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Tumors contain heterogeneous and dynamic populations of cells that do not all display the fast-proliferating properties that traditional chemotherapies target. There is a need therefore, to develop novel treatment strategies that target diverse tumor cell properties. Identifying therapy combinations is challenging however. Current approaches have relied on cell lines cultured in monolayers with treatment response being assessed using endpoint metabolic assays, which although enable large-scale throughput, do not capture tumor heterogeneity. Here, a 3D in vitro tumor model using micro-molded hydrogels (microgels), the Gels for Live Analysis of Compartmentalized Environments (GLAnCE) platform, is adapted into a 96-well plate format (96-GLAnCE) that integrates patient-derived organoids (PDOs) and is combined with longitudinal automated imaging to address these limitations. Using 96-GLAnCE, two measures of tumor aggressiveness are quantified, tumor cell growth and in situ regrowth after drug treatment, in both cell lines and PDOs. The use of longitudinal image-based readouts enables the identification of tumor cell phenotypes with cell population and subpopulation resolution that cannot be detected by standard bulk-soluble assays. 96-GLAnCE is a versatile and robust platform that combines 3D-ECM based models, PDOs, and real-time assay readouts, to provide an additional tool for pre-clinical anti-cancer drug discovery for the identification of novel targets with translatable clinical significance.