Discovering Breast Cancer Biomarkers in Saliva: Quantifying Low Abundance Proteins from Dynamic Range Compressed Samples

Presented by: Sue Van Riper

Breast cancer ranks second as a cause of cancer death in women.   Successfully managing and curing breast cancer requires early detection and diagnosis through palpitation, mammography and biomarkers. An ideal vessel for protein biomarkers is saliva; it is easily collected, non-invasive, abundant, and particularly important to patient groups in which it can be difficult to conduct other diagnostic tests.  Unfortunately, saliva’s wide dynamic range in protein abundance impedes shotgun proteomics’ ability to detect and quantify low-abundance-but-potentially-indicative proteins. We recently showed that hexapeptide bead dynamic range compression (DRC) integrated into our proteomics workflow increases sensitivity and allows detection of low abundance proteins in saliva. Building on this work, we next determined whether we could quantify abundance levels of proteins in DRC saliva in the context of breast cancer biomarker discovery. Pooled DRC saliva samples from healthy and metastatic breast cancer patients were trypsin digested, labeled with stable isotope tags (newly introduced mTRAQ heavy and light labels), orthogonally fractionated in three steps, and analyzed on an LTQ-Orbitrap mass spectrometer. As with many new technologies, introduction of new biochemical methods (mTRAQ) into our workflow necessitated corresponding changes in our computational proteomics software. We tackled this problem in true interdisciplinary fashion, accommodating mTRAQ by integrating relational database approaches, quantification software (ASAPRatio), and validation software (Mayu) into a new quantitative shotgun proteomics workflow. Using this novel workflow, our preliminary results show successful identification and quantification of mTRAQ labeled proteins in DRC saliva and reveal notable (> 2x) abundance changes in several proteins. Of these proteins, some are associated with existing serum/breast tissue oncogenes and biomarkers, and others are novel in that they are not known as diagnostic for breast cancer. While our work to date has been on late stage metastatic breast cancer saliva, these preliminary results suggest that it is possible to discover early stage salivary biomarkers and justify continued studies seeking to identify salivary biomarkers for early detection of breast cancer. 

Bio:  A native Minnesotan, Susan Van Riper is a 3^rd year BICB PhD graduate student with a research focus in computational proteomics. After receiving her B.S. in Computer Science from Winona State University, she held several technical and management positions during a twenty year career in industry. She returned to academia, receiving a M.S. in Software Engineering from the University of Minnesota in 2007. During her matriculation, she became interested in medical diagnostics and subsequently became a full-time PhD student applying computational and database algorithms to biological problems. Working with Professors John Carlis and Tim Griffin, her current research focuses on novel relational algebra extensions for de novo identification and quantification algorithms in ultra high resolution mass spectrometry based proteomics in the context of discovering novel diagnostic biomarkers in saliva.