A 3D multiscale model of cancer stem cell in tumor development

Multi-scale modeling of hippo signaling identifies homeostatic control by YAP-LATS negative feedback

The Hippo signaling primarily includes LATS1/2 and YAP1. Recent work has demonstrated a novel negative feedback between YAP1 and LATS1/2. However, how YAP-LATS negative feedback regulates cancer progression remains elusive. We constructed a multi-scale model which integrates angiogenesis, spatiotemporal variation of microenvironmental factors and phenotypic switch of tumor cells. Our simulation replicated the findings that YAP overexpression markedly attenuated cell proliferation owing to elevated negative feedback strength. After disruption of YAP-LATS negative feedback loop, however, YAP overexpression would promote cell proliferation. Consistently, LATS overexpression inhibited cell growth and lowered the proliferation potential. We also employed a putative LATS agonist and identified its dose-dependent tumor suppressive effects. Furthermore, targeted delivery could more effectively inhibit tumor growth. Our model has reconciled experimental findings and implied that reconstruction of functional and/or hyperactivated YAP-LATS negative feedback might be a promising strategy to homeostatic maintenance and tumor suppression.

A multi-scale agent-based model for avascular tumour growth

In this paper, we have developed a multi-scale, lattice-free, agent based model of avascular tumour growth in epithelial tissue. The model integrates different events to identify the underlying diversity within intracellular, cellular, and extracellular layer dynamics. The model considers every cell as an agent. A cellular agent may proliferate, spawns two identical daughter agents, or it may be transformed into other phenotypes during its life time depending on its internal proteins' activity as well as its external microenvironment. In this context, a simplified age-structured cell cycle model is adopted from the existing literature. The model considers that the intracellular events are regulated by p27 gene expression. In this model, p27 protein controls the overall tumour growth dynamics. Moreover, p27 is controlled by the external oxygen and nutrients that are modelled with the reaction-diffusion equations. The model also considers several biophysical forces which directly effect on the tumour growth dynamics. This modelling framework offers biologically realistic outcomes and also covers important criteria of the hallmarks of cancer which include oxygen and nutrient consumptions, micro-environmental heterogeneity, tumour cell proliferation by avoiding growth suppressor signals, replication of tumour cells at an abnormally faster rate, and resistance of apoptosis. The avascular tumour growth model is validated with immunohistochemistry and histopathology data. The outcome of the proposed model is very close to the range of the patient data, which concludes that the model is capable enough to mimic these complex biophysical phenomena.

The Optimal Tetralogy of Fallot Repair Using Generative Adversarial Networks

Background

Tetralogy of Fallot (TOF) is a type of congenital cardiac disease with pulmonary artery (PA) stenosis being the most common defect. Repair surgery needs an appropriate patch to enlarge the narrowed artery from the right ventricular (RV) to the PA.

Methods

In this work, we proposed a generative adversarial networks (GANs) based method to optimize the patch size, shape, and location. Firstly, we built the 3D PA of patients by segmentation from cardiac computed tomography angiography. After that, normal and stenotic areas of each PA were detected and labeled into two sub-images groups. Then a GAN was trained based on these sub-images. Finally, an optimal prediction model was utilized to repair the PA with patch augmentation in the new patient.

Results

The fivefold cross-validation (CV) was performed for optimal patch prediction based on GANs in the repair of TOF and the CV accuracy was 93.33%, followed by the clinical outcome. This showed that the GAN model has a significant advantage in finding the best balance point of patch optimization.

Conclusion

This approach has the potential to reduce the intraoperative misjudgment rate, thereby providing a detailed surgical plan in patients with TOF.

Implications of HIF-1α in the tumorigenesis and progression of pancreatic cancer

Pancreatic cancer is one of the leading causes of cancer-related deaths worldwide and is characterized by highly hypoxic tumor microenvironment. Hypoxia-inducible factor-1 alpha (HIF-1α) is a major regulator of cellular response to changes in oxygen concentration, supporting the adaptation of tumor cells to hypoxia in an oxygen-deficient tumor microenvironment. Numerous studies revealed the central role of HIF-1α in the carcinogenesis and progression of pancreatic cancer. This article reviewed the molecular mechanisms of how HIF-1α regulated tumorigenesis and progression of pancreatic cancer and suggested that targeting HIF-1α and its signaling pathways could be promising therapeutics for pancreatic cancer.

Role of cancer stem cells in the development of giant cell tumor of bone

The primary bone tumor is usually observed in adolescence age group which has been shown to be part of nearly 20% of the sarcomas known today. Giant cell tumor of bone (GCTB) can be benign as well as malignant tumor which exhibits localized dynamism and is usually associated with the end point of a long bone. Giant cell tumor (GCT) involves mononuclear stromal cells which proliferate at a high rate, multinucleated giant cells and stromal cells are equally present in this type of tumor. Cancer stem cells (CSCs) have been confirmed to play a potential role in the development of GCT. Cancer stem cell-based microRNAs have been shown to contribute to a greater extent in giant cell tumor of bone. CSCs and microRNAs present in the tumors specifically are a great concern today which need in-depth knowledge as well as advanced techniques to treat the bone cancer effectively. In this review, we attempted to summarize the role played by cancer stem cells involving certain important molecules/factors such as; Mesenchymal Stem Cells (MSCs), miRNAs and signaling mechanism such as; mTOR/PI3K-AKT, towards the formation of giant cell tumor of bone, in order to get an insight regarding various effective strategies and research advancements to obtain adequate knowledge related to CSCs which may help to focus on highly effective treatment procedures for bone tumors.

Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study

Background

Contemporary biological observations have revealed a large variety of mechanisms acting during the expansion of a tumor. However, there are still many qualitative and quantitative aspects of the phenomenon that remain largely unknown. In this context, mathematical and computational modeling appears as an invaluable tool providing the means for conducting in silico experiments, which are cheaper and less tedious than real laboratory experiments.

Results

This paper aims at developing an extensible and computationally efficient framework for in silico modeling of tumor growth in a 3-dimensional, inhomogeneous and time-varying chemical environment. The resulting model consists of a set of mathematically derived and algorithmically defined operators, each one addressing the effects of a particular biological mechanism on the state of the system. These operators may be extended or re-adjusted, in case a different set of starting assumptions or a different simulation scenario needs to be considered.

Conclusion

In silico modeling provides an alternative means for testing hypotheses and simulating scenarios for which exact biological knowledge remains elusive. However, finer tuning of pertinent methods presupposes qualitative and quantitative enrichment of available biological evidence. Validation in a strict sense would further require comprehensive, case-specific simulations and detailed comparisons with biomedical observations.

Mathematical modeling of bone marrow - peripheral blood dynamics in the disease state based on current emerging paradigms, part II

The cancer stem cell hypothesis has gained currency in recent times but concerns remain about its scientific foundations because of significant gaps that exist between research findings and comprehensive knowledge about cancer stem cells (CSCs). In this light, a mathematical model that considers hematopoietic dynamics in the diseased state of the bone marrow and peripheral blood is proposed and used to address findings about CSCs. The ensuing model, resulting from a modification and refinement of a recent model, develops out of the position that mathematical models of CSC development, that are few at this time, are needed to provide insightful underpinnings for biomedical findings about CSCs as the CSC idea gains traction. Accordingly, the mathematical challenges brought on by the model that mirror general challenges in dealing with nonlinear phenomena are discussed and placed in context. The proposed model describes the logical occurrence of discrete time delays, that by themselves present mathematical challenges, in the evolving cell populations under consideration. Under the challenging circumstances, the steady state properties of the model system of delay differential equations are obtained, analyzed, and the resulting mathematical predictions arising therefrom are interpreted and placed within the framework of findings regarding CSCs. Simulations of the model are carried out by considering various parameter scenarios that reflect different experimental situations involving disease evolution in human hosts. Model analyses and simulations suggest that the emergence of the cancer stem cell population alongside other malignant cells engenders higher dimensions of complexity in the evolution of malignancy in the bone marrow and peripheral blood at the expense of healthy hematopoietic development. The model predicts the evolution of an aberrant environment in which the malignant population particularly in the bone marrow shows tendencies of reaching an uncontrollable equilibrium state. Essentially, the model shows that a structural relationship exists between CSCs and non-stem malignant cells that confers on CSCs the role of temporally enhancing and stimulating the expansion of non-stem malignant cells while also benefitting from increases in their own population and these CSCs may be the main protagonists that drive the ultimate evolution of the uncontrollable equilibrium state of such malignant cells and these may have implications for treatment.

Multiscale Tumor Modeling With Drug Pharmacokinetic and Pharmacodynamic Profile Using Stochastic Hybrid System

Effective cancer treatment strategy requires an understanding of cancer behavior and development across multiple temporal and spatial scales. This has resulted into a growing interest in developing multiscale mathematical models that can simulate cancer growth, development, and response to drug treatments. This study thus investigates multiscale tumor modeling that integrates drug pharmacokinetic and pharmacodynamic (PK/PD) information using stochastic hybrid system modeling framework. Specifically, (1) pathways modeled by differential equations are adopted for gene regulations at the molecular level; (2) cellular automata (CA) model is proposed for the cellular and multicellular scales. Markov chains are used to model the cell behaviors by taking into account the gene expression levels, cell cycle, and the microenvironment. The proposed model enables the prediction of tumor growth under given molecular properties, microenvironment conditions, and drug PK/PD profile. Simulation results demonstrate the effectiveness of the proposed approach and the results agree with observed tumor behaviors.

Intraglomerular crosstalk elaborately regulates podocyte injury and repair in diabetic patients: insights from a 3D multiscale modeling study

Podocytes are mainly involved in the regulation of glomerular filtration rate (GFR) under physiological condition. Podocyte depletion is a crucial pathological alteration in diabetic nephropathy (DN) and results in a broad spectrum of clinical syndromes such as protein urine and renal insufficiency. Recent studies indicate that depleted podocytes can be regenerated via differentiation of the parietal epithelial cells (PECs), which serve as the local progenitors of podocytes. However, the podocyte regeneration process is regulated by a complicated mechanism of cell-cell interactions and cytokine stimulations, which has been studied in a piecemeal manner rather than systematically. To address this gap, we developed a high-resolution multi-scale multi-agent mathematical model in 3D, mimicking the in situ glomerulus anatomical structure and micro-environment, to simulate the podocyte regeneration process under various cytokine perturbations in healthy and diabetic conditions. Our model showed that, treatment with pigment epithelium derived factor (PEDF) or insulin-like growth factor-1 (IGF-1) alone merely ameliorated the glomerulus injury, while co-treatment with both cytokines replenished the damaged podocyte population gradually. In addition, our model suggested that continuous administration of PEDF instead of a bolus injection sustained the regeneration process of podocytes. Part of the results has been validated by our in vivo experiments. These results indicated that amelioration of the glomerular stress by PEDF and promotion of PEC differentiation by IGF-1 are equivalently critical for podocyte regeneration. Our 3D multi-scale model represents a powerful tool for understanding the signaling regulation and guiding the design of cytokine therapies in promoting podocyte regeneration.

A systems biology approach to studying the molecular mechanisms of osteoblastic differentiation under cytokine combination treatment

Recent studies revealed that sequential release of bone morphogenetic protein 2 and insulin-like growth factor 1 plays an important role in osteogenic process, suggesting that cytokines bone morphogenetic protein 2 and insulin-like growth factor 1 function in a time-dependent manner. However, the specific molecular mechanisms underlying these observations remained elusive, impeding the elaborate manipulation of cytokine sequential delivery in tissue repair. The aim of this study was to identify the key relevant pathways and processes regulating bone morphogenetic protein 2/insulin-like growth factor 1-mediated osteoblastic differentiation. Based on the microarray and proteomics data, and differentiation/growth status of mouse bone marrow stromal cells, we constructed a multiscale systems model to simulate the bone marrow stromal cells lineage commitment and bone morphogenetic protein 2 and insulin-like growth factor 1-regulated signaling dynamics. The accuracy of our model was validated using a set of independent experimental data. Our study reveals that, treatment of bone marrow stromal cells with bone morphogenetic protein 2 prior to insulin-like growth factor 1 led to the activation of transcription factor Runx2 through TAK1-p38 MAPK and SMAD1/5 signaling pathways and initiated the lineage commitment of bone marrow stromal cells. Delivery of insulin-like growth factor 1 four days after bone morphogenetic protein 2 treatment optimally activated transcription factors osterix and β-catenin through ERK and AKT pathways, which are critical to preosteoblast maturity. Our systems biology approach is expected to provide technical and scientific support in optimizing therapeutic scheme to improve osteogenesis/bone regeneration and other essential biological processes.

Microfluidics 3D gel-island chip for single cell isolation and lineage-dependent drug responses study

3D cell culture in the extracellular matrix (ECM), which not only provides structural support to cellular constituents, but also initiates regulatory biochemical cues for a variety of important cell functions in tissue, has become more and more important in understanding cancer pathology and drug testing. Although the ECM-gel has been used in cell culture both in bulk and on-chip, previous studies focused on collective cell behavior rather than single-cell heterogeneity. To track the behavior of each individual cell, we have developed a gel-island chip, which can form thousands of islands containing single cells encapsulated by the desired ECM. Optimized by Poisson's distribution, the device can attain 34% single cell capture efficiency of the exact number of single cells per island. A good culture media exchange rate and high cell viability can be achieved in the gel-islands. The cells in the islands can be automatically counted for high-throughput analysis. As a proof of concept, we monitored the proliferation and differentiation of single Notch+ (stem-like) T47D breast cancer cells. The 3D collagen gel environment was found to be favorable for the stem-like phenotype through better self-renewal and de-differentiation (Notch- to Notch+ transition). More interestingly, we found that the Notch- de-differentiated cells were more resistant to doxorubicin and cisplatin than the Notch+ cells. Combining the 3D ECM culture and single cell resolution, the presented platform can automatically analyze the individual cell behaviors of hundreds of cells using a small amount of drug and reagents.

In silico study of the impact of cancer stem cell dynamics and radiobiological hypoxia on tumour response to hyperfractionated radiotherapy

OBJECTIVES

Advanced head and neck carcinomas (HNCs) are aggressive tumours, mainly due to hypoxia and a cancer stem cell (CSC) subpopulation. The aim of this study was to simulate tumour growth and behaviour during radiotherapy of three HNC groups (governed by different growth kinetics, hypoxia levels and CSC division pattern) to determine correlation between resistance factors and responses to hyperfractionated radiotherapy.

METHODS

An in silico HNC model was developed based on biologically realistic input parameters. During radiotherapy simulation, three parameters were studied: growth kinetics, hypoxia and probability of CSC symmetrical division. Both independent and combined effects on tumour response to hyperfractionated radiotherapy were assessed.

RESULTS

Oxic and very mildly hypoxic HNCs were revealed to be controlled by hyperfractionated radiotherapy, irrespective of growth kinetics and CSC division pattern. Moderately hypoxic tumours had different responses to radiotherapy: while slowly proliferating HNCs were still controllable, tumours with higher cell turnover were more resistant. In rapidly proliferating tumours, the number of fractions needed for tumour control increased exponentially with the probability of CSC symmetrical division, whereas in moderately growing HNC, this behaviour was linear. Severely hypoxic tumours could not be controlled by radiotherapy alone. Tumours with CSCs in a severely hypoxic niche required adjuvant therapies to be eradicated.

CONCLUSIONS

Growth kinetics strongly influence tumour responses to treatment. Slowly growing tumours showed linear dependence between dose and hypoxia/CSC, whereas rapidly growing tumours followed exponential behaviour.

Mathematical modeling of bone marrow--peripheral blood dynamics in the disease state based on current emerging paradigms, part I

Stemming from current emerging paradigms related to the cancer stem cell hypothesis, an existing mathematical model is expanded and used to study cell interaction dynamics in the bone marrow and peripheral blood. The proposed mathematical model is described by a system of nonlinear differential equations with delay, to quantify the dynamics in abnormal hematopoiesis. The steady states of the model are analytically and numerically obtained. Some conditions for the local asymptotic stability of such states are investigated. Model analyses suggest that malignancy may be irreversible once it evolves from a nonmalignant state into a malignant one and no intervention takes place. This leads to the proposition that a great deal of emphasis be placed on cancer prevention. Nevertheless, should malignancy arise, treatment programs for its containment or curtailment may have to include a maximum and extensive level of effort to protect normal cells from eventual destruction. Further model analyses and simulations predict that in the untreated disease state, there is an evolution towards a situation in which malignant cells dominate the entire bone marrow - peripheral blood system. Arguments are then advanced regarding requirements for quantitatively understanding cancer stem cell behavior. Among the suggested requirements are, mathematical frameworks for describing the dynamics of cancer initiation and progression, the response to treatment, the evolution of resistance, and malignancy prevention dynamics within the bone marrow - peripheral blood architecture.

Cancer Stem Cell Plasticity as Tumor Growth Promoter and Catalyst of Population Collapse

It is increasingly argued that cancer stem cells are not a cellular phenotype but rather a transient state that cells can acquire, either through intrinsic signaling cascades or in response to environmental cues. While cancer stem cell plasticity is generally associated with increased aggressiveness and treatment resistance, we set out to thoroughly investigate the impact of different rates of plasticity on early and late tumor growth dynamics and the response to therapy. We develop an agent-based model of cancer stem cell driven tumor growth, in which plasticity is defined as a spontaneous transition between stem and nonstem cancer cell states. Simulations of the model show that plasticity can substantially increase tumor growth rate and invasion. At high rates of plasticity, however, the cells get exhausted and the tumor will undergo spontaneous remission in the long term. In a series of in silico trials, we show that such remission can be facilitated through radiotherapy. The presented study suggests that stem cell plasticity has rather complex, nonintuitive implications on tumor growth and treatment response. Further theoretical, experimental, and integrated studies are needed to fully decipher cancer stem cell plasticity and how it can be harnessed for novel therapeutic approaches.

Silibinin strongly inhibits the growth kinetics of colon cancer stem cell-enriched spheroids by modulating interleukin 4/6-mediated survival signals

Involvement of cancer stem cells (CSC) in initiation, progression, relapse, and therapy-resistance of colorectal cancer (CRC) warrants search for small molecules as ‘adjunct-therapy’ to target both colon CSC and bulk tumor population. Herein, we assessed the potential of silibinin to eradicate colon CSC together with associated molecular mechanisms. In studies examining how silibinin modulates dynamics of CSC spheroids in terms of its effect on kinetics of CSC spheroids generated in presence of mitogenic and interleukin (IL)-mediated signaling which provides an autocrine/paracrine amplification loop in CRC, silibinin strongly decreased colon CSC pool together with cell survival of bulk tumor cells. Silibinin effect on colon CSC was mediated via blocking of pro-tumorigenic signaling, notably IL-4/-6 signaling that affects CSC population. These silibinin effects were associated with decreased mRNA and protein levels of various CSC-associated transcription factors, signaling molecules and markers. Furthermore, 2D and 3D differentiation assays indicated formation of more differentiated clones by silibinin. These results highlight silibinin potential to interfere with kinetics of CSC pool by shifting CSC cell division to asymmetric type via targeting various signals associated with the survival and multiplication of colon CSC pool. Together, our findings further support clinical usefulness of silibinin in CRC intervention and therapy.

Toward a science of tumor forecasting for clinical oncology

We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. With a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time.

Cancer stem cells: small subpopulation or evolving fraction?

This review discusses quantitative modeling studies of stem and non-stem cancer cell interactions and the fraction of cancer stem cells.