Bioinformatic Approaches to Validation and Functional Analysis of 3D Lung Cancer Models

Abstract

Simple Summary Lung cancer remains a major cause of mortality worldwide. Treatment options for lung cancer have remained relatively unchanged despite a significant need, and unrepresentative preclinical cancer models contribute to stifled therapeutic development. 3D models of cancer, including xenografts, spheroids, and organoids, have the potential to improve cancer research and drug development because they are more representative of cancer biology and its diverse pathophysiology. Abstract 3D models of cancer have the potential to improve basic, translational, and clinical studies. Patient-derived xenografts, spheroids, and organoids are broad categories of 3D models of cancer, and to date, these 3D models of cancer have been established for a variety of cancer types. In lung cancer, for example, 3D models offer a promising new avenue to gain novel insights into lung tumor biology and improve outcomes for patients afflicted with the number one cancer killer worldwide. However, the adoption and utility of these 3D models of cancer vary, and demonstrating the fidelity of these models is a critical first step before seeking meaningful applications. Here, we review use cases of current 3D lung cancer models and bioinformatic approaches to assessing model fidelity. Bioinformatics approaches play a key role in both validating 3D lung cancer models and high dimensional functional analyses to support downstream applications.