Patient-derived tumor organoids (PDTOs) hold great promise for preclinical and translational research and predicting the patient therapy response from ex vivo drug screenings. However, current adenosine triphosphate (ATP)-based drug screening assays do not capture the complexity of a drug response (cytostatic or cytotoxic) and intratumor heterogeneity that has been shown to be retained in PDTOs due to a bulk readout. Live-cell imaging is a powerful tool to overcome this issue and visualize drug responses more in-depth. However, image analysis software is often not adapted to the three-dimensionality of PDTOs, requires fluorescent viability dyes, or is not compatible with a 384-well microplate format. This paper describes a semiautomated methodology to seed, treat, and image PDTOs in a high-throughput, 384-well format using conventional, widefield, live-cell imaging systems. In addition, we developed viability marker-free image analysis software to quantify growth ratebased drug response metrics that improve reproducibility and correct growth rate variations between different PDTO lines. Using the normalized drug response metric, which scores drug response based on the growth rate normalized to a positive and negative control condition, and a fluorescent cell death dye, cytotoxic and cytostatic drug responses can be easily distinguished, profoundly improving the classification of responders and non-responders. In addition, drug-response heterogeneity can by quantified from single-organoid drug response analysis to identify potential, resistant clones. Ultimately, this method aims to improve the prediction of clinical therapy response by capturing a multiparametric drug response signature, which includes kinetic growth arrest and cell death quantification.

Tweetable abstract Pretreatment UGT1A1 genotyping and a 70% irinotecan dose intensity in poor metabolizers is safe, feasible, cost-effective and essential for safe irinotecan treatment in cancer patients. It is time to update guidelines to swiftly enable the implementation of UGT1A1 genotype-guided irinotecan dosing in routine oncology care.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal diseases, characterized by a treatment-resistant and invasive nature. In-line with these inherent aggressive characteristics, only a subset of patients show a clinical response to the standard of care therapies, thereby highlighting the need for a more personalized treatment approach. In this study, we comprehensively unraveled the intra-patient response heterogeneity and intrinsic aggressive nature of PDAC on bulk and single-organoid resolution. We leveraged a fully characterized PDAC organoid panel (N=8) and matched our artificial intelligence-driven, live-cell organoid image analysis with retrospective clinical patient response. In-line with the clinical outcomes, we identified patient-specific sensitivities to the standard of care therapies (gemcitabine-paclitaxel and FOLFIRINOX) using a growth rate-based and normalized drug response metric. Moreover, the single-organoid analysis was able to detect resistant as well as invasive PDAC organoid clones, which was orchestrates on a patient, therapy, drug, concentration and time-specific level. Furthermore, our in vitro organoid analysis indicated a strong correlation with the matched patient progression-free survival (PFS) compared to the current, conventional drug response readouts. This work not only provides valuable insights on the response complexity in PDAC, but it also highlights the potential applications (extendable to other tumor types) and clinical translatability of our approach in drug discovery and the emerging era of personalized medicine.