Endoplasmic reticulum stress, the unfolded protein response, and gene network modeling in antiestrogen resistant breast cancer

Robert Clarke, Rebecca B. Riggins, Louis M. Werner, Caroline Facey, Harini Aiyer, Katherine Cook, F. Edward Hickman, Alan Zwart, Anni Wärri, Leena A. Hilakivi-Clarke, Robert Clarke, Ayesha N. Shajahan, William T. Bauman, Jianhua Xuan, Bai Zhang, Yue Wang, Iman Tavassoly, Anael Verdugo, Chun Chen, John J. TysonRobert Clarke

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

Lack of understanding of endocrine resistance remains one of the major challenges for breast cancer researchers, clinicians, and patients. Current reductionist approaches to understanding the molecular signaling driving resistance have offered mostly incremental progress over the past 10 years. As the field of systems biology has begun to mature, the approaches and network modeling tools being developed and applied therein offer a different way to think about how molecular signaling and the regulation of crucial cellular functions are integrated. To gain novel insights, we first describe some of the key challenges facing network modeling of endocrine resistance, many of which arise from the properties of the data spaces being studied. We then use activation of the unfolded protein response (UPR) following induction of endoplasmic reticulum stress in breast cancer cells by antiestrogens, to illustrate our approaches to computational modeling. Activation of UPR is a key determinant of cell fate decision-making and regulation of autophagy and apoptosis. These initial studies provide insight into a small subnetwork topology obtained using differential dependency network analysis and focused on the UPR gene XBP1. The XBP1 subnetwork topology incorporates BCAR3, BCL2, BIK, NF-kB, and other genes as nodes; the connecting edges represent the dependency structures among these nodes. As data from ongoing cellular and molecular studies become available, we will build detailed mathematical models of this XBP1-UPR network.

Original languageEnglish (US)
Pages (from-to)35-44
Number of pages10
JournalHormone Molecular Biology and Clinical Investigation
Volume5
Issue number1
DOIs
StatePublished - 2011
Externally publishedYes

Keywords

  • antiestrogen
  • apoptosis
  • autophagy
  • breast cancer
  • cell signaling
  • computational modeling
  • endoplasmic reticulum
  • estrogens
  • gene networks
  • mathematical modeling
  • systems biology
  • unfolded protein response

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