The progressive trend of modeling and drug screening systems of breast cancer bone metastasis.

Bone metastasis is considered as a considerable challenge for breast cancer patients. Various in vitro and in vivo models have been developed to examine this occurrence. In vitro models are employed to simulate the intricate tumor microenvironment, investigate the interplay between cells and their adjacent microenvironment, and evaluate the effectiveness of therapeutic interventions for tumors. The endeavor to replicate the latency period of bone metastasis in animal models has presented a challenge, primarily due to the necessity of primary tumor removal and the presence of multiple potential metastatic sites.The utilization of novel bone metastasis models, including three-dimensional (3D) models, has been proposed as a promising approach to overcome the constraints associated with conventional 2D and animal models. However, existing 3D models are limited by various factors, such as irregular cellular proliferation, autofluorescence, and changes in genetic and epigenetic expression. The imperative for the advancement of future applications of 3D models lies in their standardization and automation. The utilization of artificial intelligence exhibits the capability to predict cellular behavior through the examination of substrate materials' chemical composition, geometry, and mechanical performance. The implementation of these algorithms possesses the capability to predict the progression and proliferation of cancer. This paper reviewed the mechanisms of bone metastasis following primary breast cancer. Current models of breast cancer bone metastasis, along with their challenges, as well as the future perspectives of using these models for translational drug development, were discussed.

Bayesian inference on the Allee effect in cancer cell line populations using time-lapse microscopy images.

The Allee effect describes the phenomenon that the per capita reproduction rate increases along with the population density at low densities. Allee effects have been observed at all scales, including in microscopic environments where individual cells are taken into account. This is great interest to cancer research, as understanding critical tumour density thresholds can inform treatment plans for patients. In this paper, we introduce a simple model for cell division in the case where the cancer cell population is modelled as an interacting particle system. The rate of the cell division is dependent on the local cell density, introducing an Allee effect. We perform parameter inference of the key model parameters through Markov Chain Monte Carlo, and apply our procedure to two image sequences from a cervical cancer cell line. The inference method is verified on in silico data to accurately identify the key parameters, and results on the in vitro data strongly suggest an Allee effect.

Nano-Hydroxyapatite/PLGA Mixed Scaffolds as a Tool for Drug Development and to Study Metastatic Prostate Cancer in the Bone.

(1) Background: Three-dimensional (3D) in vitro, biorelevant culture models that recapitulate cancer progression can help elucidate physio-pathological disease cues and enhance the screening of more effective therapies. Insufficient research has been conducted to generate in vitro 3D models to replicate the spread of prostate cancer to the bone, a key metastatic site of the disease, and to understand the interplay between the key cell players. In this study, we aim to investigate PLGA and nano-hydroxyapatite (nHA)/PLGA mixed scaffolds as a predictive preclinical tool to study metastatic prostate cancer (mPC) in the bone and reduce the gap that exists with traditional 2D cultures. (2) Methods: nHA/PLGA mixed scaffolds were produced by electrospraying, compacting, and foaming PLGA polymer microparticles, +/- nano-hydroxyapatite (nHA), and a salt porogen to produce 3D, porous scaffolds. Physicochemical scaffold characterisation together with an evaluation of osteoblastic (hFOB 1.19) and mPC (PC-3) cell behaviour (RT-qPCR, viability, and differentiation) in mono- and co-culture, was undertaken. (3) Results: The results show that the addition of nHA, particularly at the higher-level impacted scaffolds in terms of mechanical and degradation behaviour. The nHA 4 mg resulted in weaker scaffolds, but cell viability increased. Qualitatively, fluorescent imaging of cultures showed an increase in PC-3 cells compared to osteoblasts despite lower initial PC-3 seeding densities. Osteoblast monocultures, in general, caused an upregulation (or at least equivalent to controls) in gene production, which was highest in plain scaffolds and decreased with increases in nHA. Additionally, the genes were downregulated in PC3 and co-cultures. Further, drug toxicity tests demonstrated a significant effect in 2D and 3D co-cultures. (4) Conclusions: The results demonstrate that culture conditions and environment (2D versus 3D, monoculture versus co-culture) and scaffold composition all impact cell behaviour and model development.

