A Precision Clinical-Omics Approach in Predicting Therapeutic Response to Pancreatic Cancer

Abstract

Background: Pancreatic ductal adenocarcinoma is one of the most aggressive solid tumours. Despite numerous advances in surgical techniques, survival has remained largely unchanged over the last decade. Pancreatic cancer is thus considered a ‘systemic disease’ where chemotherapy plays a pivotal role in both the pre- and post-surgery setting. Tools that can predict the most effective chemotherapeutic regimens for patients are urgently needed to improve survival by optimising biological effects while minimising side effects, thereby not comprising their fitness for surgery. The aim of the study was to characterise the proteome of patient derived pancreatic cancer organoids to identify biomarkers predictive of chemotherapeutic sensitivity. Method: Using quantitative mass spectrometry we performed deep proteomic profiling of patient derived pancreatic cancer organoids with matched primary tumour tissue, and performed high-throughput combinatorial drug screening on organoid lines. Results: We demonstrated that common pancreatic cancer characteristics were conserved in organoids derived from different patients, and through comparative analysis between organoids revealed individual diversity that correlated with patient specific features. By correlating personalized organoid proteomes with their matched drug sensitivity profiles derived using high-throughput combinatorial drug screening, we identified protein biomarkers that correlated with therapeutic response to both standard of care and other novel therapeutics. Candidate biomarkers were verified by immunohistochemistry of organoids, with further validation to be performed in an interventional trial using organoids to predict second-line chemotherapy in patients with metastatic disease. Conclusions: We present the integration of deep proteomic profiling with a patient derived organoid platform for therapeutic biomarker discovery in pancreatic cancer.