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Aleksander Skardal Department of Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA
The Ohio State University and Arthur G. James Comprehensive Cancer Center, Columbus, Ohio, USA
Center for Cancer Engineering, The Ohio State University, Columbus, Ohio, USA

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Hemamylammal Sivakumar Department of Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA

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Marco A Rodriguez Department of Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA

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Liudmila V Popova Division of Surgical Oncology, The Ohio State University and Arthur G. James Comprehensive Cancer Center, Columbus, Ohio, USA

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Priya H Dedhia The Ohio State University and Arthur G. James Comprehensive Cancer Center, Columbus, Ohio, USA
Center for Cancer Engineering, The Ohio State University, Columbus, Ohio, USA
Division of Surgical Oncology, The Ohio State University and Arthur G. James Comprehensive Cancer Center, Columbus, Ohio, USA

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Graphical abstract

Abstract

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.

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