Effective data sharing is key to accelerating research to improve diagnostic precision, treatment efficacy, and long-term survival in pediatric cancer and other childhood catastrophic diseases. We present St. Jude Cloud (https://www.stjude.cloud), a cloud-based data-sharing ecosystem for accessing, analyzing, and visualizing genomic data from >10,000 pediatric patients with cancer and long-term survivors, and >800 pediatric sickle cell patients. Harmonized genomic data totaling 1.25 petabytes are freely available, including 12,104 whole genomes, 7,697 whole exomes, and 2,202 transcriptomes. The resource is expanding rapidly, with regular data uploads from St. Jude's prospective clinical genomics programs. Three interconnected apps within the ecosystem—Genomics Platform, Pediatric Cancer Knowledgebase, and Visualization Community—enable simultaneously performing advanced data analysis in the cloud and enhancing the Pediatric Cancer knowledgebase. We demonstrate the value of the ecosystem through use cases that classify 135 pediatric cancer subtypes by gene expression profiling and map mutational signatures across 35 pediatric cancer subtypes.Significance: To advance research and treatment of pediatric cancer, we developed St. Jude Cloud, a data-sharing ecosystem for accessing >1.2 petabytes of raw genomic data from >10,000 pediatric patients and survivors, innovative analysis workflows, integrative multiomics visualizations, and a knowledgebase of published data contributed by the global pediatric cancer community.This article is highlighted in the In This Issue feature, p. 995

PURPOSE: SJMB03 (ClinicalTrials.gov identifier: NCT00085202 ) was a phase III risk-adapted trial that aimed to determine the frequency and clinical significance of biological variants and genetic alterations in medulloblastoma. PATIENTS AND METHODS: Patients 3-21 years old were stratified into average-risk and high-risk treatment groups based on metastatic status and extent of resection. Medulloblastomas were molecularly classified into subgroups (Wingless [WNT], Sonic Hedgehog [SHH], group 3, and group 4) and subtypes based on DNA methylation profiles and overlaid with gene mutations from next-generation sequencing. Coprimary study end points were (1) to assess the relationship between ERBB2 protein expression in tumors and progression-free survival (PFS), and (2) to estimate the frequency of mutations associated with WNT and SHH tumors. Clinical and molecular risk factors were evaluated, and the most robust were used to model new risk-classification categories. RESULTS: Three hundred thirty eligible patients with medulloblastoma were enrolled. Five-year PFS was 83.2% (95% CI, 78.4 to 88.2) for average-risk patients (n = 227) and 58.7% (95% CI, 49.8 to 69.1) for high-risk patients (n = 103). No association was found between ERBB2 status and PFS in the overall cohort ( P = .74) or when patients were stratified by clinical risk ( P = .71). Mutations in CTNNB1 (96%), DDX3X (37%), and SMARCA4 (24%) were most common in WNT tumors and PTCH1 (38%), TP53 (21%), and DDX3X (19%) in SHH tumors. Methylome profiling classified 53 WNT (17.4%), 48 SHH (15.7%), 65 group 3 (21.3%), and 139 group 4 (45.6%) tumors. A comprehensive clinicomolecular risk factor analysis identified three low-risk groups (WNT, low-risk SHH, and low-risk combined groups 3 and 4) with excellent (5-year PFS > 90%) and two very high-risk groups (high-risk SHH and high-risk combined groups 3 and 4) with poor survival (5-year PFS < 60%). CONCLUSION: These results establish a new risk stratification for future medulloblastoma trials.

Genomic studies of pediatric cancer have primarily focused on specific tumor types or high-risk disease. Here, we used a three-platform sequencing approach, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and RNA sequencing (RNA-seq), to examine tumor and germline genomes from 309 prospectively identified children with newly diagnosed (85%) or relapsed/refractory (15%) cancers, unselected for tumor type. Eighty-six percent of patients harbored diagnostic (53%), prognostic (57%), therapeutically relevant (25%), and/or cancer-predisposing (18%) variants. Inclusion of WGS enabled detection of activating gene fusions and enhancer hijacks (36% and 8% of tumors, respectively), small intragenic deletions (15% of tumors), and mutational signatures revealing of pathogenic variant effects. Evaluation of paired tumor–normal data revealed relevance to tumor development for 55% of pathogenic germline variants. This study demonstrates the power of a three-platform approach that incorporates WGS to interrogate and interpret the full range of genomic variants across newly diagnosed as well as relapsed/refractory pediatric cancers. Significance: Pediatric cancers are driven by diverse genomic lesions, and sequencing has proven useful in evaluating high-risk and relapsed/refractory cases. We show that combined WGS, WES, and RNA-seq of tumor and paired normal tissues enables identification and characterization of genetic drivers across the full spectrum of pediatric cancers. This article is highlighted in the In This Issue feature, p. 2945

Medulloblastoma (MB) is the most common CNS embryonal tumor. While the overall cure rate is around 70%, patients with high‐risk disease continue to have poor outcome and experience long‐term morbidity. MB is among the tumors for which diagnosis, risk stratification, and clinical management has shown the most rapid advancement. These advances are largely due to technological improvements in diagnosis and risk stratification which now integrate histomorphologic classification and molecular classification. MB stands as a prototype for other solid tumors in how to effectively integrate morphology and genomic data to stratify clinicopathologic risk and aid design of innovative clinical trials for precision medicine. This review explores the current diagnostic and classification of MB in modern neuropathology laboratories.