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.
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.
Authors: Chris Bauer; Frank Kleinjung; Celia J Smith; Mark W Towers; Ali Tiss; Alexandra Chadt; Tanja Dreja; Dieter Beule; Hadi Al-Hasani; Knut Reinert; Johannes Schuchhardt; Rainer Cramer Journal: BMC Bioinformatics Date: 2011-05-09 Impact factor: 3.169
Authors: Christopher R Smith; Ihor Batruch; Josep Miquel Bauça; Hari Kosanam; Julia Ridley; Marcus Q Bernardini; Felix Leung; Eleftherios P Diamandis; Vathany Kulasingam Journal: Clin Proteomics Date: 2014-06-02 Impact factor: 3.988
Authors: Thanusi Thavarajah; Claudia C Dos Santos; Arthur S Slutsky; John C Marshall; Pete Bowden; Alexander Romaschin; John G Marshall Journal: Clin Proteomics Date: 2020-07-02 Impact factor: 3.988
Authors: Neomal S Sandanayake; Stephane Camuzeaux; John Sinclair; Oleg Blyuss; Fausto Andreola; Michael H Chapman; George J Webster; Ross C Smith; John F Timms; Stephen P Pereira Journal: BMC Clin Pathol Date: 2014-02-04
Authors: Jaimie Dufresne; Pete Bowden; Thanusi Thavarajah; Angelique Florentinus-Mefailoski; Zhuo Zhen Chen; Monika Tucholska; Tenzin Norzin; Margaret Truc Ho; Morla Phan; Nargiz Mohamed; Amir Ravandi; Eric Stanton; Arthur S Slutsky; Claudia C Dos Santos; Alexander Romaschin; John C Marshall; Christina Addison; Shawn Malone; Daren Heyland; Philip Scheltens; Joep Killestein; Charlotte Teunissen; Eleftherios P Diamandis; K W M Siu; John G Marshall Journal: Clin Proteomics Date: 2019-12-23 Impact factor: 3.988