An NF-κB signature predicts low-grade glioma prognosis: a precision medicine approach based on patient-derived stem cells
- PMID: 29228370
- PMCID: PMC5961156
- DOI: 10.1093/neuonc/nox234
An NF-κB signature predicts low-grade glioma prognosis: a precision medicine approach based on patient-derived stem cells
Abstract
Background: While recent genome-wide association studies have suggested novel low-grade glioma (LGG) stratification models based on a molecular classification, we explored the potential clinical utility of patient-derived cells. Specifically, we assayed glioma-associated stem cells (GASC) that are patient-derived and representative of the glioma microenvironment.
Methods: By next-generation sequencing, we analyzed the transcriptional profile of GASC derived from patients who underwent anaplastic transformation either within 48 months (GASC-BAD) or ≥7 years (GASC-GOOD) after surgery. Gene set enrichment and pathway enrichment analyses were applied. The prognostic role of a nuclear factor-kappaB (NF-κB) signature derived from GASC-BAD was tested in 530 newly diagnosed diffuse LGG patients comprised within The Cancer Genome Atlas (TCGA) database. The prognostic value of the GASC upstream regulator p65 NF-κB was assessed, by univariate and multivariate Cox analyses, in a single center case study, including 146 grade II LGGs.
Results: The key elements differentiating the transcriptome of GASC isolated from LGG with different prognoses were mostly related to hallmarks of cancer (eg, inflammatory/immune process, NF-κB activation). Consistently, the NF-κB signature extrapolated from the GASC study was prognostic in the dataset of TCGA. Finally, the nuclear expression of the NF-kB-p65 protein, assessed using an inexpensive immunohistochemical method, was an independent predictor of both overall survival and malignant progression-free survival in 146 grade II LGGs.
Conclusion: This study demonstrates for the first time the independent prognostic role of NF-kB activation in LGG and outlines the role of patient-based stem cell models as a tool for precision medicine approaches.
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