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Model-based Predictions of Responses RTK Pathway Therapies

Joe W Gray

3 Collaborator(s)

Funding source

National Cancer Institute (NIH)
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.

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