Lee Sanghoon, Sun Min, Hu Yiheng, Wang Yue, Islam Md N, Goerlitz David, Lucas Peter C, Lee Adrian V, Swain Sandra M, Tang Gong, Wang Xiao-Song
University of Pittsburgh.
Georgetown University Medical Center.
Res Sq. 2023 Nov 30:rs.3.rs-3649238. doi: 10.21203/rs.3.rs-3649238/v1.
Multi-omics sequencing is expected to become clinically routine within the next decade and transform clinical care. However, there is a paucity of viable and interpretable genome-wide modeling methods that can facilitate rational selection of patients for tailored intervention. Here we develop an integral genomic signature-based method called iGenSig-Rx as a white-box tool for modeling therapeutic response based on clinical trial datasets with improved cross-dataset applicability and tolerance to sequencing bias. This method leverages high-dimensional redundant genomic features to address the challenges of cross-dataset modeling, a concept similar to the use of redundant steel rods to reinforce the pillars of a building. Using genomic datasets for HER2 targeted therapies, the iGenSig-Rx model demonstrates stable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We expect that iGenSig-Rx as a class of biologically interpretable multi-omics modeling methods will have broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx. : https://drive.google.com/drive/folders/1KgecmUoon9-h2Dg1rPCyEGFPOp28Ols3?usp=sharing.
多组学测序有望在未来十年内成为临床常规手段并改变临床护理。然而，目前缺乏可行且可解释的全基因组建模方法，这些方法能够促进为量身定制的干预措施合理选择患者。在此，我们开发了一种基于整合基因组特征的方法，称为iGenSig-Rx，作为一种白盒工具，用于基于临床试验数据集对治疗反应进行建模，具有更高的跨数据集适用性和对测序偏差的耐受性。该方法利用高维冗余基因组特征来应对跨数据集建模的挑战，这一概念类似于使用冗余钢条来加固建筑物的支柱。使用针对HER2靶向治疗的基因组数据集，iGenSig-Rx模型在四项独立临床试验中展现出稳定的预测能力。更重要的是，iGenSig-Rx模型提供了临床应用急需的透明度，能够清晰解释预测是如何产生的、特征如何对预测做出贡献以及潜在的关键途径是什么。我们预计，作为一类具有生物学可解释性的多组学建模方法，iGenSig-Rx将在基于大数据的精准肿瘤学中得到广泛应用。R包可在以下网址获取：https://github.com/wangxlab/iGenSig-Rx。 : https://drive.google.com/drive/folders/1KgecmUoon9-h2Dg1rPCyEGFPOp28Ols3?usp=sharing。
