Pattern of self-organization in tumour systems: complex growth dynamics in a novel brain tumour spheroid model

Dissipative scaling of development and aging in multicellular organisms

Evolution, self-replication and ontogenesis are highly dynamic, irreversible and self-organizing processes dissipating energy. While progress has been made to decipher the role of thermodynamics in cellular fission, it is not yet clear how entropic balances shape organism growth and aging. This paper derives a general dissipation theory for the life history of organisms. It implies a self-regulated energy dissipation facilitating exponential growth within a hierarchical and entropy lowering self-organization. The theory predicts ceilings in energy expenditures imposed by geometric constrains, which promote thermal optimality during development, and a dissipative scaling across organisms consistent with ecological scaling laws combining isometric and allometric terms. The theory also illustrates how growing organisms can tolerate damage through continuous extension and production of new dissipative structures low in entropy. However, when organisms reduce their rate of cell division and reach a steady adult state, they become thermodynamically unstable, increase internal entropy by accumulating damage, and age.

Fractal dimension: analyzing its potential as a neuroimaging biomarker for brain tumor diagnosis using machine learning

Purpose: The main purpose of this study was to comprehensively investigate the potential of fractal dimension (FD) measures in discriminating brain gliomas into low-grade glioma (LGG) and high-grade glioma (HGG) by examining tumor constituents and non-tumorous gray matter (GM) and white matter (WM) regions. Methods: Retrospective magnetic resonance imaging (MRI) data of 42 glioma patients (LGG, n = 27 and HGG, n = 15) were used in this study. Using MRI, we calculated different FD measures based on the general structure, boundary, and skeleton aspects of the tumorous and non-tumorous brain GM and WM regions. Texture features, namely, angular second moment, contrast, inverse difference moment, correlation, and entropy, were also measured in the tumorous and non-tumorous regions. The efficacy of FD features was assessed by comparing them with texture features. Statistical inference and machine learning approaches were used on the aforementioned measures to distinguish LGG and HGG patients. Results: FD measures from tumorous and non-tumorous regions were able to distinguish LGG and HGG patients. Among the 15 different FD measures, the general structure FD values of enhanced tumor regions yielded high accuracy (93%), sensitivity (97%), specificity (98%), and area under the receiver operating characteristic curve (AUC) score (98%). Non-tumorous GM skeleton FD values also yielded good accuracy (83.3%), sensitivity (100%), specificity (60%), and AUC score (80%) in classifying the tumor grades. These measures were also found to be significantly (p < 0.05) different between LGG and HGG patients. On the other hand, among the 25 texture features, enhanced tumor region features, namely, contrast, correlation, and entropy, revealed significant differences between LGG and HGG. In machine learning, the enhanced tumor region texture features yielded high accuracy, sensitivity, specificity, and AUC score. Conclusion: A comparison between texture and FD features revealed that FD analysis on different aspects of the tumorous and non-tumorous components not only distinguished LGG and HGG patients with high statistical significance and classification accuracy but also provided better insights into glioma grade classification. Therefore, FD features can serve as potential neuroimaging biomarkers for glioma.

Morphological instability of solid tumors in a nutrient-deficient environment

A phenomenological reaction-diffusion model that includes a nutrient-regulated growth rate of tumor cells is proposed to investigate the morphological instability of solid tumors during the avascular growth. We find that the surface instability could be induced more easily when tumor cells are placed in a harsher nutrient-deficient environment, while the instability is suppressed for tumor cells in a nutrient-rich environment due to the nutrient-regulated proliferation. In addition, the surface instability is shown to be influenced by the growth moving speed of tumor rims. Our analysis reveals that a larger growth movement of the tumor front results in a closer proximity of tumor cells to a nutrient-rich region, which tends to inhibit the surface instability. A nourished length that represents the proximity is defined to illustrate its close relation to the surface instability.

Spatio-temporal analysis of nanoparticles in live tumor spheroids impacted by cell origin and density

Nanoparticles hold great preclinical promise in cancer therapy but continue to suffer attrition through clinical trials. Advanced, three dimensional (3D) cellular models such as tumor spheroids can recapitulate elements of the tumor environment and are considered the superior model to evaluate nanoparticle designs. However, there is an important need to better understand nanoparticle penetration kinetics and determine how different cell characteristics may influence this nanoparticle uptake. A key challenge with current approaches for measuring nanoparticle accumulation in spheroids is that they are often static, losing spatial and temporal information which may be necessary for effective nanoparticle evaluation in 3D cell models. To overcome this challenge, we developed an analysis platform, termed the Determination of Nanoparticle Uptake in Tumor Spheroids (DONUTS), which retains spatial and temporal information during quantification, enabling evaluation of nanoparticle uptake in 3D tumor spheroids. Outperforming linear profiling methods, DONUTS was able to measure silica nanoparticle uptake to 10 μm accuracy in both isotropic and irregularly shaped cancer cell spheroids. This was then extended to determine penetration kinetics, first by a forward-in-time, center-in-space model, and then by mathematical modelling, which enabled the direct evaluation of nanoparticle penetration kinetics in different spheroid models. Nanoparticle uptake was shown to inversely relate to particle size and varied depending on the cell type, cell stiffness and density of the spheroid model. The automated analysis method we have developed can be applied to live spheroids in situ, for the advanced evaluation of nanoparticles as delivery agents in cancer therapy.

Is the Fixed Periodic Treatment Effective for the Tumor System without Complete Information?

Objective

The treatment plans designed with the guidance of the mathematical model and adaptive strategy can trap tumor subpopulations in a periodic and controllable loop. But this process requires detailed information about the tumor system, which is difficult to obtain. Therefore, we wondered whether the fixed periodic treatment plans designed with the typical values of population parameters could be applied to a similar tumor system without complete information.

Methods

A binary tumor system constructed by an EGFR-mutant and a KRAS-mutant cell line was used to explore the applicability of the fixed periodic treatment plans. The dynamics of this system were described by combining the Lotka-Volterra model with the framework of the nonlinear mixed-effects model. The typical values of population parameters were used to design the plans, and the robust plans were screened through parameter variation. These screened plans were examined their applicability in animal experiments and simulations.

Results

In animal experiments where system parameters vary from -30% to 30%, the "osimertinib administration, withdrawal, FK866 administration and withdrawal" plan can trap subpopulations of the system in periodic cycles. In simulation, when there was an unknown resistant subpopulation, the screened fixed periodic treatment plans can still delay the evolution of resistance. The median outcomes of screened plans were better than conventional sequential treatment in most cases. There was no significant difference between the outcomes of the screened plan with median stability and the optimal therapy. The evolutionary trajectories of these two plans were similar.

