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Clinical utility of a blood based assay for the detection of IDH1.R132H-mutant gliomas.
- Source :
- Nature Communications; 8/16/2024, Vol. 15 Issue 1, p1-14, 14p
- Publication Year :
- 2024
-
Abstract
- Glioma represents the most common central nervous system neoplasm in adults. Current classification scheme utilizes molecular alterations, particularly IDH1.R132H, to stratify lesions into distinct prognostic groups. Identification of the single nucleotide variant through traditional tissue biopsy assessment poses procedural risks and does not fully reflect the heterogeneous and evolving tumor landscape. Here, we introduce a liquid biopsy assay, mt-IDH1<subscript>dx</subscript>. The blood-based test allows minimally invasive detection of tumor-derived extracellular vesicle RNA using only 2 ml plasma volume. We perform rigorous, blinded validation testing across the study population (n = 133), comprising of IDH1.R132H patients (n = 80), IDH1 wild-type gliomas (n = 44), and age matched healthy controls (n = 9). Results from our plasma testing demonstrate an overall sensitivity of 75.0% (95% CI: 64.1%–84.0%), specificity 88.7% (95% CI: 77.0%–95.7%), positive predictive value 90.9%, and negative predictive value 70.1% compared to the tissue gold standard. In addition to fundamental diagnostic applications, the study also highlights the utility of mt-IDH1<subscript>dx</subscript> platform for blood-based monitoring and surveillance, offering valuable prognostic information. Finally, the optimized workflow enables rapid and efficient completion of both tumor tissue and plasma testing in under 4 hours from the time of sampling. Efficient and non-invasive, liquid biopsy methods could greatly improve the molecular classification of gliomas. Here, the authors develop an RNA-based Droplet Digital PCR assay to detect the key IDH1.R132H mutation in plasma-derived extracellular vesicles from glioma patients with high sensitivity, allowing accurate diagnosis, prognostication and longitudinal monitoring. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20411723
- Volume :
- 15
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Nature Communications
- Publication Type :
- Academic Journal
- Accession number :
- 179067156
- Full Text :
- https://doi.org/10.1038/s41467-024-51332-7