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

PLoS Computational Biology
Elin NymanChris Sander

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 ...Continue Reading

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Citations

Jun 25, 2021·PLoS Computational Biology·Salvatore AlaimoAlfredo Ferro

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Methods Mentioned

BETA
protein array
transgenic
gene knockouts
chips
protein
chip
Fluorescence

Software Mentioned

Python
ZeptoView
Adam
MATLAB
Incucyte
Zeptosens
TensorFlow

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