Advantages:
- A precision-guided method for identifying glioblastoma-related genes that improves assessment of the current prognostic biomarker
- Enables the identification of potential novel therapeutic targets: GLP1R agonists, which are already used for managing obesity and type 2 diabetes, could be explored as treatments for GBM
- Broader Applications: applicable to multiple cancer types, including GBM, lower-grade gliomas (LGG), and pancreatic adenocarcinoma (PAAD)
Summary:
Existing approaches to predict outcomes of multiple cancers, specifically glioblastoma, often rely on established biomarkers that do not fully capture the nuances of gene copy number variations and their association with microsatellite instability. This is especially true in genes involved in glucose metabolism. Consequently, clinicians face significant hurdles in delivering precise prognoses and optimized therapeutic strategies, underscoring the need for novel diagnostic methods that integrate the assessment of detailed genomic profiling with traditional clinical assessments.
The invention details a novel methodology for integrating precision-guided copy number variation (CNV) analysis with microsatellite instability (MSI) data. This approach establishes prognostic indicators and therapeutic strategies for brain cancers. Quantifying gene copy numbers from TCGA-derived genomic data and correlating these with MSI scores and survival outcomes via Kaplan-Meier and multivariate analyses demonstrates that higher GLP1R copy numbers in glioblastoma multiforme (GBM) is strongly associated with improved survival—and that higher GPI copies in lower-grade glioma (LGG) is correlated with worse outcomes. These findings suggest unexpected glycemic-independent mechanisms and link gene dosage to patient prognosis. This enables the repurposing of existing GLP1R agonists for GBM as well as VEGF inhibitors for LGG. Thus, our technology leverages integrated genomic and MSI analysis to offer a dual-pronged innovation that improves both the accuracy of brain cancer prognoses and targeted drug therapy.
Figure 1. Overall survival (OS) analyses for highest quartile vs. lowest quartile CNs of the GLP1R gene for TCGA-GBM dataset
KM analyses revealed that the cases representing the upper CN quartile also represented a higher survival probability for all evaluated survival parameters, specifically, OS (logrank p=0.034), DSS (logrank p=0.037) and PFS (logrank p=0.055) (Fig. 1A).
Desired Partnerships:
- License
- Sponsored Research
- Co-Development