Plasma metabolic signatures in epithelial ovarian cancer diagnosis: The application of NextGen Metabolomics in gynecologic oncology.

Abstract

e17593 Background: Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy. The lack of reliable diagnostic tests for early detection has led to the majority of EOC patients being diagnosed with advanced stage disease. Despite advances, most patients continue to suffer recurrence within 5 years following surgery and platinum (CDDP)-based chemotherapy. As malignant transformation is associated with metabolic re-programming, plasma-based biochemical signatures may offer biomarkers for the earlier detection of EOC. We previously reported the application of quantitative Mass Spectrometry (MS/MS) for the identification of CDDP resistance in EOC (D’Amora et al., Gynecol Oncol, 2021). Here we report the application of plasma MS/MS signatures for the detection of EOC. Methods: Following informed consent, EOC patients and controls provided EDTA blood samples with plasma was stored at -80 C. Quantitative MS/MS was performed using the Absolute IDQ p180 on a Sciex 6500 triple Quad MS. Amino acid (AA), lipid, acyl-carnitine, and hexose concentrations were compared with internal and external standards (NIST). Data analyses used MetaboAnalyst 3.0 to develop training sets in the original 13 EOC and 31 controls that were then validated in 34 EOC and 100 controls confirming diagnostic accuracy. Results: Plasma lipidomic profiles comparing EOC with controls provided highly significant differences that included micro-molar concentrations of Palmitic acid (C16) (p-1.07E-06), Oleic acid (C18:1) (p=1.37E-17) and the ratio of acyl-carnitine/carnitine (AcylC/C0) (p=1.07E-10). AA concentrations including Tryptophan (p=3.79E-22), branch chain amino acids (BCAA) (p=2.90E-07) and the Tryptophan/ Kynurenine ratio (TRP/KYN) (p=1.77E-11) were also significantly different for EOC versus controls. A composite equation comprised of AA and lipids (Valine/Phenylalanine)/(lysophophatidyl cholinePCaC16:0)/Trp} was found to predict disease free survival (DFS)(p =0.024). Conclusions: Malignant transformation in EOC reflects metabolic re-programming that can be identified and quantified in the plasma using targeted MS/MS. Changes in the concentrations of bioenergetic and structural lipids and AA’s reflect altered nutrient dependencies in transformed cells, while the ratio of TRP/KYN may reflect dysregulated immune response and surveillance. Plasma metabolite concentrations and select ratios provide insights into EOC pathogenesis that could be applied for diagnosis and prognosis. These MS/MS signatures have the potential to provide a new platform the earlier diagnosis of EOC.