SummaryOestrogen receptor-α (ER) is the defining and driving transcription factor in the majority of breast cancers and its target genes dictate cell growth and endocrine response, yet genomic understanding of ER function has been restricted to model systems1-3. We now map genome-wide ER binding events, by chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq), in primary breast cancers from patients with different clinical outcome and in distant ER positive (ER+) metastases. We find that drug resistant cancers still have ER-chromatin occupancy, but that ER binding is a dynamic process, with the acquisition of unique ER binding regions in tumours from patients that are likely to relapse. The acquired, poor outcome ER regulatory regions observed in primary tumours reveal gene signatures that predict clinical outcome in ER+ disease exclusively. We find that the differential ER binding programme observed in tumours from patients with poor outcome is not due to the selection of a rare subpopulation of cells, but is due to the FoxA1-mediated reprogramming of ER binding on a rapid time scale. The parallel redistribution of ER and FoxA1 cis-regulatory elements in drug resistant cellular contexts is supported by histological co-expression of ER and FoxA1 in metastatic samples. By establishing transcription factor mapping in primary tumour material, we show that there is plasticity in ER binding capacity, with distinct combinations of cis-regulatory elements linked with the different clinical outcomes.

Motivation: The Illumina Infinium 450 k DNA Methylation Beadchip is a prime candidate technology for Epigenome-Wide Association Studies (EWAS). However, a difficulty associated with these beadarrays is that probes come in two different designs, characterized by widely different DNA methylation distributions and dynamic range, which may bias downstream analyses. A key statistical issue is therefore how best to adjust for the two different probe designs.Results: Here we propose a novel model-based intra-array normalization strategy for 450 k data, called BMIQ (Beta MIxture Quantile dilation), to adjust the beta-values of type2 design probes into a statistical distribution characteristic of type1 probes. The strategy involves application of a three-state beta-mixture model to assign probes to methylation states, subsequent transformation of probabilities into quantiles and finally a methylation-dependent dilation transformation to preserve the monotonicity and continuity of the data. We validate our method on cell-line data, fresh frozen and paraffin-embedded tumour tissue samples and demonstrate that BMIQ compares favourably with two competing methods. Specifically, we show that BMIQ improves the robustness of the normalization procedure, reduces the technical variation and bias of type2 probe values and successfully eliminates the type1 enrichment bias caused by the lower dynamic range of type2 probes. BMIQ will be useful as a preprocessing step for any study using the Illumina Infinium 450 k platform.Availability: BMIQ is freely available from http://code.google.com/p/bmiq/.Contact: a.teschendorff@ucl.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online

(http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. microRNA expression profiling of human breast cancer

Integrated analysis of miRNA expression and genomic changes in human breast tumors allows the classification of tumor subtypes. Abstract Background: MicroRNAs (miRNAs), a class of short non-coding RNAs found in many plants and animals, often act post-transcriptionally to inhibit gene expression.