The DIONESUS algorithm provides scalable and accurate reconstruction of dynamic phosphoproteomic networks to reveal new drug targets

Mark F. Ciaccio, Vincent C. Chen, Richard B. Jones, Neda Bagheri*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Many drug candidates fail in clinical trials due to an incomplete understanding of how small-molecule perturbations affect cell phenotype. Cellular responses can be non-intuitive due to systems-level properties such as redundant pathways caused by co-activation of multiple receptor tyrosine kinases. We therefore created a scalable algorithm, DIONESUS, based on partial least squares regression with variable selection to reconstruct a cellular signaling network in a human carcinoma cell line driven by EGFR overexpression. We perturbed the cells with 26 diverse growth factors and/or small molecules chosen to activate or inhibit specific subsets of receptor tyrosine kinases. We then quantified the abundance of 60 phosphosites at four time points using a modified microwestern array, a high-confidence assay of protein abundance and modification. DIONESUS, after being validated using three in silico networks, was applied to connect perturbations, phosphorylation, and cell phenotype from the high-confidence, microwestern dataset. We identified enhancement of STAT1 activity as a potential strategy to treat EGFR-hyperactive cancers and PTEN as a target of the antioxidant, N-acetylcysteine. Quantification of the relationship between drug dosage and cell viability in a panel of triple-negative breast cancer cell lines validated proposed therapeutic strategies.

Original languageEnglish (US)
Pages (from-to)776-791
Number of pages16
JournalIntegrative Biology (United Kingdom)
Volume7
Issue number7
DOIs
StatePublished - Jul 1 2015

ASJC Scopus subject areas

  • Biophysics
  • Biochemistry

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