Peptides generated ex vivo from serum proteins by tumor-specific exopeptidases are not useful biomarkers in ovarian cancer
| Autor: | Brian Burford, Ali Tiss, Aleksandra Gentry-Maharaj, Ian Jacobs, Celia Smith, Zhiyuan Luo, Alexander Gammerman, Usha Menon, Dmitry Devetyarov, Rainer Cramer, Ilia Nouretdinov, Stephane Camuzeaux, John F. Timms, Jeremy Ford |
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| Rok vydání: | 2010 |
| Předmět: |
Pathology
medicine.medical_specialty Clinical Biochemistry Peptide Biology Diagnosis Differential Text mining Exopeptidases medicine Biomarkers Tumor Humans Aged chemistry.chemical_classification Ovarian Neoplasms business.industry Biochemistry (medical) Cancer Reproducibility of Results Blood Proteins Middle Aged medicine.disease Blood proteins chemistry Case-Control Studies Spectrometry Mass Matrix-Assisted Laser Desorption-Ionization Proteome Cancer biomarkers Female Ovarian cancer business Ex vivo |
| Zdroj: | Clinical chemistry. 56(2) |
| ISSN: | 1530-8561 |
| Popis: | Background: The serum peptidome may be a valuable source of diagnostic cancer biomarkers. Previous mass spectrometry (MS) studies have suggested that groups of related peptides discriminatory for different cancer types are generated ex vivo from abundant serum proteins by tumor-specific exopeptidases. We tested 2 complementary serum profiling strategies to see if similar peptides could be found that discriminate ovarian cancer from benign cases and healthy controls. Methods: We subjected identically collected and processed serum samples from healthy volunteers and patients to automated polypeptide extraction on octadecylsilane-coated magnetic beads and separately on ZipTips before MALDI-TOF MS profiling at 2 centers. The 2 platforms were compared and case control profiling data analyzed to find altered MS peak intensities. We tested models built from training datasets for both methods for their ability to classify a blinded test set. Results: Both profiling platforms had CVs of approximately 15% and could be applied for high-throughput analysis of clinical samples. The 2 methods generated overlapping peptide profiles, with some differences in peak intensity in different mass regions. In cross-validation, models from training data gave diagnostic accuracies up to 87% for discriminating malignant ovarian cancer from healthy controls and up to 81% for discriminating malignant from benign samples. Diagnostic accuracies up to 71% (malignant vs healthy) and up to 65% (malignant vs benign) were obtained when the models were validated on the blinded test set. Conclusions: For ovarian cancer, altered MALDI-TOF MS peptide profiles alone cannot be used for accurate diagnoses. |
| Databáze: | OpenAIRE |
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