The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers
Anahita Fathi Kazerooni, Hamed Akbari, Xiaoju Hu, Vikas Bommineni, Dimitris Grigoriadis, Erik Toorens, Chiharu Sako, Elizabeth Mamourian, Dominique Ballinger, Robyn T. Sussman, Ashish Singh, Ioannis I. Verginadis, Nadia Dahmane, Constantinos Koumenis, Zev A. Binder, Stephen Bagley, Suyash Mohan, Artemis G. Hatzigeorgiou, Donald M. O’Rourke, Tapan Ganguly, Subhajyoti De, Spyridon Bakas, MacLean P. Nasrallah, Christos Davatzikos
Abstract
Glioblastoma is a highly heterogeneous brain tumor, posing challenges for precision therapies and patient stratification in clinical trials. Understanding how genetic mutations influence tumor imaging may improve patient management and treatment outcomes. This study investigates the relationship between imaging features, spatial patterns of tumor location, and genetic alterations in IDH-wildtype glioblastoma, as well as the likely sequence of mutational events. We conducted a retrospective analysis of 357 IDH-wildtype glioblastomas with pre-operative multiparametric MRI and targeted genetic sequencing data. Radiogenomic signatures and spatial distribution maps were generated for key mutations in genes such as EGFR, PTEN, TP53, and NF1 and their corresponding pathways. Machine and deep learning models were used to identify imaging biomarkers and stratify tumors based on their genetic profiles and molecular heterogeneity. Here, we show that glioblastoma mutations produce distinctive imaging signatures, which are more pronounced in tumors with less molecular heterogeneity. These signatures provide insights into how mutations affect tumor characteristics such as neovascularization, cell density, invasion, and vascular leakage. We also found that tumor location and spatial distribution correlate with genetic profiles, revealing associations between tumor regions and specific oncogenic drivers. Additionally, imaging features reflect the cross-sectionally inferred evolutionary trajectories of glioblastomas. This study establishes clinically accessible imaging biomarkers that capture the molecular composition and oncogenic drivers of glioblastoma. These findings have potential implications for noninvasive tumor profiling, personalized therapies, and improved patient stratification in clinical trials. Glioblastoma is a type of brain cancer that grows and spreads quickly, making treatment challenging. The changes that lead to cancer and parts of the brain in which it grows are also very variable. We explored how specific genetic changes in glioblastoma influence its appearance on brain scans and how tumor location within the brain relates the genetic changes seen. We applied computational models to the imaging data to identify patterns in where cancers with particular genetic changes are found in the brain. These findings could help doctors predict genetic changes in tumors without the need for invasive procedures, improving patient selection for targeted therapies and clinical trials. Kazerooni et al. explore the associations between tumor imaging and spatial characteristics with cancer gene mutations and the inferred sequence of mutational events. Radiogenomics reflects glioblastoma's molecular heterogeneity and imaging biomarkers and spatial patterns can reveal key oncogenic drivers.