Dataset Information

Human somatic cell mutagenesis creates genetically tractable sarcomas

ABSTRACT: Creating spontaneous yet genetically tractable human tumors from normal cells presents a fundamental challenge. Retroviruses and transposons have been separately used as somatic cell insertional mutagens to identify cancer drivers in model organisms. Here we combined these mutagenic elements to enable cancer gene discovery starting with normal human cells. Lentivirus was used to seed gain- and loss-of-function gene disruption elements which were further deployed by Sleeping Beauty transposons throughout the genome of human bone explant mesenchymal cells. De novo tumors rapidly generated in this context were high-grade sarcomas corresponding to the spectrum of myxofibrosarcoma and undifferentiated pleomorphic sarcoma, aggressive neoplasms with a predilection for older adults. Tumor insertion sites were genome-wide and enriched in regions of recurrent somatic copy number alteration found in multiple cancers, with a bias towards those of sarcomas. Novel driver genes which sustain somatic alterations in cancer patients were pinpointed. We identify the gene HDLBP, which codes for the RNA binding protein vigilin, as a candidate tumor suppressor deleted at 2q37.3 in greater than one in ten tumors across multiple tissues of origin. Hybrid viral-transposon systems will accelerate the functional annotation of cancer genomes by enabling insertional mutagenesis screens in higher eukaryotes that are not amenable to germline transgenesis.

ORGANISM(S): Homo sapiens

PROVIDER: GSE59720 | GEO | 2014/07/25

SECONDARY ACCESSION(S): PRJNA256035

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Somatic transposon mutagenesis in mice is an efficient strategy to investigate the genetic mechanisms of tumorigenesis. The identification of tumor driving transposon insertions traditionally requires the generation of large tumor cohorts to obtain information about common insertion sites. Tumor driving insertions are also characterized by their clonal expansion in tumor tissue, a phenomenon that is facilitated by the slow and evolving transformation process of transposon mutagenesis. We describe here an improved approach for the detection of tumor driving insertions that assesses the clonal expansion of insertions by quantifying the relative proportion of sequence reads obtained in individual tumors. To this end, we have developed a protocol for insertion site sequencing that utilizes acoustic shearing of tumor DNA and Illumina sequencing. We analyzed various solid tumors generated by PiggyBac mutagenesis and for each tumor >10^6 reads corresponding to >10^4 insertion sites were obtained. In each tumor, 9 to 25 insertions stood out by their enriched sequence read frequencies when compared to frequencies obtained from tail DNA controls. These enriched insertions are potential clonally expanded tumor driving insertions, and thus identify candidate cancer genes. The candidate cancer genes of our study comprised many established cancer genes, but also novel candidate genes such as Mastermind-like1 (Mamld1) and Diacylglycerolkinase delta (Dgkd). We show that clonal expansion analysis by high-throughput sequencing is a robust approach for the identification of candidate cancer genes in insertional mutagenesis screens on the level of individual tumors. Solid tumors in mice were generated by somatic transposon mutagenesis with a PiggyBac transposon system. Insertion sites of transposons in 11 tumors and 6 non-cancerous tail controls were determined by Illumina high-throughput sequencing. Insertions were determined both on 5' and 3' sides of the transposon (PB5 and PB3, respectively). Quantitative analysis of read numbers revealed enrichment of certain insertions in tumors, but not in controls, and these enriched insertions identify candidate cancer genes.

Dataset Information

Human somatic cell mutagenesis creates genetically tractable sarcomas

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets