Concepts in Cancer Modeling: A Brief History

Fabrication of a self-assembled and vascularized tumor array via bioprinting on a microfluidic chip

A tumor microenvironment (TME) is a complex system that comprises various components, including blood vessels that play a crucial role in supplying nutrients, oxygen, and growth factors, as well as delivering chemotherapy drugs to the tumor mass through the vascular endothelial barrier. To replicate the TME in vitro, several bioprinting and microfluidic organ-on-a-chip technologies have been developed. However, these technologies have not been fully exploited in terms of potential benefits of bioprinting and microfluidics, such as precise spatial control for biological samples, construction of multiple TMEs per microfluidic device, and the ability to adjust culture environments for better biological similarity. In addition, the complex transport phenomena within the vascular endothelial barrier and the aggregated tumor mass in the TME model should be considered before applying the model to drug treatment and screening. In this study, we describe a novel integrative technology that addresses these issues by introducing a self-organized TME array bioprinted on a microfluidic chip consisting of a vascular endothelial barrier surrounding breast cancer spheroids. To integrate the TME array onto the microfluidic platform, a microfluidic substrate for extrusion bioprinting was developed for a cell culture platform, which enables diffusivity control by microstructures and establishes a perfusion culture environment inside the culture channel. We also analyzed the cellular behaviors within the TME array to investigate the influence of the diffusivity on the self-organization process required to form the vascular endothelial barrier surrounding breast cancer spheroids.

MAST: a hybrid Multi-Agent Spatio-Temporal model of tumor microenvironment informed using a data-driven approach

Motivation

Recently, several computational modeling approaches, such as agent-based models, have been applied to study the interaction dynamics between immune and tumor cells in human cancer. However, each tumor is characterized by a specific and unique tumor microenvironment, emphasizing the need for specialized and personalized studies of each cancer scenario.

Results

We present MAST, a hybrid Multi-Agent Spatio-Temporal model which can be informed using a data-driven approach to simulate unique tumor subtypes and tumor-immune dynamics starting from high-throughput sequencing data. It captures essential components of the tumor microenvironment by coupling a discrete agent-based model with a continuous partial differential equations-based model.The application to real data of human colorectal cancer tissue investigating the spatio-temporal evolution and emergent properties of four simulated human colorectal cancer subtypes, along with their agreement with current biological knowledge of tumors and clinical outcome endpoints in a patient cohort, endorse the validity of our approach.

Availability and implementation

MAST, implemented in Python language, is freely available with an open-source license through GitLab (https://gitlab.com/sysbiobig/mast), and a Docker image is provided to ease its deployment. The submitted software version and test data are available in Zenodo at https://dx.doi.org/10.5281/zenodo.7267745.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Cancer Stem Cells in Tumor Modeling: Challenges and Future Directions

Microfluidic tumors-on-chips models have revolutionized anticancer therapeutic research by creating an ideal microenvironment for cancer cells. The tumor microenvironment (TME) includes various cell types and cancer stem cells (CSCs), which are postulated to regulate the growth, invasion, and migratory behavior of tumor cells. In this review, the biological niches of the TME and cancer cell behavior focusing on the behavior of CSCs are summarized. Conventional cancer models such as three-dimensional cultures and organoid models are reviewed. Opportunities for the incorporation of CSCs with tumors-on-chips are then discussed for creating tumor invasion models. Such models will represent a paradigm shift in the cancer community by allowing oncologists and clinicians to predict better which cancer patients will benefit from chemotherapy treatments.

Designing Relevant Preclinical Rodent Models for Studying Links Between Nutrition, Obesity, Metabolism, and Cancer

Diet and nutrition are intricately related to cancer prevention, growth, and treatment response. Preclinical rodent models are a cornerstone to biomedical research and remain instrumental in our understanding of the relationship between cancer and diet and in the development of effective therapeutics. However, the success rate of translating promising findings from the bench to the bedside is suboptimal. Well-designed rodent models will be crucial to improving the impact basic science has on clinical treatment options. This review discusses essential experimental factors to consider when designing a preclinical cancer model with an emphasis on incorporating these models into studies interrogating diet, nutrition, and metabolism. The aims of this review are to (a) provide insight into relevant considerations when designing cancer models for obesity, nutrition, and metabolism research; (b) identify common pitfalls when selecting a rodent model; and (c) discuss strengths and limitations of available preclinical models. Expected final online publication date for the Annual Review of Nutrition, Volume 41 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Toward improved models of human cancer

Human cancer is a complex and heterogeneous collection of diseases that kills more than 18 million people every year worldwide. Despite advances in detection, diagnosis, and treatments for cancers, new strategies are needed to combat deadly cancers. Models of human cancer continue to evolve for preclinical research and have culminated in patient-derived systems that better represent the diversity and complexity of cancer. Still, no model is perfect. This Perspective attempts to address ways that we can improve the clinical translatability of models used for cancer research, from the point of view of researchers who mainly conduct cancer studies in vivo.

