Clinical Chemistry · 2007 · 112 citations · 33 references
Life SciencesBiochemistryOncologyMass Spectrometric AnalysisMolecular OncologyProteomicsCancer BiomarkersCancer ProteomicsMass Spectrometry-based ProteomicsPeptide AnalysisPeptide IdentificationProtein IdentificationBiomarker PanelsMass Spectrometry ProteomicsOvarian Cancer BiomarkersOvarian Cancer Early DetectionOvarian Tumor BiomarkersPeptide Marker DiscoveryPeptide Mass SpectrometrySerum Biomarkers
BACKGROUND: Most cases of ovarian cancer are detected at later stages when the 5-year survival is approximately 15%, but 5-year survival approaches 90% when the cancer is detected early (stage I). To use mass spectrometry (MS) of serum proteins for early detection, a seamless workflow is needed that provides an opportunity for rapid profiling along with direct identification of the underpinning ions. METHODS: We used carrier protein-bound affinity enrichment of serum samples directly coupled with MALDI orthagonal TOF MS profiling to rapidly search for potential ion signatures that contained discriminatory power. These ions were subsequently directly subjected to tandem MS for sequence identification. RESULTS: We discovered several biomarker panels that enabled differentiation of stage I ovarian cancer from unaffected (age-matched) patients with no evidence of ovarian cancer, with positive results in >93% of samples from patients with disease-negative results and in 97% of disease-free controls. The carrier protein-based approach identified additional protein fragments, many from low-abundance proteins or proteins not previously seen in serum. CONCLUSIONS: This workflow system using a highly reproducible, high-resolution MALDI-TOF platform enables rapid enrichment and profiling of large numbers of clinical samples for discovery of ion signatures and integration of direct sequencing and identification of the ions without need for additional offline, time-consuming purification strategies.
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ROCR: visualizing classifier performance in R
Tobias Sing, Oliver Sander, Niko Beerenwinkel et al. · Computer applications in the biosciences · 2005 · 3.3K citations · Full text
Use of proteomic patterns in serum to identify ovarian cancer
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Medicine, Gynecology, Oncology +17
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