The overall focus of this project is the development of experimentally validated computational models that predict responses of ductal breast cancers to therapeutic agents that target aspects of aberrant receptor tyrosine kinase (RTK) signaling with emphasis on HER-family RTKs and their downstream targets. Optimal deployment of a broad range of RTK pathway targeted drugs has not yet been achieved clinically. It is already clear in breast cancer that HER-family RTK pathway signaling differs between tumors and as a consequence, response to pathway inhibitors is quite variable and often not durable. To understand this, the diverse resistance and feedback response mechanisms involved will need to be characterized. Improvements of treatment efficacy or durability will almost certainly require use of drug combinations designed to counter resistance mechanisms. Optimal selection of drug combinations is complicated by the fact that resistance may result from multiple, interacting crosstalk and feedback mechanisms that operate over time scales ranging from minutes to days. Understanding how best to counter resistance requires a quantitative understanding of the relative importance of the resistance mechanisms, how they interact in complex feedback loops and the time scales over which they operate. Our experimental and computational work suggests that the signaling dynamics through this pathway varies considerably between breast cancer subtypes. This general observation guides efforts proposed in this renewal application to continue to define subtype specific HER-family signaling pathway connectivity using Bayesian modeling approaches and to use the resulting information to develop subtype specific dynamic models of response to inhibitors that target aspects of HER-family signaling. This work will be accomplished in four projects and a Developmental Program: (1) Signaling network inference for breast cancer subtypes to support development of subtype specific connectivity models. (2) Modeling response to MEK inhibitors in Claudin-Low breast cancers. (3) Modeling response to HER2 targeted therapies in HER2-amplified breast cancers, (4) Modeling response to PISK signaling in luminal subtype breast cancers and (5) Modeling the bioenergetic effects of PISK signaling. The central premise of this ICBP project is that selection of optimal RTK pathway targeted drug combinations will require experimentally validated, cancer subtype and mutation-specific computational models of the diverse feedback, resistance and response mechanisms that will allow drug combinations to be tested in silico so that the most promising can be prioritized for clinical evaluation.

Public Health Relevance

Optimal deployment of a broad range of receptor tyrosine kinase (RTK) pathway targeted drugs, many in clinical trials, has not yet been achieved. The diverse resistance and feedback response mechanisms involved will need to be characterized. This project will provide experimentally validated, cancer subtype and mutation specific computational models allowing RTK targeted drug combinations to be tested in silico so that selection of optimal therapeutic combinations can be prioritized for clinical evaluation in patients.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
7U54CA112970-08
Application #
8234879
Study Section
Special Emphasis Panel (ZCA1-SRLB-C (J1))
Program Officer
Gallahan, Daniel L
Project Start
2004-09-30
Project End
2015-02-28
Budget Start
2012-03-01
Budget End
2013-02-28
Support Year
8
Fiscal Year
2012
Total Cost
$2,370,950
Indirect Cost
$296,228
Name
Oregon Health and Science University
Department
Biomedical Engineering
Type
Schools of Medicine
DUNS #
096997515
City
Portland
State
OR
Country
United States
Zip Code
97239
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
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
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

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