The Medulloblastoma Meta-Analysis Portal Enables Integrative Exploration of Clinical and Genomic Data from Patients

Abstract

Medulloblastoma is a prevalent, highly heterogeneous malignant pediatric brain cancer. In this study, we developed the Medulloblastoma Meta-Analysis portal, a web-based data analysis and visualization platform that consolidates clinical, molecular, and survival data of 898 patients from three major clinical trials (ACNS0331, ACNS0332, and SJMB03). The portal integrated clinical data with genomic mutations and DNA methylomes, enabling in-depth analyses of tumor subgroups and survival outcomes. Dynamic exploration and custom stratification of these data were made possible by the portal's user-friendly visualization tools powered by the ProteinPaint framework, allowing users to select from a wide range of variables or customize their own. The Cox regression analysis tool in the portal demonstrated utility in examining the prognostic impact of variables across a heterogeneous dataset. Additionally, an in-depth exploration of medulloblastomas that harbor KBTBD4 mutations and their corresponding methylomes generated leads that could help improve molecular classification. These use cases underscore the portal's utility in enhancing risk stratification, informing tailored therapeutic strategies, and revealing the complexities of molecular heterogeneity in patients with medulloblastoma. The portal, along with links to the tutorials and live figures, is freely available at https://viz.stjude.cloud/st-jude-childrens-research-hospital/visualization/medulloblastoma-integrative-analysis-portal~2882.

Significance: The MB-meta portal facilitates risk stratification of patients using molecular, genomic, clinical, and demographic data, representing a significant step in neuro-oncology data sharing by offering an intuitive web-based platform for complex analyses.

Figure 1.

Data dictionary variables in the MB-meta cohort. A, Breakdown of dictionary variables with different data types. Numbers in genomics data variables indicate the number of tumors with that data. B, Medulloblastoma cohort summaries for major clinical variables. AR, average risk; CM, clinico molecular; CSI, craniospinal irradiation; HDCSI, high-dose craniospinal irradiation; HR, high risk; LDCSI, low-dose craniospinal irradiation; LR, low risk; rCM, revised clinico molecular; SDCSI, standard-dose craniospinal irradiation; VHR, very high risk; WCA, whole chromosome aberration; WES, whole-exome sequencing; WGS, whole-genome sequencing.

Figure 2.

Overview of the visualizations in the MB-meta portal. A, Summary plot showing the “age at diagnosis” for different subgroups (shown by different colors). B, Kaplan–Meier curves comparing PFS between the molecular subgroups G3/G4-2 and G3/G4-7. P values are based on all survival data. C, Cox regression analyses showing risks for different molecular groups on progression of the disease. D, Lollipop plot showing CTNNB1 mutations for the WNT group. E, Sample matrix showing demographic, clinical, mutational, and copy-number variation data. Rows indicate variables, and columns indicate samples. F, Methylome t-SNE showing molecular subgroup classification, and lassoing shows selection of groups for survival analysis. G, Sample view showing complete variable annotation for a sample, genome-wide mutation, and copy-number variation profile in the disco plot. CNV, copy-number variation; CSI, craniospinal irradiation; IFRT, involved-field radiotherapy; LC/A, large cell/anaplastic; RT, radiotherapy. H, Sample view showing genome-wide copy-number variation plot derived by DNA methylation plot and locus view.

Figure 3.

Using custom variables for regression and survival analyses. A, G3/G4 cases filtered for G3/G4 patients who received regimens containing standard- or high-dose craniospinal irradiation without carboplatin during radiotherapy. B, Groups are created for different combinations of presence or absence of MYC amplifications and iso17 using custom variable functionality within the portal. CNV, copy-number variation; CSI, craniospinal irradiation; LDCSI, low-dose craniospinal irradiation. C, Univariate and multivariate Cox regression analyses using custom variables and other cofactors with PFS as the outcome. D, Survival plots launched from custom variables with the presence or absence of MYC amplifications and iso17. P values are based on all survival data.

Figure 4.

KBTBD4 mutations within medulloblastoma subgroups visualized on the methylome t-SNE. A, Lollipop plot showing all samples and mutations for KBTBD4. B, Methylome t-SNE showing molecular subgroup (as shapes) and KBTBD4 mutations (as colors). The t-SNE plot is limited to G3/G4 samples with verified KBTBD4 mutation status. Circles highlight two clusters of KBTBD4-mutated samples and are zoomed in F and D. C, Lollipop plot showing KBTBD4 mutations in subgroup G3/G4-2. Arrow, position of the hotspot mutation. Each distinct mutation is assigned a number (circled) and is used to label all mutated samples in D and F. D, Subgroup G3/G4-2 KBTBD4 mutations visualized on the t-SNE plot, with samples with rescued mutations highlighted in cyan boxes and by arrows. E, Lollipop plot showing KBTBD4 mutations in subgroup G3/G4-7, with the rescued mutation highlighted in cyan. Arrow, position of the hotspot mutation. F, Subgroup G3/G4-7 KBTBD4 mutations visualized on the t-SNE plot. A sample with rescued mutation is highlighted in cyan. MB, medulloblastoma; MNP, molecular neuropathology; MYO, medulloblastoma with myogenic differentiation.