【工具】PathNetDRP一种基于通路和蛋白质-蛋白质相互作用的新型生物标志物发现框架

介绍

在癌症免疫治疗中，预测免疫检查点抑制剂（ICI）的疗效仍是一个重大挑战。现有的许多方法依赖于差异基因表达分析或预先设定的免疫特征，这些方法可能无法捕捉免疫反应背后的复杂调控机制。基于网络的模型试图整合生物学相互作用，但它们往往缺乏评估单个基因在通路中所起作用的定量框架，这限制了生物标志物的特异性和可解释性。鉴于这些局限性，我们开发了 PathNetDRP，这是一个整合了生物学通路、蛋白质-蛋白质相互作用网络和机器学习的框架，用于识别与 ICI 响应预测相关的功能相关生物标志物。

我们推出了 PathNetDRP 这种新型的生物标志物发现方法，该方法将 PageRank 算法应用于对与免疫检查点抑制剂（ICI）相关的基因进行优先排序，将这些基因映射到相关的生物途径中，并计算 PathNetGene 分数以量化它们对免疫反应的贡献。与仅关注基因表达差异的传统方法不同，PathNetDRP 系统性地纳入了生物学背景，从而提高了生物标志物的选择准确性。在多个独立的癌症队列上的验证表明，PathNetDRP 实现了强大的预测性能，交叉验证下的受试者工作特征曲线下面积从 0.780 提高到了 0.940。有趣的是，PathNetDRP 不仅仅是提高了预测准确性；它还提供了有关关键免疫相关途径的见解，进一步巩固了其在识别临床相关生物标志物方面的潜力。

PathNetDRP 所识别的生物标志物在交叉验证和独立验证数据集上均表现出强大的预测性能，这表明其在临床应用中具有潜在的实用性。此外，富集分析突出了关键的免疫相关通路，为它们在免疫检查点抑制反应调节中的作用提供了更深入的理解。尽管这些发现突显了 PathNetDRP 的前景，但未来的工作将探索整合更多的预测特征，如肿瘤突变负荷和微卫星不稳定性，以进一步优化其适用性。

Background
Predicting immune checkpoint inhibitor (ICI) response remains a significant challenge in cancer immunotherapy. Many existing approaches rely on differential gene expression analysis or predefined immune signatures, which may fail to capture the complex regulatory mechanisms underlying immune response. Network-based models attempt to integrate biological interactions, but they often lack a quantitative framework to assess how individual genes contribute within pathways, limiting the specificity and interpretability of biomarkers. Given these limitations, we developed PathNetDRP, a framework that integrates biological pathways, protein-protein interaction networks, and machine learning to identify functionally relevant biomarkers for ICI response prediction.

Results
We introduce PathNetDRP, a novel biomarker discovery approach that applies the PageRank algorithm to prioritize ICI-associated genes, maps them to relevant biological pathways, and calculates PathNetGene scores to quantify their contribution to immune response. Unlike conventional methods that focus solely on gene expression differences, PathNetDRP systematically incorporates biological context to improve biomarker selection. Validation across multiple independent cancer cohorts showed that PathNetDRP achieved strong predictive performance, with cross-validation the area under the receiver operating characteristic curves increasing from 0.780 to 0.940. Interestingly, PathNetDRP did not merely improve predictive accuracy; it also provided insights into key immune-related pathways, reinforcing its potential for identifying clinically relevant biomarkers.

Conclusion
The biomarkers identified by PathNetDRP demonstrated robust predictive performance across cross-validation and independent validation datasets, suggesting their potential utility in clinical applications. Furthermore, enrichment analysis highlighted key immune-related pathways, providing a deeper understanding of their role in ICI response regulation. While these findings underscore the promise of PathNetDRP, future work will explore the integration of additional predictive features, such as tumor mutational burden and microsatellite instability, to further refine its applicability.

代码

https://github.com/doohee94/PathNetDRP

参考

• PathNetDRP: A Novel Biomarker Discovery Framework Using Pathway and Protein-Protein Interaction Networks for Immune Checkpoint Inhibitor Response Prediction
• https://github.com/doohee94/PathNetDRP