Optimization of enrichment for MALDI TOF/TOF identification of cancer biomarkers

Georgetown University, Washington DC

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Abstract

[unreadable] DESCRIPTION (provided by applicant): Early detection of cancer and precancerous conditions improves patient survival. This study optimizes methods for discovery of a diagnostic set of peptides and proteins (P/P) in serum of patients with hepatocellular carcinoma (HCC). To identify biomarkers of HCC, we developed enrichment of low molecular weight (LMW) fraction of serum for matrix assisted laser desorption ionization-time of flight (MALDI- TOF/TOF) mass spectrometry (MS). This method allows high-throughput population screening as well as identification of the peptides of interest by TOF/TOF sequencing. We tested the biomarker discovery method on a pilot set of HCC cases and matched controls, which identified a set of P/P predictive of HCC with greater than 90% prediction accuracy. In this study, we propose to adjust the method to complementary enrichment using WCX and Cu-IMAC magnetic beads, expand the study to include a comparison with premalignant liver disease (150 cases, 150 matched controls, 50 cirrhosis), and to begin identification of the biomarker-candidates by sequencing. The samples come from our ongoing case-control study of HCC in Egypt, a country with an epidemic of hepatitis C infection and HCC. Our goal is to identify biomarkers that would improved early detection of HCC and track the natural progression of chronic viral infection to cancer. The identity of the biomarkers will be verified by MALDI-TOF/TOF sequencing and complementary liquid chromatography/mass spectrometry methods. Defining clinically applicable biomarkers of early-stage cancer has potentially far-reaching consequences for disease management and patient health. The biomarkers could be used to screen high risk populations for early signs of disease; to design and test improved chemoprevention strategies; and to follow disease progression after treatment. Identification of the P/P is expected to generate new hypotheses for targeted disease management. [unreadable] [unreadable] [unreadable]