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A Computational Assay of Estrogen Receptor α Antagonists Reveals the Key Common Structural Traits of Drugs Effectively Fighting Refractory Breast Cancers

Somatic mutations of the Estrogen Receptor α (ERα) occur with an up to 40% incidence in ER sensitive breast cancer (BC) patients undergoing prolonged endocrine treatments. These polymorphisms are implicated in acquired resistance, disease relapse, and increased mortality rates, hence representing a...

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Detalles Bibliográficos
Autores principales: Pavlin, Matic, Spinello, Angelo, Pennati, Marzia, Zaffaroni, Nadia, Gobbi, Silvia, Bisi, Alessandra, Colombo, Giorgio, Magistrato, Alessandra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5766519/
https://www.ncbi.nlm.nih.gov/pubmed/29330437
http://dx.doi.org/10.1038/s41598-017-17364-4
Descripción
Sumario:Somatic mutations of the Estrogen Receptor α (ERα) occur with an up to 40% incidence in ER sensitive breast cancer (BC) patients undergoing prolonged endocrine treatments. These polymorphisms are implicated in acquired resistance, disease relapse, and increased mortality rates, hence representing a current major clinical challenge. Here, multi-microseconds (12.5 µs) molecular dynamics simulations revealed that recurrent ERα polymorphisms (i. e. L536Q, Y537S, Y537N, D538G) (mERα) are constitutively active in their apo form and that they prompt the selection of an agonist (active)-like conformation even upon antagonists binding. Interestingly, our simulations rationalize, for the first time, the efficacy profile of (pre)clinically used Selective Estrogen Receptor Modulators/Downregulators (SERMs/SERDs) against these variants, enlightening, at atomistic level of detail, the key common structural traits needed by drugs able to effectively fight refractory BC types. This knowledge represents a key advancement for mechanism-based therapeutics targeting resistant ERα isoforms, potentially allowing the community to move a step closer to ‘precision medicine’ calibrated on patients’ genetic profiles and disease progression.