Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types.

Cancer cell lines are a cornerstone of cancer research but previous studies have shown that not all cell lines are equal in their ability to model primary tumors. Here we present a comprehensive pan-cancer analysis utilizing transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 tumor types. We perform correlation analysis and gene set enrichment analysis to understand the differences between cell lines and primary tumors. Additionally, we classify cell lines into tumor subtypes in 9 tumor types. We present our pancreatic cancer results as a case study and find that the commonly used cell line MIA PaCa-2 is transcriptionally unrepresentative of primary pancreatic adenocarcinomas. Lastly, we propose a new cell line panel, the TCGA-110-CL, for pan-cancer studies. This study provides a resource to help researchers select more representative cell line models

Pharmacoproteomic characterisation of human colon and rectal cancer

Most molecular cancer therapies act on protein targets but data on the proteome status of patients and cellular models for proteome-guided pre-clinical drug sensitivity studies are only beginning to emerge. Here, we profiled the proteomes of 65 colorectal cancer (CRC) cell lines to a depth of > 10,000 proteins using mass spectrometry. Integration with proteomes of 90 CRC patients and matched transcriptomics data defined integrated CRC subtypes, highlighting cell lines representative of each tumour subtype. Modelling the responses of 52 CRC cell lines to 577 drugs as a function of proteome profiles enabled predicting drug sensitivity for cell lines and patients. Among many novel associations, MERTK was identified as a predictive marker for resistance towards MEK1/2 inhibitors and immunohistochemistry of 1,074 CRC tumours confirmed MERTK as a prognostic survival marker. We provide the proteomic and pharmacological data as a resource to the community to, for example, facilitate the design of innovative prospective clinical trials. © 2017 The Authors. Published under the terms of the CC BY 4.0 licens

The influence of adipocyte secretome on selected metabolic fingerprints of breast cancer cell lines representing the four major breast cancer subtypes

Molecular subtype (MS) is one of the most used classifications of breast cancer (BC). Four MSs are widely accepted according to receptor expression of estrogen, progesterone, and HER2. The impact of adipose tissue on BC MS metabolic impairment is still unclear. The present work aims to elucidate the metabolic alterations in breast cancer cell lines representing different MSs subjected to adipocyte associated factors. Preadipocytes isolated from human subcutaneous adipose tissue were differentiated into mature adipocytes. MS representative cell lines were exposed to mature adipocyte secretome. Extracellular medium was collected for metabolomics and RNA was extracted to evaluate enzymatic expression by RT-PCR. Adipocyte secretome exposure resulted in a decrease in the Warburg effect rate and an increase in cholesterol release. HER2+ cell lines (BT-474 and SK-BR-3) exhibited a similar metabolic pattern, in contrast to luminal A (MCF-7) and triple negative (TN) (MDA-MB-231), both presenting identical metabolisms. Anaplerosis was found in luminal A and TN representative cells, whereas cataplerotic reactions were likely to occur in HER2+ cell lines. Our results indicate that adipocyte secretome affects the central metabolism distinctly in each BC MS representative cell line.info:eu-repo/semantics/publishedVersio

Activity of the multikinase inhibitor dasatinib against ovarian cancer cells

BackgroundHere, we explore the therapeutic potential of dasatinib, a small-molecule inhibitor that targets multiple cytosolic and membrane-bound tyrosine kinases, including members of the Src kinase family, EphA2, and focal adhesion kinase for the treatment of ovarian cancer.MethodsWe examined the effects of dasatinib on proliferation, invasion, apoptosis, cell-cycle arrest, and kinase activity using a panel of 34 established human ovarian cancer cell lines. Molecular markers for response prediction were studied using gene expression profiling. Multiple drug effect/combination index (CI) isobologram analysis was used to study the interactions with chemotherapeutic drugs.ResultsConcentration-dependent anti-proliferative effects of dasatinib were seen in all ovarian cancer cell lines tested, but varied significantly between individual cell lines with up to a 3 log-fold difference in the IC(50) values (IC(50) range: 0.001-11.3 micromol l(-1)). Dasatinib significantly inhibited invasion, and induced cell apoptosis, but less cell-cycle arrest. At a wide range of clinically achievable drug concentrations, additive and synergistic interactions were observed for dasatinib plus carboplatin (mean CI values, range: 0.73-1.11) or paclitaxel (mean CI values, range: 0.76-1.05). In this study, 24 out of 34 (71%) representative ovarian cancer cell lines were highly sensitive to dasatinib, compared with only 8 out of 39 (21%) representative breast cancer cell lines previously reported. Cell lines with high expression of Yes, Lyn, Eph2A, caveolin-1 and 2, moesin, annexin-1, and uPA were particularly sensitive to dasatinib.ConclusionsThese data provide a clear biological rationale to test dasatinib as a single agent or in combination with chemotherapy in patients with ovarian cancer

iPSCORE: A Resource of 222 iPSC Lines Enabling Functional Characterization of Genetic Variation across a Variety of Cell Types.

