Biologically relevant 3D tumor arrays: Imaging-based methods for quantification of reproducible growth and analysis of treatment response

Three-dimensional in vitro tumor models have emerged as powerful research tools in cancer biology, though the vast potential of these systems as high-throughput, biologically relevant reporters of treatment response has yet to be adequately explored. Here, building on previous studies, we demonstrate the utility of using 3D models for ovarian and pancreatic cancers in conjunction with quantitative image processing to reveal aspects of growth behavior and treatment response that would not be evident without either modeling or quantitative analysis component. In this report we specifically focus on recent improvements in the imaging component of this integrative research platform and emphasize analysis to establish reproducible growth properties in 3D tumor arrays, a key consideration in establishing the utility of this platform as a reliable reporter of therapeutic response. Building on previous studies using automated segmentation of low magnification image fields containing large numbers of nodules to study size dependent treatment effects, we introduce an improvement to this method using multiresolution decomposition to remove gradient background from transmitted light images for more reliable feature identification. This approach facilitates the development of a new treatment response metric, disruption fraction (Dfrac), which quantifies dose dependent distribution shifts from nodular fragmentation induced by cytotoxic therapies. Using this approach we show that PDT treatment is associated with significant dose-dependent increases in Dfrac, while this is not observed with carboplatin treatment. The ability to quantify this response to therapy could play a key role in design of combination regimens involving these two modalities.