Cancer has recently overtaken heart disease as the world’s biggest killer. Cancer is initiated by gene mutations that result in local proliferation of abnormal cells and their migration to other parts of the human body, a process called metastasis. The metastasized cancer cells then interfere with the normal functions of the body, eventually leading to death. There are two hundred types of cancer, classified by their point of origin. Most of them share some common features, but they also have their specific character. In this article we review mathematical models of such common features and then proceed to describe models of specific cancer diseases.

When invading the tissue, malignant tumour cells (i.e. cancer cells) need to detach from neighbouring cells, degrade the basement membrane, and migrate through the extracellular matrix. These processes require loss of cell-cell adhesion and enhancement of cell-matrix adhesion. In this paper we present a mathematical model of an intracellular pathway for the interactions between a cancer cell and the extracellular matrix. Cancer cells use similar mechanisms as with normal cells for their interactions with the extracellular matrix. We develop a model of cell-matrix adhesion that accounts for reactions between the cell surface receptor integrins, the matrix glycoprotein fibronectin, and the actin filaments in the cytoskeleton. Each represents components for an intermediate compartment, the extracellular compartment, and the intracellular compartment, respectively. Binding of fibronectin with integrins triggers a clustering of protein complexes, which then activates and phosphorylates regulatory proteins that are involved in actin reorganisation causing actin polymerization and stress fibre assembly. Rearrangement of actin filaments with integrin/fibronectin complexes near adhesion sites and interaction with fibrillar fibronectin produces the force necessary for cell migration, accounting for cell-matrix adhesion.

A computational framework for testing the effects of cytotoxic molecules, specific to a given phase of the cell cycle, and vascular disrupting agents (VDAs) is presented. The model is based on a cellular automaton to describe tumour cell states transitions from proliferation to death. It is coupled with a model describing the tumour vasculature and its adaptation to the blood rheological constraints when alterations are induced by VDAs treatment. Several therapeutic protocols in two structurally different vascular networks were tested by varying the duration of cytotoxic drug perfusion and the periodicity of treatment cycles. The impact of VDAs were also tested both experimentally from intravital microscopy through a dorsal skinfold chamber on a mouse and numerically. Simulation results show that combining cytotoxic treatment with a post treatment of VDA through a judicious timing could favour the rapid eradication of the tumour. The computational framework thus gives some insights into the outcome of cytotoxic and VDAs treatments on a qualitative basis. Future validation from our experimental setup could open up new perspectives towards Computer-Assisted Therapeutic Strategies.

The invasive capability is fundamental in determining the malignancy of a solid tumor. Revealing biomedical strategies that are able to partially decrease cancer invasiveness is therefore an important approach in the treatment of the disease and has given rise to multiple in vitro and in silico models. We here develop a hybrid computational framework, whose aim is to characterize the effects of the different cellular and subcellular mechanisms involved in the invasion of a malignant mass. In particular, a discrete Cellular Potts Model is used to represent the population of cancer cells at the mesoscopic scale, while a continuous approach of reaction-diffusion equations is employed to describe the evolution of microscopic variables, as the nutrients and the proteins present in the microenvironment and the matrix degrading enzymes secreted by the tumor. The behavior of each cell is then determined by a balance of forces, such as homotypic (cell-cell) and heterotypic (cell-matrix) adhesions and haptotaxis, and is mediated by the internal state of the individual, i.e. its motility. The resulting composite model quantifies the influence of changes in the mechanisms involved in tumor invasion and, more interestingly, puts in evidence possible therapeutic approaches, that are potentially effective in decreasing the malignancy of the disease, such as the alteration in the adhesive properties of the cells, the inhibition in their ability to remodel and the disruption of the haptotactic movement. We also extend the simulation framework by including cell proliferation which, following experimental evidence, is regulated by the intracellular level of growth factors. Interestingly, in spite of the increment in cellular density, the depth of invasion is not significantly increased, as one could have expected.

Gliomas are highly invasive brain tumors that exhibit high and spatially heterogeneous cell proliferation and motility rates. The interplay of proliferation and migration dynamics plays an important role in the invasion of these malignant tumors. We analyze the regulation of proliferation and migration processes with a lattice-gas cellular automaton (LGCA). We study and characterize the influence of the migration/proliferation dichotomy (also known as the “GO-or-Grow" mechanism) on avascular glioma invasion, in terms of invasion speed and width of the infiltration zone. We show that the invasive behavior of the (macroscopic) tumor colony is a highly complex phenomenon that cannot be extrapolated by the sole knowledge of the (microscopic) individual cell phenotype.

There is evidence that cancer develops when cells acquire a sequence of mutations that alter normal cell characteristics. This sequence determines a hierarchy among the cells, based on how many more mutations they need to accumulate in order to become cancerous. When cells divide, they exhibit telomere loss and differentiate, which defines another cell hierarchy, on top of which is the stem cell. We propose a mutation-generation model, which combines the mutation-accumulation hierarchy with the differentiation hierarchy of the cells, allowing us to take a step further in examining cancer development and growth. The results of the model support the hypothesis of the cancer stem cell’s role in cancer pathogenesis: a very small fraction of the cancer cell population is responsible for the cancer growth and development. Also, according to the model, the nature of mutation accumulation is sufficient to explain the faster growth of the cancer cell population. However, numerical results show that in order for a cancer to develop within a reasonable time frame, cancer cells need to exhibit a higher proliferation rate than normal cells.

The cancer stem cell hypothesis has evolved to one of the most important paradigms in biomedical research. During recent years evidence has been accumulating for the existence of stem cell-like populations in different cancers, especially in leukemias. In the current work we propose a mathematical model of cancer stem cell dynamics in leukemias. We apply the model to compare cellular properties of leukemic stem cells to those of their benign counterparts. Using linear stability analysis we derive conditions necessary and sufficient for expansion of malignant cell clones, based on fundamental cellular properties. This approach reveals different scenarios of cancer initiation and provides qualitative hints to possible treatment strategies.

