Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning.
Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning.
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DOI：
10.3389/fimmu.2023.1090373
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发表时间：
2023
影响因子：
7.3
通讯作者：
Xiao, Kunhong
中科院分区：
文献类型：
--
作者：
Fang, Fei;Liu, Peng;Song, Lei;Wagner, Patrick;Bartlett, David;Ma, Liane;Li, Xue;Rahimian, M. Amin;Tseng, George;Randhawa, Parmjeet;Xiao, Kunhong
关键词：
通过确保及时治疗移植并发症，基于活检的诊断对于维持同种异体肾移植物的寿命至关重要。尽管组织学评估仍然是金标准，但它具有显着的局限性，例如主观解释、再现性不佳和疾病负担定量不精确。人们希望分子诊断能够提高传统组织学方法的效率、准确性和可重复性。对肾移植患者的一组福尔马林固定石蜡包埋 (FFPE) 活检组织进行定量无标记质谱分析，其中包括五个样本，每个样本均诊断为 T 细胞介导的排斥反应 (TCMR)、多瘤病毒 BK 肾病 (BKPyVN) 和稳定 (STA) 肾功能对照组织。使用差异蛋白表达结果作为分类器，测试了三种不同的机器学习算法，以构建 TCMR 的分子诊断模型。无标记蛋白质组学方法产生了 800-1350 种蛋白质，可以通过单次测量对每个样品进行高置信度定量。在这些候选蛋白中，与 STA 和 BKPyVN 相比，分别有 329 个和 467 个蛋白被定义为 TCMR 的差异表达蛋白 (DEP)。将本研究中使用无标记方法获得的 FFPE 定量蛋白质组数据集与我们之前使用同量异位标记技术报告的数据集进行比较，生成一个由两个数据集中通常量化的 DEP 特征组成的分类器池，用于 TCMR 预测。留一法交叉验证结果表明，基于随机森林（RF）的模型具有最佳的预测能力。在使用独立样本集进行的后续盲测中，基于 RF 的模型对于 TCMR 的准确度为 80%，对于 STA 的准确度为 100%。当将建立的基于 RF 的模型应用于两个公共转录组数据集时，分别实现了 78.1%-82.9% 的敏感性和 58.7%-64.4% 的特异性。这项原理验证研究证明了使用准确、高效且经济高效的平台进行 FFPE 活检蛋白质组学分析的临床可行性，该平台将定量无标记质谱分析与基于机器学习的诊断模型相结合。每次测试的费用不到 10 美元。
Biopsy-based diagnosis is essential for maintaining kidney allograft longevity by ensuring prompt treatment for graft complications. Although histologic assessment remains the gold standard, it carries significant limitations such as subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden. It is hoped that molecular diagnostics could enhance the efficiency, accuracy, and reproducibility of traditional histologic methods. Quantitative label-free mass spectrometry analysis was performed on a set of formalin-fixed, paraffin-embedded (FFPE) biopsies from kidney transplant patients, including five samples each with diagnosis of T-cell-mediated rejection (TCMR), polyomavirus BK nephropathy (BKPyVN), and stable (STA) kidney function control tissue. Using the differential protein expression result as a classifier, three different machine learning algorithms were tested to build a molecular diagnostic model for TCMR. The label-free proteomics method yielded 800-1350 proteins that could be quantified with high confidence per sample by single-shot measurements. Among these candidate proteins, 329 and 467 proteins were defined as differentially expressed proteins (DEPs) for TCMR in comparison with STA and BKPyVN, respectively. Comparing the FFPE quantitative proteomics data set obtained in this study using label-free method with a data set we previously reported using isobaric labeling technology, a classifier pool comprised of features from DEPs commonly quantified in both data sets, was generated for TCMR prediction. Leave-one-out cross-validation result demonstrated that the random forest (RF)-based model achieved the best predictive power. In a follow-up blind test using an independent sample set, the RF-based model yields 80% accuracy for TCMR and 100% for STA. When applying the established RF-based model to two public transcriptome datasets, 78.1%-82.9% sensitivity and 58.7%-64.4% specificity was achieved respectively. This proof-of-principle study demonstrates the clinical feasibility of proteomics profiling for FFPE biopsies using an accurate, efficient, and cost-effective platform integrated of quantitative label-free mass spectrometry analysis with a machine learning-based diagnostic model. It costs less than 10 dollars per test.
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DOI：
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DOI：
10.1080/13510002.2020.1857157
发表时间：
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期刊：
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影响因子：
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