A pharmacologically based multiscale mathematical model of angiogenesis and its use in investigating the efficacy of a new cancer treatment strategy

Tumor angiogenesis is the process by which new blood vessels are formed and enhance the oxygenation and growth of tumors. As angiogenesis is recognized as being a critical event in cancer development, considerable efforts have been made to identify inhibitors of this process. Cytostatic treatments that target the molecular events of the angiogenesis process have been developed, and have met with some success. However, it is usually difficult to preclinically assess the effectiveness of targeted therapies, and apparently promising compounds sometimes fail in clinical trials.We have developed a multiscale mathematical model of angiogenesis and tumor growth. At the molecular level, the model focuses on molecular competition between pro- and anti-angiogenic substances modeled on the basis of pharmacological laws. At the tissue scale, the model uses partial differential equations to describe the spatio-temporal changes in cancer cells during three stages of the cell cycle, as well as those of the endothelial cells that constitute the blood vessel walls.This model is used to qualitatively assess how efficient endostatin gene therapy is. Endostatin is an anti-angiogenic endogenous substance. The gene therapy entails overexpressing endostatin in the tumor and in the surrounding tissue. Simulations show that there is a critical treatment dose below which increasing the duration of treatment leads to a loss of efficacy.This theoretical model may be useful to evaluate the efficacy of therapies targeting angiogenesis, and could therefore contribute to designing prospective clinical trials.     

A mathematical model of tumor growth and its response to single irradiation

Background

Mathematical modeling of biological processes is widely used to enhance quantitative understanding of bio-medical phenomena. This quantitative knowledge can be applied in both clinical and experimental settings. Recently, many investigators began studying mathematical models of tumor response to radiation therapy. We developed a simple mathematical model to simulate the growth of tumor volume and its response to a single fraction of high dose irradiation. The modelling study may provide clinicians important insights on radiation therapy strategies through identification of biological factors significantly influencing the treatment effectiveness.

Methods

We made several key assumptions of the model. Tumor volume is composed of proliferating (or dividing) cancer cells and non-dividing (or dead) cells. Tumor growth rate (or tumor volume doubling time) is proportional to the ratio of the volumes of tumor vasculature and the tumor. The vascular volume grows slower than the tumor by introducing the vascular growth retardation factor, θ. Upon irradiation, the proliferating cells gradually die over a fixed time period after irradiation. Dead cells are cleared away with cell clearance time. The model was applied to simulate pre-treatment growth and post-treatment radiation response of rat rhabdomyosarcoma tumors and metastatic brain tumors of five patients who were treated with Gamma Knife stereotactic radiosurgery (GKSRS).

Results

By selecting appropriate model parameters, we showed the temporal variation of the tumors for both the rat experiment and the clinical GKSRS cases could be easily replicated by the simple model. Additionally, the application of our model to the GKSRS cases showed that the α-value, which is an indicator of radiation sensitivity in the LQ model, and the value of θ could be predictors of the post-treatment volume change.

Conclusions

The proposed model was successful in representing both the animal experimental data and the clinically observed tumor volume changes. We showed that the model can be used to find the potential biological parameters, which may be able to predict the treatment outcome. However, there is a large statistical uncertainty of the result due to the small sample size. Therefore, a future clinical study with a larger number of patients is needed to confirm the finding.

     

A nonlinear mathematical model of DNA and its use in calculating neutron scatter

A simple nonlinear mathematical model is applied to calculate spectrum of inelastic coherent scattering of neutrons by DNA.     

