How detecting change-points in biomarkers time series can support the diagnostic approaches of ovarian cancer

Abstract

Ovarian cancer is the fifth most common cause of cancer-related deaths among women, with more than 150,000 deceases worldwide each year. One of the main problems of ovarian cancer is that, usually, is detected at later stages, for which the survival rates are relatively low. Thus, the development of new approaches for longitudinal multi-marker analysis that result in earlier detection may significantly impact on mortality. An issue of specific interest is to determine whether the baseline level of a biomarker changes significantly at some time instant (change-point) prior to the clinical diagnosis of cancer. Here we apply a hierarchical Bayesian change-point model to jointly study the features of time series from multiple biomarkers, such as the serum biomarker Cancer Antigen 125, the Human Epididymis Protein 4, matrix metalloproteinase-7, Cytokeratin 19 fragment, glycodelin and mesothelin, using data from a nested case-control study of women diagnosed with ovarian cancer in the UK Collaborative Trial of Ovarian Cancer Screening. In this way we assess whether some of these biomarkers can play a role in change-point detection and, therefore, aid in the diagnosis of the disease. The main conclusion of our study is that the combined analysis of a group of specific biomarkers may possibly improve the detection of changepoints in time series data (compared to the analysis of Cancer Antigen 125 alone, the most commonly used oncomarker in the screening of ovarian cancer) which, in turn, are relevant for the early diagnosis of ovarian cancer.