The use of high-throughput blood-based metabolomics for ovarian cancer early detection and diagnosis

Background: Ovarian cancer, a leading cause of gynaecological cancer-related mortality, poses significant challenges due to its late diagnosis and lack of effective preventative strategies. Most cases are diagnosed at advanced stages, resulting in a 5-year survival rate of 25%.

Previously explored screening methods, involving ultrasound or the serum CA125 biomarker, have shown insufficient efficacy with at least half of early-stage cases exhibiting normal CA125 levels.

Exploration of standalone blood biomarkers, such as HE4, IGF-II, and transferrin, for ovarian cancer diagnostics has yielded mediocre results, rendering them unsuitable for clinical use; their accuracy when in combination is still under investigation.

Current diagnostics also have moderate ability to determine the nature of the cyst (malignant/benign) and the probability of invasive disease in women presenting with pelvic mass(es), with CA125 and imaging modalities achieving variable sensitivity/specificity. Accurate characterisation of a mass would enable tailored surgical treatments and management strategies.

Aims: Building on our promising preliminary results, we will employ a novel high-throughput metabolomics platform to enhance early detection and diagnosis of ovarian cancer by identifying differentially expressed metabolites and lipids in blood. The aim is to assess the diagnostic accuracy of the proposed blood test in discriminating: A.

Women with ovarian cancer from healthy controls. B. Malignant, borderline and benign cysts in women presenting with ovarian masses on imaging.

Methods: Metabolomics, focusing on lipids and small metabolites linked to an individual's phenotype, offer valuable insights into a patient's pathophysiological status.

Metabolomic approaches provide information in a multimarker manner and have been extensively used in investigating cancer metabolism and identifying disease biomarkers.

Traditional metabolomic platforms, like liquid chromatography-mass spectrometry, have been previously employed for ovarian cancer diagnostics but are hindered by laborious procedures and time-consuming sample preparation.

The advent of ambient ionization mass spectrometry, exemplified by Rapid Evaporative Ionization Mass Spectrometry (REIMS), has transformed metabolomic studies. REIMS analyses biological samples by generating a molecule-rich aerosol through electrosurgical tools or lasers. We will employ REIMS to facilitate rapid disease detection by identifying altered blood metabolites/lipids.

How results of this research will be used: This study represents the first assessment of a high-throughput platform in ovarian cancer, conducting real-time blood sample analysis. Our goal is to enhance early detection and diagnosis, ultimately improving prognosis.

Following successful completion, we will pursue funding for a series of extensive translational studies to validate our findings and formulate a commercialisation strategy, bringing the proposed test closer to clinical implementation.