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

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.

Predicting Radiotherapy Patient Outcomes with Real-Time Clinical Data Using Mathematical Modelling

Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed to predict treatment outcome in this context, and have the potential to guide clinical decision-making and inform personalised fractionation protocols. Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses. In this work, we present a compartment model of tumour volume and tumour composition, which, despite relative simplicity, is capable of producing a wide range of patient responses. We then develop novel statistical methodology and leverage a cohort of existing clinical data to produce a predictive model of both tumour volume progression and the associated level of uncertainty that evolves throughout a patient's course of treatment. To capture inter-patient variability, all model parameters are patient specific, with a bootstrap particle filter-like Bayesian approach developed to model a set of training data as prior knowledge. We validate our approach against a subset of unseen data, and demonstrate both the predictive ability of our trained model and its limitations.

Low Dose and Non-Targeted Radiation Effects in Environmental Protection and Medicine-A New Model Focusing on Electromagnetic Signaling

The role of signalling in initiating and perpetuating effects triggered by deposition of ionising radiation energy in parts of a system is very clear. Less clear are the very early steps involved in converting energy to chemical and biological effects in non-targeted parts of the system. The paper aims to present a new model, which could aid our understanding of the role of low dose effects in determining ultimate disease outcomes. We propose a key role for electromagnetic signals resulting from physico-chemical processes such as excitation decay, and acoustic waves. These lead to the initiation of damage response pathways such as elevation of reactive oxygen species and membrane associated changes in key ion channels. Critically, these signalling pathways allow coordination of responses across system levels. For example, depending on how these perturbations are transduced, adverse or beneficial outcomes may predominate. We suggest that by appreciating the importance of signalling and communication between multiple levels of organisation, a unified theory could emerge. This would allow the development of models incorporating time, space and system level to position data in appropriate areas of a multidimensional domain. We propose the use of the term "infosome" to capture the nature of radiation-induced communication systems which include physical as well as chemical signals. We have named our model "the variable response model" or "VRM" which allows for multiple outcomes following exposure to low doses or to signals from low dose irradiated cells, tissues or organisms. We suggest that the use of both dose and infosome in radiation protection might open up new conceptual avenues that could allow intrinsic uncertainty to be embraced within a holistic protection framework.

Datasets of in vitro clonogenic assays showing low dose hyper-radiosensitivity and induced radioresistance

Low dose hyper-radiosensitivity and induced radioresistance are primarily observed in surviving fractions of cell populations exposed to ionizing radiation, plotted as the function of absorbed dose. Several biophysical models have been developed to quantitatively describe these phenomena. However, there is a lack of raw, openly available experimental data to support the development and validation of quantitative models. The aim of this study was to set up a database of experimental data from the public literature. Using Google Scholar search, 46 publications with 101 datasets on the dose-dependence of surviving fractions, with clear evidence of low dose hyper-radiosensitivity, were identified. Surviving fractions, their uncertainties, and the corresponding absorbed doses were digitized from graphs of the publications. The characteristics of the cell line and the irradiation were also recorded, along with the parameters of the linear-quadratic model and/or the induced repair model if they were provided. The database is available in STOREDB, and can be used for meta-analysis, for comparison with new experiments, and for development and validation of biophysical models.

Low dose radiation mechanisms: The certainty of uncertainty

This paper reviews the current understanding of low dose radiobiology, and how it has evolved from classical target theory. It highlights the uncertainty around low dose effects, which is due in part to the complexity of "context" surrounding the ultimate expression of biological effects following low dose exposure. The paper makes special reference to low dose non-targeted effects which, are currently ignored in radiation protection and population level risk assessment, because it is unclear what they mean for risk. The view of the authors is that this "lack of clarity" about what the effects mean is precisely the point. It indicates the uncertainty of outcomes after a given exposure. The uncertainty stems from multiple outcome options resulting from the intrinsic uncertainty of the stochastic interaction of low dose radiation with matter. This uncertainty should be embraced rather than eschewed. The impacts of the uncertainties identified in this paper is explored and an approach to quantifying mutation probability in relation to dose is presented.

Role of nano-sensitizers in radiation therapy of metastatic tumors

Cancer metastasis remains the major cause of global cancer deaths. Radiation therapy remains one of the golden standards for cancer treatment. Nanomedicine based strategies have been designed and developed in order to improve the clinical outcomes of cancer therapy and diagnosis at molecular levels. Over the years, several researchers have shown their interest in using radiosensitizers made of high Z elements. Metal-based nanosystems also play a dual role by enhancing the synergistic effect of cell killing via various biological immune responses. This review summarizes the role of Nano-sensitizers in boosting radiation (ionizing/non-ionizing radiations) induced biological responses in treatment of metastatic cancer models.

