Metabolism-based isolation of invasive glioblastoma cells with specific gene signatures and tumorigenic potential.
Neurooncol Adv
; 2(1): vdaa087, 2020.
Article
em En
| MEDLINE
| ID: mdl-32904996
BACKGROUND: Glioblastoma (GBM) is a highly aggressive brain tumor with rapid subclonal diversification, harboring molecular abnormalities that vary temporospatially, a contributor to therapy resistance. Fluorescence-guided neurosurgical resection utilizes the administration of 5-aminolevulinic acid (5-ALA) generating individually fluorescent tumor cells within a background population of non-neoplastic cells in the invasive tumor region. The aim of the study was to specifically isolate and interrogate the invasive GBM cell population using a novel 5-ALA-based method. METHODS: We have isolated the critical invasive GBM cell population by developing 5-ALA-based metabolic fluorescence-activated cell sorting. This allows purification and study of invasive cells from GBM without an overwhelming background "normal brain" signal to confound data. The population was studied using RNAseq, real-time PCR, and immunohistochemistry, with gene targets functionally interrogated on proliferation and migration assays using siRNA knockdown and known drug inhibitors. RESULTS: RNAseq analysis identifies specific genes such as SERPINE1 which is highly expressed in invasive GBM cells but at low levels in the surrounding normal brain parenchyma. siRNA knockdown and pharmacological inhibition with specific inhibitors of SERPINE1 reduced the capacity of GBM cells to invade in an in vitro assay. Rodent xenografts of 5-ALA-positive cells were established and serially transplanted, confirming tumorigenicity of the fluorescent patient-derived cells but not the 5-ALA-negative cells. CONCLUSIONS: Identification of unique molecular features in the invasive GBM population offers hope for developing more efficacious targeted therapies compared to targeting the tumor core and for isolating tumor subpopulations based upon intrinsic metabolic properties.
Texto completo:
1
Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article