‘Gain’ of supernumerary copies of the 8q24.21 chromosomal region has been shown to be common in many human cancers1–13 and is associated with poor prognosis7,10,14. The well-characterized myelocytomatosis (MYC) oncogene resides in the 8q24.21 region and is consistently co-gained with an adjacent ‘gene desert’ of approximately 2 megabases that contains the long non-coding RNA gene PVT1, the CCDC26 gene candidate and the GSDMC gene. Whether low copy-number gain of one or more of these genes drives neoplasia is not known. Here we use chromosome engineering in mice to show that a single extra copy of either the Myc gene or the region encompassing Pvt1, Ccdc26 and Gsdmc fails to advance cancer measurably, whereas a single supernumerary segment encompassing all four genes successfully promotes cancer. Gain of PVT1 long non-coding RNA expression was required for high MYC protein levels in 8q24-amplified human cancer cells. PVT1 RNA and MYC protein expression correlated in primary human tumours, and copy number of PVT1 was co-increased in more than 98% of MYC-copy-increase cancers. Ablation of PVT1 from MYC-driven colon cancer line HCT116 diminished its tumorigenic potency. As MYC protein has been refractory to small-molecule inhibition, the dependence of high MYC protein levels on PVT1 long non-coding RNA provides a much needed therapeutic target.
BackgroundThe single cell RNA sequencing (scRNA-seq) technique begin a new era by allowing the observation of gene expression at the single cell level. However, there is also a large amount of technical and biological noise. Because of the low number of RNA transcriptomes and the stochastic nature of the gene expression pattern, there is a high chance of missing nonzero entries as zero, which are called dropout events.ResultsWe develop DrImpute to impute dropout events in scRNA-seq data. We show that DrImpute has significantly better performance on the separation of the dropout zeros from true zeros than existing imputation algorithms. We also demonstrate that DrImpute can significantly improve the performance of existing tools for clustering, visualization and lineage reconstruction of nine published scRNA-seq datasets.ConclusionsDrImpute can serve as a very useful addition to the currently existing statistical tools for single cell RNA-seq analysis. DrImpute is implemented in R and is available at https://github.com/gongx030/DrImpute.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2226-y) contains supplementary material, which is available to authorized users.
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