Meningioma transcriptomic landscape demonstrates novel subtypes with regional associated biology and patient outcome
H. Nayanga Thirimanne, Damian Almiron-Bonnin, Nicholas Nuechterlein, Sonali Arora, Matt Jensen, Carolina A. Parada, Chengxiang Qiu, Frank Szulzewsky, Collin W. English, William Chen, Philipp Sievers, Farshad Nassiri, Justin Z. Wang, Tiemo J. Klisch, Kenneth D. Aldape, Akash J. Patel, Patrick J. Cimino, Gelareh Zadeh, Felix Sahm, David R. Raleigh, Jay Shendure, Manuel Ferreira, Eric C. Holland
Abstract
Meningiomas, although mostly benign, can be recurrent and fatal. World Health Organization (WHO) grading of the tumor does not always identify high-risk meningioma, and better characterizations of their aggressive biology are needed. To approach this problem, we combined 13 bulk RNA sequencing (RNA-seq) datasets to create a dimension-reduced reference landscape of 1,298 meningiomas. The clinical and genomic metadata effectively correlated with landscape regions, which led to the identification of meningioma subtypes with specific biological signatures. The time to recurrence also correlated with the map location. Further, we developed an algorithm that maps new patients onto this landscape, where the nearest neighbors predict outcome. This study highlights the utility of combining bulk transcriptomic datasets to visualize the complexity of tumor populations. Further, we provide an interactive tool for understanding the disease and predicting patient outcomes. This resource is accessible via the online tool Oncoscape, where the scientific community can explore the meningioma landscape.