Blackboard to Bedside: A Mathematical Modeling Bottom-Up Approach Toward Personalized Cancer Treatments

Cancers present with high variability across patients and tumors; thus, cancer care, in terms of disease prevention, detection, and control, can highly benefit from a personalized approach. For a comprehensive personalized oncology practice, this personalization should ideally consider data gathered from various information levels, which range from the macroscale population level down to the microscale tumor level, without omission of the central patient level. Appropriate data mined from each of these levels can significantly contribute in devising personalized treatment plans tailored to the individual patient and tumor. Mathematical models of solid tumors, combined with patient-specific tumor profiles, present a unique opportunity to personalize cancer treatments after detection using a bottom-up approach. Here, we discuss how information harvested from mathematical models and from corresponding in silico experiments can be implemented in preclinical and clinical applications. To conceptually illustrate the power of these models, one such model is presented, and various pertinent tumor and treatment scenarios are demonstrated in silico. The presented model, specifically a multiscale, hybrid cellular automaton, has been fully validated in vitro using multiple cell-line–specific data. We discuss various insights provided by this model and other models like it and their role in designing predictive tools that are both patient, and tumor specific. After refinement and parametrization with appropriate data, such in silico tools have the potential to be used in a clinical setting to aid in treatment protocols and decision making.Publisher PDFPeer reviewe

Bystander effects and their implications for clinical radiation therapy : insights from multiscale in silico experiments

GGP and MAJC thank University of Dundee, where this research was carried out. The authors gratefully acknowledge the support of the ERC Advanced Investigator Grant 227619, M5CGS - From Mutations to Metastases: Multiscale Mathematical Modelling of Cancer Growth and Spread. AJM Acknowledges support from EU BIOMICS Project DG-CNECT Contract 318202.Radiotherapy is a commonly used treatment for cancer and is usually given in varying doses. At low radiation doses relatively few cells die as a direct response to radiation but secondary radiation effects, such as DNA mutation or bystander phenomena, may affect many cells. Consequently it is at low radiation levels where an understanding of bystander effects is essential in designing novel therapies with superior clinical outcomes. In this article, we use a hybrid multiscale mathematical model to study the direct effects of radiation as well as radiation-induced bystander effects on both tumour cells and normal cells. We show that bystander responses play a major role in mediating radiation damage to cells at low-doses of radiotherapy, doing more damage than that due to direct radiation. The survival curves derived from our computational simulations showed an area of hyper-radiosensitivity at low-doses that are not obtained using a traditional radiobiological model.PostprintPeer reviewe

Simulation and sensitivity analysis on the parameter of non-targeted irradiation effects model

Real-life situations showed damage effects on non-targeted cells located in the vicinity of an irradiation region, due to danger signal molecules released by the targeted cells. This effect is widely known as radiation-induced bystander effects (RIBE). The purpose of this paper is to model the interaction of non-targeted cells towards bystander factors released by the irradiated cells by using a system of structured ordinary differential equations. The mathematical model and its simulations are presented in this paper. In the model, the cells are grouped based on the number of double-strand breaks (DSBs) and mis-repair DSBs because the DSBs are formed in non-targeted cells. After performing the model's simulations, the analysis continued with sensitivity analysis. Sensitivity analysis will determine which parameter in the model is the most sensitive to the survival fraction of non-targeted cells. The proposed mathematical model can explain the survival fraction of non-targeted cells affected by the bystander factors

Development of a coupled simulation toolkit for computational radiation biology based on Geant4 and CompuCell3D

