Abstract
Focusing on defining metabolite-based inter-tumoral heterogeneity in ovarian cancer, we investigated the metabolic diversity of a panel of high-grade serous ovarian carcinoma (HGSOC) cell-lines using a metabolomics platform that interrogate 731 compounds. Metabolic fingerprinting followed by 2-dimensional and 3-dimensional principal component analysis established the heterogeneity of the HGSOC cells by clustering them into five distinct metabolic groups compared to the fallopian tube epithelial cell line control. An overall increase in the metabolites associated with aerobic glycolysis and phospholipid metabolism were observed in the majority of the cancer cells. A preponderant increase in the levels of metabolites involved in trans-sulphuration and glutathione synthesis was also observed. More significantly, subsets of HGSOC cells showed an increase in the levels of 5-Hydroxytryptamine, γ-aminobutyrate, or glutamate. Additionally, 5-hydroxytryptamin synthesis inhibitor as well as antagonists of γ-aminobutyrate and glutamate receptors prohibited the proliferation of HGSOC cells, pointing to their potential roles as oncometabolites and ligands for receptor-mediated autocrine signaling in cancer cells. Consistent with this role, 5-Hydroxytryptamine synthesis inhibitor as well as receptor antagonists of γ-aminobutyrate and Glutamate-receptors inhibited the proliferation of HGSOC cells. These antagonists also inhibited the three-dimensional spheroid growth of TYKNU cells, a representative HGSOC cell-line. These results identify 5-HT, GABA, and Glutamate as putative oncometabolites in ovarian cancer metabolic sub-type and point to them as therapeutic targets in a metabolomic fingerprinting-based therapeutic strategy.
Highlights
In addition to establishing a metabolome-based sub-classification of high-grade serous ovarian carcinoma (HGSOC) cells, our results identify glutamic acid (Glu), GABA, and 5-HT as oncometabolites that can be effectively targeted by specific receptor- or synthesis-antagonists for therapy in ovarian cancer subtypes
A total of 731 metabolites of known identity was analyzed for their presence in fourteen HGSOC cell lines and the control Fallopian Tube-derived epithelial cell line, FTE188
An unsupervised principal component analysis (PCA) was carried out using all the samples to determine whether the ovarian cancer cell lines can be segregated from the FTE188 control and from each other, based on differences in their overall metabolite signature
Summary
Ovarian cancer is the eighth most common cancer in women and ranks fifth in cancer deaths [1,2]. The high mortality rate is largely due to the heterogeneous nature of the disease along with the lack of an efficacious targeted therapy [3,4]. The heterogeneity in ovarian cancer is contributed by histological subtypes of the disease as well as genetic and epigenetic diversity among the tumor cells [5,6]. Recent studies have shown that the intra- and inter tumor differences in metabolic profile contribute significantly to the heterogeneity of the disease [7]. Metabolic reprogramming is long recognized as one of the hallmarks of cancers and altered cancer metabolome has been associated with
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