigation into the biological parameters that enhance/prevent anticancer drug transport to the tumour provides a means to evaluate the effects of these parameters on the treatment process. Sensitivity analysis of these provides useful insights concerning anticancer drug transport mechanisms from the vasculature to the solid tumour for a non-specified drug and non-specified solid tumour by revealing the conditions that promote or prevent effective drug transport. The effect of the vasculature on transport efficiency is studied using a parametric analysis of some of the transport and biological parameters. Understanding the various transport mechanisms provides a basis to evaluate the effectiveness of the drug treatment a priori.

RESULTS

It was found that increases in the capillary hydrostatic pressure, diffusive permeability coefficient and hydraulic conductivity all result in a decrease in tumour size. Similarly, decreases in the interstitium hydrostatic pressure and filtration constant result in a decrease in tumour size. Dependence of the change in the tumour size to changes in these parameters is non-linear. These results demonstrate the potential of the integrated computational model of the tumour and its vasculature to estimate efficacy of a particular treatment process. Regardless of the dependency of the outcome on the assumed model parameters and the assumed kinetics, mathematical models of this type can provide more explanation on the issues related to the transport barriers, the efficacy of the treatment, and the development of effective anticancer drugs. A case study also is presented to demonstrate the model's flexibility to accommodate a two-cell-glioma population.

Local controlled drug delivery to the brain: mathematical modeling of the underlying mass transport mechanisms

The mass transport mechanisms involved in the controlled delivery of drugs to living brain tissue are complex and yet not fully understood. Often the drug is embedded within a polymeric or lipidic matrix, which is directly administered into the brain tissue, that is, intracranially. Different types of systems, including microparticles and disc- or rod-shaped implants are used to control the release rate and, thus, to optimize the drug concentrations at the site of action in the brain over prolonged periods of time. Most of these dosage forms are biodegradable to avoid the need for the removal of empty remnants after drug exhaustion. Various physical and chemical processes are involved in the control of drug release from these systems, including water penetration, drug dissolution, degradation of the matrix and drug diffusion. Once the drug has been released from the delivery system, it has to be transported through the living brain tissue to the target site(s). Again, a variety of phenomena, including diffusion, drug metabolism and degradation, passive or active uptake into CNS tissue and convection can be of importance for the fate of the drug. An overview is given of the current knowledge of the nature of barriers to free access of drug to tumour sites within the brain and the state of the art of: (i) mathematical modeling approaches describing the physical transport processes and chemical reactions which can occur in different types of intracranially administered drug delivery systems, and of (ii) theories quantifying the mass transport phenomena occurring after drug release in the living tissue. Both, simplified as well as complex mathematical models are presented and their major advantages and shortcomings discussed. Interestingly, there is a significant lack of mechanistically realistic, comprehensive theories describing both parts in detail, namely, drug transport in the dosage form and in the living brain tissue. High quality experimental data on drug concentrations in the brain tissue are difficult to obtain, hence this is itself an issue in testing mathematical approaches. As a future perspective, the potential benefits and limitations of these mathematical theories aiming to facilitate the design of advanced intracranial drug delivery systems and to improve the efficiency of the respective pharmacotherapies are discussed.

Wave optics of Liesegang rings

Liesegang rings refract and reflect at the interface between the regions of the same gel but of different thickness. The incident and the refracted rings obey a refraction law analogous to the Snell's law of classical optics, with a reverse of the spacing coefficient being a counterpart of the refraction index. The wavelike behavior of the rings at the interface is explained by geometrical arguments derived from the Jablczynski's spacing principle, and is reproduced in numerical simulations based on a three-dimensional minimalistic version of the nucleation-growth model.

Cancer Cell Invasion of Brain Tissue: Guided by a Prepattern?

The malignant brain tumourGlioblastoma multiforme(GBM) displays a highly invasive behaviour. Spreading of the malignant cells appears to be guided by the white matter fibre tracts within the brain. In order to understand the global growth process we introduce a lattice-gas cellular automaton model which describes the local interaction between individual malignant cells and their neighbourhood. We consider interactions between cells (brain cells and tumour cells) and between malignant cells and the fibre tracts in the brain, which are considered as a prepattern. The prepattern implies persistent individual cell motion along the fibre structure. Simulations with the model show that only the inclusion of the prepattern results in invading tumour and growing tumour islets in front of the expanding tumour bulk (i.e. the growth pattern observed in clinical practice). Our results imply that the infiltrative growth of GBMs is, in part, determined by the physical structure of the surrounding brain rather than by intrinsic properties of the tumour cells.

Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion

Over the last 10 years increasingly complex mathematical models of cancerous growths have been developed, especially on solid tumors, in which growth primarily comes from cellular proliferation. The invasiveness of gliomas, however, requires a change in the concept to include cellular motility in addition to proliferative growth. In this article we review some of the recent developments in mathematical modeling of gliomas. We begin with a model of untreated gliomas and continue with models of polyclonal gliomas following chemotherapy or surgical resection. From relatively simple assumptions involving homogeneous brain tissue bounded by a few gross anatomical landmarks (ventricles and skull) the models have recently been expanded to include heterogeneous brain tissue with different motilities of glioma cells in grey and white matter on a geometrically complex brain domain, including sulcal boundaries, with a resolution of 1 mm(3) voxels. We conclude that the velocity of expansion is linear with time and varies about 10-fold, from about 4 mm/year for low-grade gliomas to about 3 mm/month for high-grade ones.

Speed of reaction-diffusion fronts in spatially heterogeneous media

The front speed problem for nonuniform reaction rate and diffusion coefficient is studied by using singular perturbation analysis, the geometric approach of Hamilton-Jacobi dynamics, and the local speed approach. Exact and perturbed expressions for the front speed are obtained in the limit of large times. For linear and fractal heterogeneities, the analytic results have been compared with numerical results exhibiting a good agreement. Finally we reach a general expression for the speed of the front in the case of smooth and weak heterogeneities.