REVIEW OF QUANTITATIVE MECHANISTIC MODELS OF RADIATION-INDUCED NON-TARGETED EFFECTS (NTE)

Quantitative mechanistic modeling of the biological effects of ionizing radiation has a long rich history. Initially, it was dominated by target theory, which quantifies damage caused by traversal of cellular targets like DNA by ionizing tracks. The discovery that mutagenesis, death and/or altered behavior sometimes occur in cells that were not themselves traversed by any radiation tracks but merely interacted with traversed cells was initially seen as surprising. As more evidence of such 'non-targeted' or 'bystander' effects accumulated, the importance of their contribution to radiation-induced damage became more recognized. Understanding and modeling these processes is important for quantifying and predicting radiation-induced health risks. Here we review the variety of mechanistic mathematical models of nontargeted effects that emerged over the past 2-3 decades. This review is not intended to be exhaustive, but focuses on the main assumptions and approaches shared or distinct between models, and on identifying areas for future research.

Microdosimetry-based determination of tumour control probability curves for treatments with 225Ac-PSMA of metastatic castration resistant prostate cancer

We performed Monte Carlo simulations in order to determine, by means of microdosimetry calculations, tumour control probability (TCP) curves for treatments with ²²⁵Ac-PSMA of metastatic castration resistant prostate cancer (mCRPC). Realistic values of cell radiosensitivity, nucleus size and lesion size were used for calculations. As the cell radiosensitivity decreased, the nucleus size decreased and the lesion size increased, the absorbed dose to reach a given TCP increased. The widest variations occurred with regard to the cell radiosensitivity. For the Monte Carlo simulations, in order to address a non-uniform PSMA expression, different ²²⁵Ac-PSMA distributions were considered. The effect of these different PSMA distributions resulted in small variations in the TCP curves (maximum variation of 5%). Absorbed doses to reach a TCP of 0.9 for a uniform ²²⁵Ac-PSMA distribution, considering a relative biological effectiveness (RBE) of 5, ranged between 35.0 Gy and 116.5 Gy. The lesion absorbed doses per administered activity reported in a study on treatments with ²²⁵Ac-PSMA of mCRPC ranged between 1.3 Gy MBq^-1 and 9.8 Gy MBq^-1 for a RBE = 5. For a 70 kg-patient to whom 100 kBq kg^-1 of ²²⁵Ac-PSMA are administered, the range of lesion absorbed doses would be between 9.1 Gy and 68.6 Gy. Thus, for a single cycle of 100 kBq kg^-1, a number of lesions would not receive an absorbed dose high enough to reach a TCP of 0.9.

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.

The malignancy of liver cancer cells is increased by IL-4/ERK/AKT signaling axis activity triggered by irradiated endothelial cells

The malignant traits involved in tumor relapse, metastasis and the expansion of cancer stem-like cells are acquired via the epithelial-mesenchymal transition (EMT) process in the tumor microenvironment. In addition, the tumor microenvironment strongly supports the survival and growth of malignant tumor cells and further contributes to the reduced efficacy of anticancer therapy. Ionizing radiation can influence the tumor microenvironment, because it alters the biological functions of endothelial cells composing tumor vascular systems. However, to date, studies on the pivotal role of these endothelial cells in mediating the malignancy of cancer cells in the irradiated tumor microenvironment are rare. We previously evaluated the effects of irradiated endothelial cells on the malignant traits of human liver cancer cells and reported that endothelial cells irradiated with 2 Gy reinforce the malignant properties of these cancer cells. In this study, we investigated the signaling mechanisms underlying these events. We revealed that the increased expression level of IL-4 in endothelial cells irradiated with 2 Gy eventually led to enhanced migration and invasion of cancer cells and further expansion of cancer stem-like cells. In addition, this increased level of IL-4 activated the ERK and AKT signaling pathways to reinforce these events in cancer cells. Taken together, our data indicate that ionizing radiation may indirectly modulate malignancy by affecting endothelial cells in the tumor microenvironment. Importantly, these indirect effects on malignancy are thought to offer valuable clues or targets for overcoming the tumor recurrence after radiotherapy.

