Integrated Pipeline for Mass Spectrometry-Based Discovery and Confirmation of Biomarkers Demonstrated in a Mouse Model of Breast Cancer

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical
use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally
interrogate proteomes) to biological (genetic and environmental differences among patients and their
tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and
facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically,
we performed LC−MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven
mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a
novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC−MS/MS
datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the
overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and
extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the
mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for
biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is
capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore,
the approach can be extended to find biomarkers relevant to human disease.
Keywords: biomarker • breast cancer • mouse • HER2 • mass spectrometry • osteopontin • multiple reaction
monitoring • SISCAPA