Clinical Implementation of MetaFusion for Accurate Cancer-Driving Fusion Detection from RNA Sequencing.
J Mol Diagn
; 25(12): 921-931, 2023 12.
Article
em En
| MEDLINE
| ID: mdl-37748705
ABSTRACT
Oncogenic fusion genes may be identified from next-generation sequencing data, typically RNA-sequencing. However, in a clinical setting, identifying these alterations is challenging against a background of nonrelevant fusion calls that reduce workflow precision and specificity. Furthermore, although numerous algorithms have been developed to detect fusions in RNA-sequencing, there are variations in their individual sensitivities. Here this problem was addressed by introducing MetaFusion into clinical use. Its utility was illustrated when applied to both whole-transcriptome and targeted sequencing data sets. MetaFusion combines ensemble fusion calls from eight individual fusion-calling algorithms with practice-informed identification of gene fusions that are known to be clinically relevant. In doing so, it allows oncogenic fusions to be identified with near-perfect sensitivity and high precision and specificity, significantly outperforming the individual fusion callers it uses as well as existing clinical-grade software. MetaFusion enhances clinical yield over existing methods and is able to identify fusions that have patient relevance for the purposes of diagnosis, prognosis, and treatment.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Software
/
Neoplasias
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
Idioma:
En
Revista:
J Mol Diagn
Assunto da revista:
BIOLOGIA MOLECULAR
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Canadá