Diagnosis of epithelial ovarian cancer using a combined protein biomarker panel

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Abstract

Background An early detection tool for EOC was constructed from analysis of biomarker expression data from serum collected during the UKCTOCS. Methods This study included 49 EOC cases (19 Type I and 30 Type II) and 31 controls, representing 482 serial samples spanning seven years pre-diagnosis. A logit model was trained by analysis of dysregulation of expression data of four putative biomarkers, (CA125, phosphatidylcholine-sterol acyltransferase, vitamin K-dependent protein Z and C-reactive protein); by scoring the specificity associated with dysregulation from the baseline expression for each individual. Results The model is discriminatory, passes k-fold and leave-one-out cross-validations and was further validated in a Type I EOC set. Samples were analysed as a simulated annual screening programme, the algorithm diagnosed cases with >30% PPV 1–2 years pre-diagnosis. For Type II cases (~80% were HGS) the algorithm classified 64% at 1 year and 28% at 2 years tDx as severe. Conclusions The panel has the potential to diagnose EOC one-two years earlier than current diagnosis. This analysis provides a tangible worked example demonstrating the potential for development as a screening tool and scrutiny of its properties. Limits on interpretation imposed by the number of samples available are discussed.
Original languageUndefined
JournalBritish Journal of Cancer
Early online date7 Aug 2019
DOIs
Publication statusPublished - 10 Sept 2019

Research Beacons, Institutes and Platforms

  • Manchester Cancer Research Centre

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