Breast cancer quantitative proteome and proteogenomic landscape

Area of Science:

• Oncology
• Proteomics
• Genomics

Background:

• Molecular characterization has transformed breast cancer (BC) research and treatment.
• Current BC classification may be incomplete, particularly for aggressive subtypes.

Purpose of the Study:

• To recapitulate BC subtypes using an unbiased proteome-wide analysis.
• To identify novel protein-based biomarkers for improved BC classification and prognostication.
• To explore potential new immunotherapeutic targets from protein products in non-coding genomic regions.

Main Methods:

• Unbiased analysis of breast tumor proteomes, quantifying 9995 proteins.
• Proteome-based network analysis to identify functional protein modules.
• Correlation analysis of mRNA and protein levels, and assessment of gene copy number alterations at the protein level.

Main Results:

• BC subtypes were recapitulated based on proteomic data.
• Poor-prognosis basal-like and luminal B tumors were further subdivided by immune infiltration.
• Co-expression of EGFR and MET identified specific ductal carcinoma in situ regions.
• Protein quantification demonstrated higher value than mRNA for prognostication and classification.
• Identified protein products mapping to non-coding genomic regions.

Conclusions:

• Proteomic analysis provides a more refined classification of breast cancer subtypes.
• Immune infiltration plays a significant role in aggressive BC subtypes.
• Proteogenomics offers a powerful approach for BC prognostication and identifying novel therapeutic targets, including those from non-coding regions.