Proteomics profiling of research models for studying pancreatic ductal adenocarcinoma

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 system 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. Preparation and validation of the research models and the proteomics were described in detail. The assembly datasets we present here may contribute to the data collection on PDAC, which will be useful for systems modeling, data mining, knowledge discovery in databases, and bioinformatics of individual models. Further data analysis may lead to generation of research hypotheses, predictions of targets for diagnosis and treatment and relationships between data variables. bridging the gap between preclinical research and clinical trials, thus enhancing the possibilities for discovering early diagnostic biomarkers and effective therapeutic targets.

胰腺导管腺癌（Pancreatic ductal adenocarcinoma, PDAC）仍是致死率最高的恶性肿瘤之一，由于确诊时多已处于晚期且现有治疗手段疗效有限，其五年生存率仅为10%~15%。本研究采用基于蛋白质组学的系统建模方法，从多种研究模型中生成多模态数据集，这些模型包括胰腺导管腺癌细胞、细胞球、类器官，以及小鼠和人类样本来源的组织。所有研究模型均采用统一的基于质谱的蛋白质组学分析方案。研究模型的制备、验证及蛋白质组学实验流程均已详细阐述。本研究发布的组装数据集可丰富胰腺导管腺癌相关的数据储备，有望为各类模型的系统建模、数据挖掘、数据库知识发现以及生物信息学研究提供支撑。后续数据分析可用于生成研究假说、预测诊断与治疗靶点，同时阐明数据变量间的关联，从而弥合临床前研究与临床试验之间的鸿沟，为早期诊断生物标志物的发掘以及有效治疗靶点的发现提供更多可能性。