RL acknowledges support from Consortium for Risk Evaluation and Stakeholder Participation (http://cresp.org). JAG acknowledges support from National Science Foundation grant NSF 1720625 and National Institutes of Health, National Institute of General Medical Sciences grants U01 GM111243 and R01 GM076692, JAG and MS acknowledge support from National Institutes of Health, National Institute of General Medical Sciences grant R01 GM122424.Understanding and designing clinical radiation therapy is one of the most important areas of state-of-the-art oncological treatment regimens. Decades of research have gone into developing sophisticated treatment devices and optimization protocols for schedules and dosages. In this paper, we presented a comprehensive computational platform that facilitates building of the sophisticated multi-cell-based model of how radiation affects the biology of living tissue. We designed and implemented a coupled simulation method, including a radiation transport model, and a cell biology model, to simulate the tumor response after irradiation. The radiation transport simulation was implemented through Geant4 which is an open-source Monte Carlo simulation platform that provides many flexibilities for users, as well as low energy DNA damage simulation physics, Geant4-DNA. The cell biology simulation was implemented using CompuCell3D (CC3D) which is a cell biology simulation platform. In order to couple Geant4 solver with CC3D, we developed a "bridging" module, RADCELL, that extracts tumor cellular geometry of the CC3D simulation (including specification of the individual cells) and ported it to the Geant4 for radiation transport simulation. The cell dose and cell DNA damage distribution in multicellular system were obtained using Geant4. The tumor response was simulated using cell-based tissue models based on CC3D, and the cell dose and cell DNA damage information were fed back through RADCELL to CC3D for updating the cell properties. By merging two powerful and widely used modeling platforms, CC3D and Geant4, we delivered a novel tool that can give us the ability to simulate the dynamics of biological tissue in the presence of ionizing radiation, which provides a framework for quantifying the biological consequences of radiation therapy. In this introductory methods paper, we described our modeling platform in detail and showed how it can be applied to study the application of radiotherapy to a vascularized tumor.PostprintPeer reviewe

A computer model of in vitro cellular response to radiation

It is believed that irradiation interacts with biological tissues to break or modify the DNA, which is the molecule contained in the nuclei of cells that carries all the relevant information for the organism. As such, radiation is dangerous for individuals; however, its properties can also be used in medicine, e.g.. in cancer treatments. Nevertheless, the exact mechanisms of cellular response to radiation are not fully understood yet, especially for low doses (below 50 cGy), where non-targeted effects, i.e. that do not involve only the interactions radiation-DNA, are taking place. In order to deepen the knowledge of those non-targeted effects,a.computer model of a population of cells irradiated in vitro was written, taking into account the phenomena in the low dose domain. As a start, two non-targeted effects were studied, the bystander effect and the low dose hyperradiosensitivity. The program was written in C++ and the technique of the cellular automaton was used. The clonogenic assay was reproduced; cells were seeded in a dish and if the colony they formed after a given period of time was bigger than 50 cells, the seeded cells were assumed to have survived. The direct effect of radiation was calculated by the traditional linear quadratic model and in addition cells were subjected to the bystander effect. Some simulations were run in the case of two cell lines, the hamster cell line V79 and the glioma cell line T98G. The results show that the bystander effect is unlikely to be limited to one period of the cell cycle, but that the low dose hyper-radiosensitivity and the bystander effect could be the same phenomenon. This work also suggests that the bystander effect may be significant after low doses of conventional radiotherapy. Such a model represents a very useful tool for solving problems that at the moment cannot be investigated experimentally.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Low-Dose Hypersensitivity and Bystander Effect are Not Mutually Exclusive in A549 Lung Carcinoma Cells after Irradiation with Charged Particles

The purpose of this study was to measure survival fraction of A549 lung carcinoma cells irradiated with charged particles of various LET and to determine mechanisms responsible for enhanced cell killing in the low-dose region. A549 cells were irradiated with a broadbeam of either 10 and 25 keV/μm protons or 100 keV/μm alpha particles and then processed for clonogenic assays and phospho-histone H3 staining. The survival fraction of unirradiated A549 cells co-cultured with irradiated cells was also evaluated. A549 cells were shown to exhibit low-dose hypersensitivity (HRS) for both protons and alpha particles. The dose threshold at which HRS occurs decreased with increasing linear energy transfer (LET), whereas αs, the initial survival curve slope, increased with increasing LET. In addition, the enhanced cell killing observed after irradiation with alpha particles was partly attributed to the bystander effect, due to the low proportion of hit cells at very low doses. Co-culture experiments suggest a gap junction-mediated bystander signal. Our results indicate that HRS is likely to be dependent on LET, and that a bystander effect and low-dose hypersensitivity may co-exist within a given cell line