A Patient-derived Organoid and Artificial Intelligence-based Workflow for Optimising Chemotherapeutics to Improve Outcomes in Pancreatic Cancer

Pancreatic ductal adenocarcinoma is one of the most aggressive solid tumors. Despite numerous advances in surgical techniques, survival has remained largely unchanged over the last decade. As such, pancreatic cancer has been described as a 'systemic disease' - and one which chemotherapy plays a pivotal role both pre- and post-surgery. Tools that can predict the most effective chemotherapeutic regimens for patients are urgently needed to improve survival in this dismal disease so as to optimise biological effects while minimising side effects and fitness for surgery. We have established a biobank of patient-derived organoids from endoscopic ultrasound biopsies of pancreatic adenocarcinoma. Using an optimised technique these are generated within a clinically applicable timeframe for high-throughput drug screening that can inform therapeutic decisions. By applying artificial intelligence, we are able to optimize drug combinations to maximise clinical efficacy while minimising toxicity. The inclusion of preclinical drugs in our screening panel in addition to standard therapeutics allows for the derivation of novel drug combinations. This work forms the basis for an interventional trial at our centre. We also employ the use of highly sensitive proteomics to profile patients that demonstrate similar therapeutic responses to identify predictive biomarkers of chemosensitivity. We present a platform for personalized medicine in pancreatic cancer that employs organoid technology and artificial intelligence to predict chemosensitivity and therapeutic response for individual patients.