Conclusion

According to the results, these fixed periodic plans should be tried in treatment even the information of the tumor system was incomplete.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

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.

Challenges of applying multicellular tumor spheroids in preclinical phase

The three-dimensional (3D) multicellular tumor spheroids (MCTs) model is becoming an essential tool in cancer research as it expresses an intermediate complexity between 2D monolayer models and in vivo solid tumors. MCTs closely resemble in vivo solid tumors in many aspects, such as the heterogeneous architecture, internal gradients of signaling factors, nutrients, and oxygenation. MCTs have growth kinetics similar to those of in vivo tumors, and the cells in spheroid mimic the physical interaction of the tumors, such as cell-to-cell and cell-to-extracellular matrix interactions. These similarities provide great potential for studying the biological properties of tumors and a promising platform for drug screening and therapeutic efficacy evaluation. However, MCTs are not well adopted as preclinical tools for studying tumor behavior and therapeutic efficacy up to now. In this review, we addressed the challenges with MCTs application and discussed various efforts to overcome the challenges.

3D Tumor Spheroid Models for In Vitro Therapeutic Screening of Nanoparticles

The anticancer activity of compounds and nanoparticles is most often determined in the cell monolayer. However, three-dimensional (3D) systems, such as tumor spheroids, are more representing the natural tumor microenvironment. They have been shown to have higher invasiveness and resistance to cytotoxic agents and radiotherapy compared to cells growing in 2D monolayer. Furthermore, to improve the prediction of clinical efficacy of drugs, in the past decades, even more sophisticated systems, such as multicellular 3D cultures, closely representing natural tumor microenvironment have been developed. Those cultures are formed from either cell lines or patient-derived tumor cells. Such models are very attractive and could improve the selection of tested materials for clinical trials avoiding unnecessary expensive tests in vivo. The microenvironment in tumor spheroids is different, and those differences or the interaction between several cell populations may contribute to different tumor response to the treatment. Also, different types of nanoparticles may have different behavior in 3D models, depending on their nature, physicochemical properties, the presence of targeting ligands on the surface, etc. Therefore, it is very important to understand in which cases which type of tumor spheroid is more suitable for testing specific types of nanoparticles, which conditions should be used, and which analytical method should be applied.

Head-to-Head Comparison of the Penetration Efficiency of Lipid-Based Nanoparticles into Tumor Spheroids

Most tumor-targeted drug delivery systems must overcome a large variety of physiological barriers before reaching the tumor site and diffuse through the tight network of tumor cells. Many studies focus on optimizing the first part, the accumulation of drug carriers at the tumor site, ignoring the penetration efficiency, i.e., a measure of the ability of a drug delivery system to overcome tumor surface adherence and uptake. We used three-dimensional (3D) tumor spheroids in combination with light-sheet fluorescence microscopy in a head-to-head comparison of a variety of commonly used lipid-based nanoparticles, including liposomes, PEGylated liposomes, lipoplexes, and reconstituted high-density lipoproteins (rHDL). Whilst PEGylation of liposomes only had minor effects on the penetration efficiency, we show that lipoplexes are mainly associated with the periphery of tumor spheroids, possibly due to their positive surface charge, leading to fusion with the cells at the spheroid surface or aggregation. Surprisingly, the rHDL showed significantly higher penetration efficiency and high accumulation inside the spheroid. While these findings indeed could be relevant when designing novel drug delivery systems based on lipid-based nanoparticles, we stress that the used platform and the detailed image analysis are a versatile tool for in vitro studies of the penetration efficiency of nanoparticles in tumors.

Seven Properties of Self-Organization in the Human Brain

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: (1) modular connectivity, (2) unsupervised learning, (3) adaptive ability, (4) functional resiliency, (5) functional plasticity, (6) from-local-to-global functional organization, and (7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward.

Self-organization in brain tumors: How cell morphology and cell density influence glioma pattern formation

Modeling cancer cells is essential to better understand the dynamic nature of brain tumors and glioma cells, including their invasion of normal brain. Our goal is to study how the morphology of the glioma cell influences the formation of patterns of collective behavior such as flocks (cells moving in the same direction) or streams (cells moving in opposite direction) referred to as oncostream. We have observed experimentally that the presence of oncostreams correlates with tumor progression. We propose an original agent-based model that considers each cell as an ellipsoid. We show that stretching cells from round to ellipsoid increases stream formation. A systematic numerical investigation of the model was implemented in [Formula: see text]. We deduce a phase diagram identifying key regimes for the dynamics (e.g. formation of flocks, streams, scattering). Moreover, we study the effect of cellular density and show that, in contrast to classical models of flocking, increasing cellular density reduces the formation of flocks. We observe similar patterns in [Formula: see text] with the noticeable difference that stream formation is more ubiquitous compared to flock formation.

The mechanical and pharmacological regulation of glioblastoma cell migration in 3D matrices

The invasion of glioblastoma is a complex process based on the interactions of tumor cells and the extracellular matrix. Tumors that are engineered using biomaterials are more physiologically relevant than a two-dimensional (2D) cell culture system. Matrix metalloproteinases and the plasminogen activator generated by tumor cells regulate a tumor's invasive behavior. In this study, microtumors were fabricated by encapsulating U87 glioma cells in Type I collagen and then glioma cell migration in the collagen hydrogels was investigated. Crosslinking of collagen with 8S-StarPEG increased the hydrogel viscosity and reduced the tumor cell migration speed in the hydrogels. The higher migration speed corresponded to the increased gene expression of MMP-2, MMP-9, urokinase plasminogen activator (uPA), and tissue plasminogen activator (tPA) in glioma cells grown in non-crosslinked collagen hydrogels. Inhibitors of these molecules hindered U87 and A172 cell migration in collagen hydrogels. Aprotinin and tranexamic acid did not inhibit U87 and A172 migration on the culture dish. This study demonstrated the differential effect of pharmacologic molecules on tumor cell motility in either a 2D or three-dimensional culture environment.

Integrating in vitro experiments with in silico approaches for Glioblastoma invasion: the role of cell-to-cell adhesion heterogeneity

Glioblastoma cells adopt migration strategies to invade into the brain parenchyma ranging from individual to collective mechanisms, whose role and dynamics are not yet fully understood. In this work, we explore Glioblastoma heterogeneity and recapitulate its invasive patterns both in vitro, by utilizing primary cells along with the U87MG cell line, and in silico, by adopting discrete, individual cell-based mathematics. Glioblastoma cells are cultured three-dimensionally in an ECM-like substrate. The primary Glioblastoma spheroids adopt a novel cohesive pattern, mimicking perivascular invasion in the brain, while the U87MG adopt a typical, starburst invasive pattern under the same experimental setup. Mathematically, we focus on the role of the intrinsic heterogeneity with respect to cell-to-cell adhesion. Our proposed mathematical approach mimics the invasive morphologies observed in vitro and predicts the dynamics of tumour expansion. The role of the proliferation and migration is also explored showing that their effect on tumour morphology is different per cell type. The proposed model suggests that allowing cell-to-cell adhesive heterogeneity within the tumour population is sufficient for variable invasive morphologies to emerge which remain originally undetectable by conventional imaging, indicating that exploration in pathological samples is needed to improve our understanding and reveal potential patient-specific therapeutic targets.