A mathematical model of breast cancer development, local treatment and recurrence

Cancer development is a stepwise process through which normal somatic cells acquire mutations which enable them to escape their normal function in the tissue and become self-sufficient in survival. The number of mutations depends on the patient's age, genetic susceptibility and on the exposure of the patient to carcinogens throughout their life. It is believed that in every malignancy 4-6 crucial similar mutations have to occur on cancer-related genes. These genes are classified as oncogenes and tumour suppressor genes (TSGs) which gain or lose their function respectively, after they have received one mutative hit or both of their alleles have been knocked out. With the acquisition of each of the necessary mutations the transformed cell gains a selective advantage over normal cells, and the mutation will spread throughout the tissue via clonal expansion. We present a simplified model of this mutation and expansion process, in which we assume that the loss of two TSGs is sufficient to give rise to a cancer. Our mathematical model of the stepwise development of breast cancer verifies the idea that the normal mutation rate in genes is only sufficient to give rise to a tumour within a clinically observable time if a high number of breast stem cells and TSGs exist or genetic instability is involved as a driving force of the mutation pathway. Furthermore, our model shows that if a mutation occurred in stem cells pre-puberty, and formed a field of cells with this mutation through clonal formation of the breast, it is most likely that a tumour will arise from within this area. We then apply different treatment strategies, namely surgery and adjuvant external beam radiotherapy and targeted intraoperative radiotherapy (TARGIT) and use the model to identify different sources of local recurrence and analyse their prevention.     

A mathematical model of cancer treatment by immunotherapy

In this paper, a detailed mathematical study of cancer immunotherapy will be presented. General principles of cancer immunotherapy and the model equations and hypotheses will be discussed. Mathematical analyses of the model equations with regard to dissipativity, boundedness of solutions, invariance of non-negativity, nature of equilibria, persistence, extinction and global stability will be analyzed. It will also be shown that bifurcations can occur, and criteria for total cure will also be derived.     

A mathematical model of the course of the DNA synthesis in mammalian cells after ultraviolet irradiation and its use in the determination of the length of the replicon

The chromosome of mammalian cells is divided into several shorter segments — replicons which replicate separately. When the cells are irradiated with ultraviolet radiation the pyrimidine flimers preventing the DNA-polymerase in its normal function and creating obstacles for a timely completing of the implication are formed. Using these facts as a basis we have derived a mathematical model of the course of the DNA synthesis in mammalian cells after ultraviolet irradiation, it has been proved that for the determination of the average length of units of replication in chromosomes of these cells the measurement of the intensity of post-radiation DNA synthesis in an asynchronous culture of mammalian cells can be used. Directions for the determination of this length using results of the measurement of the post-radiation synthesis are given, too. The whole method has already beer, tested on mammalian L-cells.     

The use of the orthotopic model to validate antivascular therapies for cancer

The orthotopic model reproduces aspects of the tumour microenvironment and emulates a number of important biological features of cancer progression, angiogenesis, metastasis and resistance. Due to its parallels with human cancer, the model can be used to evaluate therapeutic responses to various therapies. This review outlines the importance of using the orthotopic implantation of tumour cells in mice models for evaluating the effectiveness of antivascular therapies.     

A mathematical model for cell cycle-specific cancer virotherapy

Oncolytic viruses preferentially infect and replicate in cancerous cells, leading to elimination of tumour populations, while sparing most healthy cells. Here, we study the cell cycle-specific activity of viruses such as vesicular stomatitis virus (VSV). In spite of its capacity as a robust cytolytic agent, VSV cannot effectively attack certain tumour cell types during the quiescent, or resting, phase of the cell cycle. In an effort to understand the interplay between the time course of the cell cycle and the specificity of VSV, we develop a mathematical model for cycle-specific virus therapeutics. We incorporate the minimum biologically required time spent in the non-quiescent cell cycle phases using systems of differential equations with incorporated time delays. Through analysis and simulation of the model, we describe how varying the minimum cycling time and the parameters that govern viral dynamics affect the stability of the cancer-free equilibrium, which represents therapeutic success.     