Coupling of cell fate selection model enhances DNA damage response and may underlie BE phenomenon

Double-strand break-induced (DSB) cells send signal that induces DSBs in neighbour cells, resulting in the interaction among cells sharing the same medium. Since p53 network gives oscillatory response to DSBs, such interaction among cells could be modelled as an excitatory coupling of p53 network oscillators. This study proposes a plausible coupling model of three-mode two-dimensional oscillators, which models the p53-mediated cell fate selection in globally coupled DSB-induced cells. The coupled model consists of ATM and Wip1 proteins as variables. The coupling mechanism is realised through ATM variable via a mean-field modelling the bystander signal in the intercellular medium. Investigation of the model reveals that the coupling generates more sensitive DNA damage response by affecting cell fate selection. Additionally, the authors search for the cause-effect relationship between coupled p53 network oscillators and bystander effect (BE) endpoints. For this, they search for the possible values of uncertain parameters that may replicate BE experiments' results. At certain parametric regions, there is a correlation between the outcomes of cell fate and endpoints of BE, suggesting that the intercellular coupling of p53 network may manifest itself as the form of observed BEs.

Cell death and survival due to cytotoxic exposure modelled as a two-state Ising system

Cancer chemotherapy agents are assessed for their therapeutic utility primarily by their ability to cause apoptosis of cancer cells and their potency is given by an IC50 value. Chemotherapy uses both target-specific and systemic-action drugs and drug combinations to treat cancer. It is important to judiciously choose a drug type, its dosage and schedule for optimized drug selection and administration. Consequently, the precise mathematical formulation of cancer cells' response to chemotherapy may assist in the selection process. In this paper, we propose a mathematical description of the cancer cell response to chemotherapeutic agent exposure based on a time-tested physical model of two-state multiple-component systems near criticality. We describe the Ising model methodology and apply it to a diverse panel of cytotoxic drugs administered against numerous cancer cell lines in a dose-response manner. The analysed dataset was generated by the Netherlands Translational Research Center B.V. (Oncolines). This approach allows for an accurate and consistent analysis of cytotoxic agents' effects on cancer cell lines and reveals the presence or absence of the bystander effect through the interaction constant. By calculating the susceptibility function, we see the value of IC50 coinciding with the peak of this measure of the system's sensitivity to external perturbations.

Hadrontherapy Interactions in Molecular and Cellular Biology

The resistance of cancer cells to radiotherapy is a major issue in the curative treatment of cancer patients. This resistance can be intrinsic or acquired after irradiation and has various definitions, depending on the endpoint that is chosen in assessing the response to radiation. This phenomenon might be strengthened by the radiosensitivity of surrounding healthy tissues. Sensitive organs near the tumor that is to be treated can be affected by direct irradiation or experience nontargeted reactions, leading to early or late effects that disrupt the quality of life of patients. For several decades, new modalities of irradiation that involve accelerated particles have been available, such as proton therapy and carbon therapy, raising the possibility of specifically targeting the tumor volume. The goal of this review is to examine the up-to-date radiobiological and clinical aspects of hadrontherapy, a discipline that is maturing, with promising applications. We first describe the physical and biological advantages of particles and their application in cancer treatment. The contribution of the microenvironment and surrounding healthy tissues to tumor radioresistance is then discussed, in relation to imaging and accurate visualization of potentially resistant hypoxic areas using dedicated markers, to identify patients and tumors that could benefit from hadrontherapy over conventional irradiation. Finally, we consider combined treatment strategies to improve the particle therapy of radioresistant cancers.

Stochastic multicellular modeling of x-ray irradiation, DNA damage induction, DNA free-end misrejoining and cell death

The repair or misrepair of DNA double-strand breaks (DSBs) largely determines whether a cell will survive radiation insult or die. A new computational model of multicellular, track structure-based and pO₂-dependent radiation-induced cell death was developed and used to investigate the contribution to cell killing by the mechanism of DNA free-end misrejoining for low-LET radiation. A simulated tumor of 1224 squamous cells was irradiated with 6 MV x-rays using the Monte Carlo toolkit Geant4 with low-energy Geant4-DNA physics and chemistry modules up to a uniform dose of 1 Gy. DNA damage including DSBs were simulated from ionizations, excitations and hydroxyl radical interactions along track segments through cell nuclei, with a higher cellular pO₂ enhancing the conversion of DNA radicals to strand breaks. DNA free-ends produced by complex DSBs (cDSBs) were able to misrejoin and produce exchange-type chromosome aberrations, some of which were asymmetric and lethal. A sensitivity analysis was performed and conditions of full oxia and anoxia were simulated. The linear component of cell killing from misrejoining was consistently small compared to values in the literature for the linear component of cell killing for head and neck squamous cell carcinoma (HNSCC). This indicated that misrejoinings involving DSBs from the same x-ray (including all associated secondary electrons) were rare and that other mechanisms (e.g. unrejoined ends) may be important. Ignoring the contribution by the indirect effect toward DNA damage caused the DSB yield to drop to a third of its original value and the cDSB yield to drop to a tenth of its original value. Track structure-based cell killing was simulated in all 135306 viable cells of a 1 mm³ hypoxic HNSCC tumor for a uniform dose of 1 Gy.

