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ESR1 ligand binding domain mutations in hormone-resistant breast cancer

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      Abstract

      Seventy percent of breast cancers express estrogen receptor (ER) and most of these are sensitive to ER inhibition. However, many such tumors become refractory to inhibition of estrogen action in the metastatic setting for unknown reasons. We conducted a comprehensive genetic analysis of two independent cohorts of metastatic ER+ breast tumors and identified mutations in the ligand binding domain (LBD) of ESR1 in 14/80 cases. These included highly recurrent mutations p.Tyr537Ser/Asn and p.Asp538Gly. Molecular dynamics simulations suggest the Tyr537Ser and Asp538Gly structures lead to hydrogen bonding of the mutant amino acid with Asp351, thus favoring the receptor’s agonist conformation. Consistent with this model, mutant receptors drive ER-dependent transcription and proliferation in the absence of hormone and reduce the efficacy of ER antagonists. These data implicate LBD mutant forms of ER in mediating clinical resistance to hormonal therapy and suggest that more potent ER antagonists may have significant therapeutic benefit.

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      The Sequence Alignment/Map format and SAMtools

      Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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        Fast and accurate short read alignment with Burrows–Wheeler transform

        Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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          A framework for variation discovery and genotyping using next-generation DNA sequencing data

          Recent advances in sequencing technology make it possible to comprehensively catalogue genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (1) initial read mapping; (2) local realignment around indels; (3) base quality score recalibration; (4) SNP discovery and genotyping to find all potential variants; and (5) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We discuss the application of these tools, instantiated in the Genome Analysis Toolkit (GATK), to deep whole-genome, whole-exome capture, and multi-sample low-pass (~4×) 1000 Genomes Project datasets.
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            Author and article information

            Affiliations
            [1 ]Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center (MSKCC), New York, NY, USA
            [2 ]Toyota Technological Institute at Chicago, Chicago, IL, USA
            [3 ]Ben May Department of Cancer Research, University of Chicago, Chicago, IL, USA
            [4 ]Breast Service, Department of Surgery, MSKCC, New York, NY, USA
            [5 ]Weill Cornell Medical College, New York, NY, USA
            [6 ]Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA
            [7 ]Breast Cancer Medicine Service, Solid Tumor Division, Department of Medicine, MSKCC, New York, NY, USA
            [8 ]Department of Breast Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
            Author notes
            Correspondence: Sarat Chandarlapaty, MD, PhD, Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY, 10065 USA, (T) 646-888-3387, chandars@ 123456mskcc.org
            Journal
            9216904
            2419
            Nat Genet
            Nat. Genet.
            Nature genetics
            1061-4036
            1546-1718
            27 November 2013
            03 November 2013
            December 2013
            01 June 2014
            : 45
            : 12
            : 1439-1445
            24185512
            3903423
            10.1038/ng.2822
            NIHMS531250

            Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

            Funding
            Funded by: National Cancer Institute : NCI
            Award ID: K08 CA134833 || CA
            Categories
            Article

            Genetics

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