Large-scale collections of induced pluripotent stem cells (iPSCs) could serve as powerful model systems for examining how genetic variation affects biology and disease. Here we describe the iPSCORE resource: a collection of systematically derived and characterized iPSC lines from 222 ethnically diverse individuals that allows for both familial and association-based genetic studies. iPSCORE lines are pluripotent with high genomic integrity (no or low numbers of somatic copy-number variants) as determined using high-throughput RNA-sequencing and genotyping arrays, respectively. Using iPSCs from a family of individuals, we show that iPSC-derived cardiomyocytes demonstrate gene expression patterns that cluster by genetic background, and can be used to examine variants associated with physiological and disease phenotypes. The iPSCORE collection contains representative individuals for risk and non-risk alleles for 95% of SNPs associated with human phenotypes through genome-wide association studies. Our study demonstrates the utility of iPSCORE for examining how genetic variants influence molecular and physiological traits in iPSCs and derived cell lines

Molecular and phenotypic characterisation of paediatric glioma cell lines as models for preclinical drug development.

Although paediatric high grade gliomas resemble their adult counterparts in many ways, there appear to be distinct clinical and biological differences. One important factor hampering the development of new targeted therapies is the relative lack of cell lines derived from childhood glioma patients, as it is unclear whether the well-established adult lines commonly used are representative of the underlying molecular genetics of childhood tumours. We have carried out a detailed molecular and phenotypic characterisation of a series of paediatric high grade glioma cell lines in comparison to routinely used adult lines

Delayed luminescence to monitor programmed cell death induced by berberine on thyroid cancer cells

Correlation between apoptosis and UVA-induced ultraweak photon emission delayed luminescence (DL) from tumor thyroid cell lines was investigated. In particular, the effects of berberine, an alkaloid that has been reported to have anticancer activities, on two cancer cell lines were studied. The FTC-133 and 8305C cell lines, as representative of follicular and anaplastic thyroid human cancer, respectively, were chosen. The results show that berberine is able to arrest cell cycle and activate apoptotic pathway as shown in both cell lines by deoxyribonucleic acid fragmentation, caspase-3 cleavage, p53 and p27 protein overexpression. In parallel, changes in DL spectral components after berberine treatment support the hypothesis that DL from human cells originates mainly from mitochondria, since berberine acts especially at the mitochondrial level. The decrease of DL blue component for both cell lines could be related to the decrease of intra-mitochondrial nicotinamide adenine dinucleotide and may be a hallmark of induced apoptosis. In contrast, the response in the red spectral range is different for the two cell lines and may be ascribed to a different iron homeostasis

Association of Epstein-Barr virus with nasopharyngeal carcinoma and current status of development of cancer-derived cell lines

It is well known that the Epstein-Barr virus (EBV) contributes directly to tumourigenesis in nasopharyngeal carcinoma (NPC), primarily in the undifferentiated form of NPC (WHO type III; UNPC or UC), which is commonly found in South East Asia. Unfortunately, research in NPC has been severely hampered by the lack of authentic EBV-positive (EBV+) human NPC cell lines for study. Since 1975, there have been more than 20 reported NPC cell lines. However, many of these NPC-derived cell lines do not express EBV transcripts in long-term culture, and therefore that finding may dispute the fundamental theory of NPC carcinogenesis. In fact, currently only one EBV+ human NPC cell line (C-666) in long-term culture has been reported. Hence, most of the NPC cell lines may not be representative of the disease itself. In order to better understand and treat NPC, there is an urgent need to develop more EBV+ human NPC cell lines. In this review, we discuss the authenticity of existing NPC cell lines and the impact of our understanding of NPC biology on the treatment of the disease and the relationship of EBV to NPC in the context of cell lines

Distinct transcriptional programs stratify ovarian cancer cell lines into the five major histological subtypes.

From Europe PMC via Jisc Publications RouterHistory: epub 2021-09-01, ppub 2021-09-01Publication status: PublishedFunder: Cancer Research UK; Grant(s): C147/A25254, C1422/A19842Funder: Manchester Biomedical Research Centre; Grant(s): R120700/CAA070107BackgroundEpithelial 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