The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers
Abstract Background 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...
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Nature Portfolio
2025-03-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-025-00767-0 |
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| author | Anahita Fathi Kazerooni Hamed Akbari Xiaoju Hu Vikas Bommineni Dimitris Grigoriadis Erik Toorens Chiharu Sako Elizabeth Mamourian Dominique Ballinger Robyn Sussman Ashish Singh Ioannis I. Verginadis Nadia Dahmane Constantinos Koumenis Zev A. Binder Stephen J. Bagley Suyash Mohan Artemis Hatzigeorgiou Donald M. O’Rourke Tapan Ganguly Subhajyoti De Spyridon Bakas MacLean P. Nasrallah Christos Davatzikos |
| author_facet | Anahita Fathi Kazerooni Hamed Akbari Xiaoju Hu Vikas Bommineni Dimitris Grigoriadis Erik Toorens Chiharu Sako Elizabeth Mamourian Dominique Ballinger Robyn Sussman Ashish Singh Ioannis I. Verginadis Nadia Dahmane Constantinos Koumenis Zev A. Binder Stephen J. Bagley Suyash Mohan Artemis Hatzigeorgiou Donald M. O’Rourke Tapan Ganguly Subhajyoti De Spyridon Bakas MacLean P. Nasrallah Christos Davatzikos |
| author_sort | Anahita Fathi Kazerooni |
| collection | DOAJ |
| description | Abstract Background 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. Methods 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. Results 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. Conclusions 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. |
| format | Article |
| id | doaj-art-3d91c3ea09bc488d8dcb7db28cb40853 |
| institution | DOAJ |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-3d91c3ea09bc488d8dcb7db28cb408532025-08-20T02:54:37ZengNature PortfolioCommunications Medicine2730-664X2025-03-015111510.1038/s43856-025-00767-0The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic driversAnahita Fathi Kazerooni0Hamed Akbari1Xiaoju Hu2Vikas Bommineni3Dimitris Grigoriadis4Erik Toorens5Chiharu Sako6Elizabeth Mamourian7Dominique Ballinger8Robyn Sussman9Ashish Singh10Ioannis I. Verginadis11Nadia Dahmane12Constantinos Koumenis13Zev A. Binder14Stephen J. Bagley15Suyash Mohan16Artemis Hatzigeorgiou17Donald M. O’Rourke18Tapan Ganguly19Subhajyoti De20Spyridon Bakas21MacLean P. Nasrallah22Christos Davatzikos23AI2D Center for AI and Data Science for Integrated Diagnostics, University of PennsylvaniaDepartment of Bioengineering, School of Engineering, Santa Clara UniversityRutgers Cancer Institute of New Jersey, Rutgers the State University of New JerseyAI2D Center for AI and Data Science for Integrated Diagnostics, University of PennsylvaniaDepartment of Computer Science and Biomedical Informatics, University of ThessalyPenn Genomic Analysis Core, Perelman School of Medicine, University of PennsylvaniaDepartment of Radiology, Perelman School of Medicine, University of PennsylvaniaDepartment of Radiology, Perelman School of Medicine, University of PennsylvaniaDepartment of Pathology & Laboratory Medicine, Perelman School of Medicine, University of PennsylvaniaDepartment of Pathology & Laboratory Medicine, Perelman School of Medicine, University of PennsylvaniaAI2D Center for AI and Data Science for Integrated Diagnostics, University of PennsylvaniaDepartment of Radiation Oncology, Perelman School of Medicine, University of PennsylvaniaDepartment of Neurological Surgery, Weill Cornell MedicineDepartment of Radiation Oncology, Perelman School of Medicine, University of PennsylvaniaDepartment of Neurosurgery, Perelman School of Medicine, University of PennsylvaniaDepartment of Radiation Oncology, Perelman School of Medicine, University of PennsylvaniaDepartment of Radiology, Perelman School of Medicine, University of PennsylvaniaDepartment of Computer Science and Biomedical Informatics, University of ThessalyDepartment of Neurosurgery, Perelman School of Medicine, University of PennsylvaniaPenn Genomic Analysis Core, Perelman School of Medicine, University of PennsylvaniaRutgers Cancer Institute of New Jersey, Rutgers the State University of New JerseyDepartment of Pathology & Laboratory Medicine, Indiana University School of MedicineAI2D Center for AI and Data Science for Integrated Diagnostics, University of PennsylvaniaAI2D Center for AI and Data Science for Integrated Diagnostics, University of PennsylvaniaAbstract Background 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. Methods 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. Results 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. Conclusions 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.https://doi.org/10.1038/s43856-025-00767-0 |
| spellingShingle | Anahita Fathi Kazerooni Hamed Akbari Xiaoju Hu Vikas Bommineni Dimitris Grigoriadis Erik Toorens Chiharu Sako Elizabeth Mamourian Dominique Ballinger Robyn Sussman Ashish Singh Ioannis I. Verginadis Nadia Dahmane Constantinos Koumenis Zev A. Binder Stephen J. Bagley Suyash Mohan Artemis Hatzigeorgiou Donald M. O’Rourke Tapan Ganguly Subhajyoti De Spyridon Bakas MacLean P. Nasrallah Christos Davatzikos The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers Communications Medicine |
| title | The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers |
| title_full | The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers |
| title_fullStr | The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers |
| title_full_unstemmed | The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers |
| title_short | The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers |
| title_sort | radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers |
| url | https://doi.org/10.1038/s43856-025-00767-0 |
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