Multiview classification of pediatric medulloblastoma refines prognostic stratification and enables imaging-based risk prediction

Adam Kraya; Komal Rathi; Anahita Fathi Kazerooni; Ammar S. Naqvi; Ariana M. Familiar; Yiran Guo; Qi Li; Varun Kesherwani; Angela N. Viaene; Mariarita Santi; Deep Gandhi; Yuankun Zhu; Xiaoyan Huang; Bo Zhang; Arastoo Vossough; Ali Nabavizadeh; Phillip B. Storm; Adam C Resnick; Brian R. Rood

doi:10.59717/j.xinn-oncol.2026.100025

The Innovation Oncology > 2026 Vol. 1 > No. 2 > 100025

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Multiview classification of pediatric medulloblastoma refines prognostic stratification and enables imaging-based risk prediction

1.
Division of Neurosurgery, The Children's Hospital of Philadelphia, Philadelphia 19104, USA
2.
Center for Data-Driven Discovery in Biomedicine (D3b), The Children’s Hospital of Philadelphia, Philadelphia 19104, USA
3.
Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia 19104, USA
4.
Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia 19104, USA
5.
Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA
6.
Brain Tumor Institute, Children’s National Research Institute, Washington DC 20010, USA
7.
Department of Radiology, University of Pennsylvania, Philadelphia 19104, USA
8.
These authors contributed equally

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Corresponding authors: krayaa@chop.edu (A.K.); brood@childrensnational.org (B.R.R)
Received Date: February 28, 2026

Accepted Date: July 02, 2026

Published Online: July 10, 2026

The Innovation Oncology 1(2), https://doi.org/10.59717/j.xinn-oncol.2026.100025 | Cite this article

GRAPHICAL ABSTRACT

GRAPHICAL ABSTRACT

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PUBLIC SUMMARY

PUBLIC SUMMARY
1. We developed novel multiomic classifications for pediatric medulloblastoma to improve risk stratification.
  
  14 distinct molecular clusters reveal high- and low-risk populations that were previously grouped together.
  
  Magnetic resonance and pathology images can predict molecular risk groups, improving accessibility.
  
  This model offers a path toward reducing toxic drug effects and improving survival for aggressive disease.

ABSTRACT

ABSTRACT

Despite advances in molecular subclassification, significant heterogeneity within the four primary medulloblastoma subgroups complicates therapeutic design. High-risk SHH and Group 3/4 cases, particularly those with metastases, frequently fail conventional therapy, highlighting the urgent need for molecularly-based risk refinement and improved disease estimation. By integrating five distinct molecular layers—non-synonymous variants, copy number variations (CNVs), DNA-methylation, gene expression, and alternative splicing—we identified 14 multi-omic clusters (MOCs) across 152 primary tumors from the Children’s Brain Tumor Network (CBTN). Within Group 3, we identified two prognostically distinct MOCs (p = 0.00024); favorable outcomes were linked to high-fidelity genomic maintenance and proteostasis, while poor prognosis was characterized by Notch signaling and metabolic reprogramming (IDH2/3B). Similarly, Group 4 MOCs showed divergent outcomes (p = 0.039) driven by either RTK-mediated proliferation or epigenetic stability. Germline analysis revealed that chromatin-modifying variants (SIRT2, PRDM2) associated with favorable clusters, while invasion-associated variants (PTPRT, EPHA1) linked to tumor aggression. In an effort to explore potential avenues for clinical translation, we modeled MRI and histopathology features as surrogates for these molecularly-defined risks. This integrated multi-omic approach refines our understanding of medulloblastoma outcomes, suggesting that integrated omics coupled with clinical imaging can significantly enhance precision medicine and prognostication.
- Risk Classification /
- Multiview Clustering

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Kraya A., Rathi K., Kazerooni A. F., et al. (2026). Multiview classification of pediatric medulloblastoma refines prognostic stratification and enables imaging-based risk prediction. The Innovation Oncology 1:100025. https://doi.org/10.59717/j.xinn-oncol.2026.100025

Kraya A., Rathi K., Kazerooni A. F., et al. (2026). Multiview classification of pediatric medulloblastoma refines prognostic stratification and enables imaging-based risk prediction. The Innovation Oncology 1:100025. https://doi.org/10.59717/j.xinn-oncol.2026.100025

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Multiview classification of pediatric medulloblastoma refines prognostic stratification and enables imaging-based risk prediction

GRAPHICAL ABSTRACT

PUBLIC SUMMARY

ABSTRACT