Context Specificity in Causal Signaling Networks Revealed by Phosphoprotein Profiling

Steven M. Hill, Nicole K. Nesser, Katie Johnson-Camacho, Mara Jeffress, Aimee Johnson, Chris Boniface, Simon E.F. Spencer, Yiling Lu, Laura M. Heiser, Yancey Lawrence, Nupur T. Pande, James E. Korkola, Joe W. Gray, Gordon B. Mills, Sach Mukherjee, Paul T. Spellman

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Signaling networks downstream of receptor tyrosine kinases are among the most extensively studied biological networks, but new approaches are needed to elucidate causal relationships between network components and understand how such relationships are influenced by biological context and disease. Here, we investigate the context specificity of signaling networks within a causal conceptual framework using reverse-phase protein array time-course assays and network analysis approaches. We focus on a well-defined set of signaling proteins profiled under inhibition with five kinase inhibitors in 32 contexts: four breast cancer cell lines (MCF7, UACC812, BT20, and BT549) under eight stimulus conditions. The data, spanning multiple pathways and comprising ∼70,000 phosphoprotein and ∼260,000 protein measurements, provide a wealth of testable, context-specific hypotheses, several of which we experimentally validate. Furthermore, the data provide a unique resource for computational methods development, permitting empirical assessment of causal network learning in a complex, mammalian setting.

Original languageEnglish (US)
Pages (from-to)73-83.e10
JournalCell Systems
Volume4
Issue number1
DOIs
StatePublished - Jan 25 2017

Keywords

  • breast cancer cell lines
  • casual networks
  • computational systems biology
  • context-specific networks
  • data resource
  • empirical assessment
  • network inference
  • protein signaling networks
  • reverse-phase protein array data

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

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