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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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Steroid receptor/coactivator binding inhibitors: An update.

TL;DR: Small-molecule and peptide coactivator binding inhibitors for steroid receptors, with a particular focus on recent compounds that have been assayed in cell-based models, are highlighted.
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Clinical Review on the Management of Hormone Receptor-Positive Metastatic Breast Cancer.

TL;DR: In this article, the authors present a treatment plan for hormone receptor-positive breast cancer patients, taking into account traditional prognostic factors such as menopausal status, previous treatments, disease-free interval for those patients with early breast cancer that has recurred, and tumor burden.
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Transcriptomic Profiling of Sequential Tumors from Breast Cancer Patients Provides a Global View of Metastatic Expression Changes Following Endocrine Therapy.

TL;DR: A novel global analysis highlights the influence of primary tumor biology in determining the transcriptomic profile of metastatic tumors, as well as the need for adaptations in cell–cell communications to facilitate successful tumor cell colonization of distant host organs.
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The current use and attitudes towards tumor genome sequencing in breast cancer.

TL;DR: The survey indicates that tumor genome sequencing is sometimes used, albeit not widely, in guiding management of breast cancer patients, and that institutional guidelines for molecular sequencing were associated with a higher probability of use.
References
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Journal ArticleDOI

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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

Daniel C. Koboldt, +355 more
- 04 Oct 2012 - 
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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