Dataset Information

Robust in-silico identification of Cancer Cell Lines based on RNA and targeted DNA sequencing data.

ABSTRACT: Cancer cell lines (CCL) are an integral part of modern cancer research but are susceptible to misidentification. The increasing popularity of sequencing technologies motivates the in-silico identification of CCLs based on their mutational fingerprint, but care must be taken when identifying heterogeneous data. We recently developed the proof-of-concept Uniquorn 1 method which could reliably identify heterogeneous sequencing data from selected sequencing technologies. Here we present Uniquorn 2, a generic and robust in-silico identification method for CCLs with DNA/RNA-seq and panel-seq information. We benchmarked Uniquorn 2 by cross-identifying 1612?RNA and 3596 panel-sized NGS profiles derived from 1516 CCLs, five repositories, four technologies and three major cancer panel-designs. Our method achieves an accuracy of 96% for RNA-seq and 95% for mixed DNA-seq and RNA-seq identification. Even for a panel of only 94 cancer-related genes, accuracy remains at 82% but decreases when using smaller panels. Uniquorn 2 is freely available as R-Bioconductor-package 'Uniquorn'.

SUBMITTER: Otto R

PROVIDER: S-EPMC6344579 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Publications

Robust in-silico identification of Cancer Cell Lines based on RNA and targeted DNA sequencing data.

Otto Raik R Rössler Jan-Niklas JN Sers Christine C Mamlouk Soulafa S Leser Ulf U

Scientific reports 20190123 1

Cancer cell lines (CCL) are an integral part of modern cancer research but are susceptible to misidentification. The increasing popularity of sequencing technologies motivates the in-silico identification of CCLs based on their mutational fingerprint, but care must be taken when identifying heterogeneous data. We recently developed the proof-of-concept Uniquorn 1 method which could reliably identify heterogeneous sequencing data from selected sequencing technologies. Here we present Uniquorn 2, ...[more]

PMID: 30674903

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Project description:Astrocytomas are the most common and aggressive type of primary brain tumors in adults. The World Health Organization (WHO) assorts them into grades, from I to IV, based on histopathological features that reflect their malignancy [1]. Alongside with tumor progression, comes an increased proliferation, genomic instability, infiltration in normal brain tissue and resistance to treatments. The high genomic instability forges tumor cells enhancing key proteins that avoid cells from collapsing and favor therapy resistance [2]. To explore genes and pathways associated with tumor progression phenotypes we analyzed gene expression in a panel of non-tumor and glioma cell lines, namely: ACBRI371, non-tumor human astrocytes; HDPC, fibroblasts derived from dental pulp; Res186, Res259, Res286 and UW467 that include grade I, II and III astrocytoma cell lines derived from pediatric tumors; and T98G, U343MG, U87MG, U138MG and U251MG, all derived from GBM (grade IV). We also profiled gene expression changes caused by exogenously induced replicative stress, performing RNA sequencing with camptothecin (CPT)-treated cells. Here we describe the RNA-sequencing data set acquired, including quality of reads and sequencing consistency, as well as the bioinformatics strategy used to analyze it. We also compared gene expression patterns and pathway enrichment between non-tumor versus lower-grade (LGG), non-tumor versus GBM, LGG versus GBM, and CPT-treated versus non-treated cells. In brief, a total of 6467 genes showed differential expression and 5 pathways were enriched in tumor progression, while 2279 genes and 7 pathways were altered under the replication stress condition. The raw data was deposited in the NCBI BioProject database under the accession number PRJNA631805. Our dataset is valuable for researchers interested in differential gene expression among different astrocytoma grades and in expression changes caused by replicative stress, facilitating studies that seek novel biomarkers of glioma progression and treatment resistance.

Dataset Information

Robust in-silico identification of Cancer Cell Lines based on RNA and targeted DNA sequencing data.

Publications

Robust in-silico identification of Cancer Cell Lines based on RNA and targeted DNA sequencing data.

Similar Datasets

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