Epigenetic reprogramming at estrogen-receptor binding sites alters 3D chromatin landscape in endocrine-resistant breast cancer

Endocrine therapy resistance frequently develops in estrogen receptor positive (ER+) breast cancer, but the underlying molecular mechanisms are largely unknown. Here, we show that 3-dimensional (3D) chromatin interactions both within and between topologically associating domains (TADs) frequently change in ER+ endocrine-resistant breast cancer cells and that the differential interactions are enriched for resistance-associated genetic variants at CTCF-bound anchors. Ectopic chromatin interactions are preferentially enriched at active enhancers and promoters and ER binding sites, and are associated with altered expression of ER-regulated genes, consistent with dynamic remodelling of ER pathways accompanying the development of endocrine resistance. We observe that loss of 3D chromatin interactions often occurs coincidently with hypermethylation and loss of ER binding. Alterations in active A and inactive B chromosomal compartments are also associated with decreased ER binding and atypical interactions and gene expression. Together, our results suggest that 3D epigenome remodelling is a key mechanism underlying endocrine resistance in ER+ breast cancer

Meeting the challenges of implementing rapid genomic testing in acute pediatric care

Authors: Stark, Z. Lunke, S. Brett, G. Tan, N. Stapleton, R. Kumble, S. Yeung, A. Phelan, D. Chong, B. Fernandez, M.F. Marum, J.E. Hunter, M. Jarmolowicz, A. Yael Prawer, Y. Riseley, J.R. Regan, M. Elliott, J. Melissa Martyn, M. Best, S. Tan, T. Clara L Gaff, C.L. and White, S.M

Ximmer: a system for improving accuracy and consistency of CNV calling from exome data

Background: While exome and targeted next-generation DNA sequencing are primarily used for detecting single nucleotide changes and small indels, detection of copy number variants (CNVs) can provide highly valuable additional information from the data. Although there are dozens of exome CNV detection methods available, these are often difficult to use, and accuracy varies unpredictably between and within datasets. Findings: We present Ximmer, a tool that supports an end-to-end process for evaluating, tuning, and running analysis methods for detection of CNVs in germline samples. Ximmer includes a simulation framework, implementations of several commonly used CNV detection methods, and a visualization and curation tool that together enable interactive exploration and quality control of CNV results. Using Ximmer, we comprehensively evaluate CNV detection on four datasets using five different detection methods. We show that application of Ximmer can improve accuracy and aid in quality control of CNV detection results. In addition, Ximmer can be used to run analyses and explore CNV results in exome data. Conclusions: Ximmer offers a comprehensive tool and method for applying and improving accuracy of CNV detection methods for exome data