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3D bioprinting of gastrointestinal cancer models: A comprehensive review on processing, properties, and therapeutic implications.

Kalappa PrashanthaAmita KrishnappaMalini Muthappa
Published in: Biointerphases (2023)
Gastrointestinal tract (GIT) malignancies are an important public health problem considering the increased incidence in recent years and the high morbidity and mortality associated with it. GIT malignancies constitute 26% of the global cancer incidence burden and 35% of all cancer-related deaths. Gastrointestinal cancers are complex and heterogenous diseases caused by the interplay of genetic and environmental factors. The tumor microenvironment (TME) of gastrointestinal tract carcinomas is dynamic and complex; it cannot be recapitulated in the basic two-dimensional cell culture systems. In contrast, three-dimensional (3D) in vitro models can mimic the TME more closely, enabling an improved understanding of the microenvironmental cues involved in the various stages of cancer initiation, progression, and metastasis. However, the heterogeneity of the TME is incompletely reproduced in these 3D culture models, as they fail to regulate the orientation and interaction of various cell types in a complex architecture. To emulate the TME, 3D bioprinting has emerged as a useful technique to engineer cancer tissue models. Bioprinted cancer tissue models can potentially recapitulate cancer pathology and increase drug resistance in an organ-mimicking 3D environment. In this review, we describe the 3D bioprinting methods, bioinks, characterization of 3D bioprinted constructs, and their application in developing gastrointestinal tumor models that integrate their microenvironment with different cell types and substrates, as well as bioprinting modalities and their application in therapy and drug screening. We review prominent studies on the 3D bioprinted esophageal, hepatobiliary, and colorectal cancer models. In addition, this review provides a comprehensive understanding of the cancer microenvironment in printed tumor models, highlights current challenges with respect to their clinical translation, and summarizes future perspectives.
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