Network Entropy Reveals that Cancer Resistance to MEK Inhibitors Is Driven by the Resilience of Proliferative Signaling

  • Joel Maust
  • Judith Leopold
  • Andrej BugrimEmail author
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 882)


Recently MEK kinase inhibitors emerged as a promising treatment for KRAS-mutant tumors. However, clinical success remains elusive due to drug resistance. To better understand the mechanism of such resistance, we consider drug response as a transition from proliferative to apoptotic state of the molecular network and search for network metrics linked to likelihood of such transition. We focus on the dynamic network entropy – a statistical property related to stability and robustness of network states. We calculate network entropy metrics for approximately 400 cell lines from the Cancer Cell Line Encyclopedia, representing a broad variety of cancers. We investigate correlation between these metrics and cellular response to a MEK-kinase inhibitor drug PD-0325901. We find that network entropy rates of proteins and pathways related to the cell cycle exhibit the most significant differences between groups of sensitive and resistant cell lines. Our results suggest that resistance to MEK kinase inhibition is driven by the overall resilience of the network of proliferative signaling. We confirm this experimentally by observing synergy between MEK and CDK4/6 inhibitors in select cancer cell lines with high network entropy rates of the G2/M transition pathway. Our findings show that network entropy metrics can become a promising predictor of drug sensitivity. They can be used where gene-level markers are not available, provide insights into functional mechanisms of resistance and guide combination therapy selection.


Protein interaction networks Network entropy Drug resistance Network robustness 



This work was supported in part by the NIH Cancer Center Support Grant to the Rogel Cancer Center at the University of Michigan (P30 CA046592-29).


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of Michigan Medical SchoolAnn ArborUSA
  2. 2.Silver Beach Analytics, Inc.St. JosephUSA

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