Integrating meta-analysis of microarray data and targeted proteomics for biomarker identification: application in breast cancer.

The development of signature biomarkers has gained considerable attention in the past decade. Although the most well-known examples of biomarker panels stem from gene expression studies, proteomic panels are becoming more relevant, with the advent of targeted mass spectrometry-based methodologies. At the same time, the development of multigene prognostic classifiers for early stage breast cancer patients has resulted in a wealth of publicly available gene expression data from thousands of breast cancer specimens. In the present study, we integrated transcriptome and proteome-based platforms to identify genes and proteins related to patient survival. Candidate biomarker proteins have been identified in a previously generated breast cancer tissue extract proteome. A mass-spectrometry-based assay was then developed for the simultaneous quantification of these 20 proteins in breast cancer tissue extracts. We quantified the relative expression levels of the 20 potential biomarkers in a cohort of 96 tissue samples from patients with early stage breast cancer. We identified two proteins, KPNA2 and CDK1, which showed potential to discriminate between estrogen receptor positive patients of high and low risk of disease recurrence. The role of these proteins in breast cancer prognosis warrants further investigation.