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Dynamic rearrangement of cell states detected by systematic screening of sequential anticancer treatments

Abstract

Signaling networks are nonlinear and complex, involving a large ensemble of dynamic interaction states that fluctuate in space and time. However, therapeutic strategies, such as combination chemotherapy, rarely consider the timing of drug perturbations. If we are to advance drug discovery for complex diseases, it will be essential to develop methods capable of identifying dynamic cellular responses to clinically relevant perturbations. Here, we present a Bayesian dose-response framework and the screening of an oncological drug matrix, comprising 10,000 drug combinations in melanoma and pancreatic cancer cell lines, from which we predict sequentially effective drug combinations. Approximately 23% of the tested combinations showed high-confidence sequential effects (either synergistic or antagonistic), demonstrating that cellular perturbations of many drug combinations have temporal aspects, which are currently both underutilized and poorly understood.

Type Journal
ISBN 2211-1247 (Electronic)
Authors Koplev, S.; Longden, J.; Ferkinghoff-Borg, J.; Blicher Bjerregard, M.; Cox, T. R.; Erler, J. T.; Pedersen, J. T.; Voellmy, F.; Sommer, M. O. A.; Linding, R.
Responsible Garvan Author Dr Thomas Cox
Publisher Name Cell Reports
Published Date 2017-09-19
Published Volume 20
Published Issue 12
Published Pages 2784-2791
Status Published in-print
DOI 10.1016/j.celrep.2017.08.095
URL link to publisher's version https://www.ncbi.nlm.nih.gov/pubmed/28930675