Emerging Neuroblastoma 3D In Vitro Models for Pre-Clinical Assessments

• Main Authors: Diana Corallo, Stella Frabetti, Olivia Candini, Elisa Gregianin, Massimo Dominici, Horst Fischer, Sanja Aveic
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-11-01
• Series: Frontiers in Immunology
• Subjects: 3D in vitro models; neuroblastoma; pediatric oncology; immunotherapy; drug screening; extracellular matrix
• Online Access: https://www.frontiersin.org/articles/10.3389/fimmu.2020.584214/full

The potential of tumor three-dimensional (3D) in vitro models for the validation of existing or novel anti-cancer therapies has been largely recognized. During the last decade, diverse in vitro 3D cell systems have been proposed as a bridging link between two-dimensional (2D) cell cultures and in vivo animal models, both considered gold standards in pre-clinical settings. The latest awareness about the power of tailored therapies and cell-based therapies in eradicating tumor cells raises the need for versatile 3D cell culture systems through which we might rapidly understand the specificity of promising anti-cancer approaches. Yet, a faithful reproduction of the complex tumor microenvironment is demanding as it implies a suitable organization of several cell types and extracellular matrix components. The proposed 3D tumor models discussed here are expected to offer the required structural complexity while also assuring cost-effectiveness during pre-selection of the most promising therapies. As neuroblastoma is an extremely heterogenous extracranial solid tumor, translation from 2D cultures into innovative 3D in vitro systems is particularly challenging. In recent years, the number of 3D in vitro models mimicking native neuroblastoma tumors has been rapidly increasing. However, in vitro platforms that efficiently sustain patient-derived tumor cell growth, thus allowing comprehensive drug discovery studies on tailored therapies, are still lacking. In this review, the latest neuroblastoma 3D in vitro models are presented and their applicability for a more accurate prediction of therapy outcomes is discussed.