Session: Proteomics: Cancer Biomarkers Code: MP27 Time Slot/Poster Number: 632

A Mass Spectrometry Based Platform for Biomarker Discovery in Archived Prostate Tissues

Armann A. Andaya1;Harryl D. Martinez1; Rohini J. Jasavala1; David K. Han2; Michael E. Wright1
1University of California Davis Genome Center, Davis, CA; 2University of Connecticut Health Center, Farmington, CT

Introduction:
Prostate cancer is the second leading cause of cancer deaths among males in the United States. The identification and validation of novel protein biomarkers will play an important role in treating life-threatening forms of this disease in men. However, obtaining sufficient amounts of diseased specimens to interrogate the robustness of protein biomarkers poses a serious limitation in protein biomarker development in prostate cancer. In this study, we have extracted proteins from formalin-fixed paraffin embedded prostate tissues and identified a complement of proteins through tandem mass spectrometry (MS/MS) in a process called the Direct Tissue Proteomics (DTP) method.
Methods:
We have employed a multi-step approach to identify potential protein biomarkers in formalin-fixed human prostate tissues using the DTP method. First, proteins were extracted from commercially available prostate cancer tissue microarrays. A total of 38 prostate cancer and 8 normal tissue samples were analyzed by tandem MS/MS. RAW MS files were converted to mzXML format and searched using the SEQUEST, and subsequently processed through the transproteomic pipeline (TPP) (PeptideProphet, ProteinProphet). Results were uploaded into the Computational Portal and Analysis System (CPAS) database and subsequently interrogated to look for individual proteins or protein signatures that were unique to cancerous and normal prostate tissue samples.
Preliminary Results:
This proof-of-principle analysis utilizes our workflow to elucidate potential protein biomarkers in prostate cancer, and greater than 2,000 unique proteins were robustly detected (ProteinProphet ≥0.95). We have corroborated previously published data and have identified candidate proteins and/or protein signatures/networks as candidate biomarkers in prostate cancer. Lastly, these studies identified a subset of proteins that were capable of segregating prostate cancers of different Gleason grades.