Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30-50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers.

In an article in the journal Cancers, researchers show that recent advances in the throughput of ultra-high sensitivity proteomics technologies, such as PEA Explore, have enabled characterization of an increasingly large fraction of the plasma proteome using very small quantities of input material. Coupling such sensitive analysis technologies with machine learning approaches to detect combinations of biomarkers with robust predictive power, is a powerful approach to break new ground and enable progress beyond the current knowledge. The PEA Explore technology can be applied not only to liquid plasma samples, but also to other clinical sample matrices, such as dried blood spots. This opens the possibility to establish screening programs based on self-collected clinical samples, coupled with highly sensitive analysis of precise molecular biomarkers, as a cost-efficient solution for the early detection of ovarian cancer.

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