Multiscale modelling of cancer progression and treatment control : the role of intracellular heterogeneities in chemotherapy treatment

Cancer is a complex, multiscale process involving interactions at intracellular, intercellular and tissue scales that are in turn susceptible to microenvironmental changes. Each individual cancer cell within a cancer cell mass is unique, with its own internal cellular pathways and biochemical interactions. These interactions contribute to the functional changes at the cellular and tissue scale, creating a heterogenous cancer cell population. Anticancer drugs are effective in controlling cancer growth by inflicting damage to various target molecules and thereby triggering multiple cellular and intracellular pathways, leading to cell death or cell-cycle arrest. One of the major impediments in the chemotherapy treatment of cancer is drug resistance driven by multiple mechanisms, including multi-drug and cell-cycle mediated resistance to chemotherapy drugs. In this article, we discuss two hybrid multiscale modelling approaches, incorporating multiple interactions involved in the sub-cellular, cellular and microenvironmental levels to study the effects of cell-cycle, phase-specific chemotherapy on the growth and progression of cancer cells.PostprintPeer reviewe

Hybrid data-based modelling in oncology: successes, challenges and hopes

International audienceIn this review we make the statement that hybrid models in oncology are required as a mean for enhanced data integration. In the context of systems oncology, experimental and clinical data need to be at the heart of the models developments from conception to validation to ensure a relevant use of the models in the clinical context. The main applications pursued are to improve diagnosis and to optimize therapies.We first present the Successes achieved thanks to hybrid modelling approaches to advance knowledge, treatments or drug discovery. Then we present the Challenges than need to be addressed to allow for a better integration of the model parts and of the data into the models. And Finally, the Hopes with a focus towards making personalised medicine a reality. Mathematics Subject Classification. 35Q92, 68U20, 68T05, 92-08, 92B05

Modeling the Spatial Distribution of Chronic Tumor Hypoxia: Implications for Experimental and Clinical Studies

Tumor oxygenation status is considered one of the important prognostic markers in cancer since it strongly influences the response of cancer cells to various treatments; in particular, to radiation therapy. Thus, a proper and accurate assessment of tumor oxygen distribution before the treatment may highly affect the outcome of the treatment. The heterogeneous nature of tumor hypoxia, mainly influenced by the complex tumor microenvironment, often makes its quantification very difficult. The usual methods used to measure tumor hypoxia are biomarkers and the polarographic needle electrode. Although these techniques may provide an acceptable assessment of hypoxia, they are invasive and may not always give a spatial distribution of hypoxia, which is very useful for treatment planning. An alternative method to quantify the tumor hypoxia is to use theoretical simulations with the knowledge of tumor vasculature. The purpose of this paper is to model tumor hypoxia using a known spatial distribution of tumor vasculature obtained from image data, to analyze the accuracy of polarographic needle electrode measurements in quantifying hypoxia, to quantify the optimum number of measurements required to satisfactorily evaluate the tumor oxygenation status, and to study the effects of hypoxia on radiation response. Our results indicate that the model successfully generated an accurate oxygenation map for tumor cross-sections with known vascular distribution. The method developed here provides a way to estimate tumor hypoxia and provides guidance in planning accurate and effective therapeutic strategies and invasive estimation techniques. Our results agree with the previous findings that the needle electrode technique gives a good estimate of tumor hypoxia if the sampling is done in a uniform way with 5-6 tracks of 20–30 measurements each. Moreover, the analysis indicates that the accurate measurement of oxygen profile can be very useful in determining right radiation doses to the patients

Modeling of Brain Tumors: Effects of Microenvironment and Associated Therapeutic Strategies

Gliomas are the most common and aggressive primary brain tumors. The most common treatment protocols for these brain tumors are combinations of surgery, chemotherapy and radiotherapy. However, even with the most aggressive combination of surgery and radiotherapy and/or chemotherapy schedules, gliomas almost always recur resulting in a median survival time for patients of not more than 12 months. This highly diffusive and invasive nature of brain tumors makes it very important to study the effects of these combined therapeutic strategies in an effort to improve the survival time of patients. It is also important to study the tumor microenvironment, since the complex nature of the cerebral vasculature, including the blood brain barrier and several other tumor-induced conditions such as hypoxia, high interstitial pressure, and cerebral edema affect drug delivery as well as the effectiveness of radiotherapy. Recently, a novel strategy using antiangiogenic therapy has been studied for the treatment of brain tumors. Antiangiogenic therapy interferes with the development of tumor vasculature and indirectly helps in the control of tumor growth. Recent clinical trials suggest that anti-angiogenic therapy is usually more effective when given in combination with other therapeutic strategies. In an effort to study the effects of the aforementioned therapeutic strategies, a spatio-temporal model is considered here that incorporates the tumor cell growth and the effects of radiotherapy and chemotherapy. The effects of different schedules of radiation therapy is then studied using a generalized linear quadratic model and compared against the published clinical data. The model is then extended to include the interactions of tumor vasculature and oxygen concentration, to explain tumor hypoxia and to study various methods of hypoxia characterizations including biomarker estimates and needle electrode measurements. The model predicted hypoxia is also used to analyze the effects of tumor oxygenation status on radiation response as it is known that tumor hypoxia negatively influences the radiotherapy outcome. This thesis also presents a detailed analysis of the effects of heterogenous tumor vasculature on tumor interstitial fluid pressure and interstitial fluid velocity. A mathematical modeling approach is then used to analyze the changes in interstitial fluid pressure with or without antiangiogenic therapy

A heat and mass transfer model for bread baking: An investigation using numerical schemes

Master'sMASTER OF SCIENC

Process Algebra with Layers: Multi-scale Integration Modelling applied to Cancer Therapy

We present a novel Process Algebra designed for multi-scale integration modelling: Process Algebra with Layers (PAL). The unique feature of PAL is the modularisation of scale into integrated layers: Object and Population. An Object can represent a molecule, organelle, cell, tissue, organ or any organism. Populations hold specific types of Object, for example, life stages, cell phases and infectious states. The syntax and semantics of this novel language are presented. A PAL model of the multi-scale system of cell growth and damage from cancer treatment is given. This model allows the analysis of different scales of the system. The Object and Population levels give insight into the length of a cell cycle and cell population growth respectively. The PAL model results are compared to wet laboratory survival fractions of cells given different doses of radiation treatment [1]. This comparison shows how PAL can be used to aid in investigations of cancer treatment in systems biology