Abstract
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.