Evaluation of a Proteomics-Guided Protein Signature for Breast Cancer Detection in Breast Tissue

• Published in: Journal of proteome research Vol. 23; no. 11; pp. 4907 - 4923
• Main Authors: Moreno-Ulloa, Aldo, Zárate-Córdova, Vareska L., Ramírez-Sánchez, Israel, Cruz-López, Juan Carlos, Perez-Ortiz, Andric, Villarreal-Garza, Cynthia, Díaz-Chávez, José, Estrada-Mena, Benito, Antonio-Aguirre, Bani, López-Almanza, Perla Ximena, Lira-Romero, Esmeralda, Estrada-Mena, Fco. Javier
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 01-11-2024
• Subjects: mass spectrometry; Breast cancer; biomarkers; quantitative proteomics
• ISSN: 1535-3893; 1535-3907; 1535-3907
• DOI: 10.1021/acs.jproteome.4c00295

The distinction between noncancerous and cancerous breast tissues is challenging in clinical settings, and discovering new proteomics-based biomarkers remains underexplored. Through a pilot proteomic study (discovery cohort), we aimed to identify a protein signature indicative of breast cancer for subsequent validation using six published proteomics/transcriptomics data sets (validation cohorts). Sequential window acquisition of all theoretical (SWATH)-based mass spectrometry revealed 370 differentially abundant proteins between noncancerous tissue and breast cancer. Protein–protein interaction-based networks and enrichment analyses revealed dysregulation in pathways associated with extracellular matrix organization, platelet degranulation, the innate immune system, and RNA metabolism in breast cancer. Through multivariate unsupervised analysis, we identified a four-protein signature (OGN, LUM, DCN, and COL14A1) capable of distinguishing breast cancer. This dysregulation pattern was consistently verified across diverse proteomics and transcriptomics data sets. Dysregulation magnitude was notably higher in poor-prognosis breast cancer subtypes like Basal-Like and HER2 compared to Luminal A. Diagnostic evaluation (receiver operating characteristic (ROC) curves) of the signature in distinguishing breast cancer from noncancerous tissue revealed area under the curve (AUC) ranging from 0.87 to 0.9 with predictive accuracy of 80% to 82%. Upon stratifying, to solely include the Basal-Like/Triple-Negative subtype, the ROC AUC increased to 0.922–0.959 with predictive accuracy of 84.2%–89%. These findings suggest a potential role for the identified signature in distinguishing cancerous from noncancerous breast tissue, offering insights into enhancing diagnostic accuracy.