A three-dimensional (3D) organotypic microfluidic model for glioma stem cells - Vascular interactions

Glioblastoma (GBM) is one of the deadliest forms of cancer. Despite many treatment options, prognosis of GBM remains dismal with a 5-year survival rate of 4.7%. Even then, tumors often recur after treatment. Tumor recurrence is hypothesized to be driven by glioma stem cell (GSC) populations which are highly tumorigenic, invasive, and resistant to several forms of therapy. GSCs are often concentrated around the tumor vasculature, referred to as the vascular niche, which are known to provide microenvironmental cues to maintain GSC stemness, promote invasion, and resistance to therapies. In this work, we developed a 3D organotypic microfluidic platform, integrated with hydrogel-based biomaterials, to mimic the GSC vascular niche and study the influence of endothelial cells (ECs) on patient-derived GSC behavior and identify signaling cues that mediate their invasion and phenotype. The established microvascular network enhanced GSC migration within a 3D hydrogel, promoted invasive morphology as well as maintained GSC proliferation rates and phenotype (Nestin, SOX2, CD44). Notably, we compared migration behavior to in vivo mice model and found similar invasive morphology suggesting that our microfluidic system could represent a physiologically relevant in vivo microenvironment. Moreover, we confirmed that CXCL12-CXCR4 signaling is involved in promoting GSC invasion in a 3D vascular microenvironment by utilizing a CXCR4 antagonist (AMD3100), while also demonstrating the effectiveness of the microfluidic as a drug screening assay. Our model presents a potential ex vivo platform for studying the interplay of GSCs with its surrounding microenvironment as well as development of future therapeutic strategies tailored toward disrupting key molecular pathways involved in GSC regulatory mechanisms.

Embedded Spheroids as Models of the Cancer Microenvironment

To more accurately study the complex mechanisms behind cancer invasion, progression, and response to treatment, researchers require models that replicate both the multicellular nature and 3D stromal environment present in an in vivo tumor. Multicellular aggregates (i.e., spheroids) embedded in an extracellular matrix mimic are a prevalent model. Recently, quantitative metrics that fully utilize the capability of spheroids are described along with conventional experiments, such as invasion into a matrix, to provide additional details and insights into the underlying cancer biology. The review begins with a discussion of the salient features of the tumor microenvironment, introduces the early work on non-embedded spheroids as tumor models, and then concentrates on the successes achieved with the study of embedded spheroids. Examples of studies include cell movement, drug response, tumor cellular heterogeneity, stromal effects, and cancer progression. Additionally, new methodologies and those borrowed from other research fields (e.g., vascularization and tissue engineering) are highlighted that expand the capability of spheroids to aid future users in designing their cancer-related experiments. The convergence of spheroid research among the various fields catalyzes new applications and leads to a natural synergy. Finally, the review concludes with a reflection and future perspectives for cancer spheroid research.

Dynamics of cancerous tissue correlates with invasiveness

Two of the classical hallmarks of cancer are uncontrolled cell division and tissue invasion, which turn the disease into a systemic, life-threatening condition. Although both processes are studied, a clear correlation between cell division and motility of cancer cells has not been described previously. Here, we experimentally characterize the dynamics of invasive and non-invasive breast cancer tissues using human and murine model systems. The intrinsic tissue velocities, as well as the divergence and vorticity around a dividing cell correlate strongly with the invasive potential of the tissue, thus showing a distinct correlation between tissue dynamics and aggressiveness. We formulate a model which treats the tissue as a visco-elastic continuum. This model provides a valid reproduction of the cancerous tissue dynamics, thus, biological signaling is not needed to explain the observed tissue dynamics. The model returns the characteristic force exerted by an invading cell and reveals a strong correlation between force and invasiveness of breast cancer cells, thus pinpointing the importance of mechanics for cancer invasion.

Identifying the necrotic zone boundary in tumour spheroids with pair-correlation functions

Automatic identification of the necrotic zone boundary is important in the assessment of treatments on in vitro tumour spheroids. This has been difficult especially when the difference in cell density between the necrotic and viable zones of a tumour spheroid is small. To help overcome this problem, we developed novel one-dimensional pair-correlation functions (PCFs) to provide quantitative estimates of the radial distance of the necrotic zone boundary from the centre of a tumour spheroid. We validate our approach on synthetic tumour spheroids in which the position of the necrotic zone boundary is known a priori It is then applied to nine real tumour spheroids imaged with light sheet-based fluorescence microscopy. PCF estimates of the necrotic zone boundary are compared with those of a human expert and an existing standard computational method.

Tumor proliferation and diffusion on percolation clusters

We study in silico the influence of host tissue inhomogeneity on tumor cell proliferation and diffusion by simulating the mobility of a tumor on percolation clusters with different homogeneities of surrounding tissues. The proliferation and diffusion of a tumor in an inhomogeneous tissue could be characterized in the framework of the percolation theory, which displays similar thresholds (0.54, 0.44, and 0.37, respectively) for tumor proliferation and diffusion in three kinds of lattices with 4, 6, and 8 connecting near neighbors. Our study reveals the existence of a critical transition concerning the survival and diffusion of tumor cells with leaping metastatic diffusion movement in the host tissues. Tumor cells usually flow in the direction of greater pressure variation during their diffusing and infiltrating to a further location in the host tissue. Some specific sites suitable for tumor invasion were observed on the percolation cluster and around these specific sites a tumor can develop into scattered tumors linked by some advantage tunnels that facilitate tumor invasion. We also investigate the manner that tissue inhomogeneity surrounding a tumor may influence the velocity of tumor diffusion and invasion. Our simulation suggested that invasion of a tumor is controlled by the homogeneity of the tumor microenvironment, which is basically consistent with the experimental report by Riching et al. as well as our clinical observation of medical imaging. Both simulation and clinical observation proved that tumor diffusion and invasion into the surrounding host tissue is positively correlated with the homogeneity of the tissue.

In vitro models of medulloblastoma: Choosing the right tool for the job

The recently-defined four molecular subgroups of medulloblastoma have required updating of our understanding of in vitro models to include molecular classification and risk stratification features from clinical practice. This review seeks to build a more comprehensive picture of the in vitro systems available for modelling medulloblastoma. The subtype classification and molecular characterisation for over 40 medulloblastoma cell-lines has been compiled, making it possible to identify the strengths and weaknesses in current model systems. Less than half (18/44) of established medulloblastoma cell-lines have been subgrouped. The majority of the subgrouped cell-lines (11/18) are Group 3 with MYC-amplification. SHH cell-lines are the next most common (4/18), half of which exhibit TP53 mutation. WNT and Group 4 subgroups, accounting for 50% of patients, remain underrepresented with 1 and 2 cell-lines respectively. In vitro modelling relies not only on incorporating appropriate tumour cells, but also on using systems with the relevant tissue architecture and phenotype as well as normal tissues. Novel ways of improving the clinical relevance of in vitro models are reviewed, focusing on 3D cell culture, extracellular matrix, co-cultures with normal cells and organotypic slices. This paper champions the establishment of a collaborative online-database and linked cell-bank to catalyse preclinical medulloblastoma research.

Anti-gastric cancer activity in three-dimensional tumor spheroids of bufadienolides

Multicellular spheroids of cancer cells have been increasingly used to screen anti-tumor compounds, owing to their in vivo like microenvironment and structure as well as compatibility to high-throughput/high-content screening. Here we report the potency and efficacy of a family of bufadienolides to inhibit the growth of gastric cancer cell line HGC-27 in three-dimensional (3D) spheroidal models. Examining the morphological and growth patterns of several cell lines in round-bottomed ultra-low attachment microplate suggested that HGC-27 cells formed reproducibly multicellular spheroidal structures. Profiling of 15 natural bufadienolides isolated from toad skin indicated that 8 14-hydroxy bufadienolides displayed inhibitory activity of the growth of HGC-27 spheroids in a dose-dependent manner. Notably, compared to clinical drugs taxol and epirubicin, active bufadienolides were found to penetrate more effectively into the HGC-27 spheroids, but with a narrower effective concentration range and a shorter lasting inhibitory effect. Furthermore, compared to two-dimensional (2D) cell monolayer assays, active bufadienolides exhibited weaker efficacy and different potency in 3D spheroid model, demonstrating the great potential of 3D multicellular cell spheroid models in anti-cancer drug discovery and development.

Impact of multicellular tumor spheroids as an in vivo‑like tumor model on anticancer drug response

The incidence of colorectal cancer is higher in men than in women, amounting to 15% of cancer-related diseases as a whole. As such, undesirable effects, arising from the administration of current chemotherapeutic agents (the FOLFIRI/FOLFOX combinations), which are exerted on the remaining non-cancerous tissues and/or cells, have contributed to the occurrence of resistance to multiple drugs, thus markedly reducing their efficacy. However, the delivery of chemotherapeutic agents may be improved and their action may be more selectively targeted to diseased tissues/cells by means of developing biotechnologies and nano-techniques. Thus, the current focus is on creating biological tissue and related tumor models, by means of three-dimensional (3D) spheres, in an attempt to bridge the gap between results obtained in the pre-clinical phase and promising outcomes obtained in clinical trials. For this purpose, the characterization and use of so-called ‘multicellular tumor spheroids’, may prove to be invaluable. In this study, we focus on describing the efficacy of a model 3D system as compared to the traditional 2D tumor spheres in determining drug response, highlighting a potentially greater effect of the drugs following the encapsulation of respective liposomes. The results obtained demonstrate the successful preparation of a suspension of liposomes loaded with folinic acid, oxaliplatin and 5-fluorouracil (5-FU), and loaded with meso-tetra (4-sulfonatophenyl) porphyrin. Following its use on HT-29 colorectal cancer cells, an important comparative reduction was noted in the viability of the HT-29 cells, demonstrating the efficacy of multicellular tumor spheroids carrying liposomes loaded with therapeutic drugs. These findings indicate that the method of drug encapsulation in liposomes may improve the treatment efficacy of chemotherapeutic agents.

Mathematical Modelling of a Brain Tumour Initiation and Early Development: A Coupled Model of Glioblastoma Growth, Pre-Existing Vessel Co-Option, Angiogenesis and Blood Perfusion

We propose a coupled mathematical modelling system to investigate glioblastoma growth in response to dynamic changes in chemical and haemodynamic microenvironments caused by pre-existing vessel co-option, remodelling, collapse and angiogenesis. A typical tree-like architecture network with different orders for vessel diameter is designed to model pre-existing vasculature in host tissue. The chemical substances including oxygen, vascular endothelial growth factor, extra-cellular matrix and matrix degradation enzymes are calculated based on the haemodynamic environment which is obtained by coupled modelling of intravascular blood flow with interstitial fluid flow. The haemodynamic changes, including vessel diameter and permeability, are introduced to reflect a series of pathological characteristics of abnormal tumour vessels including vessel dilation, leakage, angiogenesis, regression and collapse. Migrating cells are included as a new phenotype to describe the migration behaviour of malignant tumour cells. The simulation focuses on the avascular phase of tumour development and stops at an early phase of angiogenesis. The model is able to demonstrate the main features of glioblastoma growth in this phase such as the formation of pseudopalisades, cell migration along the host vessels, the pre-existing vasculature co-option, angiogenesis and remodelling. The model also enables us to examine the influence of initial conditions and local environment on the early phase of glioblastoma growth.

Three-dimensional microtissues essentially contribute to preclinical validations of therapeutic targets in breast cancer

A 3D microtissues using T47D and JIMT‐1 cells were generated to analyze tissue‐like response of breast cancer cells after combined human epidermal growth factor receptor 2 (HER2)‐targeted treatment and radiation. Following lentiviral knockdown of HER2, we compared growth rate alterations using 2D monolayers, 3D microtissues, and mouse xenografts. Additionally, to model combined therapeutic strategies, we treated HER2‐depleted T47D cells and 3D microtissues using trastuzumab (anti‐HER2 antibody) in combination with irradiation. Comparison of HER2 knockdown with corresponding controls revealed growth impairment due to HER2 knockdown in T47D 2D monolayers, 3D microtissues, and xenografts (after 2, 12, and ≥40 days, respectively). In contrast, HER2 knockdown was less effective in inhibiting growth of trastuzumab‐resistant JIMT‐1 cells in vitro and in vivo. Combined administration of trastuzumab and radiation treatment was also analyzed using T47D 3D microtissues. Administration of both, radiation (5 Gy) and trastuzumab, significantly enhanced the growth inhibiting effect in 3D microtissues. To improve the predictive power of potential drugs—as single agents or in combination—here, we show that regarding tumor growth analyses, 3D microtissues are highly comparable to outcomes derived from xenografts. Considering increased limitations for animal experiments on the one hand and strong need of novel drugs on the other hand, it is indispensable to include highly reproducible 3D microtissue platform in preclinical analyses to validate more accurately the capacity of future drug‐combined radiotherapy.

Integrating Multiscale Modeling with Drug Effects for Cancer Treatment

In this paper, we review multiscale modeling for cancer treatment with the incorporation of drug effects from an applied system's pharmacology perspective. Both the classical pharmacology and systems biology are inherently quantitative; however, systems biology focuses more on networks and multi factorial controls over biological processes rather than on drugs and targets in isolation, whereas systems pharmacology has a strong focus on studying drugs with regard to the pharmacokinetic (PK) and pharmacodynamic (PD) relations accompanying drug interactions with multiscale physiology as well as the prediction of dosage-exposure responses and economic potentials of drugs. Thus, it requires multiscale methods to address the need for integrating models from the molecular levels to the cellular, tissue, and organism levels. It is a common belief that tumorigenesis and tumor growth can be best understood and tackled by employing and integrating a multifaceted approach that includes in vivo and in vitro experiments, in silico models, multiscale tumor modeling, continuous/discrete modeling, agent-based modeling, and multiscale modeling with PK/PD drug effect inputs. We provide an example application of multiscale modeling employing stochastic hybrid system for a colon cancer cell line HCT-116 with the application of Lapatinib drug. It is observed that the simulation results are similar to those observed from the setup of the wet-lab experiments at the Translational Genomics Research Institute.

Introduction of a Framework for Dynamic Knowledge Representation of the Control Structure of Transplant Immunology: Employing the Power of Abstraction with a Solid Organ Transplant Agent-Based Model

Agent-based modeling has been used to characterize the nested control loops and non-linear dynamics associated with inflammatory and immune responses, particularly as a means of visualizing putative mechanistic hypotheses. This process is termed dynamic knowledge representation and serves a critical role in facilitating the ability to test and potentially falsify hypotheses in the current data- and hypothesis-rich biomedical research environment. Importantly, dynamic computational modeling aids in identifying useful abstractions, a fundamental scientific principle that pervades the physical sciences. Recognizing the critical scientific role of abstraction provides an intellectual and methodological counterweight to the tendency in biology to emphasize comprehensive description as the primary manifestation of biological knowledge. Transplant immunology represents yet another example of the challenge of identifying sufficient understanding of the inflammatory/immune response in order to develop and refine clinically effective interventions. Advances in immunosuppressive therapies have greatly improved solid organ transplant (SOT) outcomes, most notably by reducing and treating acute rejection. The end goal of these transplant immune strategies is to facilitate effective control of the balance between regulatory T cells and the effector/cytotoxic T-cell populations in order to generate, and ideally maintain, a tolerant phenotype. Characterizing the dynamics of immune cell populations and the interactive feedback loops that lead to graft rejection or tolerance is extremely challenging, but is necessary if rational modulation to induce transplant tolerance is to be accomplished. Herein is presented the solid organ agent-based model (SOTABM) as an initial example of an agent-based model (ABM) that abstractly reproduces the cellular and molecular components of the immune response to SOT. Despite its abstract nature, the SOTABM is able to qualitatively reproduce acute rejection and the suppression of acute rejection by immunosuppression to generate transplant tolerance. The SOTABM is intended as an initial example of how ABMs can be used to dynamically represent mechanistic knowledge concerning transplant immunology in a scalable and expandable form and can thus potentially serve as useful adjuncts to the investigation and development of control strategies to induce transplant tolerance.

The Importance of Neighborhood Scheme Selection in Agent-based Tumor Growth Modeling

Modeling tumor growth has proven a very challenging problem, mainly due to the fact that tumors are highly complex systems that involve dynamic interactions spanning multiple scales both in time and space. The desire to describe interactions in various scales has given rise to modeling approaches that use both continuous and discrete variables, known as hybrid approaches. This work refers to a hybrid model on a 2D square lattice focusing on cell movement dynamics as they play an important role in tumor morphology, invasion and metastasis and are considered as indicators for the stage of malignancy used for early prognosis and effective treatment. Considering various distributions of the microenvironment, we explore how Neumann vs. Moore neighborhood schemes affects tumor growth and morphology. The results indicate that the importance of neighborhood selection is critical under specific conditions that include i) increased hapto/chemo-tactic coefficient, ii) a rugged microenvironment and iii) ECM degradation.

Mathematically modeling the biological properties of gliomas: A review

Although mathematical modeling is a mainstay for industrial and many scientific studies, such approaches have found little application in neurosurgery. However, the fusion of biological studies and applied mathematics is rapidly changing this environment, especially for cancer research. This review focuses on the exciting potential for mathematical models to provide new avenues for studying the growth of gliomas to practical use. In vitro studies are often used to simulate the effects of specific model parameters that would be difficult in a larger-scale model. With regard to glioma invasive properties, metabolic and vascular attributes can be modeled to gain insight into the infiltrative mechanisms that are attributable to the tumor's aggressive behavior. Morphologically, gliomas show different characteristics that may allow their growth stage and invasive properties to be predicted, and models continue to offer insight about how these attributes are manifested visually. Recent studies have attempted to predict the efficacy of certain treatment modalities and exactly how they should be administered relative to each other. Imaging is also a crucial component in simulating clinically relevant tumors and their influence on the surrounding anatomical structures in the brain.

Towards the Personalized Treatment of Glioblastoma: Integrating Patient-Specific Clinical Data in a Continuous Mechanical Model

Glioblastoma multiforme (GBM) is the most aggressive and malignant among brain tumors. In addition to uncontrolled proliferation and genetic instability, GBM is characterized by a diffuse infiltration, developing long protrusions that penetrate deeply along the fibers of the white matter. These features, combined with the underestimation of the invading GBM area by available imaging techniques, make a definitive treatment of GBM particularly difficult. A multidisciplinary approach combining mathematical, clinical and radiological data has the potential to foster our understanding of GBM evolution in every single patient throughout his/her oncological history, in order to target therapeutic weapons in a patient-specific manner. In this work, we propose a continuous mechanical model and we perform numerical simulations of GBM invasion combining the main mechano-biological characteristics of GBM with the micro-structural information extracted from radiological images, i.e. by elaborating patient-specific Diffusion Tensor Imaging (DTI) data. The numerical simulations highlight the influence of the different biological parameters on tumor progression and they demonstrate the fundamental importance of including anisotropic and heterogeneous patient-specific DTI data in order to obtain a more accurate prediction of GBM evolution. The results of the proposed mathematical model have the potential to provide a relevant benefit for clinicians involved in the treatment of this particularly aggressive disease and, more importantly, they might drive progress towards improving tumor control and patient’s prognosis.

Collective ordering of microscale matters in natural analogy

Collective interaction occurs in many natural and artificial matters in broad scales. In a biological system, collective spatial organization of live individuals in a colony is important for their viability determination. Interactive motions between a single individual and an agglomerate are critical for whole procedure of the collective behaviors, but few has been clarified for these intermediate range behaviors. Here, collective interactions of microscale matters are investigated with human cells, plant seeds and artificial microspheres in terms of commonly occurring spatial arrangements. Human cancer cells are inherently attractive to form an agglomerate by cohesive motion, while plant chia seeds are repulsive by excreting mucilage. Microsphere model is employed to investigate the dynamic assembly equilibrated by an attraction and repulsion. There is a fundamental analogy in terms of an onset of regular pattern formation even without physical contact of individuals. The collective interactions are suggested to start before the individual components become physically agglomerated. This study contributes to fundamental understanding on the microscale particulate matters and natural pattern formation which are further useful for various applications both in academic and industrial areas.

Three-dimensional (3D) tumor spheroid invasion assay

Invasion of surrounding normal tissues is generally considered to be a key hallmark of malignant (as opposed to benign) tumors. For some cancers in particular (e.g., brain tumors such as glioblastoma multiforme and squamous cell carcinoma of the head and neck – SCCHN) it is a cause of severe morbidity and can be life-threatening even in the absence of distant metastases. In addition, cancers which have relapsed following treatment unfortunately often present with a more aggressive phenotype. Therefore, there is an opportunity to target the process of invasion to provide novel therapies that could be complementary to standard anti-proliferative agents. Until now, this strategy has been hampered by the lack of robust, reproducible assays suitable for a detailed analysis of invasion and for drug screening. Here we provide a simple micro-plate method (based on uniform, self-assembling 3D tumor spheroids) which has great potential for such studies. We exemplify the assay platform using a human glioblastoma cell line and also an SCCHN model where the development of resistance against targeted epidermal growth factor receptor (EGFR) inhibitors is associated with enhanced matrix-invasive potential. We also provide two alternative methods of semi-automated quantification: one using an imaging cytometer and a second which simply requires standard microscopy and image capture with digital image analysis.

Modeling contact guidance and invasion by cancer cells

The first step in the spread of cancer is invasion by malignant cells of the normal tissue surrounding a tumor. There is considerable evidence both in vitro and in vivo that mechanical interactions with the tissue, in particular with the biopolymer network that makes up the extracellular matrix (ECM), are important factors in invasion. The interactions take two forms: (i) contractile cells on the surface of the tumor act on the nearby ECM and remodel it; in some cases, they align the fibers of the biopolymers; (ii) the aligned fibers can enhance invasion via contact guidance, the tendency of motile cells to follow alignment. Here, we give evidence, mainly for in vitro systems, that both effects are important. We discuss how alignment occurs in biopolymers such as collagen-I (a major component of the ECM). We propose a modeling framework for computing alignment and propose phenomenologic models for contact guidance. See all articles in this Cancer Research section, "Physics in Cancer Research."

The anomalous diffusion of a tumor invading with different surrounding tissues

We simulated the invasion of a proliferating, diffusing tumor within different surrounding tissue conditions using a hybrid mathematical model. The in silico invasion of a tumor was addressed systematically for the first time within the framework of a generalized diffusion theory. Our results reveal that a tumor not only migrates using typical Fickian diffusion, but also migrates more generally using subdiffusion, superdiffusion, and even ballistic diffusion, with increasing mobility of the tumor cell when haptotaxis and chemotaxis toward the host tissue surrounding the proliferative tumor are involved. Five functional terms were included in the hybrid model and their effects on a tumor's invasion were investigated quantitatively: haptotaxis toward the extracellular matrix tissue that is degraded by matrix metalloproteinases; chemotaxis toward nutrients; cell-cell adhesion; the proliferation of the tumor; and the immune response toward the tumor. Haptotaxis and chemotaxis, which are initiated by extracellular matrix and nutrient supply (i.e., glucose) respectively, as well as cell-cell adhesions all drastically affect a tumor's diffusion mode when a tumor invades its surrounding host tissue and proliferates. We verified the in silico invasive behavior of a tumor by analyzing experimental data gathered from the in vitro culturing of different tumor cells and clinical imaging observations that used the same approach as was used to process the simulation data. The different migration modes of a tumor suggested by the simulations generally conform to the results observed in cell cultures and in clinical imaging. Our study not only discloses some migration modes of a tumor that proliferates and invades under different host tissues conditions, but also provides a heuristic method to characterize the invasion of a tumor in clinical medical imaging analysis.

Response-predictive gene expression profiling of glioma progenitor cells in vitro

Background

High-grade gliomas are amongst the most deadly human tumors. Treatment results are disappointing. Still, in several trials around 20% of patients respond to therapy. To date, diagnostic strategies to identify patients that will profit from a specific therapy do not exist.

Methods

In this study, we used serum-free short-term treated in vitro cell cultures to predict treatment response in vitro. This approach allowed us (a) to enrich specimens for brain tumor initiating cells and (b) to confront cells with a therapeutic agent before expression profiling.

Results

As a proof of principle we analyzed gene expression in 18 short-term serum-free cultures of high-grade gliomas enhanced for brain tumor initiating cells (BTIC) before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated progenitor cells allowed to predict therapy-induced impairment of proliferation in vitro.

Conclusion

For the tyrosine kinase inhibitor Sunitinib used in this dataset, the approach revealed additional predictive information in comparison to the evaluation of classical signaling analysis.

Response patterns of recurrent glioblastomas treated with tumor-treating fields

Glioblastoma multiforme (GBM) is the most common form of primary malignant brain cancer. Median overall survival (OS) for newly diagnosed patients is only about 12 to 18 months. GBM tumors invariably recur, and there is no widely recognized and effective standard treatment for recurrent GBM. NovoTTF Therapy is a novel and US Food and Drug Administration (FDA)-approved antimitotic treatment for recurrent GBM with potential benefits compared with other options. Recurrent GBM patients from two prior trials with demonstrated radiologic tumor response to single-agent NovoTTF Therapy were analyzed to better characterize tumor response patterns and evaluate the associations between response, compliance, and OS. In addition, a compartmental tumor growth model was developed and evaluated for its ability to predict GBM response to tumor-treating fields (TTFields). The overall response rate across both trials was 15% (4% complete responses): 14% in the phase III trial (14/120) and 20% (2/10) in a pilot study. Tumor responses to NovoTTF Therapy developed slowly (median time to response, 5.2 months) but were durable (median duration, 12.9 months). Response duration was highly correlated with OS (r(2) = .92, P<.0001), and median OS for responders was 24.8 months. Seven of 16 responders exhibited initial tumor growth on magnetic resonance imaging. Compliance appeared to be linked with both improved response and survival. The tumor growth model predicted tumor arrest and shrinkage only after several weeks of continuous NovoTTF Therapy, consistent with the observed clinical findings of initial transient tumor growth in some patients. NovoTTF Therapy is a novel antimitotic treatment for recurrent GBM associated with slowly developing but durable tumor responses in approximately 15% of patients. Some responders exhibit initial tumor growth before shrinkage, indicating treatment should not be terminated prior to allowing for the full effect of NovoTTF Therapy to be realized. OS is longer in responders than in nonresponders. High daily compliance rates may be associated with increased likelihood of an objective response and are predictive of improved survival.

