Abstract 3765: Population pharmacodynamics: Mechanism-based modeling of receptor tyrosine kinase networks in cancer
Authors: Bender R. Joseph , Mac Gabhann Feilim 
Published: August 2015

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, but 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.

We have developed 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 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.

Note: This abstract was not presented at the meeting.

Citation Format: R. Joseph Bender, Feilim Mac Gabhann. Population pharmacodynamics: Mechanism-based modeling of receptor tyrosine kinase networks in cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3765. doi:10.1158/1538-7445.AM2015-3765