Novel risk models for early detection and screening of ovarian cancer.

Matthew R Russell,Ian Jacobs,Anthony D Whetton,Robert L.J Graham,Michael Walker,Evangelia-Ourania Fourkala,Aleksandra Gentry-Maharaj,Alfonsina D’Amato,Jatinderpal K Kalsi,Usha Menon,Caroline Dive,Ciaren Graham,Emma J Crosbie,Andy Ryan

doi:10.18632/oncotarget.13648

Abstract

PurposeOvarian 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).ResultsModel 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 MethodsThis 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.ConclusionsThese 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.

Highlights

The estimation of cancer risk has the potential to improve patient survival through earlier diagnosis and treatment
These models have encouraging sensitivities for detecting preclinical ovarian cancer, demonstrating improved sensitivity compared to CA125 alone
In addition we demonstrate how the models improve on Risk of Ovarian Cancer Algorithm (ROCA) for some cases and outline their potential future use as clinical tools

Summary

Introduction

The estimation of cancer risk has the potential to improve patient survival through earlier diagnosis and treatment. This approach may prove especially beneficial in ovarian cancer (OC) which is largely asymptomatic in the early stages. Type I OC includes low grade serous, endometrioid, clear cell, and mucinous carcinomas which are generally indolent, relatively genetically stable and lack TP53 mutations. Type II OC includes high-grade serous and endometrioid, undifferentiated carcinomas and carcinosarcomas that are highly aggressive, evolve rapidly and display TP53 mutations in over 80% of cases [1, 2]. When detected early prognosis is much better, with approximately 90% of women diagnosed at Stage I surviving five years or more [5]

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Conclusion