Molecular Biomarkers & Diagnosis

Peptide Phage Display for Discovery of Novel Biomarkers for Imaging and Therapy of Cell Subpopulations in Ovarian Cancer

Abstract

Mette Soendergaard, Jessica R Newton-Northup, Mark O Palmier and Susan L Deutscher

Ovarian cancer is a very aggressive disease that is mostly asymptomatic at early onset. Approximately 85% of patients are diagnosed at late-stage disease, which greatly compromises full recovery. Standard detection methods include measurement of the ovarian cancer biomarker CA-125. However, CA-125 is associated with false positive diagnosis and is largely limited to late-stage disease. As a result, there is a great need to discover new biomarkers and develop novel detection and imaging methods for ovarian cancer. Patients with ovarian cancer often respond to initial chemotherapy but most will succumb to recurrent disease. Such poor prognosis is associated with a drug resistant subpopulation of cancer cells with stem-like properties known as cancer stem cells (CSC). Traditional chemotherapy fails to target CSC, and it is widely accepted that this process leads to the recurrence of more aggressive tumors. Therefore, it is essential to discover new ovarian CSC biomarkers and develop therapies that specifically target this subpopulation. Bacteriophage (phage) display technology allows identification of high affinity peptides by screening of peptide libraries against cellular targets. The large amount of unique peptides in a library facilitates high throughput selections both in vivo and in vitro. Here we discuss how phage display can be utilized to discover novel peptides with high binding affinity for normal ovarian cancer cells and ovarian CSC. Such peptides may be radiolabeled and employed in SPECT and PET imaging as well as in therapeutic settings. Further, both phage and phage display derived peptides can be employed in identification of targeted antigens and novel ovarian cancer biomarkers using mass spectrometry analysis. Such biomarkers may be utilized in diagnosis and in identification and selection of ovarian cancer subpopulations.