4 - Predictive biology of ovarian cancer

Summary

Introduction

Over the past 5 years, new insights into the molecular basis of epithelial ovarian cancer (EOC) have brought about rapid change in our understanding of the disease. However, survival rates for women with advanced EOC have not changed significantly over the past two decades and fewer than 30% remain alive 5 years after diagnosis despite multiple clinical trials of novel chemotherapy combinations. The most important clinical question for the management of EOC is how to identify individuals with different risks of response or relapse, and how to implement personalised selection of optimum systemic therapies. Since 1999 there have been extensive efforts to use new genomic tools, particularly expression microarrays, to identify better biomarkers for ovarian cancer treatment. This chapter concentrates on recent robust advances that are likely to affect clinical care over the short to medium term.

The phrase ‘stratified medicine’ describes the use of better classifiers, both clinical and molecular, to separate patients into defined groups for the selection of optimum treatment. With the rapid reduction in cost of DNA sequencing, there is now a strong scientific justification to use next-generation sequencing technologies to discover a more detailed molecular stratification. ‘Personalised’ medicine describes the ability to choose a specific treatment for an individual patient and requires biomarkers with high predictive value. The terms ‘predictive’ and ‘prognostic’ are often used imprecisely to describe biomarkers. In this chapter we use the term ‘predictive’ to refer to biomarkers that choose therapy for patients.