Combining radiation with hyperthermia: a multiscale model informed by in vitro experiments

Abstract

Combined radiotherapy and hyperthermia offer great potential for the successful treatment of radio-resistant tumours through thermo-radiosensitization. Tumour response heterogeneity, due to intrinsic, or micro-environmentally induced factors, may greatly influence treatment outcome, but is difficult to account for using traditional treatment planning approaches. Systems oncology simulation, using mathematical models designed to predict tumour growth and treatment response, provides a powerful tool for analysis and optimization of combined treatments. We present a framework that simulates such combination treatments on a cellular level. This multiscale hybrid cellular automaton simulates large cell populations (up to 107 cells) in vitro, while allowing individual cell-cycle progression, and treatment response by modelling radiation-induced mitotic cell death, and immediate cell kill in response to heating. Based on a calibration using a number of experimental growth, cell cycle and survival datasets for HCT116 cells, model predictions agreed well (R2 > 0.95) with experimental data within the range of (thermal and radiation) doses tested (0–40 CEM43, 0–5 Gy). The proposed framework offers flexibility for modelling multimodality treatment combinations in different scenarios. It may therefore provide an important step towards the modelling of personalized therapies using a virtual patient tumour.

Combining hypoxia-activated prodrugs and radiotherapy in silico: Impact of treatment scheduling and the intra-tumoural oxygen landscape

“…Upon the availability of in vitro and in vivo data, this mathematical framework can be calibrated in order to serve as an in silico testbed for predicting HAP-IR treatment scenarios. As a result of interdisciplinary collaborations, the mathematical framework used in this study has previously been validated in vitro and in vivo for applications other than HAP-IR combination treatments [54,36]. The multiscale nature of the framework enables integration of data from various scales, be it from the subcellular scale, the cellular scale or the tissue scale.…”

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

“…Today, there exists a wide array of mathematical models that are able to capture various phases of tumor progression and associated mechanisms, such as tumor growth, invasion and metastasis, [20][21][22][23][24][25] angiogenesis, [26][27][28][29] and treatment responses. [30][31][32][33][34][35][36] A comprehensive overview of the field may be found in the review article by Lowengrub et al 37 Some of these models have successfully conferred with both in vitro and in vivo experiments or clinical observations [38][39][40] ; consequently, mathematical tumor modeling is steadily gaining acceptance in the medical community. Recently, several multiscale models have been developed to fully capture the spatiotemporal, multiscale nature of tumor dynamics.…”

Targeting cellular DNA damage responses in cancer: Anin vitro-calibrated agent-based model simulating monolayer and spheroid treatment responses to ATR-inhibiting drugs

“…We use Robustness Analysis to investigate how sensitive the output is to local parameter perturbations, that is to say when input parameters are varied one at a time. Figures 7,8,9,10,11,12,13 provide boxplots andÂ-measures that demonstrate the effect that local perturbations of the input variables µ, σ, Π D−s , Θ D−S , EC 50 , γ and T L→D respectively have on the output variables X 1 and X 2 . Key findings are listed below, discussing the impact of one input parameter at a time.…”

“…The in vitro and in vivo data used in our current study is gathered from this previous work by Checkley et al [7]. The mathematical framework used in our study is an extension of a mathematical model introduced by Powathil et al [8] that has previously been used to study tumour growth, chemotherapy responses, radiotherapy responses, drug resistance and more [8,9,10,11,12].…”

“…We demonstrated that the mathematical framework used in this study can successfully simulate growth and drug (AZD6738) responses both in cancer cell populations (in vitro) and in tumour xenografts (in vivo). This versatile mathematical framework is an extension of a multiscale, hybrid, agent-based, on-lattice model that has previously been used to study tumour growth and treatment responses to chemotherapy, radiotherapy, hypoxia-activated prodrugs and more [8,9,10,11,12]. By only adjusting the rules in the mathematical framework, whilst keeping model parameters intact, the mathematical model can first be calibrated by in vitro data and thereafter be used to successfully predict treatment responses in human tumour xenografts in vivo.…”