In Vitro Insertional Mutagenesis Screen Identifies Novel Genes Driving Breast Cancer Metastasis.
Journal
Molecular cancer research : MCR
ISSN: 1557-3125
Titre abrégé: Mol Cancer Res
Pays: United States
ID NLM: 101150042
Informations de publication
Date de publication:
04 10 2022
04 10 2022
Historique:
received:
10
09
2021
revised:
07
02
2022
accepted:
08
06
2022
pubmed:
11
6
2022
medline:
6
10
2022
entrez:
10
6
2022
Statut:
ppublish
Résumé
Metastasis, a complex, multistep process, is responsible for the overwhelming majority of cancer-related deaths. Despite its devastating consequences, it is not possible to effectively treat cancer that has spread to vital organs, the mechanisms leading to metastasis are still poorly understood, and the catalog of metastasis promoting genes is still incomprehensive. To identify new driver genes of metastasis development, we performed an in vitro Sleeping Beauty transposon-based forward genetic screen in nonmetastatic SKBR3 human breast cancer cells. Boyden chamber-based matrix invasion assays were used to harvest cells that acquired a de novo invasive phenotype. Using targeted RNA sequencing data from 18 pools of invasive cells, we carried out a gene-centric candidate gene prediction and identified established and novel metastasis driver genes. Analysis of these genes revealed their association with metastasis related processes and we further established their clinical relevance in metastatic breast cancer. Two novel candidate genes, G protein-coupled receptor kinase interacting ArfGAP 2 (GIT2) and muscle-associated receptor tyrosine kinase (MUSK), were functionally validated as metastasis driver genes in a series of in vitro and in vivo experimental metastasis models. We propose that our robust and scalable approach will be a useful addition to the toolkit of methodologic resources used to identify genes driving cancer metastasis. Novel metastasis drivers were identified in a human breast cancer cell line by performing an in vitro, Sleeping Beauty transposon-based forward genetic screen and an RNA fusion-based candidate gene prediction.
Identifiants
pubmed: 35687718
pii: 704871
doi: 10.1158/1541-7786.MCR-21-0772
doi:
Substances chimiques
DNA Transposable Elements
0
Receptors, G-Protein-Coupled
0
RNA
63231-63-0
Protein-Tyrosine Kinases
EC 2.7.10.1
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1502-1515Informations de copyright
©2022 American Association for Cancer Research.