Multimodal fusion of radio-pathology and proteogenomics identify integrated glioma subtypes with prognostic and therapeutic opportunities

Integrating multimodal data can uncover causal features hidden in single-modality analyses, offering a comprehensive understanding of disease complexity. This study introduces a multimodal fusion subtyping (MOFS) framework that integrates radiological, pathological, genomic, transcriptomic, and proteomic data from 122 patients with IDH-wildtype adult glioma, identifying three subtypes: MOFS1 (proneural) with favorable prognosis, elevated neurodevelopmental activity, and abundant neurocyte infiltration; MOFS2 (proliferative) with the worst prognosis, superior proliferative activity, and genome instability; MOFS3 (TME-rich) with intermediate prognosis, abundant immune and stromal components, and sensitive to anti-PD-1 immunotherapy. STRAP emerges as a prognostic biomarker and potential therapeutic target for MOFS2, associated with its proliferative phenotype. Stromal infiltration in MOFS3 serves as a crucial prognostic indicator, allowing for further prognostic stratification. Additionally, we develop a deep neural network (DNN) classifier based on radiological features to further enhance the clinical translatability, providing a non-invasive tool for predicting MOFS subtypes. Overall, these findings highlight the potential of multimodal fusion in improving the classification, prognostic accuracy, and precision therapy of IDH-wildtype glioma, offering an avenue for personalized management.

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Publication Details

Journal
Nature Communications
Published
2025-04-13
DOI
https://doi.org/10.1038/s41467-025-58675-9
Citations
18
Primary Topic
Glioma Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
29.26

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article

Multimodal fusion of radio-pathology and proteogenomics identify integrated glioma subtypes with prognostic and therapeutic opportunities

Dongming Yan, Hairong Zheng, Shuang Chen, Yingwei Zhen et al.
18 citations
Nature Communications
Glioma Diagnosis and Treatment
29.26
article

Multimodal fusion of radio-pathology and proteogenomics identify integrated glioma subtypes with prognostic and therapeutic opportunities

Dongming Yan, Hairong Zheng, Shuang Chen, Yingwei Zhen, Yushuai Wu, Wenchao Duan, Dingyuan Su, Xianzhi Liu, Li Cui, Minkai Wang, Siyuan Weng, Dongling Pei, Yuanshen Zhao, Xinwei Han, Bin Yu, Jingxian Duan, Yuning Qiu, Te Chen, Wenyuan Li, Zilong Wang, Zhenyu Zhang, Ran Li, Sen Li, Haoran Liu, Zhicheng Li, Zeyu Ma, Jing Yan, Jing Fu, Hui Xu, Fubing Wang, Weiwei Wang, Yinhui Yu, Caoyuan Ma, Zaoqu Liu, Yuchen Ji, Miaomiao Yu, Dong Liang
article en
18 citations

Abstract

Integrating multimodal data can uncover causal features hidden in single-modality analyses, offering a comprehensive understanding of disease complexity. This study introduces a multimodal fusion subtyping (MOFS) framework that integrates radiological, pathological, genomic, transcriptomic, and proteomic data from 122 patients with IDH-wildtype adult glioma, identifying three subtypes: MOFS1 (proneural) with favorable prognosis, elevated neurodevelopmental activity, and abundant neurocyte infiltration; MOFS2 (proliferative) with the worst prognosis, superior proliferative activity, and genome instability; MOFS3 (TME-rich) with intermediate prognosis, abundant immune and stromal components, and sensitive to anti-PD-1 immunotherapy. STRAP emerges as a prognostic biomarker and potential therapeutic target for MOFS2, associated with its proliferative phenotype. Stromal infiltration in MOFS3 serves as a crucial prognostic indicator, allowing for further prognostic stratification. Additionally, we develop a deep neural network (DNN) classifier based on radiological features to further enhance the clinical translatability, providing a non-invasive tool for predicting MOFS subtypes. Overall, these findings highlight the potential of multimodal fusion in improving the classification, prognostic accuracy, and precision therapy of IDH-wildtype glioma, offering an avenue for personalized management.

Nature CommunicationsVol. 16(1)
Chinese Academy of Sciences (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Wuhan University (CN), Zhengzhou University (CN), Henan Energy & Chemical Industry Group (China) (CN), Beijing Academy of Artificial Intelligence (CN), Wuhan Institute of Virology (CN), Zhongnan Hospital of Wuhan University (CN), Academy of Mathematics and Systems Science (CN), Shenzhen Institutes of Advanced Technology (CN), First Affiliated Hospital of Zhengzhou University (CN), Hangzhou City University
National Natural Science Foundation of China, Natural Science Foundation of Henan Province, Henan Provincial Science and Technology Research Project
Openalex Percentile: Top 0%
Glioma Diagnosis and Treatment
29.26
DOIOpen Access PDFOpenAlex
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