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Bioenergetic analysis of ovarian cancer cell lines: profiling of histological subtypes and identification of a mitochondria-defective cell line.

ABSTRACT: Epithelial ovarian cancer (EOC) is the most lethal of all gynecological cancers, and encompasses distinct histological subtypes that have specific genetic and tissues-of-origin differences. Ovarian clear cell carcinoma (OCCC) represents approximately 10% of cases and has been termed a stress responsive cancer. OCCC is characterized by increased expression of oxidative stress and glycolysis-related genes. In the present study, we hypothesized that bioenergetic profiling might uniquely distinguish OCCC from other EOC histological subtypes. Using an extracellular flux analyzer, OCCC lines (ES-2, TOV-21-G) were shown to be highly metabolically active, with high oxygen consumption rate (OCR) and high extracellular acidification rate (ECAR), indicative of enhanced mitochondrial oxidative phosphorylation and glycolytic rate, respectively. A high bioenergetics profile was associated with the cell lines' ability to form anchorage independent spheroids. Given their high glycolytic and mitochondrial activity, OCCC cells displayed strong sensitivity to 2-deoxy-D-glucose and Rotenone growth inhibition, although this chemosensitivity profile was not specific to only OCCC cells. Bioenergetic profiling also identified a non-OCCC cell line, OVCA420, to have severely compromised mitochondrial function, based on low OCR and a lack of stimulation of maximal respiration following application of the uncoupler FCCP. This was accompanied by mitochondrial morphology changes indicative of enhanced fission, increased expression of the mitochondrial fission protein Drp1, a loss of mitochondrial membrane potential and dependence on glycolysis. Importantly, this loss of mitochondrial function was accompanied by the inability of OVCA420 cells to cope with hypoxic stress, and a compromised ability to stabilize HIF-1? in response to 1% O2 hypoxia. This knowledge may be imperative for researchers planning to utilize this cell line for further studies of metabolism and hypoxia, and suggests that altered mitochondrial fission dynamics represents a phenotype of a subpopulation of EOCs.

SUBMITTER: Dier U

PROVIDER: S-EPMC4032324 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Bioenergetic analysis of ovarian cancer cell lines: profiling of histological subtypes and identification of a mitochondria-defective cell line.

Dier Usawadee U Shin Dong-Hui DH Hemachandra L P Madhubhani P LP Uusitalo Larissa M LM Hempel Nadine N

PloS one 20140523 5

Epithelial ovarian cancer (EOC) is the most lethal of all gynecological cancers, and encompasses distinct histological subtypes that have specific genetic and tissues-of-origin differences. Ovarian clear cell carcinoma (OCCC) represents approximately 10% of cases and has been termed a stress responsive cancer. OCCC is characterized by increased expression of oxidative stress and glycolysis-related genes. In the present study, we hypothesized that bioenergetic profiling might uniquely distinguish ...[more]

PMID: 24858344

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Project description:BackgroundEpithelial ovarian cancer (OC) is a heterogenous disease consisting of five major histologically distinct subtypes: high-grade serous (HGSOC), low-grade serous (LGSOC), endometrioid (ENOC), clear cell (CCOC) and mucinous (MOC). Although HGSOC is the most prevalent subtype, representing 70-80% of cases, a 2013 landmark study by Domcke et al. found that the most frequently used OC cell lines are not molecularly representative of this subtype. This raises the question, if not HGSOC, from which subtype do these cell lines derive? Indeed, non-HGSOC subtypes often respond poorly to chemotherapy; therefore, representative models are imperative for developing new targeted therapeutics.MethodsNon-negative matrix factorisation (NMF) was applied to transcriptomic data from 44 OC cell lines in the Cancer Cell Line Encyclopedia, assessing the quality of clustering into 2-10 groups. Epithelial OC subtypes were assigned to cell lines optimally clustered into five transcriptionally distinct classes, confirmed by integration with subtype-specific mutations. A transcriptional subtype classifier was then developed by trialling three machine learning algorithms using subtype-specific metagenes defined by NMF. The ability of classifiers to predict subtype was tested using RNA sequencing of a living biobank of patient-derived OC models.ResultsApplication of NMF optimally clustered the 44 cell lines into five transcriptionally distinct groups. Close inspection of orthogonal datasets revealed this five-cluster delineation corresponds to the five major OC subtypes. This NMF-based classification validates the Domcke et al. analysis, in identifying lines most representative of HGSOC, and additionally identifies models representing the four other subtypes. However, NMF of the cell lines into two clusters did not align with the dualistic model of OC and suggests this classification is an oversimplification. Subtype designation of patient-derived models by a random forest transcriptional classifier aligned with prior diagnosis in 76% of unambiguous cases. In cases where there was disagreement, this often indicated potential alternative diagnosis, supported by a review of histological, molecular and clinical features.ConclusionsThis robust classification informs the selection of the most appropriate models for all five histotypes. Following further refinement on larger training cohorts, the transcriptional classification may represent a useful tool to support the classification of new model systems of OC subtypes.

Project description:Epithelial ovarian cancer is a highly heterogeneous disease and remains the most lethal gynaecological malignancy in the Western world. Therapeutic approaches need to account for inter-patient and intra-tumoural heterogeneity and detailed characterization of in vitro models representing the different histological and molecular ovarian cancer subtypes is critical to enable reliable preclinical testing. There are approximately 100 publicly available ovarian cancer cell lines but their cellular and molecular characteristics are largely undescribed. We have characterized 39 ovarian cancer cell lines under uniform conditions for growth characteristics, mRNA/microRNA expression, exon sequencing, drug response for clinically-relevant therapeutics and collated all available information on the original clinical features and site of origin. We tested for statistical associations between the cellular and molecular features of the lines and clinical features. Of the 39 ovarian cancer cell lines, 14 were assigned as high-grade serous, four serous-type, one low-grade serous and 20 non-serous type. Three morphological subtypes: Epithelial (n = 21), Round (n = 7) and Spindle (n = 12) were identified that showed distinct biological and molecular characteristics, including overexpression of cell movement and migration-associated genes in the Spindle subtype. Comparison with the original clinical data showed association of the spindle-like tumours with metastasis, advanced stage, suboptimal debulking and poor prognosis. In addition, the expression profiles of Spindle, Round and Epithelial morphologies clustered with the previously described C1-stromal, C5-mesenchymal and C4 ovarian subtype expression profiles respectively. Comprehensive profiling of 39 ovarian cancer cell lines under controlled, uniform conditions demonstrates clinically relevant cellular and genomic characteristics. This data provides a rational basis for selecting models to develop specific treatment approaches for histological and molecular subtypes of ovarian cancer.

Dataset Information

Bioenergetic analysis of ovarian cancer cell lines: profiling of histological subtypes and identification of a mitochondria-defective cell line.

Publications

Bioenergetic analysis of ovarian cancer cell lines: profiling of histological subtypes and identification of a mitochondria-defective cell line.

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