Transcriptomics profiling of some commonly used cell lines at the base-line culture condition
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA565366 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR10113120.fastq.gz | Fastqsanger.gz | |||
| SRR10113121.fastq.gz | Fastqsanger.gz | |||
| SRR10113122.fastq.gz | Fastqsanger.gz | |||
| SRR10113123.fastq.gz | Fastqsanger.gz | |||
| SRR10113124.fastq.gz | Fastqsanger.gz |
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Project description:Tumor-derived cell lines have served as vital models to advance our understanding of oncogene function and therapeutic response1. Although substantial effort has been directed to defining the genomic constitution of cancer cell line panels24, the transcriptome which represents the active program of a cell remains understudied. Here, we describe RNA sequencing and SNP array analysis of 675 commonly used human cancer cell lines. We explore numerous transcriptome features including coding and non-coding gene expression, transcribed mutations, gene fusion and expression of non-human sequences. Aside from many known aberrations we find new surprising characteristics, including more than 2200 unique fusion gene pairs representing a vast, testable repertoire of oncogenic fusions, many of which have analogs found in primary human tumors. We show that a combination of multiple genome and transcriptome features in a novel pathway-based approach enhances prediction of response to various targeted therapeutics. Our results provide valuable new insights into these critical pre-clinical models and provide added context for interpreting the numerous studies that employ these widely used cell lines. The raw sequence files were submitted to the European Genome-Phenome Archive (EGA) under accession EGAS00001000610 ( https://www.ebi.ac.uk/ega/datasets/EGAD00001000725 ). Processed, human non-identifiable data, together with a README file describing the data (140625_Klijn_README.txt) are available here in E-MTAB-2706.additional.*.zip files: https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-2706/files .
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Project description:Humans and other commonly used model organisms are resistant to cycloheximide-mediated biases in ribosome profiling experiments
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Project description:Background: Despite advances in therapeutics, outcomes for hepatocellular carcinoma (HCC) remain poor and there is an urgent need for efficacious systemic therapy. Unfortunately, drugs that are successful in preclinical studies often fail in the clinical setting, and we hypothesize that this is due to functional differences between primary tumors and commonly used preclinical models. In this study, we attempt to answer this question by comparing tumor morphology and gene expression profiles between primary tumors, xenografts and HCC cell lines. Methods: Hep G2 cell lines and tumor cells from patient tumor explants were subcutaneously (ectopically) injected into the flank and orthotopically into liver parenchyma of Mus Musculus SCID mice. The mice were euthanized after two weeks. RNA was extracted from the tumors, and gene expression profiling was performed using the Gene Chip Human Genome U133 Plus 2.0. Principal component analyses (PCA) and construction of dendrograms were conducted using Partek genomics suite. Results: PCA showed that the commonly used HepG2 cell line model and its xenograft counterparts were vastly different from all fresh, primary tumors. Expression profiles of primary tumors were also significantly divergent from their counterpart patient-derived xenograft (PDX) models, regardless of the site of implantation. Xenografts from the same primary tumors were more likely to cluster together regardless of site of implantation, although heat maps showed distinct differences in gene expression profiles between orthotopic and ectopic models. Conclusions: The data presented here challenges the utility of routinely used preclinical models. Models using HepG2 were vastly different from primary tumors and PDXs, suggesting that this is not clinically representative. Surprisingly, site of implantation (orthotopic versus ectopic) resulted in limited impact on gene expression profiles, and in both scenarios xenografts differed significantly from the original primary tumors, challenging the long-held notion that orthotopic PDX model is the gold standard preclinical model for HCC.
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