A non-invasive molecular diagnostic tool that reliably predicts the tumor characteristics and risk of recurrence of meningiomas.
Meningiomas are the most common type of tumor that grows from membranes that surround the brain and spinal cord. They comprise approximately 39.7% of all central nervous system (CNS) tumors and 55.4% of all non-malignant tumors of the CNS. Meningioma patient outcomes can be variable due to limitations in predicting the clinical behavior of the graded tumor. Current diagnostic technologies for meningiomas primarily include MRI, CT scans, and biopsy. While MRI and CT scans are great for initial diagnosis, they do not always provide enough information to predict how the tumor will behave over time. Advanced techniques, such as subcellular tumor profiling, offer insights into tumor infiltration and genetic mutations; however, they rely more heavily on statistical regression models and mathematical correlations. There is a great need for non-invasive diagnostic tools that reliably predict tumor progression and recurrence.
Inventors at Emory University have developed a non-invasive diagnostic platform that reliably predicts tumor grade and risk of recurrence by generating a gene interaction map based on differentially expressed genes. The algorithm identified core genes that effectively classified meningiomas according to the World Health Organization grading system. The algorithm also predicted tumor recurrence and need for radiation with over 90% accuracy on externally validated datasets. The handling from specimen to results is completed in less than 6 hours. The inventors have developed a reproducible pipeline for diagnosing meningiomas and accurately predicting tumor behavior, which guides personalized treatment options.
The algorithm was validated using data from external GEO databases and discovery cohort.
Publication Zohdy, Y. M., Alawieh, A. M., Jahangiri, A., Siciliano, B., Tariciotti, L., Rodas, A., Maldonado, J., Hoang, K., Nduom, E., Howard, B. M., Barrow, D. L., Aksionau, A., Neill, S. G., Wen, Z., Pradilla, G., & Garzon-Muvdi, T. (2025). Integrative transcriptomics and network analysis reveals core genes driving meningioma pathogenesis and clinical outcomes. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-29334-2