Abstract: Background This study explores the repurposing of Auranofin (AF), an anti-rheumatic drug, for treating non-small cell lung cancer (NSCLC) adenocarcinoma and pancreatic ductal adenocarcinoma (PDAC). Drug repurposing in oncology offers a cost-effective and time-efficient approach to developing new cancer therapies. Our research focuses on evaluating AF's selective cytotoxicity against cancer cells, identifying RNAseq-based biomarkers to predict AF response, and finding the most effective co-therapeutic agents for combination with AF. Methods Our investigation employed a comprehensive drug screening of AF in combination with eleven anticancer agents in cancerous PDAC and NSCLC patient-derived organoids (n = 7), and non-cancerous pulmonary organoids (n = 2). Additionally, we conducted RNA sequencing to identify potential biomarkers for AF sensitivity and experimented with various drug combinations to optimize AF's therapeutic efficacy. Results The results revealed that AF demonstrates a preferential cytotoxic effect on NSCLC and PDAC cancer cells at clinically relevant concentrations below 1 µM, sparing normal epithelial cells. We identified Carbonic Anhydrase 12 (CA12) as a significant RNAseq-based biomarker, closely associated with the NF-κB survival signaling pathway, which is crucial in cancer cell response to oxidative stress. Our findings suggest that cancer cells with low CA12 expression are more susceptible to AF treatment. Furthermore, the combination of AF with the AKT inhibitor MK2206 was found to be particularly effective, exhibiting potent and selective cytotoxic synergy, especially in tumor organoid models classified as intermediate responders to AF, without adverse effects on healthy organoids. Conclusion Our research offers valuable insights into the use of AF for treating NSCLC and PDAC. It highlights AF's cancer cell selectivity, establishes CA12 as a predictive biomarker for AF sensitivity, and underscores the enhanced efficacy of AF when combined with MK2206 and other therapeutics. These findings pave the way for further exploration of AF in cancer treatment, particularly in identifying patient populations most likely to benefit from its use and in optimizing combination therapies for improved patient outcomes.

Abstract: 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 semi-automated 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 rate-based 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. ,