PO.CH02.01 · 化学
患者来源肿瘤模型的先进蛋白质组学分析揭示高度跨模型一致性和改善的转化保真度
Advanced proteomic profiling of patient-derived tumor models reveals high cross-model concordance and improved translational fidelity
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
引言：临床前肿瘤模型的高通量蛋白质组学分析在肿瘤学研究和抗癌药物开发的转化研究中日益关键。然而，如既往所示，患者来源异种移植物（PDX）中的小鼠基质成分会干扰准确的蛋白质组学定量。例如，30%的PDX肿瘤样本含有超过25%的小鼠基质细胞，这会在下游蛋白质组学分析中导致错误结果。
方法：为克服这一问题，我们开发了一种优化的"先分离后运行"方法，在质谱蛋白质组学分析前有效分离人和小鼠细胞，从而能够从PDX样本中获取精确的人肿瘤蛋白质组学特征。对于类器官模型，在质谱实验前未进行小鼠细胞去除。
结果：使用优化的数据分析流程，我们分析了418个PDX模型和89个类器官，包括59个患者来源类器官（PDO）和30个PDX来源类器官（PDXO）模型，每个样本定量了中位数9916个人蛋白（范围：8514-11206），在批次校正和标准化后具有高重现性（Pearson相关系数范围：0.93-0.98）。为评估跨平台保真度，我们还开始对配对的体内和体外肿瘤模型进行蛋白质组学分析；初步结果显示配对模型之间蛋白质组表达高度一致。例如，3对PDO-PDOX的Pearson相关系数为0.93-0.96，7对PDX-PDXO为0.91-0.96；两者均远高于PDO、PDOX、PDX和PDXO内部的模型间相关性（Wilcox检验p值 < 0.01）。进一步正在进行的研究将提供更多关于肿瘤模型内/间蛋白质组相似性的证据。
结论：总之，我们建立了一个先进的蛋白质组学分析平台，相比传统方法显著提高了临床前肿瘤模型的蛋白检测覆盖度，并验证了配对体外和体内肿瘤模型之间蛋白表达的高度一致性。
查看英文原文 English abstract
Introduction: High-throughput proteomic profiling of preclinical tumor models is increasingly critical in oncology studies and translational research for anti-cancer drug development. However, the murine stromal compartment in patient-derived xenografts (PDXs) confounds accurate proteomic quantification, as previously demonstrated. For instance, 30% of PDX tumor samples contain over 25% mouse stromal cells, which causes false results in downstream proteomic analysis.
Methods: To overcome this issue, we developed an optimized "separate-then-run" methodology that effectively separates human and mouse cells before proteomics profiling by mass spectrometry, thus enabling the acquisition of precise human tumor proteomic signatures from PDX samples. For organoid models, no mouse cell removal was performed before the mass spectrometry experiment.
Results: Using an optimized data-analysis pipeline, we profiled 418 PDX models and 89 organoids, including 59 patient-derived organoid (PDO) and 30 PDX-derived organoid (PDXO) models, quantifying a median of 9,916 human proteins per sample (range: 8,514-11,206) with high reproducibility after batch correction and normalization (Pearson correlation coefficient range: 0.93-0.98). To evaluate cross-platform fidelity, we also start to perform proteomic profiling of paired in vivo and in vitro tumor models; preliminary results show high consistency of proteome expression between paired models. For example, Pearson correlation coefficients are 0.93-0.96 for 3 PDO-PDOX pairs and 0.91-0.96 for 7 PDX-PDXO pairs; both are much higher than inter-model correlation within PDO, PDOX, PDX, and PDXO (Wilcox test p-value < 0.01). Further ongoing studies will provide more evidence on within/between tumor model proteome similarity.
Conclusions: In summary, we have established an advanced proteomic profiling platform that significantly enhances protein detection coverage in preclinical tumor models over conventional methods and validates the high concordance of protein expression between paired in vitro and in vivo tumor models.
利益披露 Disclosure
J. Xue, None..
H. Liu, None..
S. Guo, None.