A multiscale mathematical model of avascular tumor growth to investigate the therapeutic benefit of anti-invasive agents

With the aim of inhibiting cancer growth and reducing the risk of metastasis, pharmaceutical companies in the early 1990s developed anti-metastatic agents called inhibitors of metalloproteinases (MMPi). Despite the promising results obtained in pre-clinical studies, results of Phase III trials have been somewhat disappointing for late stage cancer patients. With the aim of mathematically investigating this therapeutic failure, we developed a mechanistically based model which integrates cell cycle regulation and macroscopic tumor dynamics. By simulating the model, we evaluated the efficacy of MMPi therapy. Simulation results predict the lack of efficacy of MMPi in advanced cancer patients. The theoretical model may aid in evaluating the efficacy of anti-metastatic therapies, thus benefiting the design of prospective clinical trials.     

A mathematical model for progression and heterogeneity in colorectal cancer dynamics

This paper deals with the development of a mathematical model that describes cancer dynamics at the cellular scale.The selected case study concerns colon and rectum cancer, which originates in colorectal crypts. Cells inside the crypts are assumed to be organized according to a compartmental-like arrangement and to be homogeneously mixing. A mathematical model for cancer progression is proposed here. This model describes the generation of multiple clonal sub-populations of cells at different progression stages in a single crypt.Asymptotic analysis and simulations are developed with an exploratory aim. The obtained results offer some insights into the role played by mutation, proliferation and differentiation phenomena on cancer dynamics. In particular, the acquisition of an additional growing power and a reduction for cellular differentiation seem more likely to be the driving force behind carcinogenesis rather than an increase in the mutation rate. The mutation rate instead seems to affect progression dynamics and intra-tumor heterogeneity. The role played by cells, at different differentiation stages, in the onset and progression of colorectal cancer is highlighted. The results support the fact that stem cells play a key role in cancer development and the idea that transit-amplifying cells could also take on an active role in carcinogenesis.     

A mathematical model of the processes of mitochondrial death and reproduction during body irradiation

A method of construction of mathematical model of processes of death and reproduction of mitochondria based on morphometric measurements is proposed. Using the model the time of mitochondria death and reproduction during ionized irradiation of the organism is identified.     

A multiscale hybrid approach for vasculogenesis and related potential blocking therapies

Solid tumors must recruit and form new blood vessels for maintenance, growth and detachments of metastases. Discovering drugs that block malignant angiogenesis is thus an important approach in cancer treatment and has given rise to multiple in vitro and in silico models. The present hybrid individual cell-based model incorporates some underlying biochemical events relating more closely the classical Cellular Potts Model (CPM) parameters to subcellular mechanisms and to the activation of specific signaling pathways. The model spans the three fundamental biological levels: at the extracellular level a continuous model describes secretion, diffusion, uptake and decay of the autocrine VEGF; at the cellular level, an extended lattice CPM, based on a system energy reduction, reproduces cell dynamics such as migration, adhesion and chemotaxis; at the subcellular level, a set of reaction-diffusion equations describes a simplified VEGF-induced calcium-dependent intracellular pathway. The results agree with the known interplay between calcium signals and VEGF dynamics and with their role in malignant vasculogenesis. Moreover, the analysis of the link between the microscopic subcellular dynamics and the macroscopic cell behaviors confirms the efficiency of some pharmacological interventions that are currently in use and, more interestingly, proposes some new therapeutic approaches, that are counter-intuitive but potentially effective.     

A mathematical model for the burden of diabetes and its complications

Background  

The incidence and prevalence of diabetes are increasing all over the world. Complications of diabetes constitute a burden for the individuals and the whole society.     

A paired-quadrat method for use in multiscale ordination

Multiscale ordination is a technique for examining spatial patterns of several species at several scales. We present a paired-quadrat method (paired quadrat covariance; PQC) to be used in multiscale ordination and test it with artificial data. Multiscale ordination with PQC successfully extracted the salient features of the data set. The method appears to be more sensitive than blocked-quadrat techniques for extracting small-scale patterns. We suggest that PQC will be useful as a complement to existing procedures or as a tool for analysing data from scattered quadrat arrangements.Abbreviations PQC = Paired Quadrat Covariance - PQV = Paired Quadrat Variance - TTLC = Two-Term Local Covariance - TTLQV = Two-Term Local Quadrat Variance     

