Integration of Drug Perturbed Dose Dependent Response Data in Predictive Computational Models of Chemotherapeutic Response Using Multiple‐Kernel‐Learning

Abstract

Recently there has been a great deal of work to understand the dynamics of genome expression under small molecule perturbation. This has been demonstrated by the recent expansion of the Connectivity Map (CMAP) ( www.broadinstitute.org/connectivity‐map‐cmap) with roughly 5000 small molecule signatures. While this has proven to be invaluable resource it is still limited due to many drugs being constrained to a handful of cancer cell genomes, because of this, it is difficult to leverage these datasets to make an accurate and robust predictive model. Outside CMAP there is limited data that systematically characterize small molecule induced genomic changes, however, recently Monks et al. (Cancer Res 2018 DEC 15) has made available data in the NCI60 cell line panel for treated and untreated cell lines at 2, 6, and 24hr and two different dosing concentrations for 15 anti‐cancer agents. In our work we explore how each dataset can be optimally leveraged to build a predictive model of cell IC50 within each of the 15 drugs using a multiple kernel learning approach. With this method we can reach correlations as high as 0.88 (p ~1e−6) between predicted and measured IC50 with an average correlation of 0.57 on an independent test set. Additionally, perturbation with higher drug dose yields better individual models with average correlation around 0.451 compared to non‐treated data of 0.391 suggesting drug induced genomic changes are an important predictor of drug performance. Furthermore, we present a novel method that derives signatures present in perturbed data that can be captured in basal expression data. This approach to selecting features yields model performance that is equal or better than a signature derived basally alone. Our work demonstrates the importance of both basal and dynamic gene expression in modeling and understanding of drug response. We hope this work motivates further experiments to characterize drug induced gene dynamics for both modeling applications and understanding the underlying genomic influences in drug response.Support or Funding InformationShipley Chair in Comparative Oncology