Endoplasmic reticulum stress, the unfolded protein response, and gene network modeling in antiestrogen resistant breast cancer.
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 critical 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, NFκB, and other genes as nodes; the connecting edges represent the dependency structures amongst these nodes. As data from ongoing cellular and molecular studies become available, we will build detailed mathematical models of this XBP1-UPR network.
Department of Oncology, Georgetown University School of Medicine, Washington, DC 20057, U.S.A. ; Lombardi Comprehensive Cancer Center, Georgetown University School of Medicine, Washington, DC 20057, U.S.A.
Robert
R
Clarke
Ayesha N
AN
Shajahan
Yue
Y
Wang
John J
JJ
Tyson
Rebecca B
RB
Riggins
Louis M
LM
Weiner
William T
WT
Bauman
Jianhua
J
Xuan
Bai
B
Zhang
Caroline
C
Facey
Harini
H
Aiyer
Katherine
K
Cook
F Edward
FE
Hickman
Iman
I
Tavassoly
Anael
A
Verdugo
Chun
C
Chen
Alan
A
Zwart
Anni
A
Wärri
Leena A
LA
Hilakivi-Clarke
Antiestrogen; apoptosis; autophagy; breast cancer; cell signaling; computational modeling; endoplasmic reticulum; estrogens; gene networks; mathematical modeling; systems biology; unfolded protein response