Populational adaptive evolution, chemotherapeutic resistance and multiple anti-cancer therapies

Resistance to chemotherapies, particularly to anticancer treatments, is an increasing medical concern. Among the many mechanisms at work in cancers, one of the most important is the selection of tumor cells expressing resistance genes or phenotypes. Motivated by the theory of mutation-selection in adaptive evolution, we propose a model based on a continuous variable that represents the expression level of a resistance gene (or genes, yielding a phenotype) influencing in healthy and tumor cells birth/death rates, effects of chemotherapies (both cytotoxic and cytostatic) and mutations. We extend previous work by demonstrating how qualitatively different actions of chemotherapeutic and cytostatic treatments may induce different levels of resistance. The mathematical interest of our study is in the formalism of constrained Hamilton-Jacobi equations in the framework of viscosity solutions. We derive the long-term temporal dynamics of the fittest traits in the regime of small mutations. In the context of adaptive cancer management, we also analyse whether an optimal drug level is better than the maximal tolerated dose

Combination of direct methods and homotopy in numerical optimal control: application to the optimization of chemotherapy in cancer

We consider a state-constrained optimal control problem of a system of two non-local partial-differential equations, which is an extension of the one introduced in a previous work in mathematical oncology. The aim is to minimize the tumor size through chemotherapy while avoiding the emergence of resistance to the drugs. The numerical approach to solve the problem was the combination of direct methods and continuation on discretization parameters, which happen to be insufficient for the more complicated model, where diffusion is added to account for mutations. In the present paper, we propose an approach relying on changing the problem so that it can theoretically be solved thanks to a Pontryagin Maximum Principle in infinite dimension. This provides an excellent starting point for a much more reliable and efficient algorithm combining direct methods and continuations. The global idea is new and can be thought of as an alternative to other numerical optimal control techniques

Optimisation of cancer drug treatments using cell population dynamics

International audienceCancer is primarily a disease of the physiological control on cell population proliferation. Tissue proliferation relies on the cell division cycle: one cell becomes two after a sequence of molecular events that are physiologically controlled at each step of the cycle at so-called checkpoints, in particular at transitions between phases of the cycle [105]. Tissue proliferation is the main physiological process occurring in development and later in maintaining the permanence of the organism in adults, at that late stage mainly in fast renewing tissues such as bone marrow, gut and skin

Populational adaptive evolution, chemotherapeutic resistance and multiple anti-cancer therapies

Resistance to chemotherapies, particularly to anticancer treatments, is an increasing medical concern. Among the many mechanisms at work in cancers, one of the most important is the selection of tumor cells expressing resistance genes or phenotypes. Motivated by the theory of mutation-selection in adaptive evolution, we propose a model based on a continuous variable that represents the expression level of a resistance gene (or genes, yielding a phenotype) influencing in healthy and tumor cells birth/death rates, effects of chemotherapies (both cytotoxic and cytostatic) and mutations. We extend previous work by demonstrating how qualitatively different actions of chemotherapeutic and cytostatic treatments may induce different levels of resistance. The mathematical interest of our study is in the formalism of constrained Hamilton-Jacobi equations in the framework of viscosity solutions. We derive the long-term temporal dynamics of the fittest traits in the regime of small mutations. In the context of adaptive cancer management, we also analyse whether an optimal drug level is better than the maximal tolerated dose. © EDP Sciences, SMAI, 2013

Physiologically structured cell population dynamic models with applications to combined drug delivery optimisation in oncology

International audienceOptimising drug delivery in the general circulation targeted towards cancer cell pop- ulations, but inevitably reaching also proliferating healthy cell populations imposes to design optimised drug infusion algorithms in a dynamic way, i.e., controlling the growth of both populations simultaneously by the action of the drugs in use, wanted for cancer cells, and unwanted for toxic side effects on healthy cells. Towards this goal, we design models and methods, with optional representation of circadian clock control on proliferation in both populations, according to three axes [15, 16]: a) representing the oncologist's main weapons, drugs, and their fates in the organism by molecular-based pharmacokinetic-pharmacodynamic equations; b) representing the cell populations under attack by drugs, and their proliferation dynamics, including in the models molecular and functional targets for the drugs at stake, by physiologically structured equations; c) using numerical algorithms, optimising drug delivery under different constraints at the whole organism level, representing impacts of multiple drugs with different targets on cell populations. In the present study, two molecular pharmacological ODE models, one for oxali- platin, and one for 5-Fluorouracil, have been designed, using law of mass action and enzyme kinetics, to describe the fate of these two cytotoxic drugs in the organism. An age-structured PDE cell population model has been designed with drug control. targets to represent the effects of oxaliplatin and 5-Fluorouracil on the cell cycle in proliferating cell populations. The models for proliferating cell population dynam- ics involve possible physiological fixed (i.e., out of reach of therapeutic influence) circadian clock control, and varying drug control to be optimised, connected with pharmacological models. Concentrations of drugs, represented by outputs of ODEs, are assumed to be homogeneous in the cell populations under attack by cytotoxic drugs. The possi- bility to describe the effects of other drugs, cytostatic (including in this category anti-angiogenic drugs, considered as acting on the G1 phase, choking its entries and slowing it down), is also presented, but not put in pharmacokinetic equations and actual simulations in this study, that is focused on the combination of 5-FU and oxaliplatin, a classic therapeutic association in the treatment of colorectal cancer. We then set conditions to numerically solve drug delivery optimisation problems (maximisation of cancer cell kill under the constraint of preserving healthy cells over a tolerability threshold) by considering a trade-off between therapeutic and toxic effects. The observed effects on proliferation are growth exponents, i.e., first eigenvalues of the linear PDE systems, in the two populations, healthy and cancer. The solutions to an optimisation problem taking into account circadian clock control are presented as best delivery time schedules for the two drugs used in combined treatments, to be implemented in programmable delivery pumps in the clinic

Modeling the Effects of Space Structure and Combination Therapies on Phenotypic Heterogeneity and Drug Resistance in Solid Tumors

Histopathological evidence supports the idea that the emergence of phenotypic heterogeneity and resistance to cytotoxic drugs can be considered as a process of selection in tumor cell populations. In this framework, can we explain intra-tumor heterogeneity in terms of selection driven by the local cell environment? Can we overcome the emergence of resistance and favor the eradication of cancer cells by using combination therapies? Bearing these questions in mind, we develop a model describing cell dynamics inside a tumor spheroid under the effects of cytotoxic and cytostatic drugs. Cancer cells are assumed to be structured as a population by two real variables standing for space position and the expression level of a phenotype of resistance to cytotoxic drugs. The model takes explicitly into account the dynamics of resources and anticancer drugs as well as their interactions with the cell population under treatment. We analyze the effects of space structure and combination therapies on phenotypic heterogeneity and chemotherapeutic resistance. Furthermore, we study the efficacy of combined therapy protocols based on constant infusion and bang–bang delivery of cytotoxic and cytostatic drugs

Mathematical modelling and survival prediction in cancer

Cancer is one of the leading causes of death, thus opening a vast need for extensive research and insights. The survival prospects, along with treatment benefits and costs (economical or health-related), can be predicted with tools from mathematical modelling and regression analysis. Promising results have been gained on suggesting mono- and combination therapies, that could potentially improve the treatment strategies. Furthermore, multiple biological features have been recognized as important predictors of treatment outcome. However, since cancer remains a challenging and unpredictable enemy, the need for more effective and personalized predictions and treatment suggestions remains. In this dissertation, various modelling approaches were used to predict cancer behavior, treatment outcomes and patient survival. An ordinary differential equation model was developed to investigate the changes in the cancer cell density as different treatment regimen were applied. In addition, we included the immune system along with immunotherapy, since the immune response is an important part of cancer development and has a potential to eradicate tumors. It was noted, that an adaptive treatment resulted in lower cancer burden and less time in treatment. In addition, combination treatments (immunotherapy with either chemo- or targeted therapy) generally resulted in smaller cancer burden than monotherapies, however, the potential additional side effects of two therapies have to be considered. A metapopulation model was developed for the cancer development, in which the focus was on emergence of angiogenesis and cancer cell emigration. We investigated, in which conditions cancer cells would become angiogenic with or without treatments (anti-angiogenic, cytotoxic or combination). In general, angiogenesis contribution was desired quality for cancer cells, if no anti-angiogenic treatment was administrated. With anti-angiogenic treatment, angiogenesis diminished, however the risk of resistance against anti-angiogenic treatment also increased. Two new regression methods were developed with focus on survival prediction. A greedy budget-constrained Cox regression (Greedy Cox) utilizes L2-penalty and considers the cost of selected parameters. It was also compared to LASSO selection (L1). Optimal Subset CArdinality Regression (OSCAR) method was developed with L0-pseudonorm penalty to provide sparse models. The costs of measuring the selected model features were also considered in comparison to prediction accuracy. The methods were validated on clinical prostate cancer data and it was noted that a comparable level of prediction accuracy was already reached with a few parameters, resulting in relatively low costs. All of the investigated methods also selected reasonable, cancer-related parameters such as prostate specific antigen (PSA). Taken together, this dissertation provides a comprehensive research of novel tools for modelling and predicting cancer behavior and patient survival. Important hallmarks of cancer development, such as immune response and angiogenic switch have been included along with corresponding treatments that have potential to change the traditional treatment regimens