UCLA

Functional precision medicine (FPM) in cancer is an emerging treatment paradigm involving exposure of patient-derived tumor material to drugs to assess their efficacy and to guide therapeutic selection. Tumor organoids, in addition to being versatile disease models for basic and translational research, are of particular interest for FPM applications. However, improved approaches for high-throughput drug screening of three-dimensional (3D) tumor organoids, capable of resolving both population-level and single organoid–level data and allowing for consideration of heterogeneity in drug screening readouts, are needed. Quantitative phase imaging (QPI) is a label-free microscopy technique for imaging optically transparent biological samples and quantifying various biophysical properties such as cell biomass, mass density, and growth. This thesis demonstrates a new application of high-speed live cell interferometry (HSLCI), a high-throughput QPI platform, for screening 3D-cultured organoid models of cancer, and describes the development of this method. The method presented combines bioprinting, HSLCI, and machine learning technologies to enable accurate, label-free, and highly time-resolved biomass measurements of thousands of organoids in parallel, and rapid identification of drug sensitivity and resistance with temporal monitoring and single-organoid resolution. The complete QPI-based organoid screening pipeline can be leveraged for fundamental studies of disease biology, in addition to FPM studies aiming to establish clinical correlations and ultimately improve treatment selection for patients with solid cancers.