Literature DB >> 30894502

Intrinsic cell-penetrating activity propels Omomyc from proof of concept to viable anti-MYC therapy.

Marie-Eve Beaulieu1,2, Toni Jauset1,2, Daniel Massó-Vallés1,2, Sandra Martínez-Martín2, Peter Rahl3, Loïka Maltais4, Mariano F Zacarias-Fluck2, Sílvia Casacuberta-Serra1,2, Erika Serrano Del Pozo2, Christopher Fiore3, Laia Foradada1, Virginia Castillo Cano2, Meritxell Sánchez-Hervás2, Matthew Guenther3, Eduardo Romero Sanz5, Marta Oteo5, Cynthia Tremblay4, Génesis Martín2, Danny Letourneau4, Martin Montagne4, Miguel Ángel Morcillo Alonso5, Jonathan R Whitfield2, Pierre Lavigne4, Laura Soucek6,2,7,8.   

Abstract

Inhibiting MYC has long been considered unfeasible, although its key role in human cancers makes it a desirable target for therapeutic intervention. One reason for its perceived undruggability was the fear of catastrophic side effects in normal tissues. However, we previously designed a dominant-negative form of MYC called Omomyc and used its conditional transgenic expression to inhibit MYC function both in vitro and in vivo. MYC inhibition by Omomyc exerted a potent therapeutic impact in various mouse models of cancer, causing only mild, well-tolerated, and reversible side effects. Nevertheless, Omomyc has been so far considered only a proof of principle. In contrast with that preconceived notion, here, we show that the purified Omomyc mini-protein itself spontaneously penetrates into cancer cells and effectively interferes with MYC transcriptional activity therein. Efficacy of the Omomyc mini-protein in various experimental models of non-small cell lung cancer harboring different oncogenic mutation profiles establishes its therapeutic potential after both direct tissue delivery and systemic administration, providing evidence that the Omomyc mini-protein is an effective MYC inhibitor worthy of clinical development.
Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

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Year:  2019        PMID: 30894502      PMCID: PMC6522349          DOI: 10.1126/scitranslmed.aar5012

Source DB:  PubMed          Journal:  Sci Transl Med        ISSN: 1946-6234            Impact factor:   17.956


  68 in total

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  52 in total

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9.  Deep learning to design nuclear-targeting abiotic miniproteins.

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