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

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• 作者：Jeffrey R. Whiteaker ; Heidi Zhang ; Lei ; Zhao ; Pei Wang ; Karen S. Kelly-Spratt ; Richard G. Ivey ; Brian D. Piening ; Li-Chia Feng ; Erik Kasarda ; Kay E. Gurley ; Jimmy K. Eng ; Lewis A. Chodosh ; Christopher
• 刊名：Journal of Proteome Research
• 出版年：2007
• 出版时间：October 2007
• 年：2007
• 卷：6
• 期：10
• 页码：3962 - 3975
• 全文大小：431K
• 年卷期：v.6,no.10(October 2007)
• ISSN：1535-3907

文摘

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinicaluse. Biomarker discovery is plagued with difficulties ranging from technological (inability to globallyinterrogate proteomes) to biological (genetic and environmental differences among patients and theirtumors). We urgently need paradigms for biomarker discovery. To minimize biological variation andfacilitate 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-drivenmouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed anovel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MSdatasets 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 theoverproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, andextensively characterized osteopontin as a plasma biomarker capable of early disease detection in themouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics forbiomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates iscapable 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.