Research Papers:
Novel risk models for early detection and screening of ovarian cancer
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Abstract
Matthew R. Russell1, Alfonsina D’Amato1, Ciaren Graham2, Emma J Crosbie3, Aleksandra Gentry-Maharaj4, Andy Ryan4, Jatinderpal K. Kalsi4, Evangelia-Ourania Fourkala4, Caroline Dive5, Michael Walker1, Anthony D. Whetton1, Usha Menon4, Ian Jacobs1,4,6, Robert L.J. Graham1
1Stoller Biomarker Discovery Centre and Pathology Node, Division of Molecular and Clinical Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
2School of Healthcare Science, Manchester Metropolitan University, UK
3Gynaecological Oncology Research Group, Division of Molecular and Clinical Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
4Gynaecological Cancer Research Centre, Women's Cancer, Institute for Women's Health, University College London, London, UK
5Clinical and Experimental Pharmacology Group, Cancer Research UK Manchester Institute, University of Manchester, Manchester, UK
6University of New South Wales, Australia
Correspondence to:
Robert L.J. Graham, email: [email protected]
Ian Jacobs, email: [email protected]
Keywords: ovarian cancer, UKCTOCS, early detection, logit, risk estimation
Received: August 05, 2016 Accepted: November 14, 2016 Published: November 26, 2016
ABSTRACT
Purpose: Ovarian cancer (OC) is the most lethal gynaecological cancer. Early detection is required to improve patient survival. Risk estimation models were constructed for Type I (Model I) and Type II (Model II) OC from analysis of Protein Z, Fibronectin, C-reactive protein and CA125 levels in prospectively collected samples from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS).
Results: Model I identifies cancers earlier than CA125 alone, with a potential lead time of 3-4 years. Model II detects a number of high grade serous cancers at an earlier stage (Stage I/II) than CA125 alone, with a potential lead time of 2-3 years and assigns high risk to patients that the ROCA Algorithm classified as normal.
Materials and Methods: This nested case control study included 418 individual serum samples serially collected from 49 OC cases and 31 controls up to six years pre-diagnosis. Discriminatory logit models were built combining the ELISA results for candidate proteins with CA125 levels.
Conclusions: These models have encouraging sensitivities for detecting pre-clinical ovarian cancer, demonstrating improved sensitivity compared to CA125 alone. In addition we demonstrate how the models improve on ROCA for some cases and outline their potential future use as clinical tools.
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