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

Autor: Russell MR; Stoller Biomarker Discovery Centre and Manchester Molecular Pathology Innovation Centre, Division of Cancer Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester, UK., Graham C; School of Biological Sciences, Queens University Belfast, Chlorine Gardens, Belfast, BT9 5DL, UK., D'Amato A; Department of Pharmaceutical Sciences, University of Milan, Milano, Lombardy, Italy., Gentry-Maharaj A; MRC Clinical Trials Unit at UCL, Institute of Clinical Trials & Methodology, Faculty of Population Health Sciences, University College London, London, UK., Ryan A; MRC Clinical Trials Unit at UCL, Institute of Clinical Trials & Methodology, Faculty of Population Health Sciences, University College London, London, UK., Kalsi JK; MRC Clinical Trials Unit at UCL, Institute of Clinical Trials & Methodology, Faculty of Population Health Sciences, University College London, London, UK., Whetton AD; Stoller Biomarker Discovery Centre and Manchester Molecular Pathology Innovation Centre, Division of Cancer Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester, UK., Menon U; MRC Clinical Trials Unit at UCL, Institute of Clinical Trials & Methodology, Faculty of Population Health Sciences, University College London, London, UK., Jacobs I; Stoller Biomarker Discovery Centre and Manchester Molecular Pathology Innovation Centre, Division of Cancer Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester, UK. i.jacobs@unsw.edu.au.; MRC Clinical Trials Unit at UCL, Institute of Clinical Trials & Methodology, Faculty of Population Health Sciences, University College London, London, UK. i.jacobs@unsw.edu.au.; University of New South Wales, UNSW Australia, Level 1, Chancellery Building, Sydney, NSW, 2052, Australia. i.jacobs@unsw.edu.au., Graham RLJ; School of Biological Sciences, Queens University Belfast, Chlorine Gardens, Belfast, BT9 5DL, UK. r.graham@qub.ac.uk.
Jazyk: angličtina
Zdroj: British journal of cancer [Br J Cancer] 2019 Sep; Vol. 121 (6), pp. 483-489. Date of Electronic Publication: 2019 Aug 07.
DOI: 10.1038/s41416-019-0544-0
Abstrakt: 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.
Databáze: MEDLINE
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