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FusionPathway: Prediction of pathways and therapeutic targets associated with gene fusions in cancer.
Wu, Chia-Chin; Beird, Hannah C; Zhang, Jianhua; Futreal, P Andrew.
Afiliación
  • Wu CC; Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
  • Beird HC; Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
  • Zhang J; Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
  • Futreal PA; Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
PLoS Comput Biol ; 14(7): e1006266, 2018 07.
Article en En | MEDLINE | ID: mdl-30040819
Numerous gene fusions have been uncovered across multiple cancer types. Although the ability to target several of these fusions has led to the development of some successful anti-cancer drugs, most of them are not druggable. Understanding the molecular pathways of a fusion is important in determining its function in oncogenesis and in developing therapeutic strategies for patients harboring the fusion. However, the molecular pathways have been elucidated for only a few fusions, in part because of the labor-intensive nature of the required functional assays. Therefore, we developed a domain-based network approach to infer the pathways of a fusion. Molecular interactions of a fusion are first predicted by using its protein domain composition, and its associated pathways are then inferred from these molecular interactions. We demonstrated the capabilities of this approach by primarily applying it to the well-studied BCR-ABL1 fusion. The approach was also applied to two undruggable fusions in sarcoma, EWS-FL1 and FUS-DDIT3. We successfully identified known genes and pathways associated with these fusions and satisfactorily validated these predictions using several benchmark sets. The predictions of EWS-FL1 and FUS-DDIT3 also correlate with results of high-throughput drug screening. To our best knowledge, this is the first approach for inferring pathways of fusions.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Sarcoma / Diseño de Software / Proteínas de Fusión Oncogénica / Proteínas de Fusión bcr-abl / Proteína EWS de Unión a ARN / Proteína Proto-Oncogénica c-fli-1 / Antineoplásicos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Sarcoma / Diseño de Software / Proteínas de Fusión Oncogénica / Proteínas de Fusión bcr-abl / Proteína EWS de Unión a ARN / Proteína Proto-Oncogénica c-fli-1 / Antineoplásicos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos