Cancer is one of the leading causes of death globally according to the World Health Organization. Although improved treatments and early diagnoses have reduced cancer related mortalities, metastatic disease remains a major clinical challenge. The local tumor microenvironment plays a significant role in cancer metastasis, where tumor cells respond and adapt to a plethora of biochemical and biophysical signals from stromal cells and extracellular matrix (ECM) proteins. Due to these complexities, there is a critical need to understand molecular mechanisms underlying cancer metastasis to facilitate the discovery of more effective therapies. In the past few years, the integration of advanced biomaterials and microengineering approaches has initiated the development of innovative platform technologies for cancer research. These technologies enable the creation of biomimetic in vitro models with physiologically relevant (i.e. in vivo-like) characteristics to conduct studies ranging from fundamental cancer biology to high-throughput drug screening. In this review article, we discuss the biological significance of each step of the metastatic cascade and provide a broad overview on recent progress to recapitulate these stages using advanced biomaterials and microengineered technologies. In each section, we will highlight the advantages and shortcomings of each approach and provide our perspectives on future directions.

Difficult to treat cancer patients, specifically those tumors that are metastatic and drug-resistant, prove to have the lowest survival rates when compared to more localized types. The commonplace combination therapies, surgery, chemotherapy, and radiation, do not usually result in remission and sometimes cannot be done with these specific patients. RNA interference therapeutics, especially those that use short-interfering RNA (siRNA), have given rise to a novel field that employs the mechanisms in the body to silence the gene expression post-transcriptionally. The main cell types used in this research were Ewing Sarcoma, Acute Myelogenous Leukemia, and Rhabdomyosarcoma cells. Initial assays involved the testing of the cells' responsiveness to a panel of siRNA compounds, to better understand the most effective ones. The siRNA UBBs1 proved to be the most cytotoxic to all cell lines tested, allowing for further investigation through transfection procedures for cellular assays and RNA purification for expression analysis. The data showed decreased cell viability for the UBBs1 treated group for both RD and RH-30 Rhabdomyosarcoma cell lines, especially at a much lower concentration than traditional chemotherapy drug dose response assays. The RNA purification and quantification of the transfected cells over time showed the biggest decrease in gene expression when treated with UBBs1. The use of siRNA in future therapeutics could be a highly-specific method to induce cytotoxicity of cancer cells, but more successful clinical testing and better manufacturing processes need to be established first.

Stromal cells play an important role in facilitating disease progression of ductal carcinoma. Cancer associated fibroblasts (CAFs) are an important component of the extracellular matrix (ECM) which constitutes the microenvironment of breast tumor cells. They are known to participate in chemotherapeutic drug resistance by modulating various biochemical and biophysical factors that contribute to increased matrix stiffness and collagen I density of the tumor-adjacent stroma. To address these issues in terms of patient treatment, anti-cancer drug regimes have been assembled to incorporate both chemotherapeutic as well as anti-fibrotic drugs to both target tumor cells while also diminishing the elastic modulus of the microenvironment by targeting CAFs. The quantitative assessment of these drug regimes on tumor progression is missing in terms of CAFs role alone.

A high density 3D tumor model was utilized to recapitulate the tumor microenvironment of ductal carcinoma in vitro. The tumor model consisted of MDA-MB-231 tumors seeded within micromolded collagen wells, chemically immobilized upon a surface treated PDMS substrate. CAFs were seeded within the greater collagen structure from which the microwells were formed. The combinatorial effect of anti-fibrotic drug (Tranilast) and chemotherapy drug (Doxorubicin) were studied within 3D co culture conditions. Specifically, the combinatorial effects of the drugs on tumor cell viability, proliferation, and invasion were examined dynamically upon coculture with CAFs using the microengineered model.

The results of the study showed that the combinatorial effects of Tranilast and Doxorubicin significantly decreased the proliferative ability of tumor cells, in addition to significantly decreasing the ability of tumor cells to remain viable and invade their surrounding stroma, compared to control conditions.

Malignant Pleural Mesothelioma (MPM) is an aggressive deadly tumor that has few therapeutic options. Immunotherapies have shown great potential in alleviating MPM patient symptoms. Using patient data from the Cancer Genome Atlas (TCGA) we sought to identify mutations, regulators, and immune factors driving immune cell migration. We explored computational methods to define regulatory causal flows in order to make biological predictions. These predictions were verified by cross-referencing peer-reviewed articles. A disease-relevant inference model was developed to examine the chemokine IL-18’s effect on natural killer cell (NK cell) migration.

YAP/TAZ is the key effector in the Hippo pathway, but it is also involved in many other regulatory pathways to control tissue and organ size. To better understand its regulation and effects in tumorigenesis and degeneration, a preliminary feedback network was created with the species YAP/TAZ, phosphorylated YAP/TAZ, LATS, miR-130a, VGLL4, and β-catenin. From this network a set of ordinary differential equations were written and analyzed for parameter effects. A model showing the healthy, tumorigenic, and degenerative states was created and preliminary parameter analysis identified the effects of parameter modifications on the overall levels of YAP/TAZ. Further analysis is required and connections with the underlying biology should continue to be pursued to better understand how parameter modifications could improve disease treatments.

Glioblastoma (GBM) is an extremely malignant form of brain cancer characterized by rapid progression and poor patient survival. Even after standard of care treatments, less than ten percent of patients with this disease survive five years. More effective therapeutic options are urgently needed to improve outcomes for patients with GBM. Adequate drug delivery is a critical challenge in GBM treatment, as drugs delivered systemically must be able to penetrate the blood brain barrier (BBB) and reach the tumor at therapeutic levels. To address this, we developed a resource to catalog BBB penetration information for investigational agents currently in clinical trials in cancer. Using an in silico prediction model and manual annotation to capture existing knowledge from the published literature, BBB content for ~500 investigational drugs was added to the investigational database tool. In addition to BBB content, the database also includes information on the gene targets of the included therapies. The investigational database tool was used to identify investigational agents that (1) may have increased activity against GBM based on the presence of a specific mutation in the tumor sample and (2) have evidence suggesting the compounds may penetrate the BBB. By prioritizing investigational agents for further study based on evidence for BBB penetration, this resource can help the GBM research community pursue more effective treatments for GBM.

Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far beyond the visible boundaries of the tumor and will result in a recurring tumor if not killed or removed. Since medical images are the only readily available information about the tumor, we aim to improve mathematical models of tumor growth to better estimate the missing information. Particularly, we investigate the effect of random variation in tumor cell behavior (anisotropy) using stochastic parameterizations of an established proliferation-diffusion model of tumor growth. To evaluate the performance of our mathematical model, we use MR images from an animal model consisting of Murine GL261 tumors implanted in immunocompetent mice, which provides consistency in tumor initiation and location, immune response, genetic variation, and treatment. Compared to non-stochastic simulations, stochastic simulations showed improved volume accuracy when proliferation variability was high, but diffusion variability was found to only marginally affect tumor volume estimates. Neither proliferation nor diffusion variability significantly affected the spatial distribution accuracy of the simulations. While certain cases of stochastic parameterizations improved volume accuracy, they failed to significantly improve simulation accuracy overall. Both the non-stochastic and stochastic simulations failed to achieve over 75% spatial distribution accuracy, suggesting that the underlying structure of the model fails to capture one or more biological processes that affect tumor growth. Two biological features that are candidates for further investigation are angiogenesis and anisotropy resulting from differences between white and gray matter. Time-dependent proliferation and diffusion terms could be introduced to model angiogenesis, and diffusion weighed imaging (DTI) could be used to differentiate between white and gray matter, which might allow for improved estimates brain anisotropy.

Nutritional support offered before and during chemotherapy treatment is proven to improve the outcomes of treatment (Bernhardson, 2016). This project seeks to examine current forms of nutritional support offered to patients, as well as the models of care and support teams in cancer treatment centers. The basis for this project incorporated personal experiences at M.D. Anderson Cancer Center in Gilbert, Arizona as well as research into the work of clinical oncology dietitians. An intense interest in food videos and blogs also informed this project, and was incorporated in the hope of providing chemotherapy patients a platform to discover recipes specific to their unique situation. The combination of this research was utilized to create several videos which demonstrated specific recipes beneficial for patients as well as creating a platform for this particular population. While nutritional support can take multiple forms, the focus of nutritional support surrounds symptom management. The common side effects of chemotherapy such as nausea, mucositis (mouth sores), and extreme weight loss were taken into account. Recipes were formulated to directly address these conditions and each recipe was broken down into the benefits of both macronutrients and micronutrients. In addition to formulating specific recipes and videos, barriers to proper nutritional support were examined and explained. These barriers include understaffing of clinical dietitians at cancer treatment centers, a patient's lack of transportation to and from treatments, as well as an overwhelming viewpoint that nutritional support is only required for extreme cases of malnutrition. Combatting these barriers and offering more forms of nutritional support will help to increase a patient's positive response to treatment, manage their symptoms, and improve their overall quality of life.

Breast and other solid tumors exhibit high and varying degrees of intra-tumor heterogeneity resulting in targeted therapy resistance and other challenges that make the management and treatment of these diseases rather difficult. Due to the presence of admixtures of non-neoplastic cells with polyclonal cell populations, it is difficult to define cancer genomes in patient samples. By isolating tumor cells from normal cells, and enriching distinct clonal populations, clinically relevant genomic aberrations that drive disease can be identified in patients in vivo. An in-depth analysis of clonal architecture and tumor heterogeneity was performed in a stage II chemoradiation-naïve breast cancer from a sixty-five year old patient. DAPI-based DNA content measurements and DNA content-based flow sorting was used to to isolate nuclei from distinct clonal populations of diploid and aneuploid tumor cells in surgical tumor samples. We combined DNA content-based flow cytometry and ploidy analysis with high-definition array comparative genomic hybridization (aCGH) and next-generation sequencing technologies to interrogate the genomes of multiple biopsies from the breast cancer. The detailed profiles of ploidy, copy number aberrations and mutations were used to recreate and map the lineages present within the tumor. The clonal analysis revealed driver events for tumor progression (a heterozygous germline BRCA2 mutation converted to homozygosity within the tumor by a copy number event and the constitutive activation of Notch and Akt signaling pathways. The highlighted approach has broad implications in the study of tumor heterogeneity by providing a unique ultra-high resolution of polyclonal tumors that can advance effective therapies and clinical management of patients with this disease.

Glioblastoma Multiforme (GBM) is an aggressive and deadly form of brain cancer with a median survival time of about a year with treatment. Due to the aggressive nature of these tumors and the tendency of gliomas to follow white matter tracks in the brain, each tumor mass has a unique growth pattern. Consequently it is difficult for neurosurgeons to anticipate where the tumor will spread in the brain, making treatment planning difficult. Archival patient data including MRI scans depicting the progress of tumors have been helpful in developing a model to predict Glioblastoma proliferation, but limited scans per patient make the tumor growth rate difficult to determine. Furthermore, patient treatment between scan points can significantly compound the challenge of accurately predicting the tumor growth. A partnership with Barrow Neurological Institute has allowed murine studies to be conducted in order to closely observe tumor growth and potentially improve the current model to more closely resemble intermittent stages of GBM growth without treatment effects.