Molecular and cellular characterization of two patient-derived ductal carcinoma in situ (DCIS) cell lines, ETCC-006 and ETCC-010.

BACKGROUND: Currently it is unclear how in situ breast cancer progresses to invasive disease; therefore, a better understanding of the events that occur during the transition to invasive carcinoma is warranted. Here we have conducted a detailed molecular and cellular characterization of two, patient-derived, ductal carcinoma in situ (DCIS) cell lines, ETCC-006 and ETCC-010. METHODS: Human DCIS cell lines, ETCC-006 and ETCC-010, were compared against a panel of cell lines including the immortalized, breast epithelial cell line, MCF10A, breast cancer cell lines, MCF7 and MDA-MB-231, and another DCIS line, MCF10DCIS.com. Cell morphology, hormone and HER2/ERBB2 receptor status, cell proliferation, survival, migration, anchorage-independent growth, indicators of EMT, cell signalling pathways and cell cycle proteins were examined using immunostaining, immunoblots, and quantitative, reverse transcriptase PCR (qRT-PCR), along with clonogenic, wound-closure and soft agar assays. RNA sequencing (RNAseq) was used to provide a transcriptomic profile. RESULTS: ETCC-006 and ETCC-010 cells displayed notable differences to another DCIS cell line, MCF10DCIS.com, in terms of morphology, steroid-receptor/HER status and markers of EMT. The ETCC cell lines lack ER/PR and HER, form colonies in clonogenic assays, have migratory capacity and are capable of anchorage-independent growth. Despite being isogenic, less than 30% of differentially expressed transcripts overlapped between the two lines, with enrichment in pathways involving receptor tyrosine kinases and DNA replication/cell cycle programs and in gene sets responsible for extracellular matrix organisation and ion transport. CONCLUSIONS: For the first time, we provide a molecular and cellular characterization of two, patient-derived DCIS cell lines, ETCC-006 and ETCC-010, facilitating future investigations into the molecular basis of DCIS to invasive ductal carcinoma transition.

Investigating the suitability of in vitro cell lines as models for the major subtypes of epithelial ovarian cancer.

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.

The copious capabilities of non-coding RNAs in cancer regulation, diagnosis and treatment.

Globally, cancer is one of the leading causes of mortality, accounting for 10 million deaths per year. Non-coding RNAs (ncRNAs) play integral and diverse roles in cancer, possessing the ability to both promote oncogenesis and impede tumor formation. This review discusses the various roles of microRNAs, transfer RNA-derived small RNAs, long non-coding RNAs and lncRNA-derived microproteins in cancer progression and prevention. We highlight the diagnostic and therapeutic potential of these ncRNAs, with a particular focus on detection in liquid biopsies and targeting of ncRNAs with small inhibitory molecules. Ultimately, the biological functions of cancer-associated ncRNAs, as well as the development of ncRNA-based technologies, are compelling areas for further research, holding the possibility of revolutionizing cancer treatment and diagnosis.

Exploring Evidence of Non-coding RNA Translation With Trips-Viz and GWIPS-Viz Browsers.

Detection of translation in so-called non-coding RNA provides an opportunity for identification of novel bioactive peptides and microproteins. The main methods used for these purposes are ribosome profiling and mass spectrometry. A number of publicly available datasets already exist for a substantial number of different cell types grown under various conditions, and public data mining is an attractive strategy for identification of translation in non-coding RNAs. Since the analysis of publicly available data requires intensive data processing, several data resources have been created recently for exploring processed publicly available data, such as OpenProt, GWIPS-viz, and Trips-Viz. In this work we provide a detailed demonstration of how to use the latter two tools for exploring experimental evidence for translation of RNAs hitherto classified as non-coding. For this purpose, we use a set of transcripts with substantially different patterns of ribosome footprint distributions. We discuss how certain features of these patterns can be used as evidence for or against genuine translation. During our analysis we concluded that the MTLN mRNA, previously misannotated as lncRNA LINC00116, likely encodes only a short proteoform expressed from shorter RNA transcript variants.

A bioinformatics approach to identify novel long, non-coding RNAs in breast cancer cell lines from an existing RNA-sequencing dataset.

Breast cancer research has traditionally centred on genomic alterations, hormone receptor status and changes in cancer-related proteins to provide new avenues for targeted therapies. Due to advances in next generation sequencing technologies, there has been the emergence of long, non-coding RNAs (lncRNAs) as regulators of normal cellular events, with links to various disease states, including breast cancer. Here we describe our bioinformatic analyses of a previously published RNA sequencing (RNA-seq) dataset to identify lncRNAs with altered expression levels in a subset of breast cancer cell lines. Using a previously published RNA-seq dataset of 675 cancer cell lines, a subset of 18 cell lines was selected for our analyses that included 16 breast cancer lines, one ductal carcinoma in situ line and one normal-like breast epithelial cell line. Principal component analysis demonstrated correlation with well-established categorisation methods of breast cancer (i.e. luminal A/B, HER2 enriched and basal-like A/B). Through detailed comparison of differentially expressed lncRNAs in each breast cancer sub-type with normal-like breast epithelial cells, we identified 15 lncRNAs with consistently altered expression, including three uncharacterised lncRNAs. Utilising data from The Cancer Genome Atlas (TCGA) and The Genotype Tissue Expression (GETx) project via Gene Expression Profiling Interactive Analysis (GEPIA2), we assessed clinical relevance of several identified lncRNAs with invasive breast cancer. Lastly, we determined the relative expression level of six lncRNAs across a spectrum of breast cancer cell lines to experimentally confirm the findings of our bioinformatic analyses. Overall, we show that the use of existing RNA-seq datasets, if re-analysed with modern bioinformatic tools, can provide a valuable resource to identify lncRNAs that could have important biological roles in oncogenesis and tumour progression.