Diagnosis of epithelial ovarian cancer using a combined protein biomarker panel

    Research output: Contribution to journalArticle

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    • Matthew R. Russell
    • Ciaren Graham
    • Alfonsina D’Amato
    • Aleksandra Gentry-Maharaj
    • Andy Ryan
    • Jatinderpal K. Kalsi
    • Anthony D. Whetton
    • Usha Menon
    • Ian Jacobs
    • Robert L. J. Graham

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    BackgroundAn early detection tool for EOC was constructed from analysis of biomarker expression data from serum collected during the UKCTOCS.MethodsThis 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.ResultsThe 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.ConclusionsThe 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.

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    • Diagnosis of epithelial ovarian cancer using a combined protein biomarker panel

      Rights statement: Copyright 2019 the authors. This is an open access article published under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.

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    DOI

    Original languageEnglish
    JournalBritish Journal of Cancer
    Journal publication date07 Aug 2019
    DOIs
    Publication statusPublished - 07 Aug 2019

    ID: 179518499