Liquid biopsies improve ovarian cancer diagnosis

In contrast, cervical cancer incidence and associated deaths have been reduced by up to 80% through routine cervical screening with the Papanicolaou test. Similar prediction and early detection approaches are urgently required for breast and ovarian cancer.

Towards this goal, the EU-funded EPI-FEM-CARE (Epigenetics for female personalised cancer care) consortium set out to develop novel bioassays capable of screening specific target molecules reflective of disease.

The consortium comprised both academic and industrial partners with significant clinical and scientific expertise in the field. More specifically, they wished to establish a series of blood tests based upon DNA methylation technology that would facilitate both early detection and prediction of therapeutic outcome in breast and ovarian cancer.

DNA methylation is a chemical modification known to affect gene expression and can respond to environmental cues. The EPI-FEM-CARE consortium used technology capable of detecting DNA methylation in serum against an excess background of normal contaminating DNA.

This approach relied on the detection of differentially methylated regions (DMRs) specific to breast and ovarian cancers and was validated on numerous patient samples.

In this context, partners developed specialised protocols for extracting very low concentrations of DNA in serum. In addition, new algorithms were developed for reading methylation patterns unique to tumour samples, thereby providing superior specificity and sensitivity in the clinical utility of DNA methylation markers.

Furthermore, the breast cancer specific test was applied to patient samples subjected to adjuvant treatment to assess the capacity of the test to predict therapeutic response.

Given the socioeconomic impact of gynaecological cancer, the deliverables of the study pave the way towards improved and accurate diagnosis in the future. This approach offers several advantages over gene-expression microarrays and proteomic methods. Importantly, the test detects DMR patterns in cancer cells, enabling patient stratification for determination of optimised personalised therapy.