Methodological challenges in translational drug response modeling in cancer: A systematic analysis with FORESEE

摘要

In the context of personalized medicine, finding genomic patterns in a cancer patient that can predict how a specific drug will affect the patient's survival is of great interest. Translational approaches that directly relate drug response specific processes observed in cell line experiments to their role in cancer patients have the potential to increase the clinical relevance of models. Unfortunately, existing approaches are often irreproducible in other applications. In order to address this irreproducibility aspect, our work comprises a thorough investigation of a diverse set of translational models. In contrast to other approaches that focus on one isolated model characteristic at a time, we examine the overall workflow and the interplay of all model components. Additionally, we validate our models in multiple patient data sets and identify differences between cell line and patient models. While we can establish models of high predictive performance, we also expose the deceptive potential of optimizing methods to a specific use case only by showing that those models do not necessarily depict biological processes. Thus, this study serves as a guide to interpret new approaches in a broader context to avoid the dissemination of noise-driven models that fail to serve in everyday applications.