Perturbation biology links temporal protein changes to drug responses in a melanoma cell line [article]

Elin Nyman, Richard R Stein, Xiaohong Jing, Weiqing Wang, Benjamin Marks, Ioannis K Zervantonakis, Anil Korkut, Nicholas P Gauthier, Chris Sander
2019 bioRxiv   pre-print
Data-driven mathematical modeling of biological systems has enormous potential to understand and predict the interplay between molecular and phenotypic response to perturbation, and provides a rational approach to the nomination of therapies in complex diseases such as cancer. Melanoma is a particularly debilitating disease for which most therapies eventually fail as resistance to chemotherapy and targeted drugs develop. We have previously applied an iterative experimental-computational
more » ... approach, termed perturbation biology, to predict and test effective drug combinations in melanoma cell lines. In this work, we extend our analysis framework to derive models of temporally-acquired perturbation data that do not require prior knowledge and explicit specification of the targets of individual drugs. Specifically, we characterize the response of the melanoma cell line A2058 to 54 cancer drug combinations at 8 logarithmically spaced time points from 10~minutes to 67~hours. At each time point, 124 antibodies of proteins and phospho-proteins with broad coverage of cancer-related pathways and two phenotypes (cell number and apoptosis) were measured. These data are used to infer interactions in ordinary differential equation-based models that capture temporal aspects of the drug perturbation data. This network representation of drug–protein, protein–protein, and protein–phenotype interactions can be used to identify new logical (not necessarily direct biochemical) interactions. The agreement between the predicted phenotypic response and corresponding data for unseen drug perturbations has a Pearson's correlation coefficient of 0.79. We further use model predictions to nominate effective combination therapies and perform experimental validation of the highest ranked combinations. This new data-driven modeling framework is a step forward in perturbation biology as it incorporates the temporal aspect of data. This work therefore opens the door to a new understanding of dynamic drug responses at a molecular level.
doi:10.1101/568758 fatcat:p3bm6hk4gravrj252djpnumu3a