Small molecule inhibitors targeting the RAF/MEK/ERK pathway have become potent tools in precision medicine, but their clinical efficacy is highly variable across the diversity of RAS- and BRAF-mutated cancers. Even in susceptible cancers, these inhibitors rarely give durable responses. Studying the causes of resistance, which include ?paradoxical? ERK pathway activation by RAF inhibitors, has revealed complex molecular adaptations in the complicated networks comprised of RAF and ERK pathway kinases. These complexities limit our ability to understand and predict effectiveness of targeted therapies, especially in combination ? despite decades of intense study, including mathematical modeling. Accurate predictions require understanding not only of the molecular complexities of protein kinase regulation and the intricate systems-level behavior of the networks that kinase constitute, but also of how these two levels of control are coupled. The challenge of accurately predicting effectiveness of targeted therapies and their combinations therefore demands an amalgamation of molecular and systems biology approaches. The systems biology project proposed here aims to identify optimal combinations of kinase inhibitors through mechanistic models that integrate understanding of both: 1) Conformation selectivity of kinase inhibitors ? affecting structural, thermodynamic and kinetic properties of the targeted kinase(s); and 2) Systems-level network properties, including feedback loops, mutations and kinase/scaffold abundances, which can modify feedback loops and allow normally inconsequential kinase isoforms to compensate for isoform-specific kinase inhibition. Combining these features necessitates novel approaches to modeling cell signaling that directly link molecular/structural and network facets to predict which inhibitors and their combinations can efficiently suppress oncogenic signaling while disabling or delaying signal recovery, growth, and drug resistance. We propose to develop such next-generation multiscale models of oncogenic ERK signaling and drug responses, and to establish a new conceptual foundation for discovering effective drug combinations by integrating structural, thermodynamic and kinetic information ? and combining short time-scale molecular dynamics (MD) with long time-scale modeling of systems-level dynamics. We will test our model predictions rigorously by integrating and iterating modeling and experimental studies. Experimental studies will begin in paired isogenic cancer cell lines with defined mutational differences. Once model predictions are suitably robust, we will progress to panels of cancer cell lines, then to cell line-derived xenografts in vivo, and then to patient-derived xenografts and genetically engineered mouse models (GEMMs) of melanoma ? as a presage to clinically integrated predictions. We will determine if the strategy of hitting a kinase by two (or more) inhibitors with distinct conformation selectivity ? as appears promising in our preliminary data ? is generally applicable, can be combined with inhibition of different targets within a pathway, and can be understood at a detailed mechanistic level using our multiscale models.

Public Health Relevance

Using next-generation multi-scale modeling approaches that integrate structural and systems considerations, we propose new frameworks for identifying optimal combinations of clinically available RAF, MEK and ERK kinase inhibitors in melanoma and colorectal cancer. We combine modeling and experimental approaches (in vitro and in vivo) to define rules for predicting how to apply these drugs in specific cancers for more durable inhibition of oncogenic ERK signaling in RAF- and/or RAS-driven cancers ? thereby limiting or delaying treatment resistance.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Forry, Suzanne L
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Yale University
Schools of Medicine
New Haven
United States
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