Will determine the regulatory connectivity(s) in the PISK signaling network in a panel of luminal breast tumor cell lines using an unbiased systems approach and develop and test a robust mathematical model of the signaling to predict combinations for therapy. This project is motivated by the observation that the PISK network is selectively activated by mutation in luminal and HER2 positive tumors and represents the most frequent activating event in luminal breast cancers. PISK signaling regulates breast cancer proliferation, survival, cytokine production, protein translation, cell growth, bioenergetics and metastasis. Aberrations that activate PISK signaling can occur either extrinsically at the receptor level (HER2) or intrinsically within the network (RAS, PIKSCA, PIK3CR1, PDK1, AKT or PTEN). Furthermore, as demonstrated in this CCSB, the activation state ofthe PISK network determines outcomes to drugs targeting the HER2 network. Despite the evidence defining the PISK network as a high quality target, regulatory feedback loops as well as cross talk with other networks (MAPK) make understanding and targeting the PISK network a challenging task. One of the key goals will be develop robust predictive models able to determine rational combinatorial approaches to target aberrations in the PISK network and at the same time prevent deleterious effects of feedback loops and cross talk. Experimentally, this will be accomplished by determining PISK signaling network connectivity in 4 breast cell lines by targeted knockdown of all kinases followed by phospho-proteomic analysis. This connectivity network will be combined with the dynamic data from Project 1 to develop an ODE model that describes the AKT centric network response. The model can them be used to analyze patient data and determine combinations of drugs based on the specific signaling signature in the tumor tissue. We hypothesize that nested feedback loops in the PISK network and cross talk confer complex regulatory mechanisms that will need to be targeted. As a corollary, predictive computational models will have the power to identify rational drug combinations to effectively inhibit PISK network signaling and thus cell proliferation survival and metastasis. Models developed in this project will be informed by network development efforts in Project 1 and will contribute to development of models of response to MEK and HER2/3 inhibitors in Projects 2 and 3, respectively.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Specialized Center--Cooperative Agreements (U54)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Oregon Health and Science University
United States
Zip Code
Risom, Tyler; Langer, Ellen M; Chapman, Margaret P et al. (2018) Differentiation-state plasticity is a targetable resistance mechanism in basal-like breast cancer. Nat Commun 9:3815
Gast, Charles E; Silk, Alain D; Zarour, Luai et al. (2018) Cell fusion potentiates tumor heterogeneity and reveals circulating hybrid cells that correlate with stage and survival. Sci Adv 4:eaat7828
Riesco, Adrián; Santos-Buitrago, Beatriz; De Las Rivas, Javier et al. (2017) Epidermal Growth Factor Signaling towards Proliferation: Modeling and Logic Inference Using Forward and Backward Search. Biomed Res Int 2017:1809513
Hassan, Saima; Esch, Amanda; Liby, Tiera et al. (2017) Pathway-Enriched Gene Signature Associated with 53BP1 Response to PARP Inhibition in Triple-Negative Breast Cancer. Mol Cancer Ther 16:2892-2901
Sears, Rosalie; Gray, Joe W (2017) Epigenomic Inactivation of RasGAPs Activates RAS Signaling in a Subset of Luminal B Breast Cancers. Cancer Discov 7:131-133
Gendelman, Rina; Xing, Heming; Mirzoeva, Olga K et al. (2017) Bayesian Network Inference Modeling Identifies TRIB1 as a Novel Regulator of Cell-Cycle Progression and Survival in Cancer Cells. Cancer Res 77:1575-1585
Hafner, Marc; Heiser, Laura M; Williams, Elizabeth H et al. (2017) Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics. Sci Data 4:170166
Xu, Xiaowei; De Angelis, Carmine; Burke, Kathleen A et al. (2017) HER2 Reactivation through Acquisition of the HER2 L755S Mutation as a Mechanism of Acquired Resistance to HER2-targeted Therapy in HER2+ Breast Cancer. Clin Cancer Res 23:5123-5134
Hill, Steven M; Nesser, Nicole K; Johnson-Camacho, Katie et al. (2017) Context Specificity in Causal Signaling Networks Revealed by Phosphoprotein Profiling. Cell Syst 4:73-83.e10
Seviour, E G; Sehgal, V; Mishra, D et al. (2017) Targeting KRas-dependent tumour growth, circulating tumour cells and metastasis in vivo by clinically significant miR-193a-3p. Oncogene 36:1339-1350

Showing the most recent 10 out of 193 publications