Cell cycle deregulation is a cancer hallmark that has stimulated the development of mitotic inhibitors with differing mechanisms of action. Quantitative phase imaging (QPI) is an emerging approach for determining cancer cell sensitivities to chemotherapies in vitro. Cancer cell fates in response to mitotic inhibitors are agent- and dose-dependent. Fates that lead to chromosomal instabilities may result in a survival advantage and drug resistance. Conventional techniques for quantifying cell fates are incompatible with growth inhibition assays that produce binary live/dead results. Therefore, we used QPI to quantify post-mitotic fates of G0/G1 synchronized HeLa cervical adenocarcinoma and M202 melanoma cells during 24 h of escalating-dose exposures to mitotic inhibitors, including microtubule inhibitors paclitaxel and colchicine, and an Aurora kinase A inhibitor, VX-680. QPI determined cell fates by measuring changes in cell biomass, morphology, and mean phase-shift. Cell fates fell into three groups: (1) bipolar division from drug failure; (2) cell death or sustained mitotic arrest; and (3) aberrant endocycling or multipolar division. In this proof-of-concept study, colchicine was most effective in producing desirable outcomes of sustained mitotic arrest or death throughout its dosing range, whereas both paclitaxel and VX-680 yielded dose-dependent multipolar divisions or endocycling, respectively. Furthermore, rapid completion of mitosis associated with bipolar divisions whereas prolonged mitosis associated with multipolar divisions or cell death. Overall, QPI measurement of drug-induced cancer cell fates provides a tool to inform the development of candidate agents by quantifying the dosing ranges over which suboptimal inhibitor choices lead to undesirable, aberrant cancer cell fates.

High throughput drug screening is an established approach to investigate tumor biology and identify therapeutic leads. Traditional platforms use two-dimensional cultures which do not accurately reflect the biology of human tumors. More clinically relevant model systems such as three-dimensional tumor organoids can be difficult to scale and screen. Manually seeded organoids coupled to destructive endpoint assays allow for the characterization of treatment response, but do not capture transitory changes and intra-sample heterogeneity underlying clinically observed resistance to therapy. We present a pipeline to generate bioprinted tumor organoids linked to label-free, time-resolved imaging via high-speed live cell interferometry (HSLCI) and machine learning-based quantitation of individual organoids. Bioprinting cells gives rise to 3D structures with unaltered tumor histology and gene expression profiles. HSLCI imaging in tandem with machine learning-based segmentation and classification tools enables accurate, label-free parallel mass measurements for thousands of organoids. We demonstrate that this strategy identifies organoids transiently or persistently sensitive or resistant to specific therapies, information that could be used to guide rapid therapy selection.