# Benchmarking EGF signaling pathway inference using phosphoproteomics and kinase-substrate interactions

## TL;DR

This paper benchmarks how well large-scale data can reconstruct classic signaling pathways, focusing on EGF using phosphoproteomics and kinase-substrate interactions.

## Contribution

The study provides the most comprehensive characterization of the EGF response using phosphoproteomics and compares inferred pathways to ground truth sets.

## Key findings

- Literature-curated networks recover the most ground-truth interactions.
- Up to 90% of interactions are missing from current ground truth sets.
- Network propagation methods offer modest improvements in pathway recovery.

## Abstract

Signaling pathways are useful models for interpreting molecular data, but their coverage has long been constrained by classic biochemistry methods. The growing corpus of kinase-substrate interactions, coupled to phosphoproteomics improvements, pave the way to revisit classic signaling pathways. In this study, we explore context-specific signaling pathway inference from phosphoproteomics and kinase-substrate networks. Focusing on epidermal growth factor (EGF), we conduct a meta-analysis and generate three datasets representing the most comprehensive characterization of the EGF response to date. We infer kinase-kinase pathways and compare them to different ground truth sets. Literature-curated networks consistently yield the highest recovery of ground-truth interactions, with modest gains from network propagation methods. Up to 90% of interactions are absent from current ground truth sets, indicating many unexplored interactions supported by data and knowledge. Our results demonstrate the limitations of traditional views on signaling pathways and point to opportunities for generating better mechanistic hypotheses.

To what extent can large-scale approaches accurately reconstruct classic signaling pathways? Here, authors revisit the EGF pathway using phosphoproteomics and kinase-substrate interactions