SB Driver Analysis: a Sleeping Beauty cancer driver analysis framework for identifying and prioritizing experimentally actionable oncogenes and tumor suppressors.
Nucleic Acids Res
; 46(16): e94, 2018 09 19.
Article
de En
| MEDLINE
| ID: mdl-29846651
ABSTRACT
Cancer driver prioritization for functional analysis of potential actionable therapeutic targets is a significant challenge. Meta-analyses of mutated genes across different human cancer types for driver prioritization has reaffirmed the role of major players in cancer, including KRAS, TP53 and EGFR, but has had limited success in prioritizing genes with non-recurrent mutations in specific cancer types. Sleeping Beauty (SB) insertional mutagenesis is a powerful experimental gene discovery framework to define driver genes in mouse models of human cancers. Meta-analyses of SB datasets across multiple tumor types is a potentially informative approach to prioritize drivers, and complements efforts in human cancers. Here, we report the development of SB Driver Analysis, an in-silico method for defining cancer driver genes that positively contribute to tumor initiation and progression from population-level SB insertion data sets. We demonstrate that SB Driver Analysis computationally prioritizes drivers and defines distinct driver classes from end-stage tumors that predict their putative functions during tumorigenesis. SB Driver Analysis greatly enhances our ability to analyze, interpret and prioritize drivers from SB cancer datasets and will continue to substantially increase our understanding of the genetic basis of cancer.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Oncogènes
/
Éléments transposables d'ADN
/
Transformation cellulaire néoplasique
/
Mutagenèse par insertion
/
Protéines suppresseurs de tumeurs
/
Tumeurs
Type d'étude:
Prognostic_studies
Limites:
Animals
/
Humans
Langue:
En
Journal:
Nucleic Acids Res
Année:
2018
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique