Highly Reproducible Quantitative Proteomics Analysis of Pancreatic Cancer Cells Reveals Proteome-Level Effects of a Novel Combination Drug Therapy That Induces Cancer Cell Death via Metabolic Remodeling and Activation of the Extrinsic Apoptosis Pathway.
Highly Reproducible Quantitative Proteomics Analysis of Pancreatic Cancer Cells Reveals Proteome-Level Effects of a Novel Combination Drug Therapy That Induces Cancer Cell Death via Metabolic Remodeling and Activation of the Extrinsic Apoptosis Pathway.
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DOI：
10.1021/acs.jproteome.3c00463
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发表时间：
2023-10
影响因子：
4.4
通讯作者：
Sailee Rasam;Q. Lin;S. Shen;R. Straubinger;Jun Qu
中科院分区：
文献类型：
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作者：
Sailee Rasam;Q. Lin;S. Shen;R. Straubinger;Jun Qu
胰腺癌患者存活率很低，经常使用吉西他滨(Gem)治疗。然而，最初的肿瘤敏感性往往让位于快速发展的耐药性。基于GEM的药物组合被用于提高疗效和减轻耐药性，但我们对分子水平的药物相互作用的了解有限，这可能有助于开发更有效的治疗方案。全球定量蛋白质组分析可以为药物组合相互作用提供新的机制见解，但要实现药物联合研究通常所需的大样本的高质量定量蛋白质组分析是具有挑战性的。在这里，我们使用IonStar研究了Gem与最近批准的泛FGFR抑制剂BGJ398(Infigratinib)在多个处理组(N=42个样本)中的分子水平时间相互作用。IonStar是一种强大的大规模蛋白质组学方法，采用了控制良好的超高分辨率MS1定量。样本集中共有5514个蛋白质被定量，没有遗漏数据，需要2个唯一的肽/蛋白质，<；1%的蛋白质假发现率(FDR)，<；0.1%的多肽FDR和10%的CV<；10%。对差异改变的蛋白质的功能分析揭示了药物失调的过程，如新陈代谢、细胞凋亡和抗原呈递途径。这些变化通过使用海马代谢物分析和免疫分析进行了实验验证。总体而言，对大规模蛋白质组学数据的深入分析为FGFR抑制剂补充和增强胰腺癌Gem活性的可能机制提供了新的见解。
Pancreatic cancer patients have poor survival rates and are frequently treated using gemcitabine (Gem). However, initial tumor sensitivity often gives way to rapid development of resistance. Gem-based drug combinations are employed to increase efficacy and mitigate resistance, but our understanding of molecular-level drug interactions, which could assist in the development of more effective therapeutic regimens, is limited. Global quantitative proteomic analysis could provide novel mechanistic insights into drug combination interactions, but it is challenging to achieve high-quality quantitative proteomics analysis of the large sample sets that are typically required for drug combination studies. Here, we investigated molecular-level temporal interactions of Gem with BGJ398 (infigratinib), a recently approved pan-FGFR inhibitor, in multiple treatment groups (N = 42 samples) using IonStar, a robust large-scale proteomics method that employs well-controlled, ultrahigh-resolution MS1 quantification. A total of 5514 proteins in the sample set were quantified without missing data, requiring >2 unique peptides/protein, <1% protein false discovery rate (FDR), <0.1% peptide FDR, and CV < 10%. Functional analysis of the differentially altered proteins revealed drug-dysregulated processes such as metabolism, apoptosis, and antigen presentation pathways. These changes were validated experimentally using Seahorse metabolic assays and immunoassays. Overall, in-depth analysis of large-scale proteomics data provided novel insights into possible mechanisms by which FGFR inhibitors complement and enhance Gem activity in pancreatic cancers.