Approaches to combat hypoxia in cancer therapy and the potential for in silico models in their evaluation

AIM

The negative impact of tumour hypoxia on cancer treatment outcome has been long-known, yet there has been little success combating it. This paper investigates the potential role of in silico modelling to help test emerging hypoxia-targeting treatments in cancer therapy.

METHODS

A Medline search was undertaken on the current landscape of in silico models that simulate cancer therapy and evaluate their ability to test hypoxia-targeting treatments. Techniques and treatments to combat tumour hypoxia and their current challenges are also presented.

RESULTS

Hypoxia-targeting treatments include tumour reoxygenation, hypoxic cell radiosensitization with nitroimidazoles, hypoxia-activated prodrugs and molecular targeting. Their main challenges are toxicity and not achieving adequate delivery to hypoxic regions of the tumour. There is promising research toward combining two or more of these techniques. Different types of in silico therapy models have been developed ranging from temporal to spatial and from stochastic to deterministic models. Numerous models have compared the effectiveness of different radiotherapy fractionation schedules for controlling hypoxic tumours. Similarly, models could help identify and optimize new treatments for overcoming hypoxia that utilize novel hypoxia-targeting technology.

CONCLUSION

Current therapy models should attempt to incorporate more sophisticated modelling of tumour angiogenesis/vasculature and vessel perfusion in order to become more useful for testing hypoxia-targeting treatments, which typically rely upon the tumour vasculature for delivery of additional oxygen, (pro)drugs and nanoparticles.

Enhancing low-dose risk assessment using mechanistic mathematical models of radiation effects

Mechanistic mathematical modeling of ionizing radiation (IR) effects has a long history spanning several decades. Models that mathematically represent current knowledge and hypotheses about how radiation damages cells and organs, leading to deleterious outcomes such as carcinogenesis, are particularly useful for estimating radiation risks at doses that are relevant for radiation protection, but are too low to provide a strong 'signal-to-noise ratio' in epidemiological or experimental studies with realistic sample sizes. Here, I discuss examples of models in several relevant areas, including radionuclide biokinetics, non-targeted IR effects, DNA double-strand break (DSB) rejoining and radiation carcinogenesis. I do not provide a detailed review of the vast modeling literature in these fields, but focus on concepts that we have implemented, such as using continuous probability distributions of exponential rates to model radionuclide biokinetics and DSB rejoining, and combining short and long time scales in carcinogenesis models. Improvements in models, including the ability to generate new hypotheses based on model predictions, may come from the introduction of additional novel concepts and from integrating multiple data types.

A Review of Cell-Based Computational Modeling in Cancer Biology

Cancer biology involves complex, dynamic interactions between cancer cells and their tissue microenvironments. Single-cell effects are critical drivers of clinical progression. Chemical and mechanical communication between tumor and stromal cells can co-opt normal physiologic processes to promote growth and invasion. Cancer cell heterogeneity increases cancer's ability to test strategies to adapt to microenvironmental stresses. Hypoxia and treatment can select for cancer stem cells and drive invasion and resistance. Cell-based computational models (also known as discrete models, agent-based models, or individual-based models) simulate individual cells as they interact in virtual tissues, which allows us to explore how single-cell behaviors lead to the dynamics we observe and work to control in cancer systems. In this review, we introduce the broad range of techniques available for cell-based computational modeling. The approaches can range from highly detailed models of just a few cells and their morphologies to millions of simpler cells in three-dimensional tissues. Modeling individual cells allows us to directly translate biologic observations into simulation rules. In many cases, individual cell agents include molecular-scale models. Most models also simulate the transport of oxygen, drugs, and growth factors, which allow us to link cancer development to microenvironmental conditions. We illustrate these methods with examples drawn from cancer hypoxia, angiogenesis, invasion, stem cells, and immunosurveillance. An ecosystem of interoperable cell-based simulation tools is emerging at a time when cloud computing resources make software easier to access and supercomputing resources make large-scale simulation studies possible. As the field develops, we anticipate that high-throughput simulation studies will allow us to rapidly explore the space of biologic possibilities, prescreen new therapeutic strategies, and even re-engineer tumor and stromal cells to bring cancer systems under control.

