Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications

Area of Science:

• Biochemistry and Molecular Biology
• Cancer Research
• Bioinformatics

Background:

• Aberrant cellular signaling, particularly protein phosphorylation, drives cancer and other diseases.
• Protein kinases regulate phosphorylation; their dysregulation is a key factor in tumorigenesis.
• Phosphoproteomic data from mass spectrometry can reveal upstream kinase activity.

Purpose of the Study:

• To provide a comprehensive overview of tools for inferring kinase activity from phosphoproteomic data.
• To compare algorithms, statistical frameworks, and usability of existing kinase activity inference tools.
• To highlight recent applications in clinical and hematological samples.

Main Methods:

• Review and inventory of kinase activity inference tools.
• Analysis of underlying algorithms, statistical frameworks, and kinase-substrate databases.
• In-depth comparison of widely-used tools and recent applications.

Main Results:

• Identified and inventoried various kinase activity inference tools (e.g., KSEA, PTM-SEA, INKA).
• Compared tools based on algorithms, statistics, databases, and user-friendliness.
• Demonstrated applications in clinical tissues and hematological samples for both group comparisons and single-sample analysis.

Conclusions:

• Kinase activity inference tools are crucial for understanding disease mechanisms and developing targeted therapies.
• Complementary tools (e.g., PTM-SEA, KSEA, INKA) maximize insights from large datasets.
• INKA shows promise for prioritizing actionable kinases in personalized oncology.