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- Bemis, Kylie A., et al.
(author)
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Statistical detection of differentially abundant ions in mass spectrometry-based imaging experiments with complex designs
- 2019
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In: International Journal of Mass Spectrometry. - : ELSEVIER SCIENCE BV. - 1387-3806 .- 1873-2798. ; 437, s. 49-57
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Journal article (peer-reviewed)abstract
- Mass Spectrometry Imaging (MSI) characterizes changes in chemical composition between regions of biological samples such as tissues. One goal of statistical analysis of MSI experiments is class comparison, i.e. determining analytes that change in abundance between conditions more systematically than as expected by random variation. To reach accurate and reproducible conclusions, statistical analysis must appropriately reflect the initial research question, the design of the MSI experiment, and all the associated sources of variation. This manuscript highlights the importance of following these general statistical principles. Using the example of two case studies with complex experimental designs, and with different strategies of data acquisition, we demonstrate the extent to which choices made at key points of this workflow impact the results, and provide suggestions for appropriate design and analysis of MSI experiments that aim at detecting differentially abundant analytes. (C) 2018 Elsevier B.V. All rights reserved.
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| 2. |
- Huttenhain, Ruth, et al.
(author)
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A targeted mass spectrometry strategy for developing proteomic biomarkers : a case study of epithelial ovarian cancer
- 2019
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In: Molecular and Cellular Proteomics. - 1535-9484. ; 18:9, s. 1836-1850
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Journal article (peer-reviewed)abstract
- 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.
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