The tubercular badger and the uncertain curve:- The need for a multiple stressor approach in environmental radiation protection

This article presents the results of a workshop held in Stirling, Scotland in June 2018, called to examine critically the effects of low-dose ionising radiation on the ecosphere. The meeting brought together participants from the fields of low- and high-dose radiobiology and those working in radioecology to discuss the effects that low doses of radiation have on non-human biota. In particular, the shape of the low-dose response relationship and the extent to which the effects of low-dose and chronic exposure may be predicted from high dose rate exposures were discussed. It was concluded that high dose effects were not predictive of low dose effects. It followed that the tools presently available were deemed insufficient to reliably predict risk of low dose exposures in ecosystems. The workshop participants agreed on three major recommendations for a path forward. First, as treating radiation as a single or unique stressor was considered insufficient, the development of a multidisciplinary approach is suggested to address key concerns about multiple stressors in the ecosphere. Second, agreed definitions are needed to deal with the multiplicity of factors determining outcome to low dose exposures as a term can have different meanings in different disciplines. Third, appropriate tools need to be developed to deal with the different time, space and organisation level scales. These recommendations permit a more accurate picture of prospective risks.

Irradiated endothelial cells modulate the malignancy of liver cancer cells

The tumor microenvironment is closely associated with tumor malignancy, and includes tumor relapse and metastasis trigged by epithelial-mesenchymal transition (EMT), which leads to the expansion of cancer stem-like cells. Radiotherapy is known to acutely and persistently affect changes in this tumor microenvironment by altering the vascular functions of tumor endothelial cells. However, the precise role of endothelial cells in tumor malignancy following treatment with irradiation has not been completely elucidated. The present study investigated the differences in malignant behavior of liver cancer cells in response to irradiated endothelial cells. To achieve this, a co-cultivation system was established to identify the potential role of endothelial cells in malignant liver cancer cells using medium conditioned with endothelial cells. It was observed that the medium conditioned by endothelial cells when irradiated with a single dose (2 Gy), greatly increased the migratory and invasive properties of liver cancer cells, as well as inducing mesenchymal markers, and enhancing the sphere-forming ability of liver cancer cells, The mRNA levels of genes regulating the self-renewal of cancer stem cells were increased in liver cancer cells by treatment with medium conditioned with endothelial cells. However, neither the medium conditioned by endothelial cells irradiated with fractionated doses (2 Gy × 3; 2 Gy/day for 3 days) or with a single dose (6 Gy) greatly influenced the malignancy of liver cancer cells. In conclusion, the data obtained by the present study indicated that 2 Gy irradiation of endothelial cells influenced the increase in tumor malignancy in liver cancer cells. Furthermore, the distinct differences in the indirect effects of ionizing radiation on tumor malignancy may provide valuable information for the improvement in the efficacy of radiotherapy.

An agent-based model for drug-radiation interactions in the tumour microenvironment: Hypoxia-activated prodrug SN30000 in multicellular tumour spheroids

Multicellular tumour spheroids capture many characteristics of human tumour microenvironments, including hypoxia, and represent an experimentally tractable in vitro model for studying interactions between radiotherapy and anticancer drugs. However, interpreting spheroid data is challenging because of limited ability to observe cell fate within spheroids dynamically. To overcome this limitation, we have developed a hybrid continuum/agent-based model (ABM) for HCT116 tumour spheroids, parameterised using experimental models (monolayers and multilayers) in which reaction and diffusion can be measured directly. In the ABM, cell fate is simulated as a function of local oxygen, glucose and drug concentrations, determined by solving diffusion equations and intracellular reactions. The model is lattice-based, with cells occupying discrete locations on a 3D grid embedded within a coarser grid that encompasses the culture medium; separate solvers are employed for each grid. The generated concentration fields account for depletion in the medium and specify concentration-time profiles within the spheroid. Cell growth and survival are determined by intracellular oxygen and glucose concentrations, the latter based on direct measurement of glucose diffusion/reaction (in multilayers) for the first time. The ABM reproduces known features of spheroids including overall growth rate, its oxygen and glucose dependence, peripheral cell proliferation, central hypoxia and necrosis. We extended the ABM to describe in detail the hypoxia-dependent interaction between ionising radiation and a hypoxia-activated prodrug (SN30000), again using experimentally determined parameters; the model accurately simulated clonogenic cell killing in spheroids, while inclusion of reversible cell cycle delay was required to account for the marked spheroid growth delay after combined radiation and SN30000. This ABM of spheroid growth and response exemplifies the utility of integrating computational and experimental tools for investigating radiation/drug interactions, and highlights the critical importance of understanding oxygen, glucose and drug concentration gradients in interpreting activity of therapeutic agents in spheroid models.