Mathematical Model of Muscle Wasting in Cancer Cachexia

Cancer cachexia is a debilitating condition characterized by an extreme loss of skeletal muscle mass, which negatively impacts patients' quality of life, reduces their ability to sustain anti-cancer therapies, and increases the risk of mortality. Recent discoveries have identified the myostatin/activin A/ActRIIB pathway as critical to muscle wasting by inducing satellite cell quiescence and increasing muscle-specific ubiquitin ligases responsible for atrophy. Remarkably, pharmacological blockade of the ActRIIB pathway has been shown to reverse muscle wasting and prolong the survival time of tumor-bearing animals. To explore the implications of this signaling pathway and potential therapeutic targets in cachexia, we construct a novel mathematical model of muscle tissue subjected to tumor-derived cachectic factors. The model formulation tracks the intercellular interactions between cancer cell, satellite cell, and muscle cell populations. The model is parameterized by fitting to colon-26 mouse model data, and the analysis provides insight into tissue growth in healthy, cancerous, and post-cachexia treatment conditions. Model predictions suggest that cachexia fundamentally alters muscle tissue health, as measured by the stem cell ratio, and this is only partially recovered by anti-cachexia treatment. Our mathematical findings suggest that after blocking the myostatin/activin A pathway, partial recovery of cancer-induced muscle loss requires the activation and proliferation of the satellite cell compartment with a functional differentiation program.

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

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

In Vitro and Ex Vivo Models - The Tumor Microenvironment in a Flask

Experimental tumor modeling has long supported the discovery of fundamental mechanisms of tumorigenesis and tumor progression, as well as provided platforms for the development of novel therapies. Still, the attrition rates observed today in clinical translation could be, in part, mitigated by more accurate recapitulation of environmental cues in research and preclinical models. The increasing understanding of the decisive role that tumor microenvironmental cues play in the outcome of drug response urges its integration in preclinical tumor models. In this chapter we review recent developments concerning in vitro and ex vivo approaches.

Using Mouse and Drosophila Models to Investigate the Mechanistic Links between Diet, Obesity, Type II Diabetes, and Cancer

Many of the links between diet and cancer are controversial and over simplified. To date, human epidemiological studies consistently reveal that patients who suffer diet-related obesity and/or type II diabetes have an increased risk of cancer, suffer more aggressive cancers, and respond poorly to current therapies. However, the underlying molecular mechanisms that increase cancer risk and decrease the response to cancer therapies in these patients remain largely unknown. Here, we review studies in mouse cancer models in which either dietary or genetic manipulation has been used to model obesity and/or type II diabetes. These studies demonstrate an emerging role for the conserved insulin and insulin-like growth factor signaling pathways as links between diet and cancer progression. However, these models are time consuming to develop and expensive to maintain. As the world faces an epidemic of obesity and type II diabetes we argue that the development of novel animal models is urgently required. We make the case for Drosophila as providing an unparalleled opportunity to combine dietary manipulation with models of human metabolic disease and cancer. Thus, combining diet and cancer models in Drosophila can rapidly and significantly advance our understanding of the conserved molecular mechanisms that link diet and diet-related metabolic disorders to poor cancer patient prognosis.

Genetically engineered mouse models of melanoma

Melanoma is a complex disease that exhibits highly heterogeneous etiological, histopathological, and genetic features, as well as therapeutic responses. Genetically engineered mouse (GEM) models provide powerful tools to unravel the molecular mechanisms critical for melanoma development and drug resistance. Here, we expound briefly the basis of the mouse modeling design, the available technology for genetic engineering, and the aspects influencing the use of GEMs to model melanoma. Furthermore, we describe in detail the currently available GEM models of melanoma. Cancer 2017;123:2089-103. © 2017 American Cancer Society.

Mouse models of UV-induced melanoma: genetics, pathology, and clinical relevance

Melanocytes, a neural crest cell derivative, produce pigment to protect keratinocytes from ultraviolet radiation (UVR). Although melanocytic lesions such as nevi and cutaneous malignant melanomas are known to be associated with sun exposure, the role of UVR in oncogenesis is complex and has yet to be clearly elucidated. UVR appears to have a direct mutational role in inducing or promoting melanoma formation as well as an indirect role through microenvironmental changes. Recent advances in the modeling of human melanoma in animals have built platforms upon which prospective studies can begin to investigate these questions. This review will focus exclusively on genetically engineered mouse models of UVR-induced melanoma. The role that UVR has in mouse models depends on multiple factors, including the waveband, timing, and dose of UVR, as well as the nature of the oncogenic agent(s) driving melanomagenesis in the model. Work in the field has examined the role of neonatal and adult UVR, interactions between UVR and common melanoma oncogenes, the role of sunscreen in preventing melanoma, and the effect of UVR on immune function within the skin. Here we describe relevant mouse models and discuss how these models can best be translated to the study of human skin and cutaneous melanoma.