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Endocrine tumors are a heterogeneous cluster of malignancies that originate from cells that can secrete hormones. Examples include, but are not limited to, thyroid cancer, adrenocortical carcinoma, and neuroendocrine tumors. Many endocrine tumors are relatively slow to proliferate, and as such, they often do not respond well to common antiproliferative chemotherapies. Therefore, increasing attention has been given to targeted therapies and immunotherapies in these diseases. However, in contrast to other cancers, many endocrine tumors are relatively rare, and as a result, less is understood about their biology, including specific targets for intervention. Our limited understanding of such tumors is in part due to a limitation in model systems that accurately recapitulate and enable mechanistic exploration of these tumors. While mouse models and 2D cell cultures exist for some endocrine tumors, these models often may not accurately model nuances of human endocrine tumors. Mice differ from human endocrine physiology and 2D cell cultures fail to recapitulate the heterogeneity and 3D architectures of in vivo tumors. To complement these traditional cancer models, bioengineered 3D tumor models, such as organoids and tumor-on-a-chip systems, have advanced rapidly in the past decade. However, these technologies have only recently been applied to most endocrine tumors. In this review we provide descriptions of these platforms, focusing on thyroid, adrenal, and neuroendocrine tumors and how they have been and are being applied in the context of endocrine tumors. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is the most common form of brain cancer. Even with aggressive treatment, tumor recurrence is almost universal and patient prognosis is poor because many GBM cell subpopulations, especially the mesenchymal and glioma stem cell populations, are resistant to temozolomide (TMZ), the most commonly used chemotherapeutic in GBM. For this reason, there is an urgent need for the development of new therapies that can more effectively treat GBM. Several recent studies have indicated that high expression of connexin 43 (Cx43) in GBM is associated with poor patient outcomes. It has been hypothesized that inhibition of the Cx43 hemichannels could prevent TMZ efflux and sensitize otherwise resistance cells to the treatment. In this study, we use a three‐dimensional organoid model of GBM to demonstrate that combinatorial treatment with TMZ and αCT1, a Cx43 mimetic peptide, significantly improves treatment efficacy in certain populations of GBM. Confocal imaging was used to visualize changes in Cx43 expression in response to combinatorial treatment. These results indicate that Cx43 inhibition should be pursued further as an improved treatment for GBM. [ABSTRACT FROM AUTHOR]

[Display omitted] In recent years, many research groups have begun to utilize bioengineered in vitro models of cancer to study mechanisms of disease progression, test drug candidates, and develop platforms to advance personalized drug treatment options. Due to advances in cell and tissue engineering over the last few decades, there are now a myriad of tools that can be used to create such in vitro systems. In this review, we describe the considerations one must take when developing model systems that accurately mimic the in vivo tumor microenvironment (TME) and can be used to answer specific scientific questions. We will summarize the importance of cell sourcing in models with one or multiple cell types and outline the importance of choosing biomaterials that accurately mimic the native extracellular matrix (ECM) of the tumor or tissue that is being modeled. We then provide examples of how these two components can be used in concert in a variety of model form factors and conclude by discussing how biofabrication techniques such as bioprinting and organ-on-a-chip fabrication can be used to create highly reproducible complex in vitro models. Since this topic has a broad range of applications, we use the final section of the review to dive deeper into one type of cancer, glioblastoma, to illustrate how these components come together to further our knowledge of cancer biology and move us closer to developing novel drugs and systems that improve patient outcomes. [ABSTRACT FROM AUTHOR]