A Targeted Mass Spectrometry Strategy for Developing Proteomic Biomarkers: A Case Study of Epithelial Ovarian Cancer
| Autor: | Håkan Olsson, Timothy Clough, Ruth Hüttenhain, Meena Choi, Olga Vitek, Ruedi Aebersold, Daniela M. Dinulescu, Viola Heinzelmann-Schwarz, Peter J. Wild, Laura Martin de la Fuente, Ching-Yun Veavi Chang, Kathrin Oehl, Susanne Malander, Emma Niméus, Silvia Surinova, Anne-Kathrin Zimmermann |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Proteomics
Mice Transgenic Neural Cell Adhesion Molecule L1 Disease Computational biology Carcinoma Ovarian Epithelial Biochemistry Sensitivity and Specificity Mass Spectrometry Analytical Chemistry Cohort Studies Thrombospondin 1 03 medical and health sciences Antigens Neoplasm medicine Biomarkers Tumor Animals Humans Epithelial ovarian cancer Molecular Biology 030304 developmental biology Ovarian Neoplasms 0303 health sciences Desmoglein 2 business.industry Immunoglobulin mu-Chains Research 030302 biochemistry & molecular biology Selected reaction monitoring Cancer Membrane Proteins Blood Proteins medicine.disease Targeted mass spectrometry Genetically Engineered Mouse CA-125 Antigen Case-Control Studies Biomarker (medicine) Female Ovarian cancer business Heavy Chain Disease |
| Zdroj: | Mol Cell Proteomics |
| Popis: | Protein biomarkers for epithelial ovarian cancer are critical for the early detection of the cancer to improve patient prognosis and for the clinical management of the disease to monitor treatment response and to detect recurrences. Unfortunately, the discovery of protein biomarkers is hampered by the limited availability of reliable and sensitive assays needed for the reproducible quantification of proteins in complex biological matrices such as blood plasma. In recent years, targeted mass spectrometry, exemplified by selected reaction monitoring (SRM) has emerged as a method, capable of overcoming this limitation. Here, we present a comprehensive SRM-based strategy for developing plasma-based protein biomarkers for epithelial ovarian cancer and illustrate how the SRM platform, when combined with rigorous experimental design and statistical analysis, can result in detection of predictive analytes. Our biomarker development strategy first involved a discovery-driven proteomic effort to derive potential N-glycoprotein biomarker candidates for plasma-based detection of human ovarian cancer from a genetically engineered mouse model of endometrioid ovarian cancer, which accurately recapitulates the human disease. Next, 65 candidate markers selected from proteins of different abundance in the discovery dataset were reproducibly quantified with SRM assays across a large cohort of over 200 plasma samples from ovarian cancer patients and healthy controls. Finally, these measurements were used to derive a 5-protein signature for distinguishing individuals with epithelial ovarian cancer from healthy controls. The sensitivity of the candidate biomarker signature in combination with CA125 ELISA-based measurements currently used in clinic, exceeded that of CA125 ELISA-based measurements alone. The SRM-based strategy in this study is broadly applicable. It can be used in any study that requires accurate and reproducible quantification of selected proteins in a high-throughput and multiplexed fashion. |
| Databáze: | OpenAIRE |
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