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ReCorDE: a framework for identifying drug classes targeting shared vulnerabilities with applications to synergistic drug discovery.
John, August J; Ghose, Emily T; Gao, Huanyao; Luck, Meagan; Jeong, Dabin; Kalari, Krishna R; Wang, Liewei.
Afiliación
  • John AJ; Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States.
  • Ghose ET; Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States.
  • Gao H; Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States.
  • Luck M; Department of Biological Sciences, University of Notre Dame, South Bend, IN, United States.
  • Jeong D; Biochemistry Department, Lawrence University, Appleton, WI, United States.
  • Kalari KR; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
  • Wang L; Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States.
Front Oncol ; 14: 1343091, 2024.
Article en En | MEDLINE | ID: mdl-38884087
ABSTRACT
Cancer is typically treated with combinatorial therapy, and such combinations may be synergistic. However, discovery of these combinations has proven difficult as brute force combinatorial screening approaches are both logistically complex and resource-intensive. Therefore, computational approaches to augment synergistic drug discovery are of interest, but current approaches are limited by their dependencies on combinatorial drug screening training data or molecular profiling data. These dataset dependencies can limit the number and diversity of drugs for which these approaches can make inferences. Herein, we describe a novel computational framework, ReCorDE (Recurrent Correlation of Drugs with Enrichment), that uses publicly-available cell line-derived monotherapy cytotoxicity datasets to identify drug classes targeting shared vulnerabilities across multiple cancer lineages; and we show how these inferences can be used to augment synergistic drug combination discovery. Additionally, we demonstrate in preclinical models that a drug class combination predicted by ReCorDE to target shared vulnerabilities (PARP inhibitors and Aurora kinase inhibitors) exhibits class-class synergy across lineages. ReCorDE functions independently of combinatorial drug screening and molecular profiling data, using only extensive monotherapy cytotoxicity datasets as its input. This allows ReCorDE to make robust inferences for a large, diverse array of drugs. In conclusion, we have described a novel framework for the identification of drug classes targeting shared vulnerabilities using monotherapy cytotoxicity datasets, and we showed how these inferences can be used to aid discovery of novel synergistic drug combinations.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Front Oncol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Front Oncol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos