The role of transforming growth factor-beta-mediated tumor-stroma interactions in prostate cancer progression: an integrative approach

Modeling tumors as species-rich ecological communities

Many advanced cancers resist therapeutic intervention. This process is fundamentally related to intra-tumor heterogeneity: multiple cell populations, each with different mutational and phenotypic signatures, coexist within a tumor and its metastatic nodes. Like species in an ecosystem, many cancer cell populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity nor are able to predict its consequences. Here we propose that the Generalized Lotka-Volterra model (GLV), a standard tool to describe complex, species-rich ecological communities, provides a suitable framework to describe the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties, such as outgrowth and multistability, provide a new understanding of the disease. Additionally, we discuss potential extensions of the model and their application to three active areas of cancer research, namely phenotypic plasticity, the cancer-immune interplay and the resistance of metastatic tumors to treatment. Our work outlines a set of questions and a tentative road map for further research in cancer ecology.

Mutation divergence over space in tumour expansion

Mutation accumulation in tumour evolution is one major cause of intra-tumour heterogeneity (ITH), which often leads to drug resistance during treatment. Previous studies with multi-region sequencing have shown that mutation divergence among samples within the patient is common, and the importance of spatial sampling to obtain a complete picture in tumour measurements. However, quantitative comparisons of the relationship between mutation heterogeneity and tumour expansion modes, sampling distances as well as the sampling methods are still few. Here, we investigate how mutations diverge over space by varying the sampling distance and tumour expansion modes using individual-based simulations. We measure ITH by the Jaccard index between samples and quantify how ITH increases with sampling distance, the pattern of which holds in various sampling methods and sizes. We also compare the inferred mutation rates based on the distributions of variant allele frequencies under different tumour expansion modes and sampling sizes. In exponentially fast expanding tumours, a mutation rate can always be inferred for any sampling size. However, the accuracy compared with the true value decreases when the sampling size decreases, where small sampling sizes result in a high estimate of the mutation rate. In addition, such an inference becomes unreliable when the tumour expansion is slow, such as in surface growth.

Nanoparticle-mediated cancer cell therapy: basic science to clinical applications

Every sixth person in the world dies due to cancer, making it the second leading severe cause of death after cardiovascular diseases. According to WHO, cancer claimed nearly 10 million deaths in 2020. The most common types of cancers reported have been breast (lung, colon and rectum, prostate cases), skin (non-melanoma) and stomach. In addition to surgery, the most widely used traditional types of anti-cancer treatment are radio- and chemotherapy. However, these do not distinguish between normal and malignant cells. Additional treatment methods have evolved over time for early detection and targeted therapy of cancer. However, each method has its limitations and the associated treatment costs are quite high with adverse effects on the quality of life of patients. Use of individual atoms or a cluster of atoms (nanoparticles) can cause a paradigm shift by virtue of providing point of sight sensing and diagnosis of cancer. Nanoparticles (1-100 nm in size) are 1000 times smaller in size than the human cell and endowed with safer relocation capability to attack mechanically and chemically at a precise location which is one avenue that can be used to destroy cancer cells precisely. This review summarises the extant understanding and the work done in this area to pave the way for physicians to accelerate the use of hybrid mode of treatments by leveraging the use of various nanoparticles.

What Is the Storage Effect, Why Should It Occur in Cancers, and How Can It Inform Cancer Therapy?

Intratumor heterogeneity is a feature of cancer that is associated with progression, treatment resistance, and recurrence. However, the mechanisms that allow diverse cancer cell lineages to coexist remain poorly understood. The storage effect is a coexistence mechanism that has been proposed to explain the diversity of a variety of ecological communities, including coral reef fish, plankton, and desert annual plants. Three ingredients are required for there to be a storage effect: (1) temporal variability in the environment, (2) buffered population growth, and (3) species-specific environmental responses. In this article, we argue that these conditions are observed in cancers and that it is likely that the storage effect contributes to intratumor diversity. Data that show the temporal variation within the tumor microenvironment are needed to quantify how cancer cells respond to fluctuations in the tumor microenvironment and what impact this has on interactions among cancer cell types. The presence of a storage effect within a patient’s tumors could have a substantial impact on how we understand and treat cancer.

A QSP model of prostate cancer immunotherapy to identify effective combination therapies

Immunotherapy, by enhancing the endogenous anti-tumor immune responses, is showing promising results for the treatment of numerous cancers refractory to conventional therapies. However, its effectiveness for advanced castration-resistant prostate cancer remains unsatisfactory and new therapeutic strategies need to be developed. To this end, systems pharmacology modeling provides a quantitative framework to test in silico the efficacy of new treatments and combination therapies. In this paper we present a new Quantitative Systems Pharmacology (QSP) model of prostate cancer immunotherapy, calibrated using data from pre-clinical experiments in prostate cancer mouse models. We developed the model by using Ordinary Differential Equations (ODEs) describing the tumor, key components of the immune system, and seven treatments. Numerous combination therapies were evaluated considering both the degree of tumor inhibition and the predicted synergistic effects, integrated into a decision tree. Our simulations predicted cancer vaccine combined with immune checkpoint blockade as the most effective dual-drug combination immunotherapy for subjects treated with androgen-deprivation therapy that developed resistance. Overall, the model presented here serves as a computational framework to support drug development, by generating hypotheses that can be tested experimentally in pre-clinical models.

Stromal reactivity differentially drives tumour cell evolution and prostate cancer progression

Prostate cancer (PCa) progression is a complex eco-evolutionary process driven by the feedback between evolving tumour cell phenotypes and microenvironmentally driven selection. To better understand this relationship, we used a multiscale mathematical model that integrates data from biology and pathology on the microenvironmental regulation of PCa cell behaviour. Our data indicate that the interactions between tumour cells and their environment shape the evolutionary dynamics of PCa cells and explain overall tumour aggressiveness. A key environmental determinant of this aggressiveness is the stromal ecology, which can be either inhibitory, highly reactive (supportive) or non-reactive (neutral). Our results show that stromal ecology correlates directly with tumour growth but inversely modulates tumour evolution. This suggests that aggressive, environmentally independent PCa may be a result of poor stromal ecology, supporting the concept that purely tumour epithelium-centric metrics of aggressiveness may be incomplete and that incorporating markers of stromal ecology would improve prognosis.

Hybrid Automata Library: A flexible platform for hybrid modeling with real-time visualization

The Hybrid Automata Library (HAL) is a Java Library developed for use in mathematical oncology modeling. It is made of simple, efficient, generic components that can be used to model complex spatial systems. HAL's components can broadly be classified into: on- and off-lattice agent containers, finite difference diffusion fields, a GUI building system, and additional tools and utilities for computation and data collection. These components are designed to operate independently and are standardized to make them easy to interface with one another. As a demonstration of how modeling can be simplified using our approach, we have included a complete example of a hybrid model (a spatial model with interacting agent-based and PDE components). HAL is a useful asset for researchers who wish to build performant 1D, 2D and 3D hybrid models in Java, while not starting entirely from scratch. It is available on GitHub at https://github.com/MathOnco/HAL under the MIT License. HAL requires the Java JDK version 1.8 or later to compile and run the source code.

Resolution of Cellular Heterogeneity in Human Prostate Cancers: Implications for Diagnosis and Treatment

Prostate cancers have a justified reputation as one of the most heterogeneous human tumours. Indeed, there are some who consider that advanced and castration-resistant prostate cancers are incurable, as a direct result of this heterogeneity. However, tumour heterogeneity can be defined in different ways. To a clinician, prostate cancer is a number of different diseases, the treatments for which remain equally heterogeneous and uncertain. To the pathologist, the histopathological appearances of the tumours are notoriously heterogeneous. Indeed, the genius of Donald Gleason in the 1960s was to devise a classification system designed to take into account the heterogeneity of the tumours both individually and in the whole prostate context. To the cell biologist, a prostate tumour consists of multiple epithelial cell types, inter-mingled with various fibroblasts, neuroendocrine cells, endothelial cells, macrophages and lymphocytes, all of which interact to influence treatment responses in a patient-specific manner. Finally, genetic analyses of prostate cancers have been compromised by the variable gene rearrangements and paucity of activating mutations observed, even in large numbers of patient tumours with consistent clinical diagnoses and/or outcomes. Research into familial susceptibility has even generated the least tractable outcome of such studies: the genetic loci are of low penetrance and are of course heterogeneous. By fractionating the tumour (and patient-matched non-malignant tissues) heterogeneity can be resolved, revealing homogeneous markers of patient outcomes.

The Expression of Transforming Growth Factor Beta-1 and Interleukin-6 on Human Prostate: Prostate Hyperplasia and Prostate Cancer

BACKGROUND

Prostate hyperplasia and prostate cancer are two of the most common pathological condition of the prostate to be found on male. Both of these diseases share common pathogenesis involving inflammation of prostatic tissues. Chronic inflammation will induce the release of cytokines, followed by cells injury and tissues damage. One of the cytokines that play a role in prostate pathology is IL-6. The inflammation will also induce the releases of anti-inflammatory cytokines such as TGFβ-1.

AIM

This study aims to analyse the expression of IL-6 and TGFβ-1, in prostate hyperplasia and prostate cancer.

MATERIAL AND METHODS

This is an observational study, using paraffin-embedded tissue samples of prostate hyperplasia and prostate cancer. Samples were obtained from the laboratory of Pathological Anatomy, Faculty of Medicine, Andalas University, Padang, Indonesia. Immunohistochemistry was performed to detect the cytokine expression, and a semiqunatitaves measurement according to Immunoreactive score (IRS) was performed for evaluation. For the TGFβ-1, the stromal expression was also analysed by measurement of the stromal stained area. The correlation of cytokine expression to Gleason index score was also analysed in prostate cancer.

RESULTS

The result showed that this study found that TGFβ-1 was detected both in the stromal component as well as epithelial. With the stromal being the dominant site of expression. The stromal TGFβ-1 expression was of significantly higher in prostate hyperplasia compares to prostate cancer (p < 0.05), while the epithelial expression of TGFβ-1 was not found to be significantly different. IL-6 was mostly expressed intracytoplasmic in epithelia. The IL-6 expression was significantly higher in prostate cancer compared to hyperplasia. However, there was no significant correlation to found between IL-6 expression to the Gleason Score among prostate cancers.

CONCLUSION

This study concluded that there were differences in expression of both TGFβ-1 and IL-6 between prostate hyperplasia and prostate cancer tissue by immunohistochemistry.

Integrating Models to Quantify Environment-Mediated Drug Resistance

Drug resistance is the single most important driver of cancer treatment failure for modern targeted therapies, and the dialog between tumor and stroma has been shown to modulate the response to molecularly targeted therapies through proliferative and survival signaling. In this work, we investigate interactions between a growing tumor and its surrounding stroma and their role in facilitating the emergence of drug resistance. We used mathematical modeling as a theoretical framework to bridge between experimental models and scales, with the aim of separating intrinsic and extrinsic components of resistance in BRAF-mutated melanoma; the model describes tumor-stroma dynamics both with and without treatment. Integration of experimental data into our model revealed significant variation in either the intensity of stromal promotion or intrinsic tissue carrying capacity across animal replicates. Cancer Res; 77(19); 5409-18. ©2017 AACR.

Homeostasis Back and Forth: An Ecoevolutionary Perspective of Cancer

The role of genetic mutations in cancer is indisputable: They are a key source of tumor heterogeneity and drive its evolution to malignancy. But, the success of these new mutant cells relies on their ability to disrupt the homeostasis that characterizes healthy tissues. Mutated clones unable to break free from intrinsic and extrinsic homeostatic controls will fail to establish a tumor. Here, we will discuss, through the lens of mathematical and computational modeling, why an evolutionary view of cancer needs to be complemented by an ecological perspective to understand why cancer cells invade and subsequently transform their environment during progression. Importantly, this ecological perspective needs to account for tissue homeostasis in the organs that tumors invade, because they perturb the normal regulatory dynamics of these tissues, often coopting them for its own gain. Furthermore, given our current lack of success in treating advanced metastatic cancers through tumor-centric therapeutic strategies, we propose that treatments that aim to restore homeostasis could become a promising venue of clinical research. This ecoevolutionary view of cancer requires mechanistic mathematical models to both integrate clinical with biological data from different scales but also to detangle the dynamic feedback between the tumor and its environment. Importantly, for these models to be useful, they need to embrace a higher degree of complexity than many mathematical modelers are traditionally comfortable with.

MMP-TIMP interactions in cancer invasion: An evolutionary game-theoretical framework

One of the main steps in solid cancers to invade surrounding tissues is degradation of tissue barriers in the extracellular matrix. This operation that leads to initiate, angiogenesis and metastasis to other organs, is essentially consequence of collapsing dynamic balance between matrix metalloproteinases (MMP) and tissue inhibitors of metalloproteinases (TIMP). In this work, we model the MMP-TIMP interaction in both normal tissue and invasive cancer using evolutionary game theory. Our model explains how invasive cancer cells get the upper hand in MMP-TIMP imbalance scenarios. We investigate dynamics of them over time and discuss stable and nonstable states in the population. Numerical simulations presented here provide the identification of key genotypic features in the tumor invasion and a natural description for phenotypic variability. The simulation results are consistent with the experimental results in vitro observations presented in medical literature. Finally, by the provided results the necessary conditions to inhibit cancer invasion or prolong its course are explained. In this way, two therapeutic approaches with respect to how they could meet the required conditions are considered.

Integrated gene and miRNA expression analysis of prostate cancer associated fibroblasts supports a prominent role for interleukin-6 in fibroblast activation

Tumor microenvironment coevolves with and simultaneously sustains cancer progression. In prostate carcinoma (PCa), cancer associated fibroblasts (CAF) have been shown to fuel tumor development and metastasis by mutually interacting with tumor cells. Molecular mechanisms leading to activation of CAFs from tissue-resident fibroblasts, circulating bone marrow-derived fibroblast progenitors or mesenchymal stem cells are largely unknown. Through integrated gene and microRNA expression profiling, we showed that PCa-derived CAF transcriptome strictly resembles that of normal fibroblasts stimulated in vitro with interleukin-6 (IL6), thus proving evidence, for the first time, that the cytokine is able per se to induce most of the transcriptional changes characteristic of patient-derived CAFs. Comparison with publicly available datasets, however, suggested that prostate CAFs may be alternatively characterized by IL6 and TGFβ-related signatures, indicating that either signal, depending on the context, may concur to fibroblast activation. Our analyses also highlighted novel pathways potentially relevant for induction of a reactive stroma. In addition, we revealed a role for muscle-specific miR-133b as a soluble factor secreted by activated fibroblasts to support paracrine activation of non-activated fibroblasts or promote tumor progression.

Overall, we provided insights into the molecular mechanisms driving fibroblast activation in PCa, thus contributing to identify novel hits for the development of therapeutic strategies targeting the crucial interplay between tumor cells and their microenvironment.

Predictive computational modeling to define effective treatment strategies for bone metastatic prostate cancer

The ability to rapidly assess the efficacy of therapeutic strategies for incurable bone metastatic prostate cancer is an urgent need. Pre-clinical in vivo models are limited in their ability to define the temporal effects of therapies on simultaneous multicellular interactions in the cancer-bone microenvironment. Integrating biological and computational modeling approaches can overcome this limitation. Here, we generated a biologically driven discrete hybrid cellular automaton (HCA) model of bone metastatic prostate cancer to identify the optimal therapeutic window for putative targeted therapies. As proof of principle, we focused on TGFβ because of its known pleiotropic cellular effects. HCA simulations predict an optimal effect for TGFβ inhibition in a pre-metastatic setting with quantitative outputs indicating a significant impact on prostate cancer cell viability, osteoclast formation and osteoblast differentiation. In silico predictions were validated in vivo with models of bone metastatic prostate cancer (PAIII and C4-2B). Analysis of human bone metastatic prostate cancer specimens reveals heterogeneous cancer cell use of TGFβ. Patient specific information was seeded into the HCA model to predict the effect of TGFβ inhibitor treatment on disease evolution. Collectively, we demonstrate how an integrated computational/biological approach can rapidly optimize the efficacy of potential targeted therapies on bone metastatic prostate cancer.

Theory and Experimental Validation of a Spatio-temporal Model of Chemotherapy Transport to Enhance Tumor Cell Kill

It has been hypothesized that continuously releasing drug molecules into the tumor over an extended period of time may significantly improve the chemotherapeutic efficacy by overcoming physical transport limitations of conventional bolus drug treatment. In this paper, we present a generalized space- and time-dependent mathematical model of drug transport and drug-cell interactions to quantitatively formulate this hypothesis. Model parameters describe: perfusion and tissue architecture (blood volume fraction and blood vessel radius); diffusion penetration distance of drug (i.e., a function of tissue compactness and drug uptake rates by tumor cells); and cell death rates (as function of history of drug uptake). We performed preliminary testing and validation of the mathematical model using in vivo experiments with different drug delivery methods on a breast cancer mouse model. Experimental data demonstrated a 3-fold increase in response using nano-vectored drug vs. free drug delivery, in excellent quantitative agreement with the model predictions. Our model results implicate that therapeutically targeting blood volume fraction, e.g., through vascular normalization, would achieve a better outcome due to enhanced drug delivery.

Spatial and functional heterogeneities shape collective behavior of tumor-immune networks

Tumor growth involves a dynamic interplay between cancer cells and host cells, which collectively form a tumor microenvironmental network that either suppresses or promotes tumor growth under different conditions. The transition from tumor suppression to tumor promotion is mediated by a tumor-induced shift in the local immune state, and despite the clinical challenge this shift poses, little is known about how such dysfunctional immune states are initiated. Clinical and experimental observations have indicated that differences in both the composition and spatial distribution of different cell types and/or signaling molecules within the tumor microenvironment can strongly impact tumor pathogenesis and ultimately patient prognosis. How such “functional” and “spatial” heterogeneities confer such effects, however, is not known. To investigate these phenomena at a level currently inaccessible by direct observation, we developed a computational model of a nascent metastatic tumor capturing salient features of known tumor-immune interactions that faithfully recapitulates key features of existing experimental observations. Surprisingly, over a wide range of model formulations, we observed that heterogeneity in both spatial organization and cell phenotype drove the emergence of immunosuppressive network states. We determined that this observation is general and robust to parameter choice by developing a systems-level sensitivity analysis technique, and we extended this analysis to generate other parameter-independent, experimentally testable hypotheses. Lastly, we leveraged this model as an in silico test bed to evaluate potential strategies for engineering cell-based therapies to overcome tumor associated immune dysfunction and thereby identified modes of immune modulation predicted to be most effective. Collectively, this work establishes a new integrated framework for investigating and modulating tumor-immune networks and provides insights into how such interactions may shape early stages of tumor formation.

Over the course of tumor growth, cancer cells interact with normal cells via processes that are difficult to understand by experiment alone. This challenge is particularly pronounced at early stages of tumor formation, when experimental observation is most limited. Elucidating such interactions could inform both understanding of cancer and clinical practice. To address this need we developed a computational model capturing the current understanding of how individual metastatic tumor cells and immune cells sense and contribute to the tumor environment, which in turn enabled us to investigate the complex, collective behavior of these systems. Surprisingly, we discovered that tumor escape from immune control was enhanced by the existence of small differences (or heterogeneities) in the responses of individual immune cells to their environment, as well as by heterogeneities in the way that cells and the molecules they secrete are arranged in space. These conclusions held true over a range of model formulations, suggesting that this is a general feature of these tumor-immune networks. Finally, we used this model as a test bed to evaluate potential strategies for enhancing immunological control of early tumors, ultimately predicting that specifically modulating tumor-associated immune dysfunction may be more effective than simply enhanced tumor killing.

Integrating new discoveries into the "vicious cycle" paradigm of prostate to bone metastases

In prostate to bone metastases, the "vicious cycle" paradigm has been traditionally used to illustrate how metastases manipulate the bone forming osteoblasts and resorbing osteoclasts in order to yield factors that facilitate growth and establishment. However, recent advances have illustrated that the cycle is far more complex than this simple interpretation. In this review, we will discuss the role of exosomes and hematopoietic/mesenchymal stem/stromal cells (MSC) that facilitate the establishment and activation of prostate metastases and how cells including myeloid-derived suppressor cells, macrophages, T cells, and nerve cells contribute to the momentum of the vicious cycle. The increased complexity of the tumor-bone microenvironment requires a system level approach. The evolution of computational models to interrogate the tumor-bone microenvironment is also discussed, and the application of this integrated approach should allow for the development of effective therapies to treat and cure prostate to bone metastases.

Inhibition of IL-17A suppresses enhanced-tumor growth in low dose pre-irradiated tumor beds

Ionizing radiation induces modification of the tumor microenvironment such as tumor surrounding region, which is relevant to treatment outcome after radiotherapy. In this study, the effects of pre-irradiated tumor beds on the growth of subsequently implanted tumors were investigated as well as underlying mechanism. The experimental model was set up by irradiating the right thighs of C3H/HeN mice with 5 Gy, followed by the implantation of HCa-I and MIH-2. Both implanted tumors in the pre-irradiated bed showed accelerated-growth compared to the control. Tumor-infiltrated lymphocyte (TIL) levels were increased, as well as pro-tumor factors such as IL-6 and transforming growth factor-beta1 (TGF-β1) in the pre-irradiated group. In particular, the role of pro-tumor cytokine interleukin-17A (IL-17A) was investigated as a possible target mechanism because IL-6 and TGF-β are key factors in Th17 cells differentiation from naïve T cells. IL-17A expression was increased not only in tumors, but also in CD4+ T cells isolated from the tumor draining lymph nodes. The effect of IL-17A on tumor growth was confirmed by treating tumors with IL-17A antibody, which abolished the acceleration of tumor growth. These results indicate that the upregulation of IL-17A seems to be a key factor for enhancing tumor growth in pre-irradiated tumor beds.

An integrated computational model of the bone microenvironment in bone-metastatic prostate cancer

Bone metastasis will impact most men with advanced prostate cancer. The vicious cycle of bone degradation and formation driven by metastatic prostate cells in bone yields factors that drive cancer growth. Mechanistic insights into this vicious cycle have suggested new therapeutic opportunities, but complex temporal and cellular interactions in the bone microenvironment make drug development challenging. We have integrated biologic and computational approaches to generate a hybrid cellular automata model of normal bone matrix homeostasis and the prostate cancer-bone microenvironment. The model accurately reproduces the basic multicellular unit bone coupling process, such that introduction of a single prostate cancer cell yields a vicious cycle similar in cellular composition and pathophysiology to models of prostate-to-bone metastasis. Notably, the model revealed distinct phases of osteolytic and osteogenic activity, a critical role for mesenchymal stromal cells in osteogenesis, and temporal changes in cellular composition. To evaluate the robustness of the model, we assessed the effect of established bisphosphonate and anti-RANKL therapies on bone metastases. At approximately 100% efficacy, bisphosphonates inhibited cancer progression while, in contrast with clinical observations in humans, anti-RANKL therapy fully eradicated metastases. Reducing anti-RANKL yielded clinically similar results, suggesting that better targeting or dosing could improve patient survival. Our work establishes a computational model that can be tailored for rapid assessment of experimental therapies and delivery of precision medicine to patients with prostate cancer with bone metastases.

VDR activity is differentially affected by Hic-5 in prostate cancer and stromal cells

Patients with prostate cancer treated with androgen deprivation therapy (ADT) eventually develop castrate-resistant prostate cancer (CRPC). 1,25-Dihydroxyvitamin D3 (1,25D3/calcitriol) is a potential adjuvant therapy that confers antiproliferative and pro-differentiation effects in vitro, but has had mixed results in clinical trials. The impact of the tumor microenvironment on 1,25D3 therapy in patients with CRPC has not been assessed. Transforming growth factor β (TGFβ), which is associated with the development of tumorigenic "reactive stroma" in prostate cancer, induced vitamin D3 receptor (VDR) expression in the human WPMY-1 prostate stromal cell line. Similarly, TGFβ enhanced 1,25D3-induced upregulation of CYP24A1, which metabolizes 1,25D3 and thereby limits VDR activity. Ablation of Hic-5, a TGFβ-inducible nuclear receptor coregulator, inhibited basal VDR expression, 1,25D3-induced CYP24A1 expression and metabolism of 1,25D3 and TGFβ-enhanced CYP24A1 expression. A Hic-5-responsive sequence was identified upstream (392-451 bp) of the CYP24A1 transcription start site that is occupied by VDR only in the presence of Hic-5. Ectopic expression of Hic-5 sensitized LNCaP prostate tumor cells to growth-inhibitory effects of 1,25D3 independent of CYP24A1. The sensitivity of Hic-5-expressing LNCaP cells to 1,25D3-induced growth inhibition was accentuated in coculture with Hic-5-ablated WPMY-1 cells. Therefore, these findings indicate that the search for mechanisms to sensitize prostate cancer cells to the antiproliferative effects of VDR ligands needs to account for the impact of VDR activity in the tumor microenvironment.

IMPLICATIONS

Hic-5 acts as a coregulator with distinct effects on VDR transactivation, in prostate cancer and stromal cells, and may exert diverse effects on adjuvant therapy designed to exploit VDR activity in prostate cancer.

An integrative modeling framework reveals plasticity of TGF-β signaling

Background

The TGF-β transforming growth factor is the most pleiotropic cytokine controlling a broad range of cellular responses that include proliferation, differentiation and apoptosis. The context-dependent multifunctional nature of TGF-β is associated with complex signaling pathways. Differential models describe the dynamics of the TGF-β canonical pathway, but modeling the non-canonical networks constitutes a major challenge. Here, we propose a qualitative approach to explore all TGF-β-dependent signaling pathways.

Results

Using a new formalism, CADBIOM, which is based on guarded transitions and includes temporal parameters, we have built the first discrete model of TGF-β signaling networks by automatically integrating the 137 human signaling maps from the Pathway Interaction Database into a single unified dynamic model. Temporal property-checking analyses of 15934 trajectories that regulate 145 TGF-β target genes reveal the association of specific pathways with distinct biological processes. We identify 31 different combinations of TGF-β with other extracellular stimuli involved in non-canonical TGF-β pathways that regulate specific gene networks. Extensive analysis of gene expression data further demonstrates that genes sharing CADBIOM trajectories tend to be co-regulated.

Conclusions

As applied here to TGF-β signaling, CADBIOM allows, for the first time, a full integration of highly complex signaling pathways into dynamic models that permit to explore cell responses to complex microenvironment stimuli.

Why model?

Next generation sequencing technologies are bringing about a renaissance of mining approaches. A comprehensive picture of the genetic landscape of an individual patient will be useful, for example, to identify groups of patients that do or do not respond to certain therapies. The high expectations may however not be satisfied if the number of patient groups with similar characteristics is going to be very large. I therefore doubt that mining sequence data will give us an understanding of why and when therapies work. For understanding the mechanisms underlying diseases, an alternative approach is to model small networks in quantitative mechanistic detail, to elucidate the role of gene and proteins in dynamically changing the functioning of cells. Here an obvious critique is that these models consider too few components, compared to what might be relevant for any particular cell function. I show here that mining approaches and dynamical systems theory are two ends of a spectrum of methodologies to choose from. Drawing upon personal experience in numerous interdisciplinary collaborations, I provide guidance on how to model by discussing the question "Why model?"

Microenvironmental variables must influence intrinsic phenotypic parameters of cancer stem cells to affect tumourigenicity

Since the discovery of tumour initiating cells (TICs) in solid tumours, studies focussing on their role in cancer initiation and progression have abounded. The biological interrogation of these cells continues to yield volumes of information on their pro-tumourigenic behaviour, but actionable generalised conclusions have been scarce. Further, new information suggesting a dependence of tumour composition and growth on the microenvironment has yet to be studied theoretically. To address this point, we created a hybrid, discrete/continuous computational cellular automaton model of a generalised stem-cell driven tissue with a simple microenvironment. Using the model we explored the phenotypic traits inherent to the tumour initiating cells and the effect of the microenvironment on tissue growth. We identify the regions in phenotype parameter space where TICs are able to cause a disruption in homeostasis, leading to tissue overgrowth and tumour maintenance. As our parameters and model are non-specific, they could apply to any tissue TIC and do not assume specific genetic mutations. Targeting these phenotypic traits could represent a generalizable therapeutic strategy across cancer types. Further, we find that the microenvironmental variable does not strongly affect the outcomes, suggesting a need for direct feedback from the microenvironment onto stem-cell behaviour in future modelling endeavours.

In this paper, we present a mathematical/computational model of a tumour growing according to the canonical cancer stem-cell hypothesis with a simplified microenvironment. We explore the parameters of this model and find good agreement between our model and other theoretical models in terms of the intrinsic cellular parameters, which are difficult to study biologically. We find, however, disagreement between novel biological data and our model in terms of the microenvironmental changes. We conclude that future theoretical models of stem-cell driven tumours must include specific feedback from the microenvironment onto the individual cellular behavior. Further, we identify several cell intrinsic parameters which govern loss of homeostasis into a state of uncontrolled growth.

Extracellular matrix density promotes EMT by weakening cell–cell adhesions

This paper probes the influence of extracellular matrix density on cell–cell adhesion and its relevance to EMT.

Senescent fibroblasts in melanoma initiation and progression: an integrated theoretical, experimental, and clinical approach

We present an integrated study to understand the key role of senescent fibroblasts in driving melanoma progression. Based on the hybrid cellular automata paradigm, we developed an in silico model of normal skin. The model focuses on key cellular and microenvironmental variables that regulate interactions among keratinocytes, melanocytes, and fibroblasts, key components of the skin. The model recapitulates normal skin structure and is robust enough to withstand physical as well as biochemical perturbations. Furthermore, the model predicted the important role of the skin microenvironment in melanoma initiation and progression. Our in vitro experiments showed that dermal fibroblasts, which are an important source of growth factors in the skin, adopt a secretory phenotype that facilitates cancer cell growth and invasion when they become senescent. Our coculture experiments showed that the senescent fibroblasts promoted the growth of nontumorigenic melanoma cells and enhanced the invasion of advanced melanoma cells. Motivated by these experimental results, we incorporated senescent fibroblasts into our model and showed that senescent fibroblasts transform the skin microenvironment and subsequently change the skin architecture by enhancing the growth and invasion of normal melanocytes. The interaction between senescent fibroblasts and the early-stage melanoma cells leads to melanoma initiation and progression. Of microenvironmental factors that senescent fibroblasts produce, proteases are shown to be one of the key contributing factors that promoted melanoma development from our simulations. Although not a direct validation, we also observed increased proteolytic activity in stromal fields adjacent to melanoma lesions in human histology. This leads us to the conclusion that senescent fibroblasts may create a prooncogenic skin microenvironment that cooperates with mutant melanocytes to drive melanoma initiation and progression and should therefore be considered as a potential future therapeutic target. Interestingly, our simulations to test the effects of a stroma-targeting therapy that negates the influence of proteolytic activity showed that the treatment could be effective in delaying melanoma initiation and progression.

The Need for Integrative Computational Oncology: An Illustrated Example through MMP-Mediated Tissue Degradation

Physical oncology is a growing force in cancer research, and it is enhanced by integrative computational oncology: the fusion of novel experiments with mathematical and computational modeling. Computational models must be assessed with accurate numerical methods on correctly scaled tissues to avoid numerical artifacts that can cloud analysis. Simulation-driven analyses can only be validated by careful experiments. In this perspectives piece, we evaluate a current, widespread model of matrix metalloproteinase-driven tissue degradation during cancer invasion to illustrate that integrative computational oncology may not realize its fullest potential if either of these critical steps is neglected.

The reactive stroma microenvironment and prostate cancer progression

Reactive stroma initiates during early prostate cancer development and coevolves with prostate cancer progression. Previous studies have defined the key markers of reactive stroma and have established that reactive stroma biology influences prostate tumorigenesis and progression. The stem/progenitor cells of origin and the mechanisms that regulate their recruitment and activation to myofibroblasts or carcinoma-associated fibroblasts are essentially unknown. Key regulatory factors have been identified, including transforming growth factor β, interleukin-8, fibroblast growth factors, connective tissue growth factor, wingless homologs-Wnts, and stromal cell-derived factor-1, among others. The biology of reactive stroma in cancer is similar to the more predictable biology of the stroma compartment during wound repair at sites where the epithelial barrier function is breached and a stromal response is generated. The coevolution of reactive stroma and the biology of how reactive stroma-carcinoma interactions regulate cancer progression and metastasis are targets for new therapeutic approaches. Such approaches are strategically designed to inhibit cancer progression by uncoupling the reactive stroma niche.

Distinct function of estrogen receptor α in smooth muscle and fibroblast cells in prostate development

Estrogen signaling, through estrogen receptor (ER)α, has been shown to cause hypertrophy in the prostate. Our recent report has shown that epithelial ERα knockout (KO) will not affect the normal prostate development or homeostasis. However, it remains unclear whether ERα in different types of stromal cells has distinct roles in prostate development. This study proposed to elucidate how KO of ERα in the stromal smooth muscle or fibroblast cells may interrupt cross talk between prostate stromal and epithelial cells. Smooth muscle ERαKO (smERαKO) mice showed decreased glandular infolding with the proximal area exhibiting a significant decrease. Fibroblast ERαKO mouse prostates did not exhibit this phenotype but showed a decrease in the number of ductal tips. Additionally, the amount of collagen observed in the basement membrane was reduced in smERαKO prostates. Interestingly, these phenotypes were found to be mutually exclusive among smERαKO or fibroblast ERαKO mice. Compound KO of ERα in both fibroblast and smooth muscle showed combined phenotypes from each of the single KO. Further mechanistic studies showed that IGF-I and epidermal growth factor were down-regulated in prostate smooth muscle PS-1 cells lacking ERα. Together, our results indicate the distinct functions of fibroblast vs. smERα in prostate development.

Hedgehog signaling inhibition by the small molecule smoothened inhibitor GDC-0449 in the bone forming prostate cancer xenograft MDA PCa 118b

BACKGROUND

Hedgehog signaling is a stromal-mesenchymal pathway central to the development and homeostasis of both the prostate and the bone. Aberrant Hedgehog signaling activation has been associated with prostate cancer aggressiveness. We hypothesize that Hedgehog pathway is a candidate therapeutic target in advanced prostate cancer. We confirm increased Hedgehog signaling in advanced and bone metastatic castrate resistant prostate cancer and examine the pharmacodynamic effect of Smoothened inhibition by the novel reagent GDC-0449 in an experimental prostate cancer model.

METHODS

Hedgehog signaling component expression was assessed in tissue microarrays of high grade locally advanced and bone metastatic disease. Male SCID mice subcutaneously injected with the bone forming xenograft MDA PCa 118b were treated with GDC-0449. Hedgehog signaling in the tumor microenvironment was assessed by proteomic and species specific RNA expression and compared between GDC-0449 treated and untreated animals.

RESULTS

We observe Hedgehog signaling in high grade locally advanced and bone marrow infiltrating disease. Evidence of paracrine activation of Hedgehog signaling in the tumor xenograft, was provided by increased Sonic Hedgehog expression in human tumor epithelial cells, coupled with increased Gli1 and Patched1 expression in the murine stromal compartment, while normal murine stroma did not exhibit Hh signaling expression. GDC-0449 treatment attenuated Hh signaling as evidenced by reduced expression of Gli1 and Ptch1. Reduction in proliferation (Ki67) was observed with no change in tumor volume.

CONCLUSIONS

GDC-0449 treatment is pharmacodynamically effective as evidenced by paracrine Hedgehog signaling inhibition and results in tumor cell proliferation reduction. Understanding these observations will inform the clinical development of therapy based on Hedgehog signaling inhibition.