The role of evolutionary game theory in spatial and non-spatial models of the survival of cooperation in cancer: a review.

Evolutionary game theory (EGT) is a branch of mathematics which considers populations of individuals interacting with each other to receive pay-offs. An individual's pay-off is dependent on the strategy of its opponent(s) as well as on its own, and the higher its pay-off, the higher its reproductive fitness. Its offspring generally inherit its interaction strategy, subject to random mutation. Over time, the composition of the population shifts as different strategies spread or are driven extinct. In the last 25 years there has been a flood of interest in applying EGT to cancer modelling, with the aim of explaining how cancerous mutations spread through healthy tissue and how intercellular cooperation persists in tumour-cell populations. This review traces this body of work from theoretical analyses of well-mixed infinite populations through to more realistic spatial models of the development of cooperation between epithelial cells. We also consider work in which EGT has been used to make experimental predictions about the evolution of cancer, and discuss work that remains to be done before EGT can make large-scale contributions to clinical treatment and patient outcomes.

Coupling solid and fluid stresses with brain tumour growth and white matter tract deformations in a neuroimaging-informed model.

Brain tumours are among the deadliest types of cancer, since they display a strong ability to invade the surrounding tissues and an extensive resistance to common therapeutic treatments. It is therefore important to reproduce the heterogeneity of brain microstructure through mathematical and computational models, that can provide powerful instruments to investigate cancer progression. However, only a few models include a proper mechanical and constitutive description of brain tissue, which instead may be relevant to predict the progression of the pathology and to analyse the reorganization of healthy tissues occurring during tumour growth and, possibly, after surgical resection. Motivated by the need to enrich the description of brain cancer growth through mechanics, in this paper we present a mathematical multiphase model that explicitly includes brain hyperelasticity. We find that our mechanical description allows to evaluate the impact of the growing tumour mass on the surrounding healthy tissue, quantifying the displacements, deformations, and stresses induced by its proliferation. At the same time, the knowledge of the mechanical variables may be used to model the stress-induced inhibition of growth, as well as to properly modify the preferential directions of white matter tracts as a consequence of deformations caused by the tumour. Finally, the simulations of our model are implemented in a personalized framework, which allows to incorporate the realistic brain geometry, the patient-specific diffusion and permeability tensors reconstructed from imaging data and to modify them as a consequence of the mechanical deformation due to cancer growth.

Coupling Micro-Physiological Systems and Biosensors for Improving Cancer Biomarkers Detection.

Early cancer detection is still a major clinical challenge. The development of innovative and noninvasive screening approaches for the detection of predictive biomarkers indicating the stage of the disease could save many lives. Traditional in vitro and in vivo models are not adequate to copycat the native tumor microenvironment and for the discovery of new biomarkers. Recent advances in microfluidics, biosensors, and 3D cell biology speed up the development of micro-physiological bioengineered systems that improve the discovery of new potential cancer biomarkers. This can accelerate the individualization of cancer treatments leading to precision medicine-oriented approaches that could improve patient prognosis. For this reason, it is necessary to develop point-of-care diagnostic tools that can be user-friendly, miniaturized, and easily translated into clinical practice. This chapter describes how far this new generation of cutting-edge technologies, such as microfluidics, label-free detection systems, and molecular diagnostics, are from being applied in the current clinical practice.

Nonlocal multiscale modelling of tumour-oncolytic viruses interactions within a heterogeneous fibrous/non-fibrous extracellular matrix.

