Dataset Information

Antagonists for Constitutively Active Mutant Estrogen Receptors: Insights into the Roles of Antiestrogen-Core and Side-Chain.

ABSTRACT: A major risk for patients having estrogen receptor α (ERα)-positive breast cancer is the recurrence of drug-resistant metastases after initial successful treatment with endocrine therapies. Recent studies have implicated a number of activating mutations in the ligand-binding domain of ERα that stabilize the agonist conformation as a prominent mechanism for this acquired resistance. There are several critical gaps in our knowledge regarding the specific pharmacophore requirements of an antagonist that could effectively inhibit all or most of the different mutant ERs. To address this, we screened various chemotypes for blocking mutant ER-mediated transcriptional signaling and identified RU58668 as a model compound that contains structural elements that support potent ligand-induced inhibition of mutant ERs. We designed and synthesized a focused library of novel antagonists and probed how small and large perturbations in different ligand structural regions influenced inhibitory activity on individual mutant ERs in breast cancer cells. Effective inhibition derives from both nonpolar and moderately polar motifs in a multifunctional side chain of the antagonists, with the nature of the ligand core making important contributions by increasing the potency of ligands possessing similar types of side chains. Some of our new antagonists potently blocked the transcriptional activity of the three most common mutant ERs (L536R, Y537S, D538G) and inhibited mutant ER-mediated cell proliferation. Supported by our molecular modeling, these studies provide new insights into the role of specific components, involving both the ligand core and multifunctional side chain, in suppressing wild-type and mutant ER-mediated transcription and breast cancer cell proliferation.

SUBMITTER: Sharma A

PROVIDER: S-EPMC6469989 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The most frequently diagnosed cancers in women are the estrogen receptor (ER)-positive breast cancer subtypes, which are characterized by estrogen dependency for their growth. The mainstay of clinical treatment for this tumor relies on the modulation of ERα action or on the suppression of estrogen biosynthesis via the administration of Selective ERα Modulators/Down-regulators (SERMs/SERDs) or aromatase inhibitors, respectively. Nevertheless, de novo and acquired resistance to these therapies frequently occurs and represents a major clinical concern for patient survival. Recently, somatic mutations affecting the hormone-binding domain of ERα (i.e., Y537S, Y537N, D538G) have been associated with endocrine resistance, disease relapse and increased mortality rates. Hence, devising novel therapies against these ERα isoforms represents a daunting challenge. Here, we identified five molecules active on recurrent Y537S ERα polymorphism by employing in silico virtual screening on commercial databases of molecules, complemented by ER-transactivation and MTT assays in MCF7 and MDA-MB-231 breast cancer cells expressing wild type or mutated ERα. Among them, one molecule selectively targets Y537S ERα without inducing any cytotoxicity in breast cell lines. Multi-microseconds (4.5 μs) of biased and unbiased molecular dynamics provided an atomic-level picture of the structural, thermodynamics (i.e., binding free energies) and the kinetic (i.e., dissociation free energy barriers) of these active ligands as compared to clinically used SERM/SERDs upon binding to wild type and distinct ERα variants (Y537S, Y537N, D538G). This study contributes to a dissection of the key molecular traits needed by drug-candidates to hamper the agonist (active)-like conformation of ERα, normally selected by those polymorphic variants. This information can be useful to discover mutant specific drug-candidates, enabling to move a step forward toward tailored approaches for breast cancer treatment.

Dataset Information

Antagonists for Constitutively Active Mutant Estrogen Receptors: Insights into the Roles of Antiestrogen-Core and Side-Chain.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets