From tumour perfusion to drug delivery and clinical translation of in silico cancer models

In silico cancer models have demonstrated great potential as a tool to improve drug design, optimise the delivery of drugs to target sites in the host tissue and, hence, improve therapeutic efficacy and patient outcome. However, there are significant barriers to the successful translation of in silico technology from bench to bedside. More precisely, the specification of unknown model parameters, the necessity for models to adequately reflect in vivo conditions, and the limited amount of pertinent validation data to evaluate models' accuracy and assess their reliability, pose major obstacles in the path towards their clinical translation. This review aims to capture the state-of-the-art in in silico cancer modelling of vascularised solid tumour growth, and identify the important advances and barriers to success of these models in clinical oncology. Particular emphasis has been put on continuum-based models of cancer since they - amongst the class of mechanistic spatio-temporal modelling approaches - are well-established in simulating transport phenomena and the biomechanics of tissues, and have demonstrated potential for clinical translation. Three important avenues in in silico modelling are considered in this contribution: first, since systemic therapy is a major cancer treatment approach, we start with an overview of the tumour perfusion and angiogenesis in silico models. Next, we present the state-of-the-art in silico work encompassing the delivery of chemotherapeutic agents to cancer nanomedicines through the bloodstream, and then review continuum-based modelling approaches that demonstrate great promise for successful clinical translation. We conclude with a discussion of what we view to be the key challenges and opportunities for in silico modelling in personalised and precision medicine

Multiscale Mechano-Biological Finite Element Modelling of Oncoplastic Breast Surgery-Numerical Study towards Surgical Planning and Cosmetic Outcome Prediction

Surgical treatment for early-stage breast carcinoma primarily necessitates breast conserving therapy (BCT), where the tumour is removed while preserving the breast shape. To date, there have been very few attempts to develop accurate and efficient computational tools that could be used in the clinical environment for pre-operative planning and oncoplastic breast surgery assessment. Moreover, from the breast cancer research perspective, there has been very little effort to model complex mechano-biological processes involved in wound healing. We address this by providing an integrated numerical framework that can simulate the therapeutic effects of BCT over the extended period of treatment and recovery. A validated, three-dimensional, multiscale finite element procedure that simulates breast tissue deformations and physiological wound healing is presented. In the proposed methodology, a partitioned, continuum-based mathematical model for tissue recovery and angiogenesis, and breast tissue deformation is considered. The effectiveness and accuracy of the proposed numerical scheme is illustrated through patient-specific representative examples. Wound repair and contraction numerical analyses of real MRI-derived breast geometries are investigated, and the final predictions of the breast shape are validated against post-operative follow-up optical surface scans from four patients. Mean (standard deviation) breast surface distance errors in millimetres of 3.1 (±3.1), 3.2 (±2.4), 2.8 (±2.7) and 4.1 (±3.3) were obtained, demonstrating the ability of the surgical simulation tool to predict, pre-operatively, the outcome of BCT to clinically useful accuracy

In-silico dynamic analysis of cytotoxic drug administration to solid tumours: Effect of binding affinity and vessel permeability

The delivery of blood-borne therapeutic agents to solid tumours depends on a broad range of biophysical factors. We present a novel multiscale, multiphysics, in-silico modelling framework that encompasses dynamic tumour growth, angiogenesis and drug delivery, and use this model to simulate the intravenous delivery of cytotoxic drugs. The model accounts for chemo-, hapto- and mechanotactic vessel sprouting, extracellular matrix remodelling, mechano-sensitive vascular remodelling and collapse, intra- and extravascular drug transport, and tumour regression as an effect of a cytotoxic cancer drug. The modelling framework is flexible, allowing the drug properties to be specified, which provides realistic predictions of in-vivo vascular development and structure at different tumour stages. The model also enables the effects of neoadjuvant vascular normalisation to be implicitly tested by decreasing vessel wall pore size. We use the model to test the interplay between time of treatment, drug affinity rate and the size of the vessels endothelium pores on the delivery and subsequent tumour regression and vessel remodelling. Model predictions confirm that small-molecule drug delivery is dominated by diffusive transport and further predict that the time of treatment is important for low affinity but not high affinity cytotoxic drugs, the size of the vessel wall pores plays an important role in the effect of low affinity but not high affinity drugs, that high affinity cytotoxic drugs remodel the tumour vasculature providing a large window for the normalisation of the vascular architecture, and that the combination of large pores and high affinity enhances cytotoxic drug delivery efficiency. These results have implications for treatment planning and methods to enhance drug delivery, and highlight the importance of in-silico modelling in investigating the optimisation of cancer therapy on a personalised setting

In Vitro Modeling of Mechanics in Cancer Metastasis

In addition to a multitude of genetic and biochemical alterations, abnormal morphological, structural, and mechanical changes in cells and their extracellular environment are key features of tumor invasion and metastasis. Furthermore, it is now evident that mechanical cues alongside biochemical signals contribute to critical steps of cancer initiation, progression, and spread. Despite its importance, it is very challenging to study mechanics of different steps of metastasis in the clinic or even in animal models. While considerable progress has been made in developing advanced in vitro models for studying genetic and biological aspects of cancer, less attention has been paid to models that can capture both biological and mechanical factors realistically. This is mainly due to lack of appropriate models and measurement tools. After introducing the central role of mechanics in cancer metastasis, we provide an outlook on the emergence of novel in vitro assays and their combination with advanced measurement technologies to probe and recapitulate mechanics in conditions more relevant to the metastatic disease

In silico modelling of tumour margin diffusion and infiltration: Review of current status

Extent: 16p.As a result of advanced treatment techniques, requiring precise target definitions, a need for more accurate delineation of the Clinical Target Volume (CTV) has arisen. Mathematical modelling is found to be a powerful tool to provide fairly accurate predictions for the Microscopic Extension (ME) of a tumour to be incorporated in a CTV. In general terms, biomathematical models based on a sequence of observations or development of a hypothesis assume some links between biological mechanisms involved in cancer development and progression to provide quantitative or qualitative measures of tumour behaviour as well as tumour response to treatment. Generally, two approaches are taken: deterministic and stochastic modelling. In this paper, recent mathematical models, including deterministic and stochastic methods, are reviewed and critically compared. It is concluded that stochastic models are more promising to provide a realistic description of cancer tumour behaviour due to being intrinsically probabilistic as well as discrete, which enables incorporation of patient-specific biomedical data such as tumour heterogeneity and anatomical boundaries.Fatemeh Leyla Moghaddasi, Eva Bezak, and Loredana Marc

Computational Systems Mechanobiology of Tumor-Induced Angiogenesis

Tumour-induced angiogenesis is affected by an interplay between different cell types, the mechanical stresses in the extracellular matrix (ECM), and the cell signalling networks. The morphology of the newly-created cells is highly dependent on the tip cells’ movements, yet the mechanical aspect of tip cell migration is not very well studied. The model developed here is one of the first phase-field models of angiogenesis incorporating the mechanics of the phenomenon. Besides, it is the only model to make a connection between the movement of the tip cell and the formation of matrix pathways in the extracellular matrix. Here, fracture formulas handle the modelling of the formation of matrix pathways. Furthermore, the model integrates the biochemical elements into the mechanical progression of the tip cells. This framework uses a set of equations to model different aspects of the phenomenon categorized into three modules; biomechanical, biochemical, and the vascular network module. The biomechanical module is simply a set of two partial differential equations (PDEs); the linear momentum balance equation and a phase-field equation for handling the two phases, the endothelial cells (ECs) and the ECM. We used an energy-based criterion for soft material for the fracture of the ECM. The biochemical module consists of four advection-diffusion-reaction equations, each of which is responsible for the concentration of one of the elements involved in the process namely: oxygen, TAF (tumour angiogenesis factor), MMP (matrix metalloproteinases), the ECM. The vascular network describes the movement of the tip cells and possible branching. This module uses a nonlinear equation solver and a stochastic function to find the location of the tip cell in each step. The results of this modelling approach conform with the results available from older computational models and experimental models

Cell Migration within 3D Microenvironments: an Integrative Perspective from the Membrane to the Nucleus

