通过整合L1000基因组和蛋白质组大数据进行药物效应预测

Drug Effect Prediction by Integrating L1000 Genomic and Proteomic Big Data.

作者信息

Chen Wei, Zhou Xiaobo

机构信息

Department of Radiology, Wake Forest University Medical School, Winston-Salem, NC, USA.

School of Biomedical Informatics, The University of Texas, Health Science Center at Houston, Houston, TX, USA.

出版信息

Methods Mol Biol. 2019;1939:287-297. doi: 10.1007/978-1-4939-9089-4_16.

DOI:10.1007/978-1-4939-9089-4_16

PMID:30848468

Abstract

The library of integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction. Gene expression and proteomic data in LINCS L1000 are cataloged for human cancer cells treated with compounds and genetic reagents. For understanding the related cell pathways and facilitating drug discovery, we developed binary linear programming (BLP) to infer cell-specific pathways and identify compounds' effects using L1000 gene expression and phosphoproteomics data. A generic pathway map for the MCF7 breast cancer cell line was built. Within them, BLP extracted the cell-specific pathways, which reliably predicted the compounds' effects. In this way, the potential drug effects are revealed by our models.

摘要

整合网络细胞特征（LINCS）项目的文库旨在通过编目基因表达和信号转导的变化，建立基于网络的生物学理解。LINCS L1000中的基因表达和蛋白质组学数据是针对用化合物和基因试剂处理的人类癌细胞编目的。为了理解相关细胞通路并促进药物发现，我们开发了二元线性规划（BLP），以使用L1000基因表达和磷酸化蛋白质组学数据推断细胞特异性通路并识别化合物的作用。构建了MCF7乳腺癌细胞系的通用通路图。在这些通路图中，BLP提取了细胞特异性通路，这些通路可靠地预测了化合物的作用。通过这种方式，我们的模型揭示了潜在的药物作用。