Casadonte Rita, Kriegsmann Mark, Perren Aurel, Baretton Gustavo, Deininger Sören-Oliver, Kriegsmann Katharina, Welsch Thilo, Pilarsky Christian, Kriegsmann Jörg
Proteopath GmbH, Trier, 54296, Germany.
Institute of Pathology, University of Heidelberg, Heidelberg, 69120, Germany.
Proteomics Clin Appl. 2019 Jan;13(1):e1800046. doi: 10.1002/prca.201800046. Epub 2018 Dec 19.
To define proteomic differences between pancreatic ductal adenocarcinoma (pDAC) and pancreatic neuroendocrine tumor (pNET) by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI).
Ninety-three pDAC and 126 pNET individual tissues are assembled in tissue microarrays and analyzed by MALDI MSI. The cohort is separated in a training (52 pDAC and 83 pNET) and validation set (41 pDAC and 43 pNET). Subsequently, a linear discriminant analysis (LDA) model based on 46 peptide ions is performed on the training set and evaluated on the validation cohort. Additionally, two liver metastases and a whole slide of pDAC are analyzed by the same LDA algorithm.
Classification of pDAC and pNET by the LDA model is correct in 95% (39/41) and 100% (43/43) of patients in the validation cohort, respectively. The two liver metastases and the whole slide of pDAC are also correctly classified in agreement with the histopathological diagnosis.
In the present study, a large dataset of pDAC and pNET by MALDI MSI is investigated, a class prediction model that allowed separation of both entities with high accuracy is developed, and differential peptide peaks with potential diagnostic, prognostic, and predictive values are highlighted.
通过基质辅助激光解吸/电离质谱成像（MALDI MSI）确定胰腺导管腺癌（pDAC）和胰腺神经内分泌肿瘤（pNET）之间的蛋白质组差异。
将93个pDAC和126个pNET个体组织组装到组织微阵列中，并通过MALDI MSI进行分析。该队列分为训练集（52个pDAC和83个pNET）和验证集（41个pDAC和43个pNET）。随后，在训练集上基于46个肽离子进行线性判别分析（LDA）模型，并在验证队列上进行评估。此外，通过相同的LDA算法分析了两个肝转移灶和一张pDAC全切片。
在验证队列中，LDA模型对pDAC和pNET的分类分别在95%（39/41）和100%（43/43）的患者中是正确的。两个肝转移灶和pDAC全切片也与组织病理学诊断一致地被正确分类。
在本研究中，通过MALDI MSI研究了一个大型的pDAC和pNET数据集，开发了一个能够高精度分离这两种实体的类别预测模型，并突出了具有潜在诊断、预后和预测价值的差异肽峰。
