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Glubb, D. M.

Publications and source records attributed to Glubb, D. M..

2 recordsLinked to original sources

Enhancer plasticity in endometrial tumorigenesis demarcates non-coding driver mutations and alterations in 3D genome organization to stimulate oncogene expression

The incidence and mortality of Endometrial Cancer (EC) is on the rise. 85% of ECs depend on Estrogen Receptor alpha (ER) for proliferation, but little is known about its transcriptional regulation in these tumors. We generated epigenomics, transcriptomics and Hi-C datastreams in healthy and tumor endometrial tissues, identifying robust ER reprogramming and profound alterations in 3D genome organization that lead to a gain of tumor-specific enhancer activity during EC development. Integration with endometrial cancer risk single-nucleotide polymorphisms, as well as WGS data from primary tumors and metastatic samples revealed a striking enrichment of risk variants and non-coding somatic mutations at tumor-enriched ER sites. Through machine learning-based predictions and interaction proteomics analyses, we identified an enhancer mutation which alters 3D genome conformation, impairing recruitment of the transcriptional repressor EHMT2/G9a/KMT1C, thereby alleviating transcriptional repression of ESR1 in EC. In summary, we identified a complex genomic-epigenomic interplay in EC development and progression, altering 3D genome organization to enhance expression of the critical driver ER.

cancer biology↗

SpliceAI-10k calculator for the prediction of pseudoexonization, intron retention, and exon deletion

SummarySpliceAI is a widely used splicing prediction tool and its most common application relies on the maximum delta score to assign variant impact on splicing. We developed the SpliceAI-10k calculator (SAI-10k-calc) to extend use of this tool to predict: the splicing aberration type including pseudoexonization, intron retention, partial exon deletion, and (multi)exon skipping using a 10 kb analysis window; the size of inserted or deleted sequence; the effect on reading frame; and the altered amino acid sequence. SAI-10k-calc has 95% sensitivity and 96% specificity for predicting variants that impact splicing, computed from a control dataset of 1,212 single nucleotide variants (SNVs) with curated splicing assay results. Notably, it has high performance ([≥]84% accuracy) for predicting pseudoexon and partial intron retention. The automated amino acid sequence prediction allows for efficient identification of variants that are expected to result in mRNA nonsense-mediated decay or translation of truncated proteins. Availability and implementationSAI-10k-calc is implemented in R (https://github.com/adavi4/SAI-10k-calc) and also available as a Microsoft Excel spreadsheet. Users can adjust the default thresholds to suit their target performance values. Supplementary informationSupplementary data are available online.

bioinformatics↗