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Background <p>Low-grade glioma (LGG) is a diverse group of primary brain tumors, whose molecular heterogeneity hinders classification by traditional pathological methods. Accurate phenotypic subtyping of LGG is essential for capturing tumor characteristics and optimizing clinical management. We intend to identify LGG phenotypic subtypes based on multimodal magnetic resonance imaging (MRI) data, enhancing prognosis evaluation and optimizing treatment strategy.</p> Methods <p>This was a retrospective multicenter study, and data were drawn from the First Hospital of Shanxi Medical University (FHSXMU) and Shanxi Provincial People’s Hospital (SPPH) (FHSXMU/SPPH cohort, <i>n</i> = 162), and The Cancer Genome Atlas (TCGA)/The Cancer Imaging Archive (TCIA) (TCGA/TCIA cohort, <i>n</i> = 118). In the FHSXMU/SPPH cohort, LGG phenotypic subtypes were identified using the outcome-weighted integrative clustering method (survClust) based on multimodal MRI data (CE-T1 and T2FLAIR). A multivariate Cox proportional hazards model was applied to evaluate survival differences between subtypes. Statistical comparisons between subtypes were performed, and the statistically significant MRI features were utilized to predict clinically relevant biomarkers – isocitrate dehydrogenase (IDH) mutation combined with O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation. Five models were constructed, including fused kernel partial least squares with the genetic algorithm (GA-fKPLS), logistic regression, random forest, support vector machine, and <i>k</i>-nearest neighbor. In the TCGA/TCIA cohort, we validated the identified phenotypic subtypes and further explored their biological characteristics by analyzing pathway activity and immune infiltration levels using mRNA expression data.</p> Results <p>Two distinct LGG phenotypic subtypes were identified in the FHSXMU/SPPH cohort, and validated in the TCGA/TCIA cohort. In the FHSXMU/SPPH cohort, significant differences in pathological grade, MGMT promoter status, IDH genotype, survival status, tumor volume, and survival outcome (<i>HR</i>: 2.553, 95%<i>CI</i>: [1.226–5.315]) between the two subtypes (<i>P</i> &lt; 0.05). Compared to other four models, the GA-fKPLS model exhibited superior predictive performance (AUC: 0.809). In the TCGA/TCIA cohort, two LGG phenotypic subtypes showed significant differences in pathway activities (JAK-STAT, TNF-α, p53) and immune cell infiltration (M2 macrophages, T cell regulatory, Monocytes) (<i>P</i><sub>adj</sub> &lt; 0.05).</p> Conclusion <p>This study identified two LGG phenotypic subtypes and potential biomarkers, offering supplementary information for clinical evaluation and treatment decision-making.</p> Graphical Abstract <p></p>

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Phenotypic stratification of Low-grade Glioma using multimodal MRI via outcome-weighted integrative clustering

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月4日

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  • Qi Yang,
  • Gaiqin Liu,
  • Tong Wang,
  • Zhaoyang Xu,
  • Junyu Yan,
  • Ruiling Fang,
  • Yanhong Luo,
  • Hongmei Yu,
  • Yan Tan,
  • Hui Zhang,
  • Guoqiang Yang,
  • Hongyan Cao

摘要

Background

Low-grade glioma (LGG) is a diverse group of primary brain tumors, whose molecular heterogeneity hinders classification by traditional pathological methods. Accurate phenotypic subtyping of LGG is essential for capturing tumor characteristics and optimizing clinical management. We intend to identify LGG phenotypic subtypes based on multimodal magnetic resonance imaging (MRI) data, enhancing prognosis evaluation and optimizing treatment strategy.

Methods

This was a retrospective multicenter study, and data were drawn from the First Hospital of Shanxi Medical University (FHSXMU) and Shanxi Provincial People’s Hospital (SPPH) (FHSXMU/SPPH cohort, n = 162), and The Cancer Genome Atlas (TCGA)/The Cancer Imaging Archive (TCIA) (TCGA/TCIA cohort, n = 118). In the FHSXMU/SPPH cohort, LGG phenotypic subtypes were identified using the outcome-weighted integrative clustering method (survClust) based on multimodal MRI data (CE-T1 and T2FLAIR). A multivariate Cox proportional hazards model was applied to evaluate survival differences between subtypes. Statistical comparisons between subtypes were performed, and the statistically significant MRI features were utilized to predict clinically relevant biomarkers – isocitrate dehydrogenase (IDH) mutation combined with O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation. Five models were constructed, including fused kernel partial least squares with the genetic algorithm (GA-fKPLS), logistic regression, random forest, support vector machine, and k-nearest neighbor. In the TCGA/TCIA cohort, we validated the identified phenotypic subtypes and further explored their biological characteristics by analyzing pathway activity and immune infiltration levels using mRNA expression data.

Results

Two distinct LGG phenotypic subtypes were identified in the FHSXMU/SPPH cohort, and validated in the TCGA/TCIA cohort. In the FHSXMU/SPPH cohort, significant differences in pathological grade, MGMT promoter status, IDH genotype, survival status, tumor volume, and survival outcome (HR: 2.553, 95%CI: [1.226–5.315]) between the two subtypes (P < 0.05). Compared to other four models, the GA-fKPLS model exhibited superior predictive performance (AUC: 0.809). In the TCGA/TCIA cohort, two LGG phenotypic subtypes showed significant differences in pathway activities (JAK-STAT, TNF-α, p53) and immune cell infiltration (M2 macrophages, T cell regulatory, Monocytes) (Padj < 0.05).

Conclusion

This study identified two LGG phenotypic subtypes and potential biomarkers, offering supplementary information for clinical evaluation and treatment decision-making.

Graphical Abstract