Multi-omics characterization of tumour-tissue, plasma and cervicovaginal fluid for early detection of ovarian cancer

Early detection is key for increased survival in ovarian cancer. Currently however, no precise enough and cost-efficient method exists for screening of ovarian cancer.

There is clear need for new and robust biomarkers for detection and diagnostics that can be collected and analysed in a system that does not impose additional burden on the healthcare.We have taken a data-driven approach using high-throughput technologies coupled with machine learning methodologies resulting in new, precise, combinations of biomarkers for early detection of ovarian cancer based on protein biomarkers in plasma.

We are now employing the same methodology but using a non-invasive sample that can be collected by the participating women themselves such as dried self-collected cervico-vaginal fluid (CVF) deposited on paper cards.

In this project, we have access to carefully collected sample series with multiple tissues from the same women which allows to track biomarkers through the tissue types, from tumour to dried CVF.

Specifically, we will couple RNA-expression in tumour tissue with protein expression in both plasma and CVF in the same women, providing a unique resource for detecting accurate biomarkers for early detection of ovarian cancer.

Our goal is to develop a feasible cost-efficient test for early detection of ovarian cancer that could be used for instance within the existing cervical cancer screening infrastructure.