Drug Effect Prediction by Integrating L1000 Genomic and Proteomic Big Data

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

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.

Keywords: Binary linear programming; Cell-specific pathway; Drug effect; L1000; LINCS.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Antineoplastic Agents / pharmacology
  • Breast Neoplasms / drug therapy*
  • Breast Neoplasms / genetics
  • Breast Neoplasms / metabolism
  • Drug Discovery / methods*
  • Female
  • Genomics / methods*
  • Humans
  • MCF-7 Cells
  • Programming, Linear
  • Proteomics / methods*
  • Signal Transduction / drug effects
  • Transcriptome / drug effects

Substances

  • Antineoplastic Agents