Markers of field cancerization: proposed clinical applications in prostate biopsies

Field cancerization denotes the occurrence of genetic, epigenetic, and biochemical aberrations in structurally intact cells in histologically normal tissues adjacent to cancerous lesions. This paper tabulates markers of prostate field cancerization known to date and discusses their potential clinical value in the analysis of prostate biopsies, including diagnosis, monitoring progression during active surveillance, and assessing efficacy of presurgical neoadjuvant and focal therapeutic interventions.

Primary xenografts of human prostate tissue as a model to study angiogenesis induced by reactive stroma

Characterization of the mechanism(s) of androgen-driven human angiogenesis could have significant implications for modeling new forms of anti-angiogenic therapies for CaP and for developing targeted adjuvant therapies to improve efficacy of androgen-deprivation therapy. However, models of angiogenesis by human endothelial cells localized within an intact human prostate tissue architecture are until now extremely limited. This report characterizes the burst of angiogenesis by endogenous human blood vessels in primary xenografts of fresh surgical specimens of benign prostate or prostate cancer (CaP) tissue that occurs between Days 6–14 after transplantation into SCID mice pre-implanted with testosterone pellets. The wave of human angiogenesis was preceded by androgen-mediated up-regulation of VEGF-A expression in the stromal compartment. The neo-vessel network anastomosed to the host mouse vascular system between Days 6–10 post-transplantation, the angiogenic response ceased by Day 15, and by Day 30 the vasculature had matured and stabilized, as indicated by a lack of leakage of serum components into the interstitial tissue space and by association of nascent endothelial cells with mural cells/pericytes. The angiogenic wave was concurrent with the appearance of a reactive stroma phenotype, as determined by staining for α-SMA, Vimentin, Tenascin, Calponin, Desmin and Masson's trichrome, but the reactive stroma phenotype appeared to be largely independent of androgen availability. Transplantation-induced angiogenesis by endogenous human endothelial cells present in primary xenografts of benign and malignant human prostate tissue was preceded by induction of androgen-driven expression of VEGF by the prostate stroma, and was concurrent with and the appearance of a reactive stroma phenotype. Androgen-modulated expression of VEGF-A appeared to be a causal regulator of angiogenesis, and possibly of stromal activation, in human prostate xenografts.

Fibroblasts contribute to melanoma tumor growth and drug resistance

The role of tumor-stromal interactions in progression is generally well accepted, but their role in initiation or treatment is less well understood. It is now generally agreed that, rather than consisting solely of malignant cells, tumors consist of a complex dynamic mixture of cancer cells, host fibroblasts, endothelial cells and immune cells that interact with each other and microenvironmental factors to drive tumor progression. We are particularly interested in stromal cells (for example fibroblasts) and stromal factors (for example fibronectin) as important players in tumor progression since they have also been implicated in drug resistance. Here we develop an integrated approach to understand the role of such stromal cells and factors in the growth and maintenance of tumors as well as their potential impact on treatment resistance, specifically in application to melanoma. Using a suite of experimental assays we show that melanoma cells can stimulate the recruitment of fibroblasts and activate them, resulting in melanoma cell growth by providing both structural (extracellular matrix proteins) and chemical support (growth factors). Motivated by these experimental results we construct a compartment model and use it to investigate the roles of both stromal activation and tumor aggressiveness in melanoma growth and progression. We utilize this model to investigate the role fibroblasts might play in melanoma treatment resistance and the clinically observed flare phenomenon that is seen when a patient, who appears resistant to a targeted drug, is removed from that treatment. Our model makes the unexpected prediction that targeted therapies may actually hasten tumor progression once resistance has occurred. If confirmed experimentally, this provocative prediction may bring important new insights into how drug resistance could be managed clinically.

Quantifying the role of angiogenesis in malignant progression of gliomas: in silico modeling integrates imaging and histology

Gliomas are uniformly fatal forms of primary brain neoplasms that vary from low- to high-grade (glioblastoma). Whereas low-grade gliomas are weakly angiogenic, glioblastomas are among the most angiogenic tumors. Thus, interactions between glioma cells and their tissue microenvironment may play an important role in aggressive tumor formation and progression. To quantitatively explore how tumor cells interact with their tissue microenvironment, we incorporated the interactions of normoxic glioma cells, hypoxic glioma cells, vascular endothelial cells, diffusible angiogenic factors, and necrosis formation into a first-generation, biologically based mathematical model for glioma growth and invasion. Model simulations quantitatively described the spectrum of in vivo dynamics of gliomas visualized with medical imaging. Furthermore, we investigated how proliferation and dispersal of glioma cells combine to induce increasing degrees of cellularity, mitoses, hypoxia-induced neoangiogenesis and necrosis, features that characterize increasing degrees of "malignancy," and we found that changes in the net rates of proliferation (ρ) and invasion (D) are not always necessary for malignant progression. Thus, although other factors, including the accumulation of genetic mutations, can change cellular phenotype (e.g., proliferation and invasion rates), this study suggests that these are not required for malignant progression. Simulated results are placed in the context of the current clinical World Health Organization grading scheme for studying specific patient examples. This study suggests that through the application of the proposed model for tumor-microenvironment interactions, predictable patterns of dynamic changes in glioma histology distinct from changes in cellular phenotype (e.g., proliferation and invasion rates) may be identified, thus providing a powerful clinical tool.

Hybrid models of tumor growth

Cancer is a complex, multiscale process in which genetic mutations occurring at a subcellular level manifest themselves as functional changes at the cellular and tissue scale. The multiscale nature of cancer requires mathematical modeling approaches that can handle multiple intracellular and extracellular factors acting on different time and space scales. Hybrid models provide a way to integrate both discrete and continuous variables that are used to represent individual cells and concentration or density fields, respectively. Each discrete cell can also be equipped with submodels that drive cell behavior in response to microenvironmental cues. Moreover, the individual cells can interact with one another to form and act as an integrated tissue. Hybrid models form part of a larger class of individual-based models that can naturally connect with tumor cell biology and allow for the integration of multiple interacting variables both intrinsically and extrinsically and are therefore perfectly suited to a systems biology approach to tumor growth.

Effects of androgen receptor and androgen on gene expression in prostate stromal fibroblasts and paracrine signaling to prostate cancer cells

The androgen receptor (AR) is expressed in a subset of prostate stromal cells and functional stromal cell AR is required for normal prostate developmental and influences the growth of prostate tumors. Although we are broadly aware of the specifics of the genomic actions of AR in prostate cancer cells, relatively little is known regarding the gene targets of functional AR in prostate stromal cells. Here, we describe a novel human prostate stromal cell model that enabled us to study the effects of AR on gene expression in these cells. The model involves a genetically manipulated variant of immortalized human WPMY-1 prostate stromal cells that overexpresses wildtype AR (WPMY-AR) at a level comparable to LNCaP cells and is responsive to dihydrotestosterone (DHT) stimulation. Use of WPMY-AR cells for gene expression profiling showed that the presence of AR, even in the absence of DHT, significantly altered the gene expression pattern of the cells compared to control (WPMY-Vec) cells. Treatment of WPMY-AR cells, but not WPMY-Vec control cells, with DHT resulted in further changes that affected the expression of 141 genes by 2-fold or greater compared to vehicle treated WPMY-AR cells. Remarkably, DHT significantly downregulated more genes than were upregulated but many of these changes reversed the initial effects of AR overexpression alone on individual genes. The genes most highly effected by DHT treatment were categorized based upon their role in cancer pathways or in cell signaling pathways (transforming growth factor-β, Wnt, Hedgehog and MAP Kinase) thought to be involved in stromal-epithelial crosstalk during prostate or prostate cancer development. DHT treatment of WPMY-AR cells was also sufficient to alter their paracrine potential for prostate cancer cells as conditioned medium from DHT-treated WPMY-AR significantly increased growth of LNCaP cells compared to DHT-treated WPMY-Vec cell conditioned medium.

Therapeutic targeting of the prostate cancer microenvironment

Solid tumors can be thought of as multicellular 'organs' that consist of a variety of cells as well as a scaffold of noncellular matrix. Stromal-epithelial crosstalk is integral to prostate cancer progression and metastasis, and androgen signaling is an important component of this crosstalk at both the primary and metastatic sites. Intratumoral production of androgen is an important mechanism of castration resistance and has been the focus of novel therapeutic approaches with promising results. Various other pathways are important for stromal-epithelial crosstalk and represent attractive candidate therapeutic targets. Hedgehog signaling has been associated with tumor progression, growth and survival, while Src family kinases have been implicated in tumor progression and in regulation of cancer cell migration. Fibroblast growth factors and transforming growth factor beta signaling regulate cell proliferation, apoptosis and angiogenesis in the prostate cancer microenvironment. Integrins mediate communication between the cell and the extracellular matrix, enhancing growth, migration, invasion and metastasis of cancer cells. The contribution of stromal-epithelial crosstalk to prostate cancer initiation and progression provides the impetus for combinatorial microenvironment-targeting strategies.

Computational analysis of the influence of the microenvironment on carcinogenesis

The tumour microenvironment is known to play an important role in determining the sequence of acquired phenotypic traits that characterise cancer evolution. A more precise understanding of this role could have a major influence in the understanding of cancer growth and development, and potentially in the optimisation of innovative anti-cancer treatments delivery. However, to lead such an analysis in the basis of traditional biological experiments and observations is almost utopian given the complexity of the underlying biological processes and the typical time scales involved. In this context, computer models constitute a complementary exploratory tool. In this paper we introduce a two-dimensional cellular automaton that models key cancer cell capabilities. The model has been especially designed to mimic the behaviour of a cancer colony growing in vitro and to analyse the effect of different environmental conditions on the sequence of acquisition of phenotypic traits. Our results indicate that microenvironmental factors such as the local concentration of oxygen or nutrients and cell overcrowding may determine the expansion of the tumour colony. The results also show that tumour cells evolve and that their phenotypes adapt to the microenvironment so that environmental stress determines the dominance of particular phenotypical traits.

Modeling stromal-epithelial interactions in disease progression

The role of tumor stroma in progression to malignancy has become the subject of intense experimental and clinical interest. The stromal compartment of organs is composed of all the non-epithelial cell types and maintains the proper architecture and nutrient levels required for epithelial and, ultimately, organ function. The composition of the reactive stroma surrounding tumors is vastly different from normal stromal tissue. Stromal phenotype can be correlated with, and predictive of, disease recurrence. In addition, the stroma is now seen as a legitimate target for therapeutic intervention. Although much has been learned about the role of the stromal compartment in development and disease in recent years, a number of key questions remain. Here we review how some of these questions are beginning to be addressed using new models of stromal-epithelial interaction.

Density of tumour stroma is correlated to outcome after adoptive transfer of CD4+ and CD8+ T cells in a murine mammary carcinoma model

Adoptive immunotherapy shows promise for the treatment of cancer; however, partial or mixed responses remain common outcomes due to the heterogeneity of tumours. We studied three murine mammary tumour lines that express an ovalbumin-tagged version of HER-2/neu and reproducibly undergo complete regression (CR), partial regression (PR), or progressive disease (PD) after adoptive transfer of ovalbumin-specific CD8(+) (OT-I) and CD4(+) (OT-II) T cells. The three tumour lines were implanted in immunocompetent C57Bl/6 host mice, and established tumours were treated by adoptive transfer of naive OT-I and OT-II T cells. Tumours of the CR and PR classes triggered almost indistinguishable T cell responses in terms of activation, proliferation, trafficking to the tumour site, infiltration of tumour stroma, and intratumoural T cell proliferation; however, tumours of the PR class showed reduced infiltration of tumour epithelium by donor T cells. PD responses were associated with early impairment of T cell activation and proliferation in draining lymph node, followed by negligible infiltration of tumour tissue by donor T cells. Histopathological determinants of outcome were investigated through an unsupervised analysis of 64 untreated tumours representing the three response classes. Tumours of the CR class had proportionately more stroma, which had a looser, more collagen-rich histological appearance. Thus, the amount and composition of tumour stroma distinguished successfully (CR) from unsuccessful (PR or PD) outcomes after adoptive T cell transfer, a finding that might facilitate the design of immunotherapy trials for human breast cancer.