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Activating ESR1 mutations in hormone-resistant metastatic breast cancer

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TLDR
Five new LBD-localized ESR1 mutations identified here were shown to result in constitutive activity and continued responsiveness to anti-estrogen therapies in vitro, suggesting that activating mutations in E SR1 are a key mechanism in acquired endocrine resistance in breast cancer therapy.
Abstract
Arul Chinnaiyan and colleagues report the results of prospective clinical sequencing of 11 estrogen receptor–positive metastatic breast cancers. They identify ESR1 mutations affecting the ligand-binding domain in six hormone-resistant metastatic breast cancers and show that the mutant estrogen receptors are constitutively active and continue to be responsive to anti-estrogen therapies in vitro. Breast cancer is the most prevalent cancer in women, and over two-thirds of cases express estrogen receptor-α (ER-α, encoded by ESR1). Through a prospective clinical sequencing program for advanced cancers, we enrolled 11 patients with ER-positive metastatic breast cancer. Whole-exome and transcriptome analysis showed that six cases harbored mutations of ESR1 affecting its ligand-binding domain (LBD), all of whom had been treated with anti-estrogens and estrogen deprivation therapies. A survey of The Cancer Genome Atlas (TCGA) identified four endometrial cancers with similar mutations of ESR1. The five new LBD-localized ESR1 mutations identified here (encoding p.Leu536Gln, p.Tyr537Ser, p.Tyr537Cys, p.Tyr537Asn and p.Asp538Gly) were shown to result in constitutive activity and continued responsiveness to anti-estrogen therapies in vitro. Taken together, these studies suggest that activating mutations in ESR1 are a key mechanism in acquired endocrine resistance in breast cancer therapy.

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Citations
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Catalog of genetic progression of human cancers: breast cancer

TL;DR: Novel approaches such as the analysis of circulating DNA and patient-derived xenografts are described that promise to further the understanding of the genomic changes occurring during cancer evolution and guide treatment decision making.
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The Evolving Role of Companion Diagnostics for Breast Cancer in an Era of Next-Generation Omics

TL;DR: The evolution of the biomarkers used to stratify and treat breast cancer illustrates the history of companion diagnostics and provides a lens through which to examine potential challenges.
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Subtype-specific modulation of estrogen receptor-coactivator interaction by phosphorylation.

TL;DR: A novel molecular model of a phosphorylation-induced subtype specific ER modulatory mechanism, alternative to the widely accepted ligand-induced activation mechanism is brought forward.
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Metastatic Breast Cancer With ESR1 Mutation: Clinical Management Considerations From the Molecular and Precision Medicine (MAP) Tumor Board at Massachusetts General Hospital

TL;DR: The case of a 67-year-old postmenopausal female with metastatic estrogen receptor-positive breast cancer, whose tumor progressed on multiple endocrine therapies, is presented.
References
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TL;DR: SAMtools as discussed by the authors 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.
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Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks

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ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

TL;DR: The ANNOVAR tool to annotate single nucleotide variants and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP is developed.
Journal ArticleDOI

Comprehensive molecular portraits of human breast tumours

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TL;DR: The ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity.
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