Surface-enhanced laser desorption/ionization time-of-flight proteomic profiling of breast carcinomas identifies clinicopathologically relevant groups of patients similar to previously defined clusters from cDNA expression

Introduction Microarray-based gene expression profiling represents a major breakthrough for understanding the molecular complexity of breast cancer. cDNA expression profiles cannot detect changes in activities that arise from post-translational modifications, however, and therefore do not provide a complete picture of all biologically important changes that occur in tumors. Additional opportunities to identify and/or validate molecular signatures of breast carcinomas are provided by proteomic approaches. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) offers high-throughput protein profiling, leading to extraction of protein array data, calling for effective and appropriate use of bioinformatics and statistical tools. Methods Whole tissue lysates of 105 breast carcinomas were analyzed on IMAC 30 ProteinChip Arrays (Bio-Rad, Hercules, CA, USA) using the ProteinChip Reader Model PBS IIc (BioRad) and Ciphergen ProteinChip software (Bio-Rad, Hercules, CA, USA). Cluster analysis of protein spectra was performed to identify protein patterns potentially related to established clinicopathological variables and/or tumor markers. Results Unsupervised hierarchical clustering of 130 peaks detected in spectra from breast cancer tissue lysates provided six clusters of peaks and five groups of patients differing significantly in tumor type, nuclear grade, presence of hormonal receptors, mucin 1 and cytokeratin 5/6 or cytokeratin 14. These tumor groups resembled closely luminal types A and B, basal and HER2-like carcinomas. Conclusion Our results show similar clustering of tumors to those provided by cDNA expression profiles of breast carcinomas. This fact testifies the validity of the SELDI-TOF MS proteomic approach in such a type of study. As SELDI-TOF MS provides different information from cDNA expression profiles, the results suggest the technique's potential to supplement and expand our knowledge of breast cancer, to identify novel biomarkers and to produce clinically useful classifications of breast carcinomas.