Abstract B2-37: Understanding the variability in drug response in a panel of breast cancer cell lines using computational models

Abstract

Abstract Cancer cell lines differ widely in their sensitivity to anticancer drugs. While many oncogenic drivers and drug resistance mechanisms have been discovered, it is generally unclear how these mechanisms interact in each cell line to make the cell line sensitive or resistant to a particular drug. We set out to explain this variability in drug sensitivity in a panel of 30 breast cancer cell lines. We characterized these cell lines at the DNA, RNA and protein level, and accurately measured the proliferation under treatment with various different kinase inhibitors. We then constructed computational models encompassing several of the important driver pathways and sensitivity mechanisms, and tested how well these models describe the available data. After selecting the well-fitting models, we could use these models to estimate the relative contribution of each of the interacting mechanisms to the proliferation of the cells under drug treatment. For example, the models indicated that FGF2 autocrine signaling contributes to fast proliferation in some cell lines; that SGK1 expression provides a bypass for Akt signaling in some cell lines; or that the expression level of 4E-BP1 is a key determinant of mTOR-inhibitor sensitivity on top of other genetic alterations in the PI3K-pathway. Additionally, we could use these models to thoroughly analyze which parts of the data cannot be explained. This greatly narrows down which follow-up studies are necessary to advance our understanding of drug response. These results show that knowledge-based computational models can be used to systematically study drug response in cell line panels. Such systematic, quantitative understanding of drug response will be useful in working towards precision medicine for individual cancer patients. Citation Format: Bram Thijssen, Katarzyna Jastrzebski, Roderick L. Beijersbergen, Lodewyk FA Wessels. Understanding the variability in drug response in a panel of breast cancer cell lines using computational models. [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 B2-37.