Proteomics profiling of research models for studying pancreatic ductal adenocarcinoma

Abstract

Abstract Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival rate of 10–15% due to late-stage diagnosis and limited efficacy of existing treatments. This study utilized proteomics-based systems modelling to generate multimodal datasets from various research models, including PDAC cells, spheroids, organoids, and tissues derived from murine and human samples. Identical mass spectrometry-based proteomics was applied across the different models. The preparation and validation of the research models and the proteomics were described in detail. The assembly datasets we present here contribute to the data collection on PDAC, which will be useful for systems modelling, data mining, knowledge discovery in databases, and bioinformatics of individual models. Further data analysis may lead to the generation of research hypotheses, predictions of targets for diagnosis and treatment, and relationships between data variables.