Second-generation molecular subgrouping of medulloblastoma: an international meta-analysis of Group 3 and Group 4 subtypes

Abstract

In 2012, an international consensus paper reported that medulloblastoma comprises four molecular subgroups (WNT, SHH, Group 3, and Group 4), each associated with distinct genomic features and clinical behavior. Independently, multiple recent reports have defined further intra-subgroup heterogeneity in the form of biologically and clinically relevant subtypes. However, owing to differences in patient cohorts and analytical methods, estimates of subtype number and definition have been inconsistent, especially within Group 3 and Group 4. Herein, we aimed to reconcile the definition of Group 3/Group 4 MB subtypes through the analysis of a series of 1501 medulloblastomas with DNA-methylation profiling data, including 852 with matched transcriptome data. Using multiple complementary bioinformatic approaches, we compared the concordance of subtype calls between published cohorts and analytical methods, including assessments of class-definition confidence and reproducibility. While the lowest complexity solutions continued to support the original consensus subgroups of Group 3 and Group 4, our analysis most strongly supported a definition comprising eight robust Group 3/Group 4 subtypes (types I–VIII). Subtype II was consistently identified across all component studies, while all others were supported by multiple class-definition methods. Regardless of analytical technique, increasing cohort size did not further increase the number of identified Group 3/Group 4 subtypes. Summarizing the molecular and clinico-pathological features of these eight subtypes indicated enrichment of specific driver gene alterations and cytogenetic events amongst subtypes, and identified highly disparate survival outcomes, further supporting their biological and clinical relevance. Collectively, this study provides continued support for consensus Groups 3 and 4 while enabling robust derivation of, and categorical accounting for, the extensive intertumoral heterogeneity within Groups 3 and 4, revealed by recent high-resolution subclassification approaches. Furthermore, these findings provide a basis for application of emerging methods (e.g., proteomics/single-cell approaches) which may additionally inform medulloblastoma subclassification. Outputs from this study will help shape definition of the next generation of medulloblastoma clinical protocols and facilitate the application of enhanced molecularly guided risk stratification to improve outcomes and quality of life for patients and their families. Electronic supplementary material The online version of this article (10.1007/s00401-019-02020-0) contains supplementary material, which is available to authorized users.

Invited Review: DNA methylation‐based classification of paediatric brain tumours

“…For the clinically and biologically heterogeneous Group 3 and Group 4 MB several cohorts were recently combined and expanded by Sharma et al in an attempt to reconcile the varying proposed systems for sub‐classification. This led to the recognition of eight distinct molecular subtypes largely based on DNA methylation data that showed significant differences in clinical presentation, cytogenetics, age of incidence, and/or survival outcomes . Findings such as these support the genetic and epigenetic overlap between consensus Group 3 and Group 4 tumours previously shown by DNA methylation and more recently by single‐cell transcriptomics .…”

“…Classifiers evolve by increasing the number of samples in the reference cohort and the number of distinct methylation classes. Some classifiers attempt to classify a sample into broad diagnostic categories/entities reminiscent of current traditional classifications whereas other classifiers are specialized on the sub‐classification of specific entities . The relatively small, easily exchangeable and highly standardized methylation array data seems like the perfect fuel for the newly ignited fire of machine‐learning‐based cancer classification.…”

Preclinical Models of Craniospinal Irradiation for Medulloblastoma

“…Based primarily on DNA methylation analysis, medulloblastoma is known to be a heterogeneous disease comprising four distinct molecular subgroups (WNT, SHH, G3, and G4) with significant differences in their clinical and pathologic characteristics, genetic drivers of disease development and progression patterns, and clinical prognosis [18,[46][47][48]. More recent studies employing additional genomic analyses and patient samples have identified distinct subtypes within each of these molecular subgroups [19,[49][50][51]. These data have already informed clinical trial design, with the incorporation of molecular profiling to help guide dose de-escalation in WNT medulloblastoma and intensify therapy in G3 medulloblastoma.…”

“…Thus, efforts have been directed toward maintaining or improving overall survival rates while minimizing treatment-related toxicity. These efforts have primarily focused on modifications to RT approaches that can be broadly grouped as follows: investigations of the effects of adding concurrent and/or adjuvant chemotherapy regimens [9,10]; investigations of the effects of delaying or omitting RT for very young children [11][12][13][14]; investigations of the effects of reductions in the RT dose and field size [15,16]; and, over the past decade, the identification of distinct molecular subgroups of medulloblastoma with specific alterations that drive tumorigenesis, facilitating the identification of new therapeutic targets and enhanced risk stratification [17][18][19].…”

Genotypes versus phenotypes: The potential paradigm shift in the diagnosis and management of pediatric neoplasms

“…27 Lately, further molecular subgrouping can define the prognosis even better. 28 In the past, infant with medulloblastoma is considered as having poor prognosis with the exception of those with desmoplastic histology. It was speculated that it is due to the avoidance or delay of irradiation accounting for such adverse outcome.…”