In this study we investigate computationally tumour-oncolytic virus (OV) interactions that take place within a heterogeneous extracellular matrix (ECM). The ECM is viewed as a mixture of two constitutive phases, namely a fibre phase and a non-fibre phase. The multiscale mathematical model presented here focuses on the nonlocal cell-cell and cell-ECM interactions, and how these interactions might be impacted by the infection of cancer cells with the OV. At macroscale we track the kinetics of cancer cells, virus particles and the ECM. At microscale we track (i) the degradation of ECM by matrix degrading enzymes (MDEs) produced by cancer cells, which further influences the movement of tumour boundary; (ii) the re-arrangement of the microfibres that influences the re-arrangement of macrofibres (i.e., fibres at macroscale). With the help of this new multiscale model, we investigate two questions: (i) whether the infected cancer cell fluxes are the result of local or non-local advection in response to ECM density; and (ii) what is the effect of ECM fibres on the the spatial spread of oncolytic viruses and the outcome of oncolytic virotherapy.

3D Printed Personalized Medicine for Cancer: Applications for Betterment of Diagnosis, Prognosis and Treatment.

Cancer treatment is challenging due to the tumour heterogeneity that makes personalized medicine a suitable technique for providing better cancer treatment. Personalized medicine analyses patient-related factors like genetic make-up and lifestyle and designs treatments that offer the benefits of reduced side effects and efficient drug delivery. Personalized medicine aims to provide a holistic way for prevention, diagnosis and treatment. The customization desired in personalized medicine is produced accurately by 3D printing which is an established technique known for its precision. Different 3D printing techniques exhibit their capability in producing cancer-specific medications for breast, liver, thyroid and kidney tumours. Three-dimensional printing displays major influence on cancer modelling and studies using cancer models in treatment and diagnosis. Three-dimensional printed personalized tumour models like physical 3D models, bioprinted models and tumour-on-chip models demonstrate better in vitro and in vivo correlation in drug screening, cancer metastasis and prognosis studies. Three-dimensional printing helps in cancer modelling; moreover, it has also changed the facet of cancer treatment. Improved treatment via custom-made 3D printed devices, implants and dosage forms ensures the delivery of anticancer agents efficiently. This review covers recent applications of 3D printed personalized medicine in various cancer types and comments on the possible future directions like application of 4D printing and regularization of 3D printed personalized medicine in healthcare.

Non-local multiscale approach for the impact of go or grow hypothesis on tumour-viruses interactions.

We propose and study computationally a novel non-local multiscale moving boundary mathematical model for tumour and oncolytic virus (OV) interactions when we consider the go or grow hypothesis for cancer dynamics. This spatio-temporal model focuses on two cancer cell phenotypes that can be infected with the OV or remain uninfected, and which can either move in response to the extracellular-matrix (ECM) density or proliferate. The interactions between cancer cells, those among cancer cells and ECM, and those among cells and OV occur at the macroscale. At the micro-scale, we focus on the interactions between cells and matrix degrading enzymes (MDEs) that impact the movement of tumour boundary. With the help of this multiscale model we explore the impact on tumour invasion patterns of two different assumptions that we consider in regard to cell-cell and cell-matrix interactions. In particular we investigate model dynamics when we assume that cancer cell fluxes are the result of local advection in response to the density of extracellular matrix (ECM), or of non-local advection in response to cell-ECM adhesion. We also investigate the role of the transition rates between mainly-moving and mainly-growing cancer cell sub-populations, as well as the role of virus infection rate and virus replication rate on the overall tumour dynamics.

In Silico Mathematical Modelling for Glioblastoma: A Critical Review and a Patient-Specific Case.

Glioblastoma extensively infiltrates the brain; despite surgery and aggressive therapies, the prognosis is poor. A multidisciplinary approach combining mathematical, clinical and radiological data has the potential to foster our understanding of glioblastoma evolution in every single patient, with the aim of tailoring therapeutic weapons. In particular, the ultimate goal of biomathematics for cancer is the identification of the most suitable theoretical models and simulation tools, both to describe the biological complexity of carcinogenesis and to predict tumor evolution. In this report, we describe the results of a critical review about different mathematical models in neuro-oncology with their clinical implications. A comprehensive literature search and review for English-language articles concerning mathematical modelling in glioblastoma has been conducted. The review explored the different proposed models, classifying them and indicating the significative advances of each one. Furthermore, we present a specific case of a glioblastoma patient in which our recently proposed innovative mechanical model has been applied. The results of the mathematical models have the potential to provide a relevant benefit for clinicians and, more importantly, they might drive progress towards improving tumor control and patient's prognosis. Further prospective comparative trials, however, are still necessary to prove the impact of mathematical neuro-oncology in clinical practice.