Agent-based model of fecal microbial transplant effect on bile acid metabolism on suppressing Clostridium difficile infection: an example of agent-based modeling of intestinal bacterial infection

Agent-based modeling is a computational modeling method that represents system-level behavior as arising from multiple interactions between the multiple components that make up a system. Biological systems are thus readily described using agent-based models (ABMs), as multi-cellular organisms can be viewed as populations of interacting cells, and microbial systems manifest as colonies of individual microbes. Intersections between these two domains underlie an increasing number of pathophysiological processes, and the intestinal tract represents one of the most significant locations for these inter-domain interactions, so much so that it can be considered an internal ecology of varying robustness and function. Intestinal infections represent significant disturbances of this internal ecology, and one of the most clinically relevant intestinal infections is Clostridium difficile infection (CDI). CDI is precipitated by the use of broad-spectrum antibiotics, involves the depletion of commensal microbiota, and alterations in bile acid composition in the intestinal lumen. We present an example ABM of CDI (the C. difficile Infection ABM, or CDIABM) to examine fundamental dynamics of the pathogenesis of CDI and its response to treatment with anti-CDI antibiotics and a newer treatment therapy, fecal microbial transplant. The CDIABM focuses on one specific mechanism of potential CDI suppression: commensal modulation of bile acid composition. Even given its abstraction, the CDIABM reproduces essential dynamics of CDI and its response to therapy, and identifies a paradoxical zone of behavior that provides insight into the role of intestinal nutritional status and the efficacy of anti-CDI therapies. It is hoped that this use case example of the CDIABM can demonstrate the usefulness of both agent-based modeling and the application of abstract functional representation as the biomedical community seeks to address the challenges of increasingly complex diseases with the goal of personalized medicine.

Label-free single cell kinetics of the invasion of spheroidal colon cancer cells through 3D Matrigel

This article reports label-free, real-time, and single-cell quantification of the invasion of spheroidal colon cancer cells through three-dimensional (3D) Matrigel using a resonant waveguide grating (RWG) imager. This imager employs a time-resolved swept wavelength interrogation scheme to monitor cell invasion and adhesion with a temporal resolution up to 3 s and a spatial resolution of 12 μm. As the model system, spheroids of human colorectal adenocarcinoma HT-29 cells are generated by culturing the cells in 96-well round-bottom ultralow attachment plates. 3D Matrigel is formed by its gelation in 384-well RWG biosensor microplates. The invasion and adhesion of spheroidal HT29 cells is initiated by placing individual spheroids onto the Matrigel-coated biosensors. The time series RWG images are obtained and used to extract the optical signatures arising from the adhesion after the cells are dissociated from the spheroids and invade through the 3D Matrigel. Compound profiling shows that epidermal growth factor accelerates cancer cell invasion, while vandetanib, a multitarget kinase inhibitor, dose-dependently inhibits invasion. This study demonstrates that the label-free imager can monitor in real-time the invasion of spheroidal cancer cells through 3D matrices.

Spatial invasion dynamics on random and unstructured meshes: implications for heterogeneous tumor populations

In this work we discuss a spatial evolutionary model for a heterogeneous cancer cell population. We consider the gain-of-function mutations that not only change the fitness potential of the mutant phenotypes against normal background cells but may also increase the relative motility of the mutant cells. The spatial modeling is implemented as a stochastic evolutionary system on a structured grid (a lattice, with random neighborhoods, which is not necessarily bi-directional) or on a two-dimensional unstructured mesh, i.e. a bi-directional graph with random numbers of neighbors. We present a computational approach to investigate the fixation probability of mutants in these spatial models. Additionally, we examine the effect of the migration potential on the spatial dynamics of mutants on unstructured meshes. Our results suggest that the probability of fixation is negatively correlated with the width of the distribution of the neighborhood size. Also, the fixation probability increases given a migration potential for mutants. We find that the fixation probability (of advantaged, disadvantaged and neutral mutants) on unstructured meshes is relatively smaller than the corresponding results on regular grids. More importantly, in the case of neutral mutants the introduction of a migration potential has a critical effect on the fixation probability and increases this by orders of magnitude. Further, we examine the effect of boundaries and as intuitively expected, the fixation probability is smaller on the boundary of regular grids when compared to its value in the bulk. Based on these computational results, we speculate on possible better therapeutic strategies that may delay tumor progression to some extent.

Multiple types of data are required to identify the mechanisms influencing the spatial expansion of melanoma cell colonies

BACKGROUND

The expansion of cell colonies is driven by a delicate balance of several mechanisms including cell motility, cell-to-cell adhesion and cell proliferation. New approaches that can be used to independently identify and quantify the role of each mechanism will help us understand how each mechanism contributes to the expansion process. Standard mathematical modelling approaches to describe such cell colony expansion typically neglect cell-to-cell adhesion, despite the fact that cell-to-cell adhesion is thought to play an important role.

RESULTS

We use a combined experimental and mathematical modelling approach to determine the cell diffusivity, D, cell-to-cell adhesion strength, q, and cell proliferation rate, λ, in an expanding colony of MM127 melanoma cells. Using a circular barrier assay, we extract several types of experimental data and use a mathematical model to independently estimate D, q and λ. In our first set of experiments, we suppress cell proliferation and analyse three different types of data to estimate D and q. We find that standard types of data, such as the area enclosed by the leading edge of the expanding colony and more detailed cell density profiles throughout the expanding colony, does not provide sufficient information to uniquely identify D and q. We find that additional data relating to the degree of cell-to-cell clustering is required to provide independent estimates of q, and in turn D. In our second set of experiments, where proliferation is not suppressed, we use data describing temporal changes in cell density to determine the cell proliferation rate. In summary, we find that our experiments are best described using the range D=161-243μm2 hour-1, q=0.3-0.5 (low to moderate strength) and λ=0.0305-0.0398 hour-1, and with these parameters we can accurately predict the temporal variations in the spatial extent and cell density profile throughout the expanding melanoma cell colony.

