Abstract B1-36: Population pharmacodynamics: Mechanism-based modeling of receptor tyrosine kinase networks in cancer

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Abstract The purpose of our study was to improve our ability to predict response to therapy by integrating mechanistic kinetic data of protein interactions with patient-specific gene and protein expression data. The genetic heterogeneity of cancer results in patient-to-patient variability that makes it difficult to predict whether the patient will respond to a treatment. Molecular biomarkers such as the expression levels of genes or proteins have proven useful in limited cases, for example ErbB2 and trastuzumab, particularly in the epidermal growth factor (EGF) family, but these kinds of markers are still unable to accurately predict all responsive patients. These biomarkers are typically univariate and linear, whereas multivariate, nonlinear biomarkers are needed to adequately describe the network of molecular interactions targeted by a drug. Here, we present a method for building multivariate biomarkers that incorporate BOTH patient-specific transcriptomic/proteomic data AND detailed mechanistic models of the nonlinear interactions between ligands and receptors. The mechanistic models that we use are computational pharmacodynamic models, with multiple compartments, each with multiple cell types that express the ligands and receptors under investigation. The models incorporate detailed protein-protein interaction networks to simulate the complex dynamics of growth factor families and their receptors. By integrating this molecular detail into whole-body simulations with tumors, we can evaluate many different therapeutic approaches – different drugs, doses, schedules, and routes of administration. Our models make predictions of the dynamics of receptor tyrosine kinase activity and of key blood-borne biomarkers following therapeutic intervention. These predictions can and have been validated against clinical experimental data. We applied the method to the EGFR/ErbB family in breast cancer, using individualized data from The Cancer Genome Atlas (TCGA), and showed that the personalized models were able to capture the observed variability in receptor phosphorylation. Before the addition of drugs, the models behaved in a relatively monotonic fashion, with signaling outputs closely following the expression of the key ligands. However, the response to the addition of drugs was much more complex; the baseline expression of genes/proteins was not as good a predictor of the response. We simulated the addition of three antibody drugs that each target one of EGFR, HER2, and HER3. We applied principal component analysis to characterize the output of our simulations – post-therapy time course response. We derived metrics that accounted for both target-specific and off-target effects. We then used multivariate supervised learning methods to develop predictive biomarkers. We found that biomarkers derived from gene expression data were outperformed by biomarkers derived from simulated baseline tumor behavior (i.e. that combined quantitative mechanistic information with gene expression data). This suggested that linear transformations of transcriptomic and proteomic data may not be adequate for predicting drug response; instead, the nonlinear mechanism-based transformation that is central to the computational model is more predictive. In addition, for each of the antibodies investigated, the incorporation of mechanistic protein interactions resulted in the identification of off-target effects with high inter-individual heterogeneity that have the potential to significantly blunt the response. In conclusion, transforming individualized expression data through a detailed kinetic model of molecular interactions improves predictiveness of treatment response. Citation Format: Robert Joseph Bender, Feilim Mac Gabhann. Population pharmacodynamics: Mechanism-based modeling of receptor tyrosine kinase networks in cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-36.