Perturbation biology links temporal protein changes to drug responses in a melanoma cell line.

James R. Faeder,Elin Nyman,Chris Sander,Anil Korkut,Benjamin Marks,Xiaohong Jing,Nicholas P Gauthier,Ioannis K Zervantonakis,Richard R Stein,Weiqing Wang

doi:10.1371/journal.pcbi.1007909

Abstract

Cancer cells have genetic alterations that often directly affect intracellular protein signaling processes allowing them to bypass control mechanisms for cell death, growth and division. Cancer drugs targeting these alterations often work initially, but resistance is common. Combinations of targeted drugs may overcome or prevent resistance, but their selection requires context-specific knowledge of signaling pathways including complex interactions such as feedback loops and crosstalk. To infer quantitative pathway models, we collected a rich dataset on a melanoma cell line: Following perturbation with 54 drug combinations, we measured 124 (phospho-)protein levels and phenotypic response (cell growth, apoptosis) in a time series from 10 minutes to 67 hours. From these data, we trained time-resolved mathematical models that capture molecular interactions and the coupling of molecular levels to cellular phenotype, which in turn reveal the main direct or indirect molecular responses to each drug. Systematic model simulations identified novel combinations of drugs predicted to reduce the survival of melanoma cells, with partial experimental verification. This particular application of perturbation biology demonstrates the potential impact of combining time-resolved data with modeling for the discovery of new combinations of cancer drugs.

Highlights

Targeted therapies are an important component of precision oncology as these agents—as opposed to standard chemotherapy—aim to counteract specific activating genetic or signaling pathway alterations and often have fewer side effects than conventional cytotoxic chemotherapy
These data consist of multiple drug perturbations, applied as single drugs and drug combinations in a melanoma cell line
The inferred models allow for the prediction of phenotypic responses—cell growth and apoptosis—for unseen perturbations, and can be used to generate large-scale drug discovery hypotheses

Summary

Introduction

Targeted therapies are an important component of precision oncology as these agents—as opposed to standard chemotherapy—aim to counteract specific activating genetic or signaling pathway alterations and often have fewer side effects than conventional cytotoxic chemotherapy. Tumors with the common BRAF V600E/K gain-of-function mutation have been shown to have a remarkable response to drugs that target the mutated protein kinase, such as the RAF inhibitor vemurafenib [2]. Not all patients with a BRAF V600 mutation respond to targeted therapies, and the patients that do respond often develop resistance after only a few months. Mechanisms of acquired resistance include reactivation of the MEK/ ERK pathway with new mutations in BRAF or NRAS and hyper-activation of receptor tyrosine kinases [3]. There is an urgent need for a more comprehensive understanding of resistant tumor cells in order to identify non-trivial therapeutic opportunities beyond targeting single genes

Methods

Results

Conclusion