CONCLUSIONS

Our systematic approach to identify the cell diffusivity, cell-to-cell adhesion strength and cell proliferation rate highlights the importance of integrating multiple types of data to accurately quantify the factors influencing the spatial expansion of melanoma cell colonies.

Novel Phenotypic Fluorescent Three-Dimensional Co-Culture Platforms for Recapitulating Tumor in vivo Progression and for Personalized Therapy

Because three-dimensional (3D) in vitro models are more accurate than 2D cell culture models and faster and cheaper than animal models, they have become a prospective trend in the biomedical and pharmaceutical fields, especially for personalized and targeted therapies. Because appropriate 3D models can be customized to mimic the in vivo microenvironment wherein various cell populations grow within an intricate but well organized extracellular matrix (ECM), they can accurately recapitulate physiological and pathophysiological progressions. The majority of cancers are carcinomas, which originate from epithelial cells, and dynamically interact with non-malignant cells including stromal cells (fibroblasts), vascular cells (endothelial cells and pericytes), immune cells (macrophages and mast cells), and the ECM. Employing a tumor monoclonal colony, tumor xenograft or patient cancer biopsy into an in vivo-like microenvironment, the native signaling pathways, cell-cell and cell-matrix interactions, and cell phenotypes are preserved and our fluorescent phenotypic 3D co-culture platforms can then accurately recapitulate the tumor in vivo scenario including tumor induced angiogenesis, tumor growth, and metastasis.

In this paper, we describe a robust and standardized method to co-culture a tumor colony or biopsy with different cell populations, e.g., endothelial cells, immune cells, pericytes, etc. The procedures for recovering cells from the co-culture for molecular analyses, imaging, and analyzing are also described. We selected ECM solubilized extract derived from Engelbreth-Holm-Swam sarcoma cells. Because the 3D co-culture platforms can provide drug chemosensitivity data within 9 days that is equivalent to the results generated from mouse tumor xenograft models in 50 days, the 3D co-culture platforms are more accurate, efficient, and cost-effective and may replace animal models in the near future to predict drug efficacy, personalize therapies, prevent drug resistance, and improve the quality of life.

Agent-based model of epithelial host-pathogen interactions in anastomotic leak

BACKGROUND

There is a growing recognition of the significance of host-pathogen interactions (HPIs) in gut biology leading to a reassessment of the role of bacteria in intestinal anastomotic leak. Understanding the complexities of the early postsurgical gut HPI requires integrating knowledge of both epithelial and bacterial behaviors to generate hypotheses of potential mechanisms of interaction. Agent-based modeling is a computational method well suited to achieve this goal, and we use an agent-based model (ABM) to examine alterations in the HPI affecting reestablishment of the epithelial barrier that may subsequently lead to anastomotic leak.

METHODS

Computational agents representing Pseudomonas aeruginosa were added to a previously validated ABM of epithelial restitution. Simulated experiments were performed examining the effect of radiation on bacterial binding to epithelial cells, plausibility of putative binding targets, and potential mechanisms of epithelial cell killing by virulent bacteria.

RESULTS

Simulation experiments incorporating radiation effects on epithelial monolayers produced binding patterns akin to those seen in vitro and suggested that promotility integrin-laminin associations represent potential sites for bacterial binding and disruption of restitution. Simulations of potential mechanisms of epithelial cell killing suggested that an injected cytotoxin was the means by which virulent bacteria produced the tissue destruction needed to generate an anastomotic leak, a mechanism subsequently confirmed with genotyping of the virulent P aeruginosa strain.

CONCLUSIONS

This study emphasizes the utility of ABM as an adjunct to traditional research methods and provides insights into the potentially critical role of HPI in the pathogenesis of anastomotic leak.

Different chemical reaction times between normal and solid cancer cells

Entropy generation approach has been developed in order to use it for the analysis of complex systems with particular regards to biological systems in order to evaluate their stationary states. The entropy generation is related to the transport processes related to energy flows. Moreover, cancer can be described as an open complex dynamic and self-organizing system. Using the entropy generation approach it is possible to point out different chemical reaction time between normal and solid cancer cells.

Evolution and morphology of microenvironment-enhanced malignancy of three-dimensional invasive solid tumors

The emergence of invasive and metastatic behavior in malignant tumors can often lead to fatal outcomes for patients. The collective malignant tumor behavior resulting from the complex tumor-host interactions and the interactions between the tumor cells is currently poorly understood. In this paper, we employ a cellular automaton (CA) model to investigate microenvironment-enhanced malignant behaviors and morphologies of in vitro avascular invasive solid tumors in three dimensions. Our CA model incorporates a variety of microscopic-scale tumor-host interactions, including the degradation of the extracellular matrix by the malignant cells, nutrient-driven cell migration, pressure buildup due to the deformation of the microenvironment by the growing tumor, and its effect on the local tumor-host interface stability. Moreover, the effects of cell-cell adhesion on tumor growth are explicitly taken into account. Specifically, we find that while strong cell-cell adhesion can suppress the invasive behavior of the tumors growing in soft microenvironments, cancer malignancy can be significantly enhanced by harsh microenvironmental conditions, such as exposure to high pressure levels. We infer from the simulation results a qualitative phase diagram that characterizes the expected malignant behavior of invasive solid tumors in terms of two competing malignancy effects: the rigidity of the microenvironment and cell-cell adhesion. This diagram exhibits phase transitions between noninvasive and invasive behaviors. We also discuss the implications of our results for the diagnosis, prognosis, and treatment of malignant tumors.

Regulation of cell proliferation and migration in glioblastoma: new therapeutic approach

Glioblastoma is the most aggressive brain cancer with the poor survival rate. A microRNA, miR-451, and its downstream molecules, CAB39/LKB1/STRAD/AMPK, are known to play a critical role in regulating a biochemical balance between rapid proliferation and invasion in the presence of metabolic stress in microenvironment. We develop a novel multi-scale mathematical model where cell migration and proliferation are controlled through a core intracellular control system (miR-451-AMPK complex) in response to glucose availability and physical constraints in the microenvironment. Tumor cells are modeled individually and proliferation and migration of those cells are regulated by the intracellular dynamics and reaction-diffusion equations of concentrations of glucose, chemoattractant, extracellular matrix, and MMPs. The model predicts that invasion patterns and rapid growth of tumor cells after conventional surgery depend on biophysical properties of cells, dynamics of the core control system, and microenvironment as well as glucose injection methods. We developed a new type of therapeutic approach: effective injection of chemoattractant to bring invasive cells back to the surgical site after initial surgery, followed by glucose injection at the same location. The model suggests that a good combination of chemoattractant and glucose injection at appropriate time frames may lead to an effective therapeutic strategy of eradicating tumor cells.