Quantification of ligand and mutation-induced bias in EGFR phosphorylation in direct response to ligand binding

Signaling bias is the ability of a receptor to differentially activate downstream signaling pathways in response to different ligands. Bias investigations have been hindered by inconsistent results in different cellular contexts. Here we introduce a methodology to identify and quantify bias in signal transduction across the plasma membrane without contributions from feedback loops and system bias. We apply the methodology to quantify phosphorylation efficiencies and determine absolute bias coefficients. We show that the signaling of epidermal growth factor receptor (EGFR) to EGF and TGFα is biased towards Y1068 and against Y1173 phosphorylation, but has no bias for epiregulin. We further show that the L834R mutation found in non-small-cell lung cancer induces signaling bias as it switches the preferences to Y1173 phosphorylation. The knowledge gained here challenges the current understanding of EGFR signaling in health and disease and opens avenues for the exploration of biased inhibitors as anti-cancer therapies.


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Each experimental group was comprised by vesicles imaged in the presence of the same ligand concentrations.The order of the imaging of the different groups was varied to exclude any time effects.All imaging was performed after one hour of ligand addition in agreement with the kinetics experiment.The analysis was automated in order to eliminate bias by the researcher.
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We require information from authors about some types of materials, experimental systems and methods used in many studies.Here, indicate whether each material, system or method listed is relevant to your study.If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.Dose response data are from 11570 single vesicles over 25 independent experiments for WT and 8,009 vesicles for L834R EGFR in 23 independent experiments.Transducer function measurements are from 3,085 individual vesicles over 9 independent experiments.Since errors are due to white noise, we collect data until the Gaussian distribution of data for each ligand concentration is sufficiently sampled.Occasional bad pixels in thousands of vesicle images would give unreasonable high signals.Any outliers in the datasets were removed with Matlab`s rmoutliers function in the standard settings.