Studies in 3D cultures, notably multicellular tumour spheroids that mimic many features of solid tumours, have great potential for speeding up anticancer drug development. However the increased complexity of 3D cultures makes interpretation of experiments more difficult. We have developed a hybrid continuum/agent-based mathematical model, validated by experiments, to aid interpretation of spheroid experiments in developing drugs designed to eliminate radiation-resistant hypoxic cells. This model includes key features of the tumour microenvironment including oxygen and glucose transport and regions of hypoxia where the cells are resistant to radiation, but sensitive to hypoxia-activated prodrugs such as SN30000. This enables us to predict the growth and cell response in untreated spheroids and compare the results to spheroids treated with radiation and SN30000. We demonstrate good prediction of cellular responses in spheroids treated with radiation and SN30000 and good agreement with spheroid regrowth after treatment when additional effects of cellular growth delay are added. This demonstrates that the modelling approach has potential to improve interpretation of experimental investigations of drug and radiation combinations.

Radiomodulatory effects of Aloe vera on hepatic and renal tissues of X-ray irradiated mice

The present study was aimed to explore the protective role of Aloe vera gel extract against hepatic and renal damage caused by X-ray exposure to mice. Male balb/c mice were divided into four groups: control, Aloe vera gel extract [AV] (50 mg/ kg b.w on alternate days for 30 days), X-ray (2 Gy) and AV + X-ray. X-ray irradiation enhanced the serum levels of liver function indices and chromosomal abnormalities in liver. Kidney function markers were found to be deranged and were accompanied by reduced glomerular filtration rate indicating renal dysfunction. Irradiation caused histopathological and biochemical alterations in both tissues which was associated with enhanced reactive oxygen species (ROS), lipid peroxidation (LPO) levels, lactate dehydrogenase (LDH) activity and enhanced apoptosis as revealed by TUNEL assay and DNA fragmentation. The administration of Aloe vera gel extract to X-ray exposed animals significantly improved their hepatic and renal function parameters which were associated with a reduction in ROS/LPO levels, LDH activity and chromosomal abnormalities as compared to their irradiated counterparts. In vitro assays revealed effective radical scavenging ability of Aloe vera gel extract, which may be linked to its potential in exhibiting antioxidant effects in in vivo conditions. This data suggested that Aloe vera may serve to boost the antioxidant system, thus providing protection against hepatic and renal damage caused by X-ray.

Effects of G2-checkpoint dynamics on low-dose hyper-radiosensitivity

In experimental studies, it has been found that certain cell lines are more sensitive to low-dose radiation than would be expected from the classical Linear-Quadratic model (LQ model). In fact, it is frequently observed that cells incur more damage at low dose (say 0.3 Gy) than at higher dose (say 1 Gy). This effect has been termed hyper-radiosensitivity (HRS). The effect depends on the type of cells and on their phase in the cell cycle when radiation is applied. Experiments have shown that the G2-checkpoint plays an important role in the HRS effects. Here we design and analyze a differential equation model for the cell cycle that includes G2-checkpoint dynamics and radiation treatment. We fit the model to surviving fraction data for different cell lines including glioma cells, prostate cancer cells, as well as to cell populations that are enriched in certain phases of the cell cycle. The HRS effect is measured in the literature through [Formula: see text], the ratio of slope [Formula: see text] of the surviving fraction curve at zero dose to slope [Formula: see text] of the corresponding LQ model. We derive an explicit formula for this ratio and we show that it corresponds very closely to experimental observations. Finally, we identify the dependence of this ratio on the surviving fraction at 2 Gy. It was speculated in the literature that such dependence exists. Our theoretical analysis will help to more systematically identify the HRS in cell lines, and opens doors to analyze its use in cancer treatment.

When a duck is not a duck; a new interdisciplinary synthesis for environmental radiation protection

This consensus paper presents the results of a workshop held in Essen, Germany in September 2017, called to examine critically the current approach to radiological environmental protection. The meeting brought together participants from the field of low dose radiobiology and those working in radioecology. Both groups have a common aim of identifying radiation exposures and protecting populations and individuals from harmful effects of ionising radiation exposure, but rarely work closely together. A key question in radiobiology is to understand mechanisms triggered by low doses or dose rates, leading to adverse outcomes of individuals while in radioecology a key objective is to recognise when harm is occurring at the level of the ecosystem. The discussion provided a total of six strategic recommendations which would help to address these questions.

