Skip to main content
SpringerLink
Log in

Drug Effect Prediction by Integrating L1000 Genomic and Proteomic Big Data

  • Protocol
  • First Online:
Bioinformatics and Drug Discovery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1939))

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hongwei Shao TP, Ji Z, Jing S, Zhou X (2013) Systematically studying kinase inhibitor induced signaling network signatures by integrating both therapeutic and side effects. PLoS One 8(12):e80832

    Article  Google Scholar 

  2. Saez-Rodriguez J, Goldsipe A, Muhlich J, Alexopoulos LG, Millard B et al (2008) Flexible informatics for linking experimental data to mathematical models via DataRail. Bioinformatics 24:840–847

    Article  CAS  Google Scholar 

  3. Hendriks BS, Espelin CW (2010) DataPflex: a MATLAB-based tool for the manipulation and visualization of multidimensional datasets. Bioinformatics 26:432–433

    Article  CAS  Google Scholar 

  4. Ji ZW, Su J, Liu CL, Wang HY, Huang DS, Zhou XB (2014) Integrating genomics and proteomics data to predict drug effects using binary linear programming. PLoS One 9(7):e102798

    Article  Google Scholar 

  5. Ogata H, Goto S, Fujibuchi W, Kanehisa M (1998) Computation with the KEGG pathway database. Biosystems 47:119–128

    Article  CAS  Google Scholar 

  6. Perfettini JL, Castedo M, Nardacci R, Ciccosanti F, Boya P et al (2005) Essential role of p53 phosphorylation by p38 MAPK in apoptosis induction by the HIV-1 envelope. J Exp Med 201:279–289

    Article  CAS  Google Scholar 

  7. Su JS, Woods SM, Ronen SM (2012) Metabolic consequences of treatment with AKT inhibitor perifosine in breast cancer cells. NMR Biomed 25:379–388

    Article  CAS  Google Scholar 

  8. Xue LY, Chiu SM, Oleinick NL (2003) Staurosporine-induced death of MCF-7 human breast cancer cells: a distinction between caspase-3-dependent steps of apoptosis and the critical lethal lesions. Exp Cell Res 283:135–145

    Article  CAS  Google Scholar 

  9. Mitsos A, Melas IN, Siminelakis P, Chairakaki AD, Saez-Rodriguez J et al (2009) Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on Phosphoproteomic data. PLoS Comput Biol 5:e1000591

    Article  Google Scholar 

  10. Saez-Rodriguez J, Alexopoulos LG, Epperlein J, Samaga R, Lauffenburger DA et al (2009) Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Mol Syst Biol 5:331

    Article  Google Scholar 

  11. Mather W, Bennett MR, Hasty J, Tsimring LS (2009) Delay-induced degrade-and-fire oscillations in small genetic circuits. Phys Rev Lett 102:068105

    Article  Google Scholar 

  12. Giacinti L, Giacinti C, Gabellini C, Rizzuto E, Lopez M et al (2012) Scriptaid effects on breast cancer cell lines. J Cell Physiol 227:3426–3433

    Article  CAS  Google Scholar 

  13. Rodriguez-Berriguete G, Fraile B, Paniagua R, Aller P, Royuela M (2012) Expression of NF-kappaB-related proteins and their modulation during TNFalpha-provoked apoptosis in prostate cancer cells. Prostate 72:40–50

    Article  CAS  Google Scholar 

  14. Courtois G, Gilmore TD (2006) Mutations in the NF-kappa B signaling pathway: implications for human disease. Oncogene 25:6831–6843

    Article  CAS  Google Scholar 

  15. Wang GL, Salisbury E, Shi X, Timchenko L, Medrano EE et al (2008) HDAC1 promotes liver proliferation in young mice via interactions with C/EBPbeta. J Biol Chem 283:26179–16187

    Article  CAS  Google Scholar 

  16. Schreiber M, Kolbus A, Piu F, Szabowski A, Mohle-Steinlein U et al (1999) Control of cell cycle progression by c-Jun is p53 dependent. Genes Dev 13:607–619

    Article  CAS  Google Scholar 

  17. Coulonval K, Bockstaele L, Paternot S, Roger PP (2003) Phosphorylations of cyclin-dependent kinase 2 revisited using two-dimensional gel electrophoresis. J Biol Chem 278:52052–52060

    Article  CAS  Google Scholar 

  18. Rosato RR, Almenara JA, Grant S (2003) The histone deacetylase inhibitor MS-275 promotes differentiation or apoptosis in human leukemia cells through a process regulated by generation of reactive oxygen species and induction of p21(CIP1/WAF1). Cancer Res 63:3637–3645

    CAS  PubMed  Google Scholar 

  19. Opiteck GJ, Scheffler JE (2004) Target class strategies in mass spectrometry-based proteomics. Expert Rev Proteomics 1:57–66

    Article  CAS  Google Scholar 

  20. Chiara DG, Marcocci ME, Torcia M, Lucibello M, Rosini P et al (2006) Bcl-2 phosphorylation by p38 MAPK - identification of target sites and biologic consequences. J Biol Chem 281:21353–21361

    Article  Google Scholar 

Download references

Acknowledgment

The work was supported by the grants of NIH U01HL111560-04 (Zhou) and NIH U01CA166886-03 (Zhou).

Author information

Authors and Affiliations

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

    Wei Chen & Xiaobo Zhou

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

    Xiaobo Zhou

Authors
  1. Wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaobo Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaobo Zhou .

Editor information

Editors and Affiliations

  1. Department of Pathology, University of New Mexico, Albuquerque, NM, USA

    Richard S. Larson

  2. Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA

    Tudor I. Oprea

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Chen, W., Zhou, X. (2019). Drug Effect Prediction by Integrating L1000 Genomic and Proteomic Big Data. In: Larson, R., Oprea, T. (eds) Bioinformatics and Drug Discovery. Methods in Molecular Biology, vol 1939. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9089-4_16

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9089-4_16

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9088-7

  • Online ISBN: 978-1-4939-9089-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation