Drug Signature Detection Based on L1000 Genomic and Proteomic Big Data

Methods Mol Biol. 2019:1939:273-286. doi: 10.1007/978-1-4939-9089-4_15.

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. L1000 big datasets provide gene expression profiles induced by over 10,000 compounds, shRNAs, and kinase inhibitors using L1000 platform. We developed a systematic compound signature discovery pipeline named csNMF, which covers from raw L1000 data processing to drug screening and mechanism generation. The discovered compound signatures of breast cancer were consistent with the LINCS KINOMEscan data and were clinically relevant. In this way, the potential mechanisms of compounds' efficacy are elucidated by our computational model.

Keywords: Compound efficacy; Compound signature; Drug signature; L1000; LINCS; csNMF.

Publication types

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

MeSH terms

  • Antineoplastic Agents / pharmacology
  • Big Data*
  • Breast Neoplasms / drug therapy*
  • Breast Neoplasms / genetics
  • Breast Neoplasms / metabolism
  • Drug Discovery / methods*
  • Drug Screening Assays, Antitumor / methods
  • Female
  • Gene Expression Regulation, Neoplastic / drug effects
  • Genomics / methods*
  • Humans
  • MCF-7 Cells
  • Protein Interaction Maps / drug effects
  • Protein Kinase Inhibitors / pharmacology
  • Proteomics / methods*

Substances

  • Antineoplastic Agents
  • Protein Kinase Inhibitors