Abstract A78: Serum metabolomics, cytokine measurements, and tumor RNA-seq identified phospholipids correlated with a molecular subclass as strong predictor for outcome in high-grade serous ovarian cancer.

Abstract

Abstract Background: Currently, the field of ‘omics’ is still growing, giving step by step deeper insight into the biological system of cancer. Increasing evidence suggests that alterations in cancer metabolism might have a tremendous impact on survival and could guide new treatment strategies. Therefore, including metabolic phenotyping in a study design would allow better understanding of the biological function and status of tumor cells and their hosts. Epithelial ovarian cancer (EOC) is very heterogeneous in both, local tumor spread and survival. Molecular subtypes presenting with distinct whole-genome expression profiles and significant survival differences have been described and awareness of the molecular alterations is required to identify new targets for therapy. In this study, serum metabolite changes were identified and evaluated as potential prognostic markers in a test set of 65 high grade serous EOC (HGSOC) patients. The prognostic impact was validated in an independent cohort of 165 EOC patients. Methods: Targeted metabolomics was performed on serum using the AbsoluteIDQ p180 kit (Biocrates Life Sciences), measuring 188 metabolites. In addition to a test and validation set of 65 HGSOC and 165 serous advanced stage EOC patients, the metabolomics profile of 62 healthy women was determined. In a subset of patients 16 cancer biomarkers and 40 chemokines were measured using Luminex bead-based assays and whole genome transcriptomes were assessed from different tumor tissues (primary, metastatic, and ascitic) by RNA sequencing. Results: 53 metabolites were significantly negatively correlated to overall survival (OS). Thereof, the majority (43) constituted glycerophospholipids (GPhL), followed by five acylcarnitines, four sphingolipids and one biogenic amine. A non-linear dimensionality reduction approach was used to reduce the information of the 43 GPhLs to three dimensions. The first showed the strongest impact on OS and revealed, as GPhL predictor, a significant impact on OS in uni- and multivariate (HR 0.38; p<0.001) Cox regression analyses, i.e. low GPhL concentrations in blood lead to worse OS. To determine the biologic background of these changes in the serum lipidome, correlations to cytokines and tumor markers were evaluated. IL6, CXCL2, osteopontin, and CCL23 were significantly negatively and leptin and sEGFR positively correlated to the GPhL-predictor. In a next step, RNA sequencing of different tumor tissues identified 393 genes negatively correlated (i.e. the carbohydrate-responsive element-binding protein, ChREPB) and 429 genes positively correlated (i.e. complete respiratory chain, many histones) with the GPhL-predictor. To provide more profound survival analysis, a validation was performed using expression data initially used for validation of a molecular subclassification with a tremendous impact on OS in 165 serous advanced stage EOC patients (Pils et al. Cancer Sci. 103:1334). Using the 50 most significantly up- and the 50 down-regulated genes a robust predictor was built. This gene predictor represents the GPhL-predictor and had a significant independent impact on OS (HR 0.66; p=0.004). In addition to this validation of the survival impact, a very strong correlation between the observed GPhL changes (via the gene predictor) and the molecular subclass was found, indicating a functional connection between these molecular subclasses and the serum lipidome. Conclusion: Targetd metabolomics revealed a strong independent impact of the serum concentrations of glycerophospholipids on HGSOC outcome in the test and a validation cohort. Transcriptomics data indicate changes in the energy metabolism of the tumor. Interestingly, the lipidome-changes were strongly correlated to a previously described molecular subclass in serous EOC. Citation Format: Stefanie Aust, Anna Bachmayr-Heyda, Katharina Auer, Nyamdelger Sukhbaatar, Samuel M. Meier, Christopher Gerner, Dietmar Pils. Serum metabolomics, cytokine measurements, and tumor RNA-seq identified phospholipids correlated with a molecular subclass as strong predictor for outcome in high-grade serous ovarian cancer. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; Oct 17-20, 2015; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(2 Suppl):Abstract nr A78.