CRISPR Cas System: An efficient tool for cancer modelling.

The Clustered Regularly Interspaced Short Palindromic Repeats-Cas-9 (CRISPR-Cas9) system has been a revolutionising tool in the field of molecular genetics, which provides a versatile range of editing potentials. Researchers can produce breaks or alter genomes with ease using the system. Cancer is one of the multi-gene diseases whose genes need to be studied in detail. The CRISPR-Cas9 technology may also provide a promising potential in the field of cancer genetics. The current narrative review comprised 50 research articles which were keenly analysed and the applications and outcomes of CRISPR-Cas9 system in cancer genetics were comprehensively and critically discussed. It was concluded that application of the system had great potential to help understand cancer biology of various types and could be used for its genetic modelling. However, much work is still needed to be done to apply the technology for understanding the mechanism of cancers and to help in the designing of appropriate therapies.

The epidermal growth factor receptor variant type III mutation frequently found in gliomas induces astrogenesis in human cerebral organoids.

OBJECTIVES: The epidermal growth factor receptor variant type III (EGFRvIII) is the most common mutation of EGFR in glioblastoma multiforme (GBM) and is found in approximately 25% of all GBMs. Intriguingly, EGFRvIII is mostly found in GFAP+ astrocytic tumour cells in the brain, suggesting connection of EGFRvIII to astrogenesis. In this study, we explored whether EGFRvIII mutation facilitates astrogenesis in human development setting. MATERIALS AND METHODS: Using CRISPR-Cas9, we generated EGFRvIII mutations in H9-hESCs. Wild type (wt) H9-hESCs were used as an isogenic control. Next, we generated cerebral organoids using the wt and EGFRvIII-hESCs and examined the astrogenic differentiation of the brain organoids. RESULTS: EGFRvIII-organoids showed abundant astrocytes (GFAP+ , S100ß+ ), while no astrocytes were detected in wt hESC-derived organoids at day 49. On the contrary, TUJ1+ neurons were more abundant in the wt-organoids than the EGFRvIII-organoids. This result suggested that constitutively active EGFRvIII promoted astrogenesis at the expense of neurogenesis. In addition, the EGFRvIII-organoids were larger in size and retained more Ki67+ cells than wt-organoids, indicating enhanced cell proliferation by the mutation. The EGFRvIII-organoids displayed massive apoptotic cell death after treatment with temozolomide and hence, could be used for evaluation of anti-GBM drugs. CONCLUSIONS: EGFRvIII mutation-induced astrogenesis and massive cell proliferation in a human brain development model. These results provide us new insights into the mechanisms relating EGFRvIII mutation-mediated gliogenesis and gliomagenesis.

Cerebral organoids: emerging ex vivo humanoid models of glioblastoma.

Glioblastoma is an aggressive form of brain cancer that has seen only marginal improvements in its bleak survival outlook of 12-15 months over the last forty years. There is therefore an urgent need for the development of advanced drug screening platforms and systems that can better recapitulate glioblastoma's infiltrative biology, a process largely responsible for its relentless propensity for recurrence and progression. Recent advances in stem cell biology have allowed the generation of artificial tridimensional brain-like tissue termed cerebral organoids. In addition to their potential to model brain development, these reagents are providing much needed synthetic humanoid scaffolds to model glioblastoma's infiltrative capacity in a faithful and scalable manner. Here, we highlight and review the early breakthroughs in this growing field and discuss its potential future role for glioblastoma research.

Bioengineered Tumor Organoids.

Recent advances in biofabrication technologies, such as cell culture systems, and biomaterials have led to the development of three-dimensional (3D) cell culture platforms, such as tumor organoids. Tumor organoids are more physiologically accurate to the in vivo system, which they are intended to model, compared with traditional 2D cancer cell culture systems. Tumor organoids can mimic pathological and physical characteristics of tumors as well as maintain genetic stability of the cancer cells. Furthermore tumor organoids have advantage over animal models, being made from human cells and easily controlled in the laboratory to attain the desired tissue characteristics. In this section, we describe general tumor organoid technologies, the importance of the tumor microenvironment (TME) in model culture systems, and the use of tumor organoids in drug development and precision medicine. Organoid technologies continue to develop rapidly for applications in academic, clinical, and pharmaceutical settings.

Recent Advances on Utilization of Bioprinting for Tumor Modeling.

Despite the recent rigorous studies towards a possible cure, cancer still remains as one of the most daunting problems faced by the humanity. Currently utilized two-dimensional cancer models are known to have various insuperable limitations such as insufficient biomimicry of the heterogeneous conditions of tumors and their three-dimensional structures. Discrepancies between the laboratory models and the actual tumor environment significantly impair a thorough comprehension of the carcinogenesis process and development of successful remedies against cancer. Modeling tumor microenvironments through bioprinting poses strong potential to minimize the effects of the aforementioned issues thanks to its freeform nature, adaptability, customizability, scalability and diversity. Numerous research studies involving three-dimensional modeling of various cancer types using bioprinting technologies have been reported, recently. In this review, we provide a broad summary of these studies to help better represent their potential and analyze their contribution to cancer research.

Multiscale moving boundary modelling of cancer interactions with a fusogenic oncolytic virus: The impact of syncytia dynamics.

Oncolytic viral therapies is one of the new promising strategies against cancer, due to the ability of oncolytic viruses to specifically replicate inside cancer cells and kill them. There is increasing evidence that a sub-class of viruses that contain fusion proteins (triggering the formation of syncytia) can lead to better oncolytic results. Since the details of the tumour dynamics following syncytia formation are not fully understood, in this study we consider a modelling and computational approach to describe the effect of a fusogenic oncolytic virus on the multiscale dynamics of a spreading tumour. We show that for the baseline parameter values considered here, small syncytia diffusion coefficient leads to tumour reduction. Further tumour reduction can be obtained when we increase the probability of syncytia formation, in the context of different viral burst rates and death rates for individually-infected tumour cells and syncytia structures. Finally, we show that the type of syncytia diffusion coefficient (i.e., constant or density dependent) also impacts the outcome of the oncolytic viral therapy.

Existence of solutions and numerical approximation of a non-local tumor growth model.

In order to model the evolution of a heterogeneous population of cancer stem cells and tumor cells, we analyse a nonlinear system of integro-differential equations. We provide an existence and uniqueness result by exploiting a suitable iterative scheme of functions which converge to the solution of the system. Then, we discretize the model and perform some numerical simulations. Numerical approximations are obtained by applying finite differences for space discretization and an exponential Runge-Kutta scheme for time integration. We exploit the numerical tool in order to investigate the effects that niches have on cancer development. In this respect, the numerical procedure is applied in the case when the function of cell redistribution is assumed to be spatially explicit. It allows for finding an approximate solution which is spatially inhomogeneous as time progresses. In this framework, numerical investigation may help in understanding the process of niche construction, which plays an important role in cancer population biology.

Moving toward the elimination of cervical cancer: modelling the health and economic benefits of increasing uptake of human papillomavirus vaccines.

Background: The human papillomavirus (hpv) is a common sexually transmitted infection and a primary cause of cervical cancer. The Government of Canada has set a target of reaching 90% hpv vaccine coverage among adolescents by 2025. Here, we examine hpv vaccine uptake in school-based immunization programs across Canada and explore how achieving the 90% target could affect the future incidence of cervical cancer, mortality, and health system expenditures in a cohort of Canadian women. Methods: Data for hpv vaccine uptake in the most recent reported school year available in each jurisdiction were provided in 2017 by jurisdictional school-based immunization programs and were used to estimate a national weighted average of 67%. The OncoSim microsimulation model (version 2.5) was used to compare 3 different levels of hpv vaccine uptake (0%, 67%, 90%) on health and economic outcomes for a hypothetical cohort of all 5- to 10-year-old girls in Canada in 2015. Results: Vaccine uptake for girls in school-based programs varied from 55.0% to 92.0% in the jurisdictions reviewed. The OncoSim model projects that increasing uptake to 90% from 67% would result in a 23% reduction in cervical cancer incidence rates (to 3.1 cases from 4.0 cases per 100,000, averaged across the lifetime of the cohort) and a 23% decline in the average annual mortality rate (to 1.0 deaths from 1.3 deaths per 100,000). Finally, the model projects that the health system will incur a cost of $9 million (1% increase) during the lifetime of the cohort if uptake is increased to 90% from 67%. Costs are discounted (1.5%) and expressed in 2016 Canadian dollars. Costs reflect the payer perspective. Conclusions: Our model shows that increasing hpv vaccine uptake to 90% from current levels for girls in school-based immunization programs could result in substantial reductions in the future incidence and mortality rates for cervical cancer in Canada.

Multiscale modelling of cancer response to oncolytic viral therapy.

Oncolytic viruses (OV) are viruses that can replicate selectively within cancer cells and destroy them. While the past few decades have seen significant progress related to the use of these viruses in clinical contexts, the success of oncolytic therapies is dampened by the complex spatial tumour-OV interactions. In this work, we present a novel multiscale moving boundary modelling for the tumour-OV interactions, which is based on coupled systems of partial differential equations both at macro-scale (tissue-scale) and at micro-scale (cell-scale) that are connected through a double feedback link. At the macro-scale, we account for the coupled dynamics of uninfected cancer cells, OV-infected cancer cells, extracellular matrix (ECM) and oncolytic viruses. At the same time, at the micro scale, we focus on essential dynamics of urokinase plasminogen activator (uPA) system which is one of the important proteolytic systems responsible for the degradation of the ECM, with notable influence in cancer invasion. While sourced by the cancer cells that arrive during their macro-dynamics within the outer proliferating rim of the tumour, the uPA micro-dynamics is crucial in determining the movement of the macro-scale tumour boundary (both in terms of direction and displacement magnitude). In this investigation, we consider three scenarios for the macro-scale tumour-OV interactions. While assuming the usual context of reaction-diffusion-taxis coupled PDEs, the three macro-dynamics scenarios gradually explore the influence of the ECM taxis over the tumour - OV interaction, in the form of haptotaxis of both uninfected and infected cells populations as well as the indirect ECM taxis for the oncolytic virus. Finally, the complex tumour-OV interactions is investigated numerically through the development a new multiscale moving boundary computational framework. While further investigation is needed to validate the findings of our modelling, for the parameter regimes that we considered, our numerical simulations indicate that the viral therapy leads to control and decrease of the overall cancer expansion and in certain cases this can result even in the elimination of the tumour.

Computational Modelling of Cancer Development and Growth: Modelling at Multiple Scales and Multiscale Modelling.

In this paper, we present two mathematical models related to different aspects and scales of cancer growth. The first model is a stochastic spatiotemporal model of both a synthetic gene regulatory network (the example of a three-gene repressilator is given) and an actual gene regulatory network, the NF-[Formula: see text]B pathway. The second model is a force-based individual-based model of the development of a solid avascular tumour with specific application to tumour cords, i.e. a mass of cancer cells growing around a central blood vessel. In each case, we compare our computational simulation results with experimental data. In the final discussion section, we outline how to take the work forward through the development of a multiscale model focussed at the cell level. This would incorporate key intracellular signalling pathways associated with cancer within each cell (e.g. p53-Mdm2, NF-[Formula: see text]B) and through the use of high-performance computing be capable of simulating up to [Formula: see text] cells, i.e. the tissue scale. In this way, mathematical models at multiple scales would be combined to formulate a multiscale computational model.