 Multimodal fusion of radio-pathology and proteogenomics identify integrated glioma subtypes with prognostic and therapeutic opportunitiesZaoqu Liu,
Yushuai Wu,
Hui Xu,
Minkai Wang,
Siyuan Weng,
Dongling Pei,
Shuang Chen,
WeiWei Wang,
Jing Yan,
Li Cui,
Jingxian Duan,
Yuanshen Zhao,
Zilong Wang,
Zeyu Ma,
Ran Li,
Wenchao Duan,
Yuning Qiu,
Dingyuan Su,
Sen Li,
Haoran Liu,
Wenyuan Li,
Caoyuan Ma,
Miaomiao Yu,
Yinhui Yu,
Te Chen,
Jing Fu,
YingWei Zhen,
Bin Yu,
Yuchen Ji,
Hairong Zheng,
Dong Liang,
Xianzhi Liu,
Dongming Yan,
Xinwei Han (),
Fubing Wang (),
Zhi-Cheng Li () and
Zhenyu Zhang ()
Nature Communications, 2025, vol. 16, issue 1, 1-18 Abstract: 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. Date: 2025 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58675-9 Ordering information: This journal article can be ordered from DOI: 10.1038/s41467-025-58675-9 Access Statistics for this article Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie More articles in Nature Communications from Nature |
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