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Open AccessJournal ArticleDOI

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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Journal ArticleDOI

ESR1 mutations: a new biomarker in breast cancer.

TL;DR: ESR1 mutation detection will probably become a prognostic and predictive biomarker in the future, used in clinical practice for hormone-receptor breast cancer, especially in the metastatic setting, and possibly have targeted-therapies against these mutations.
Journal ArticleDOI

Exploiting human and mouse transcriptomic data: Identification of circadian genes and pathways influencing health

TL;DR: Analyzes of human transcriptomic data show that a core set of transcripts related to processes including immune function, glucocorticoid signalling, and lipid metabolism is rhythmically expressed independently of the sleep‐wake cycle.
Journal ArticleDOI

A single droplet digital PCR for ESR1 activating mutations detection in plasma.

TL;DR: A multiplex droplet digital PCR (ddPCR) that detects, in a single reaction, the most frequent ESR1 activating mutations with good sensitivity, allows real-time liquid biopsy for E SR1 mutation monitoring in large cohorts of patients.
Book ChapterDOI

Advances in Circulating Tumor DNA Analysis

TL;DR: In this chapter, recent advances of the analysis of ctDNA are summarized starting from the discovery of cell-free DNA, to methodological approaches and the clinical applicability.
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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Journal ArticleDOI

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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