La migración celular es fundamental para la vida y el desarrollo. Desafortunadamente, la movilidad celular también está asociada con algunas de las principales causas de morbilidad y mortalidad, incluidos los trastornos inmunitarios, esqueléticos y cardiovasculares, así como la metástasis del cáncer. Las células dependen en su capacidad para percibir y responder a estímulos externos en muchos procesos fisiológicos y patológicos (p. ej., desarrollo embrionario, angiogénesis, reparación de tejidos y progresión tumoral). El objetivo global de esta tesis doctoral fue investigar la respuesta migratoria de células individuales a señales bioquímicas y biofísicas. En particular, el enfoque de esta investigación se centró en los mecanismos que permiten a las células percibir e internalizar señales bioquímicas y biofísicas y la influencia de estos estímulos en la respuesta migratoria de las células individuales.El primer estudio tuvo como objetivo establecer una metodología para facilitar la integración de estudios teóricos con datos experimentales. Al minimizar la intervención del usuario, el sistema propuesto basado en técnicas de optimización Bayesiana gestionó de manera eficiente la calibración de los modelos in silico, que de otro modo sería tediosa y propensa a errores. Posteriormente, se construyó un modelo in silico para investigar cómo los estímulos bioquímicos y biofísicos influyen en el movimiento celular en tres dimensiones. Este modelo computacional integró algunos de los principales actores que permiten a las células percibir y responder a señales externas, que pueden actuar a diferentes escalas e interactuar entre sí. Los resultados mostraron, por un lado, que las células cambian su comportamiento migratorio en función de la pendiente de los gradientes químicos y la concentración absoluta de factores químicos (por ejemplo, factores de crecimiento) a su alrededor. Por otro lado, estos resultados revelaron que la respuesta migratoria de las células a la rigidez y densidad de la matriz depende de su fenotipo. En general, la tesis destaca la dependencia de la migración celular tridimensional al fenotipo de las células (es decir, el tamaño de su núcleo, la deformabilidad del mismo) y las propiedades del microambiente circundante (por ejemplo, el perfil químico, la rigidez de la matriz, el confinamiento).Cell migration is fundamental for life and development. Unfortunately, cell motility is also associated with some of the leading causes of morbidity and mortality, including immune, skeletal, and cardiovascular disorders as well as cancer metastasis. Cells rely on their ability to perceive and respond to external stimuli in many physiological and pathological processes (e.g., embryonic development, angiogenesis, tissue repair, and tumor progression). The global objective of this doctoral thesis was to investigate the migratory response of individual cells to biochemical and biophysical cues. In particular, the focus of this research was on the mechanisms enabling cells to perceive and internalize biochemical and biophysical cues and the influence of these stimuli on the migratory response of individual cells. The first study aimed at establishing a methodology to facilitate the integration of theoretical studies with experimental data. By minimizing user intervention, the proposed framework based on Bayesian optimization techniques efficiently handled the otherwise tedious and error-prone calibration of in silico models. Afterward, an in silico model was built to investigate how biochemical and biophysical stimuli influence three-dimensional cell motion. This computational model integrated some of the main actors enabling cells to probe and respond to external cues, which may act at different scales and interact with each other. The results showed, on the one hand, that cells change their migratory behavior based on the slope of chemical gradients and the absolute concentration of chemical factors (e.g., growth factors) around them. On the other hand, these results revealed that cells’ migratory response to matrix stiffness and density depends on their phenotype. Overall, this thesis highlights the dependence of three-dimensional cell migration on both cells’ phenotype (i.e., nucleus size, deformability) and the properties of the surrounding microenvironment (e.g., chemical profile, matrix rigidity, confinement).<br /

Integrating In-Silico Models with In-Vitro Data to Generate Novel Insights into Biological Systems

Models and computational predictions are useful in identifying certain key parameters that play a central role in defining the overall behavior of the system, and thus lead to new and more informative experiments. In this thesis, in-silico models are developed over a range of individual biological scales (macroscopic, mesoscopic and microscopic) for a range of cellular phenomena (cellular interactions, migration and signalling pathways) in order to highlight the importance of combined in-vitro – in-silico investigations. It is widely accepted that Systems Biology aims to provide a simpler and more abstract framework to explain complex biological phenomena. However, integration of these models with experimental data is often underutilised. Incorporation of experimentally derived data sets into the mathematical framework of in-silico modelling results in reliable, well parameterised systems capable of replicating dynamical properties of the biological systems. Work in this thesis includes the development of a continuous macroscopic in-silico model to identify the key mechanisms of interaction between cells present within the gastric tumour microenvironment. This model of discovery is used in a predictive capacity to accept or reject hypotheses. Next, the construction of a discrete cell based model of fibroblast migration is used to determine the degree of bias fibroblast cells experience when migrating over different surface topologies. The key results from this model show that particular surface topographies can have an effect on migratory cell behaviour. Then, the parameterisation of a differential equation model is used to quantify the key mechanisms of Nrf2 regulation in the cytoplasm and nucleus. Validation with experimentally derived datasets results in the quantification of rate ratios important to the dynamics of this signalling pathway. Finally, a stochastic Petri-net model capable of simulating the dynamical behaviour of functional cross-talk between the Nrf2 and NF-κB pathways is developed. This approach allows for the evaluation of a wide array of network responses, without the need for computationally expensive parameterisation. Together, these models exhibit how integration of in-silico models with in-vitro datasets can be used to generate new knowledge, or testable hypotheses