Pan-cancer proteogenomic investigations identify post-transcriptional kinase targets

Area of Science:

• Oncology
• Proteomics
• Bioinformatics

Background:

• Genomic alterations guide targeted cancer therapies, but proteomic analysis is crucial for validation and discovering new treatment avenues.
• Current methods for identifying therapeutic targets may not capture all protein-level aberrations relevant to cancer.

Purpose of the Study:

• To develop and validate a novel algorithm, OPPTI (Over-expressed Protein Proteomics Target Identifier), for discovering overexpressed kinase proteins in cancer using mass spectrometry data.
• To identify novel protein targets for precision oncology by analyzing proteomic data across 10 cancer types.

Main Methods:

• Developed OPPTI algorithm to analyze global mass spectrometry proteomics data from 1,071 cancer cases.
• Leveraged co-expressed markers to identify kinase overexpression in tumor subsets.
• Validated findings using CRISPR screen data and immunochemistry.
• Correlated protein overexpression with genomic alterations (DNA/RNA) and pathway phosphorylation levels.

Main Results:

• OPPTI identified overexpressed ERBB2 and EGFR proteins, correlating with genomic amplifications.
• CDK4/6, PDK1, and MET protein overexpression were frequently observed without corresponding DNA/RNA alterations.
• Confirmed expression-driven dependencies for druggable and novel kinase targets.
• Demonstrated association between identified kinases and upregulated signaling pathway phosphorylation.

Conclusions:

• Protein-level aberrations, not always detectable by genomics, represent significant cancer vulnerabilities.
• OPPTI is a powerful tool for discovering novel protein targets for precision oncology.
• The findings support targeting protein expression-driven dependencies for improved cancer treatment strategies.