Epithelial ovarian cancer (EOC) is the most fatal gynaecological malignancy, accounting for over 200,000 deaths worldwide per year. EOC is a highly heterogeneous disease, classified into five major histological subtypes–high-grade serous (HGSOC), clear cell (CCOC), endometrioid (ENOC), mucinous (MOC) and low-grade serous (LGSOC) ovarian carcinomas. Classification of EOCs is clinically beneficial, as the various subtypes respond differently to chemotherapy and have distinct prognoses. Cell lines are often used as in vitro models for cancer, allowing researchers to explore pathophysiology in a relatively cheap and easy to manipulate system. However, most studies that make use of EOC cell lines fail to recognize the importance of subtype. Furthermore, the similarity of cell lines to their cognate primary tumors is often ignored. Identification of cell lines with high molecular similarity to primary tumors is needed in order to better guide pre-clinical EOC research and to improve development of targeted therapeutics and diagnostics for each distinctive subtype. This study aims to generate a reference dataset of cell lines representative of the major EOC subtypes. We found that non-negative matrix factorization (NMF) optimally clustered fifty-six cell lines into five groups, putatively corresponding to each of the five EOC subtypes. These clusters validated previous histological groupings, while also classifying other previously unannotated cell lines. We analysed the mutational and copy number landscapes of these lines to investigate whether they harboured the characteristic genomic alterations of each subtype. Finally we compared the gene expression profiles of cell lines with 93 primary tumor samples stratified by subtype, to identify lines with the highest molecular similarity to HGSOC, CCOC, ENOC, and MOC. In summary, we examined the molecular features of both EOC cell lines and primary tumors of multiple subtypes. We recommend a reference set of cell lines most suited to represent four different subtypes of EOC for both in silico and in vitro studies. We also identify lines displaying poor overall molecular similarity to EOC tumors, which we argue should be avoided in pre-clinical studies. Ultimately, our work emphasizes the importance of choosing suitable cell line models to maximise clinical relevance of experiments.

Programmed cell death (PCD) facilitates targeted elimination of redundant, damaged, or infected cells via genetically controlled pathways. In plants, PCD is often an essential component of normal development and can also mediate responses to abiotic and biotic stress stimuli. However, studying the transcriptional regulation of this fundamental process is hindered by difficulties in sampling small groups of cells undergoing PCD that are often buried within the bulk of living plant tissue. We addressed this challenge by using RNA sequencing (RNA-Seq) of Arabidopsis thaliana suspension cells, a system that allows precise monitoring of PCD activation and progression. The use of three PCD-inducing treatments (salicylic acid, heat and critical dilution), in combination with three cell death modulators (3-methyladenine, lanthanum chloride and conditioned medium), allowed isolation of candidate core and stimuli-specific PCD genes, inference of underlying gene regulatory networks and identification of putative transcriptional regulators. This analysis underscored cell cycle disturbance and the repression of both pro-survival stress responses and mitochondrial retrograde signalling as key elements of the PCD-associated transcriptional signature in plants. Further, phenotyping of twenty Arabidopsis T-DNA insertion mutants in selected candidate genes confirmed a role for several in PCD and stress tolerance regulation, and validated the potential of these generated resources to identify novel genes involved in plant PCD pathways and/or stress tolerance in plants.

SUMMARYProgrammed cell death (PCD) facilitates selective, genetically controlled elimination of redundant, damaged, or infected cells. In plants, PCD is often an essential component of normal development and can mediate responses to abiotic and biotic stress stimuli. However, studying the transcriptional regulation of PCD is hindered by difficulties in sampling small groups of dying cells that are often buried within the bulk of living plant tissue. We addressed this challenge by using RNA sequencing and Arabidopsis thaliana suspension cells, a model system that allows precise monitoring of PCD rates. The use of three PCD‐inducing treatments (salicylic acid, heat, and critical dilution), in combination with three cell death modulators (3‐methyladenine, lanthanum chloride, and conditioned medium), enabled isolation of candidate core‐ and stimuli‐specific PCD genes, inference of underlying regulatory networks and identification of putative transcriptional regulators of PCD in plants. This analysis underscored a disturbance of the cell cycle and mitochondrial retrograde signaling, and repression of pro‐survival stress responses, as key elements of the PCD‐associated transcriptional signature. Further, phenotyping of Arabidopsis T‐DNA insertion mutants in selected candidate genes validated the potential of generated resources to identify novel genes involved in plant PCD pathways and/or stress tolerance.