In order to better understand the dynamics of acute leukemia, and in particular to find theoretical conditions for the efficient delivery of drugs in acute myeloblastic leukemia, we investigate stability of a system modeling its cell dynamics.

The overall system is a cascade connection of sub-systems consisting of distributed delays and static nonlinear feedbacks. Earlier results on local asymptotic stability are improved by the analysis of the linearized system around the positive equilibrium. For the nonlinear system, we derive stability conditions by using Popov, circle and nonlinear small gain criteria. The results are illustrated with numerical examples and simulations.

Existence and stability of periodic solutions are studied for a system of delay differential equations with two delays, with periodic coefficients. It models the evolution of hematopoietic stem cells and mature neutrophil cells in chronic myelogenous leukemia under a periodic treatment that acts only on mature cells. Existence of a guiding function leads to the proof of the existence of a strictly positive periodic solution by a theorem of Krasnoselskii. The stability of this solution is analysed.

We consider the early carcinogenesis model originally proposed as a deterministic reaction-diffusion system. The model has been conceived to explore the spatial effects stemming from growth regulation of pre-cancerous cells by diffusing growth factor molecules. The model exhibited Turing instability producing transient spatial spikes in cell density, which might be considered a model counterpart of emerging foci of malignant cells. However, the process of diffusion of growth factor molecules is by its nature a stochastic random walk. An interesting question emerges to what extent the dynamics of the deterministic diffusion model approximates the stochastic process generated by the model. We address this question using simulations with a new software tool called sbioPN (spatial biological Petri Nets). The conclusion is that whereas single-realization dynamics of the stochastic process is very different from the behavior of the reaction diffusion system, it is becoming more similar when averaged over a large number of realizations. The degree of similarity depends on model parameters. Interestingly, despite the differences, typical realizations of the stochastic process include spikes of cell density, which however are spread more uniformly and are less dependent of initial conditions than those produced by the reaction-diffusion system.

We investigate optimal control of a cancer-immune cell interactive model with delay in the interphase compartment. By applying the optimal control theory, we seek to minimize the cost associated with the chemotherapy drug, minimize the accumulation of cancer cells, and increase the immune cell presence. Optimality conditions and characterization of the control are provided. Numerical analyses are given to enhance the understanding of the difficulties that occur in the control of cancer.

Recent discovery of cancer stem cells in tumorigenic tissues has raised many questions about their nature, origin, function and their behavior in cell culture. Most of current experiments reporting a dynamics of cancer stem cell populations in culture show the eventual stability of the percentages of these cell populations in the whole population of cancer cells, independently of the starting conditions. In this paper we propose a mathematical model of cancer stem cell population behavior, based on specific features of cancer stem cell divisions and including, as a mathematical formalization of cell-cell communications, an underlying field concept. We compare the qualitative behavior of mathematical models of stem cells evolution, without and with an underlying signal. In absence of an underlying field, we propose a mathematical model described by a system of ordinary differential equations, while in presence of an underlying field it is described by a system of delay differential equations, by admitting a delayed signal originated by existing cells. Under realistic assumptions on the parameters, in both cases (ODE without underlying field, and DDE with underlying field) we show in particular the stability of percentages, provided that the delay is sufficiently small. Further, for the DDE case (in presence of an underlying field) we show the possible existence of, either damped or standing, oscillations in the cell populations, in agreement with some existing mathematical literature. The outcomes of the analysis may offer to experimentalists a tool for addressing the issue regarding the possible non-stem to stem cells transition, by determining conditions under which the stability of cancer stem cells population can be obtained only in the case in which such transition can occur. Further, the provided description of the variable corresponding to an underlying field may stimulate further experiments for elucidating the nature of “instructive" signals for cell divisions, underlying a proper pattern of the biological system.

Treating cancer patients with metastatic disease remains an ultimate challenge in clinical oncology. Because invasive cancer precludes or limits the use of surgery, metastatic setting is often associated with (poor) survival, rather than sustained remission, in patients with common cancers like lung, digestive or breast carcinomas. Mathematical modeling may help us better identify non detectable metastatic status to in turn optimize treatment for patients with metastatic disease. In this paper we present a family of models for the metastatic growth. They are based on four principles : to be as simple as possible, involving the least possible number of parameters, the main informations are obtained from the primary tumor and being able to recover the variety of phenomena observed by the clinicians. Several simulations of therapeutic strategies are presented illustrating possible applications of modeling to the clinic.

Many cancer-associated genes and pathways remain to be identified in order to clarify the molecular mechanisms underlying cancer progression. In this area, genome-wide loss-of-function screens appear to be powerful biological tools, allowing the accumulation of large amounts of data. However, this approach currently lacks analytical tools to exploit the data with maximum efficiency, for which systems biology methods analyzing complex cellular networks may be extremely helpful. In this article we report such a systems biology strategy based on the construction of a Network for a biological process and specific for a given cell system (cell type). The networks are created from genome-wide loss-of-function screen datasets. We also propose tools to analyze network properties. As one of the tools, we suggest a mathematical model for discrimination between two distinct cell processes that may be affected by knocking down the activity of a gene, i. e., a decreased cell number may be caused by arrested cell proliferation or enhanced cell death. Next we show how this discrimination between the two cell processes helps to construct two corresponding subnetworks. Finally, we demonstrate an application of the proposed strategy to the identification and characterization of putative novel genes and pathways significant for the control of lung cancer cell growth, based on the results of a genome-wide proliferation/viability loss-of-function screen of human lung adenocarcinoma cells.