A multiscale, spatially distributed model of asthmatic airway hyper-responsiveness

We present a multiscale, spatially distributed model of lung and airway behaviour with the goal of furthering the understanding of airway hyper-responsiveness and asthma. The model provides an initial computational framework for linking events at the cellular and molecular levels, such as Ca²⁺ and crossbridge dynamics, to events at the level of the entire organ. At the organ level, parenchymal tissue is modelled using a continuum approach as a compressible, hyperelastic material in three dimensions, with expansion and recoil of lung tissue due to tidal breathing. The governing equations of finite elasticity deformation are solved using a finite element method. The airway tree is embedded in this tissue, where each airway is modelled with its own airway wall, smooth muscle and surrounding parenchyma. The tissue model is then linked to models of the crossbridge mechanics and their control by Ca²⁺ dynamics, thus providing a link to molecular and cellular mechanisms in airway smooth muscle cells. By incorporating and coupling the models at these scales, we obtain a detailed, computational multiscale model incorporating important physiological phenomena associated with asthma.     

A mathematical model of the effects of drug resistance in cancer chemotherapy

A mathematical model is proposed relating tumor responce under repeated doses of a single cytotoxic agent to the presence and accumulation of phenotypic drug resistance. The latter is assumed to comprise two elements: that present at diagnosis and that acquired in responce to and during treatment. New analytic expressions are presented for quantities such as the fractional tumor reduction effected by each dose, the minimum tumor size achieved under therapy, the changing composition of the tumor, and the number of doses before apparent clinical resistance (the nadir) is observed. A similar model accommodates the sequential delivery of different drugs between which there is some degree of cross-resistance, and it is shown how competing treatment strategies can be simulated and compared.     

A mathematical model for describing the metastasis of cancer in bone tissue

Metastasis is the rapid proliferation of cancer cells (secondary tumour) at a specific place, generally leading to death. This occurs at anatomical parts providing the necessary environment for vascularity, oxygen and food to hide their actions and trigger the rapid growth of cancer. Prostate and breast cancers, for example, use bone marrow for their proliferation. Bone-supporting cancer cells thus adapt to the environment, mimicking the behaviour of genetic and molecular bone cells. Evidence of this has been given in Cecchini et al. (2005, EAU Update Ser. 3:214-226), providing arguments such as how cancer cell growth is so active during bone reabsorption. This paper simulates metastasis activation in bone marrow. A mathematical model has been developed involving the activation of molecules from bone tissue cells, which are necessary for cancer to proliferate. Here, we simulate two forms of secondary tumour growth depending on the type of metastasis: osteosclerosis and osteolysis.     

Competition and natural selection in a mathematical model of cancer

A malignant tumor is a dynamic amalgamation of various cell phenotypes, both cancerous (parenchyma) and healthy (stroma). These diverse cells compete over resources as well as cooperate to maintain tumor viability. Therefore, tumors are both an ecological community and an integrated tissue. An understanding of how natural selection operates in this unique ecological context should expose unappreciated vulnerabilities shared by all cancers. In this study I address natural selection’s role in tumor evolution by developing and exploring a mathematical model of a heterogenous primary neoplasm. The model is a system of nonlinear ordinary differential equations tracking the mass of up to two different parenchyma cell types, the mass of vascular endothelial cells from which new tumor blood vessels are built and the total length of tumor microvessels. Results predict the possibility of a hypertumor—a focus of aggressively reproducing parenchyma cells that invade and destroy part or all of the tumor, perhaps before it becomes a clinical entity. If this phenomenon occurs, then we should see examples of tumors that develop an aggressive histology but are paradoxically prone to extinction. Neuroblastoma, a common childhood cancer, may sometimes fit this pattern. In addition, this model suggests that parenchyma cell diversity can be maintained by a tissue-like integration of cells specialized to provide different services.