Dataset Information

Metabolomic Fingerprinting of Human High Grade Serous Ovarian Carcinoma Cell Lines

ABSTRACT: Focusing on defining the metabolomic basis of intratumoral heterogeneity in ovarian cancer, the metabolic diversity of a panel of high grade serous ovarian carcinoma (HGSOC) cell-lines we investigated using a metabolomics platform that interrogate 731 compounds. Metabolic fingerprinting followed by 2-dimensional and 3-dimensional principal component analysis defined the heterogeneity of the HGSOC cells and clustered them into five distinct metabolic groups. 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-sulfuration and glutathione synthesis was also observed. Subsets of HGSOC cells showed an increase in the levels of 5-Hydroxytryptamine, gamma-aminobutyric acid, or glutamate, pointing to their potential role as oncometabolites. In summary, our results identify increased glycolysis, phospholipid metabolism and amino acid metabolism with the resultant increase in the levels of 5-Hydoxytryptamine, GABA, and Glutamate as metabolomic correlates underlying the heterogeneity of ovarian cancer cell lines.

ORGANISM(S): Human Homo Sapiens

TISSUE(S): Ovarian Cancer Cells

DISEASE(S): Cancer

SUBMITTER: Muralidharan Jayaraman

PROVIDER: ST001983 | MetabolomicsWorkbench | Mon Sep 20 00:00:00 BST 2021

REPOSITORIES: MetabolomicsWorkbench

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	mwtab	Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Recently, multicellular spheroids were isolated from a well-established epithelial ovarian cancer cell line, OVCAR-3, and were propagated in vitro. These spheroid derived cells displayed numerous hallmarks of cancer stem cells, which are chemo and radioresistant cells thought to be a significant cause of cancer recurrence and resultant mortality. Gene set enrichment analysis of expression data from the OVCAR-3 cells and the spheroid-derived putative cancer stem cells identified several metabolic pathways enriched in differentially expressed genes. Before this, there had been little previous knowledge or investigation of systems-scale metabolic differences between cancer cells and cancer stem cells, and no knowledge of such differences in ovarian cancer stem cells. To determine if there were substantial metabolic changes corresponding with these transcriptional differences, we used two-dimensional gas chromatography coupled to mass spectrometry to measure the metabolite profiles of the two cell lines. These two cell lines exhibited significant metabolic differences in both intracellular and extracellular metabolite measurements. Principal components analysis, an unsupervised dimensional reduction technique, showed complete separation between the two cell types based on their metabolite profiles. Pathway analysis of intracellular metabolomics data revealed close overlap with metabolic pathways identified from gene expression data, with four out of six pathways found enriched in gene-level analysis also enriched in metabolite-level analysis. Some of those pathways contained multiple metabolites that were individually statistically significantly different between the two cell lines, with one of the most broadly and consistently different pathways, arginine and proline metabolism, suggesting an interesting hypothesis about cancerous and stem-like metabolic phenotypes in this pair of cell lines. Overall, we demonstrate for the first time that metabolism in an ovarian cancer stem cell line is distinct from that of more differentiated isogenic cancer cells, supporting the potential importance of metabolism in the differences between cancer cells and cancer stem cells.

Project description:Epithelial ovarian cancer is a very heterogeneous disease and remains the most lethal gynaecological malignancy in the Western world. Rational therapeutic approaches need to account for interpatient and intratumoral heterogeneity in treatment design. Detailed characterization of in vitro models representing the different histological and molecular subtypes is therefore imperative. Strikingly, from ~100 available ovarian cancer cell lines the origin and which subtype they represent is largely unknown. We have extensively and uniformly characterized 39 ovarian cancer cell lines (with mRNA/microRNA expression, exon sequencing, dose response curves for clinically relevant therapeutics) and obtained all available information on the clinical features and tissue of origin of the original ovarian cancer to refine the putative histological subtypes. From 39 ovarian cell lines, 14 were assigned as high-grade serous, four serous-type, one low-grade serous and 20 non-serous type. Three morphological subtypes (21 Epithelial, 7 Round, 12 Spindle) were identified that showed distinct biological and molecular characteristics, including overexpression of cell movement and migration-associated genes for the Spindle subtype. Clinical validation showed a clear association of the spindle-like tumors with metastasis, advanced stage, suboptimal debulking and poor prognosis. In addition, the morphological subtypes associated with the molecular C1-6 subtypes identified by Tothill et al. [1], Spindle clustered with C1-stromal subtype, Round with C5-mesenchymal and Epithelial with C4 subtype. We provide a uniformly generated data resource for 39 ovarian cancer cell lines, the ovarian cancer cell line panel (OCCP). This should be the basis for selecting models to develop subtype specific treatment approaches, which is very much needed to prolong the survival of ovarian cancer patients. Gene expression was measured for 32 ovarian cancer cell lines using the GeneChip Human Exon 1.0 ST Array (Affymetrix). Morphological subtypes were assigned based on cell morphology, size, growth pattern and proliferation rate during culturing of the cell lines.

Dataset Information

Metabolomic Fingerprinting of Human High Grade Serous Ovarian Carcinoma Cell Lines

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets