BackgroundOesophageal cancer is one of the most deadly forms of cancer worldwide. Long non-coding RNAs (lncRNAs) are often found to have important regulatory roles.ObjectiveTo assess the lncRNA expression profile of oesophageal squamous cell carcinoma (OSCC) and identify prognosis-related lncRNAs.MethodLncRNA expression profiles were studied by microarray in paired tumour and normal tissues from 119 patients with OSCC and validated by qRT-PCR. The 119 patients were divided randomly into training (n=60) and test (n=59) groups. A prognostic signature was developed from the training group using a random Forest supervised classification algorithm and a nearest shrunken centroid algorithm, then validated in a test group and further, in an independent cohort (n=60). The independence of the signature in survival prediction was evaluated by multivariable Cox regression analysis.ResultsLncRNAs showed significantly altered expression in OSCC tissues. From the training group, we identified a three-lncRNA signature (including the lncRNAs ENST00000435885.1, XLOC_013014 and ENST00000547963.1) which classified the patients into two groups with significantly different overall survival (median survival 19.2 months vs >60 months, p<0.0001). The signature was applied to the test group (median survival 21.5 months vs >60 months, p=0.0030) and independent cohort (median survival 25.8 months vs >48 months, p=0.0187) and showed similar prognostic values in both. Multivariable Cox regression analysis showed that the signature was an independent prognostic factor for patients with OSCC. Stratified analysis suggested that the signature was prognostic within clinical stages.ConclusionsOur results suggest that the three-lncRNA signature is a new biomarker for the prognosis of patients with OSCC, enabling more accurate prediction of survival.

Li and colleagues report the genomic landscape of over 100 patients with relapsed acute lymphoblastic leukemia. Analysis of diagnosis-relapse-remission trios suggest that whereas early relapse is mediated by retained subclones, late relapse is driven by mutations induced by and conferring resistance to chemotherapy.

BackgroundSequencing errors are key confounding factors for detecting low-frequency genetic variants that are important for cancer molecular diagnosis, treatment, and surveillance using deep next-generation sequencing (NGS). However, there is a lack of comprehensive understanding of errors introduced at various steps of a conventional NGS workflow, such as sample handling, library preparation, PCR enrichment, and sequencing. In this study, we use current NGS technology to systematically investigate these questions.ResultsBy evaluating read-specific error distributions, we discover that the substitution error rate can be computationally suppressed to 10−5 to 10−4, which is 10- to 100-fold lower than generally considered achievable (10−3) in the current literature. We then quantify substitution errors attributable to sample handling, library preparation, enrichment PCR, and sequencing by using multiple deep sequencing datasets. We find that error rates differ by nucleotide substitution types, ranging from 10−5 for A>C/T>G, C>A/G>T, and C>G/G>C changes to 10−4 for A>G/T>C changes. Furthermore, C>T/G>A errors exhibit strong sequence context dependency, sample-specific effects dominate elevated C>A/G>T errors, and target-enrichment PCR led to ~ 6-fold increase of overall error rate. We also find that more than 70% of hotspot variants can be detected at 0.1 ~ 0.01% frequency with the current NGS technology by applying in silico error suppression.ConclusionsWe present the first comprehensive analysis of sequencing error sources in conventional NGS workflows. The error profiles revealed by our study highlight new directions for further improving NGS analysis accuracy both experimentally and computationally, ultimately enhancing the precision of deep sequencing.Electronic supplementary materialThe online version of this article (10.1186/s13059-019-1659-6) contains supplementary material, which is available to authorized users.

Numerous pieces of evidence support the complex, 3D spatial organization of the genome dictates gene expression. CTCF is essential to define topologically associated domain boundaries and to facilitate the formation of insulated chromatin loop structures. To understand CTCF’s direct role in global transcriptional regulation, we integrated the miniAID-mClover3 cassette to the endogenous CTCF locus in a human pediatric B-ALL cell line, SEM, and an immortal erythroid precursor cell line, HUDEP-2, to allow for acute depletion of CTCF protein by the auxin-inducible degron system. In SEM cells, CTCF loss notably disrupted intra-TAD loops and TAD integrity in concurrence with a reduction in CTCF-binding affinity, while showing no perturbation to nuclear compartment integrity. Strikingly, the overall effect of CTCF’s loss on transcription was minimal. Whole transcriptome analysis showed hundreds of genes differentially expressed in CTCF-depleted cells, among which MYC and a number of MYC target genes were specifically downregulated. Mechanically, acute depletion of CTCF disrupted the direct interaction between the MYC promoter and its distal enhancer cluster residing ∼1.8 Mb downstream. Notably, MYC expression was not profoundly affected upon CTCF loss in HUDEP-2 cells suggesting that CTCF could play a B-ALL cell line specific role in maintaining MYC expression.