Predicting drug responses using pancreatic cancer organoids and multimodal plate imaging
Pancreatic cancer is a deadly malignancy with few treatment models. Monolayer cell culture has failed to predict patient drug responses in the past. In response to this, a patient-derived organoid (PDO) methodology has been developed that enables the generation of models, from both surgically resected material and biopsies, with a success rate of 75-80%. These cultures enable the molecular dissection of treatment responses and resistance.
Here, Dannielle Engle and Christian Oberdanner will discuss the use of the Tecan Spark® Cyto to evaluate drug responses in PDO models. In short, PDO models were plated in a 384-well format, allowed to reform, and treated with compounds. The dose-responses of PDO models were compared to several agents using both CellTiter-Glo® (Promega) to measure ATP levels, as well as the automated confluence measurement. This methodology provided the same endpoint analyses previously relied upon, plus the dynamic evaluation of changes to cell morphology and confluence. Confluence measurements were found to be comparable to ATP levels. Given the diversity observed amongst pancreatic cancer patients and the PDO models, dynamic measurements provide additional flexibility and information for precision medicine approaches.
Key learning objectives:
- Discover how pancreatic cancer patient-derived organoids can be used as a tool for personalized medicine
- Explore the remaining challenges of therapeutic testing in human patient-derived organoids
- Learn how multiplexing analysis of organoids can be established using a multimode plate reader with imaging capabilities
- Find out how automated confluence assessment is applied for longitudinal analysis in organoid pharmacotyping
Who should attend:
- Professional groups: Ph.D. students, post-docs, general investigators, senior scientists, lab-heads
- Research areas: cell biology, tumor biology, oncology, pancreatic cancer research (in particular), organoid / spheroid biology
- Technologies of interest: imaging, live-cell imaging, confluence assessment, luminescence detection, organoid generation