Cancer Radiosensitizers

Radiotherapy (RT) is a mainstay treatment for many types of cancer, although it is still a large challenge to enhance radiation damage to tumor tissue and reduce side effects to healthy tissue. Radiosensitizers are promising agents that enhance injury to tumor tissue by accelerating DNA damage and producing free radicals. Several strategies have been exploited to develop highly effective and low-toxicity radiosensitizers. In this review, we highlight recent progress on radiosensitizers, including small molecules, macromolecules, and nanomaterials. First, small molecules are reviewed based on free radicals, pseudosubstrates, and other mechanisms. Second, nanomaterials, such as nanometallic materials, especially gold-based materials that have flexible surface engineering and favorable kinetic properties, have emerged as promising radiosensitizers. Finally, emerging macromolecules have shown significant advantages in RT because these molecules can be combined with biological therapy as well as drug delivery. Further research on the mechanisms of radioresistance and multidisciplinary approaches will accelerate the development of radiosensitizers.

Assessment of The Dose-Response Relationship of Radiation-Induced Bystander Effect in Two Cell Lines Exposed to High Doses of Ionizing Radiation (6 and 8 Gy)

OBJECTIVES

The dose-response relationship of radiation-induced bystander effect (RIBE) is controversial at high dose levels. The aim of the present study is to assess RIBE at high dose levels by examination of different endpoints.

MATERIALS AND METHODS

This experimental study used the medium transfer technique to induce RIBE. The cells were divided into two main groups: QU-DB cells which received medium from autologous irradiated cells and MRC5 cells which received medium from irradiated QU-DB cells. Colony, MTT, and micronucleus assays were performed to quantify bystander responses. The medium was diluted and transferred to bystander cells to investigate whether medium dilution could revive the RIBE response that disappeared at a high dose.

RESULTS

The RIBE level in QU-DB bystander cells increased in the dose range of 0.5 to 4 Gy, but decreased at 6 and 8 Gy. The Micronucleated cells per 1000 binucleated cells (MNBN) frequency of QU-DB bystander cells which received the most diluted medium from 6 and 8 Gy QU-DB irradiated cells reached the maximum level compared to the MNBN frequency of the cells that received complete medium (P<0.0001). MNBN frequency of MRC5 cells which received the most diluted medium from 4 Gy QU-DB irradiated cells reached the maximum level compared to MNBN frequency of cells that received complete medium (P<0.0001).

CONCLUSIONS

Our results showed that RIBE levels decreased at doses above 4 Gy; however, RIBE increased when diluted conditioned medium was transferred to bystander cells. This finding confirmed that a negative feedback mechanism was responsible for the decrease in RIBE response at high doses. Decrease of RIBE at high doses might be used to predict that in radiosurgery, brachytherapy and grid therapy, in which high dose per fraction is applied, normal tissue damage owing to RIBE may decrease.

Blood vessel tortuosity selects against evolution of aggressive tumor cells in confined tissue environments: A modeling approach

Cancer is a disease of cellular regulation, often initiated by genetic mutation within cells, and leading to a heterogeneous cell population within tissues. In the competition for nutrients and growth space within the tumors the phenotype of each cell determines its success. Selection in this process is imposed by both the microenvironment (neighboring cells, extracellular matrix, and diffusing substances), and the whole of the organism through for example the blood supply. In this view, the development of tumor cells is in close interaction with their increasingly changing environment: the more cells can change, the more their environment will change. Furthermore, instabilities are also introduced on the organism level: blood supply can be blocked by increased tissue pressure or the tortuosity of the tumor-neovascular vessels. This coupling between cell, microenvironment, and organism results in behavior that is hard to predict. Here we introduce a cell-based computational model to study the effect of blood flow obstruction on the micro-evolution of cells within a cancerous tissue. We demonstrate that stages of tumor development emerge naturally, without the need for sequential mutation of specific genes. Secondly, we show that instabilities in blood supply can impact the overall development of tumors and lead to the extinction of the dominant aggressive phenotype, showing a clear distinction between the fitness at the cell level and survival of the population. This provides new insights into potential side effects of recent tumor vasculature normalization approaches.

