Abstract While advances have been made in the molecular subclassification of medulloblastoma, significant molecular heterogeneity remains within subgroups with unrealized ramifications for therapeutic design. Multi-modal characterization holds promise to improve precision medicine approaches and more effectively disambiguate disease risk. Leveraging multi-omic data from 152 primary medulloblastoma tumors from the Children’s Brain Tumor Network (CBTN), we derived a novel, 13-cluster model of pediatric medulloblastoma using non-negative matrix factorization incorporating coding DNA mutations, copy number variations, functional alternative splicing events, transcript, and methylation profiling data. Wnt-driven medulloblastoma remained a relatively pure subtype, and Sonic Hedgehog (SHH), Group 3, and Group 4 medulloblastoma subdivided into additional subgroups. Kaplan-Meier analysis found that the 13 clusters showed highly distinct overall survival outcomes (p 0.0001), uncovering multi-omic clusters comprised of Group 3 (p = 0.00024) and Group 4 (p = 0.039) subgroups that were highly distinguishable. Cox regression adjusted for age at diagnosis and extent of tumor resection found that Group 3 (p 0.001) and Group 4 (p 0.001) multi-omic subgroups also differed in terms of event-free survival. Known medulloblastoma driver mutations enriched in a subset of multi-omic subgroups, and highly distinct molecular events that were correlated across the expression, methylation, and splicing layers were observed within the multi-omic clusters, among them putative regulatory methylation sites impacting expression targets known to induce oncogenesis. Among prognostically distinct Group 3 and 4 multi-omic subgroups, we found unique pathways inferred to be druggable when querying the NIH-LINCS drug response library, including VEGR2, DAG-IP3, GPCR, FLT3-STAT, PIP3-AKT, and ERBB2 signaling. When predicting multi-omic risk group membership using MR- and pathology-based imaging data, we found that intermediate-high and high-risk groups were more effectively predicted by MRI features related to edema, cellularity, and contrast enhancement (AUC-ROC: 0.9 and 0.83, respectively). Our study demonstrates that multi-omic molecular features underlie novel risk groups in medulloblastoma with potentially actionable biology and that may be effectively predicted by foundational, clinically-relevant imaging data.
Kraya et al. (Fri,) studied this question.
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