INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases

Area of Science:

• Oncology
• Biochemistry
• Bioinformatics

Background:

• Identifying specific hyperactive kinases is essential for targeted cancer therapies.
• Global phosphoproteomics can reveal kinase activity but linking it to individual kinases remains challenging.
• Current methods often focus on kinase or substrate phosphorylation, limiting comprehensive analysis.

Purpose of the Study:

• To develop and validate a novel computational approach for inferring kinase activity in individual cancer samples.
• To integrate kinase-centric and substrate-centric phosphoproteomic data for robust kinase activity profiling.
• To demonstrate the utility of the Integrative Inferred Kinase Activity (INKA) analysis in various cancer models.

Main Methods:

• Developed Integrative Inferred Kinase Activity (INKA) analysis, combining label-free kinase-centric and substrate-centric phosphoproteomic data.
• Applied INKA to analyze cancer cell lines, patient biopsies, and patient-derived xenografts.
• Compared INKA performance against its individual components and a substrate-based tool (KARP).

Main Results:

• INKA successfully ranked kinase activity and visualized kinase-substrate networks within single samples.
• The method demonstrated superior performance compared to existing approaches.
• INKA analysis identified high-ranking kinases with significant therapeutic potential across diverse cancer models.

Conclusions:

• INKA provides a powerful, integrated approach for identifying hyperactive kinases in individual tumors.
• The findings support the clinical utility of INKA for guiding personalized cancer treatment strategies.
• Further exploration of INKA's functional and clinical value is warranted for advancing precision oncology.