A SPATIOTEMPORAL MODEL TO SIMULATE CHEMOTHERAPY REGIMENS FOR HETEROGENEOUS BLADDER CANCER METASTASES TO THE LUNG

Tumors are composed of heterogeneous populations of cells. Somatic genetic aberrations are one form of heterogeneity that allows clonal cells to adapt to chemotherapeutic stress, thus providing a path for resistance to arise. In silico modeling of tumors provides a platform for rapid, quantitative experiments to inexpensively study how compositional heterogeneity contributes to drug resistance. Accordingly, we have built a spatiotemporal model of a lung metastasis originating from a primary bladder tumor, incorporating in vivo drug concentrations of first-line chemotherapy, resistance data from bladder cancer cell lines, vascular density of lung metastases, and gains in resistance in cells that survive chemotherapy. In metastatic bladder cancer, a first-line drug regimen includes six cycles of gemcitabine plus cisplatin (GC) delivered simultaneously on day 1, and gemcitabine on day 8 in each 21-day cycle. The interaction between gemcitabine and cisplatin has been shown to be synergistic in vitro, and results in better outcomes in patients. Our model shows that during simulated treatment with this regimen, GC synergy does begin to kill cells that are more resistant to cisplatin, but repopulation by resistant cells occurs. Post-regimen populations are mixtures of the original, seeded resistant clones, and/or new clones that have gained resistance to cisplatin, gemcitabine, or both drugs. The emergence of a tumor with increased resistance is qualitatively consistent with the five-year survival of 6.8% for patients with metastatic transitional cell carcinoma of the urinary bladder treated with a GC regimen. The model can be further used to explore the parameter space for clinically relevant variables, including the timing of drug delivery to optimize cell death, and patient-specific data such as vascular density, rates of resistance gain, disease progression, and molecular profiles, and can be expanded for data on toxicity. The model is specific to bladder cancer, which has not previously been modeled in this context, but can be adapted to represent other cancers.

A reaction-diffusion model for radiation-induced bystander effects

We develop and analyze a reaction-diffusion model to investigate the dynamics of the lifespan of a bystander signal emitted when cells are exposed to radiation. Experimental studies by Mothersill and Seymour 1997, using malignant epithelial cell lines, found that an emitted bystander signal can still cause bystander effects in cells even 60 h after its emission. Several other experiments have also shown that the signal can persist for months and even years. Also, bystander effects have been hypothesized as one of the factors responsible for the phenomenon of low-dose hyper-radiosensitivity and increased radioresistance (HRS/IRR). Here, we confirm this hypothesis with a mathematical model, which we fit to Joiner's data on HRS/IRR in a T98G glioma cell line. Furthermore, we use phase plane analysis to understand the full dynamics of the signal's lifespan. We find that both single and multiple radiation exposure can lead to bystander signals that either persist temporarily or permanently. We also found that, in an heterogeneous environment, the size of the domain exposed to radiation and the number of radiation exposures can determine whether a signal will persist temporarily or permanently. Finally, we use sensitivity analysis to identify those cell parameters that affect the signal's lifespan and the signal-induced cell death the most.

Inter-Relationship between Low-Dose Hyper-Radiosensitivity and Radiation-Induced Bystander Effects in the Human T98G Glioma and the Epithelial HaCaT Cell Line

Over the past several years, investigations in both low-dose hyper-radiosensitivity and increased radioresistance have been a focus of radiation oncology and biology research, since both conditions occur primarily in tumor cell lines. There has been significant progress in elucidating their signaling pathways, however uncertainties exist when they are studied together with radiation-induced bystander effects. Therefore, the aim of this work was to further investigate this relationship using the T98G glioma and HaCaT cell lines. T98G glioma cells have demonstrated a strong transition from hyper-radiosensitivity to induced radioresistance, and HaCaT cells do not show low-dose hypersensitivity. Both cell lines were paired using a mix-and-match protocol, which involved growing nonirradiated cells in culture media from irradiated cells and covering all possible combinations between them. The end points analyzed were clonogenic cell survival and live calcium measurements through the cellular membrane. Our data demonstrated that T98G cells produced bystander signals that decreased the survival of both reporter T98G and HaCaT cells. The bystander effect occurred only when T98G cells were exposed to doses below 1 Gy, which was corroborated by the induction of calcium fluxes. However, when bystander signals originated from HaCaT cells, the survival fraction increased in reporter T98G cells while it decreased in HaCaT cells. Moreover, the corresponding calcium data showed no calcium fluxes in T98G cells, while HaCaT cells displayed a biphasic calcium profile. In conclusion, our findings indicate a possible link between low-dose hyper-radiosensitivity and bystander effects. This relationship varies depending on which cell line functions as the source of bystander signals. This further suggests that the bystander mechanisms are more complex than previously expected and caution should be taken when extrapolating bystander results across all cell lines and all radiation doses.