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      ESR1 ligand binding domain mutations in hormone-resistant breast cancer

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          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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          Most cited references 41

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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: Contact:
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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: Contact:
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              VMD: Visual molecular dynamics


                Author and article information

                [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@
                Nat Genet
                Nat. Genet.
                Nature genetics
                27 November 2013
                03 November 2013
                December 2013
                01 June 2014
                : 45
                : 12
                : 1439-1445

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



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