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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

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Published on: October 3, 2025

Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on

Alexander Mitsos¹, Ioannis N Melas, Paraskeuas Siminelakis

¹Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

Plos Computational Biology

|December 10, 2009

Summary

This summary is machine-generated.

This study introduces a novel phosphoproteomic method to map cellular signaling networks and reveal drug effects. The approach uncovers unexpected drug impacts beyond primary targets, aiding in understanding drug efficacy mechanisms.

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Published on: October 24, 2025

Area of Science:

Biochemistry
Systems Biology
Pharmacology

Background:

Drug effects depend on drug properties and host cell signaling networks.
Current methods struggle to fully capture complex drug-cell interactions.
Understanding signaling pathways is crucial for pharmaceutical research.

Purpose of the Study:

To develop an unbiased, phosphoproteomic approach for identifying drug effects by monitoring signaling topology alterations.
To create cell-type specific signaling maps using Integer Linear Programming (ILP).
To uncover unanticipated drug effects by analyzing topology changes.

Main Methods:

Utilized phosphoproteomics to monitor drug-induced topology alterations in signaling networks.
Developed an Integer Linear Program (ILP) formulation to build cell-specific pathway maps.
Applied the method to HepG2 cells, evaluating four drugs, including Epidermal Growth Factor Receptor (EGFR) inhibitors.

Main Results:

Successfully mapped signaling topology for HepG2 cells and identified drug effects.
Confirmed known drug targets and uncovered unexpected effects due to drug promiscuity or cell-specific topology.
Discovered Gefitinib inhibits the Interleukin-1alpha (IL1alpha) pathway, an effect missed by binding affinity methods.

Conclusions:

The phosphoproteomic-based method provides an unbiased way to identify drug effects on signaling pathways.
The approach is scalable and applicable to various signaling interventions.
Revealing drug-induced pathway alterations is key to understanding drug efficacy.