Clinical impact of combined epigenetic and molecular analysis of pediatric low-grade gliomas

Abstract

Background: Both genetic and methylation analysis have been shown to provide insight into the diagnosis and prognosis of many brain tumors. However, the implication of methylation profiling and its interaction with genetic alterations in pediatric low-grade gliomas (PLGGs) are unclear.

Methods: We performed a comprehensive analysis of PLGG with long-term clinical follow-up. In total 152 PLGGs were analyzed from a range of pathological subtypes, including 40 gangliogliomas. Complete molecular analysis was compared with genome-wide methylation data and outcome in all patients. For further analysis of specific PLGG groups, including BRAF p.V600E mutant gliomas, we compiled an additional cohort of clinically and genetically defined tumors from 3 large centers.

Results: Unsupervised hierarchical clustering revealed 5 novel subgroups of PLGG. These were dominated by nonneoplastic factors such as tumor location and lymphocytic infiltration. Midline PLGG clustered together while deep hemispheric lesions differed from lesions in the periphery. Mutations were distributed throughout these location-driven clusters of PLGG. A novel methylation cluster suggesting high lymphocyte infiltration was confirmed pathologically and exhibited worse progression-free survival compared with PLGG harboring similar molecular alterations (P = 0.008; multivariate analysis: P = 0.035). Although the current methylation classifier revealed low confidence in 44% of cases and failed to add information in most PLGG, it was helpful in reclassifying rare cases. The addition of histopathological and molecular information to specific methylation subgroups such as pleomorphic xanthoastrocytoma-like tumors could stratify these tumors into low and high risk (P = 0.0014).

Conclusion: The PLGG methylome is affected by multiple nonneoplastic factors. Combined molecular and pathological analysis is key to provide additional information when methylation classification is used for PLGG in the clinical setting.

Keywords: lymphocytic infiltration; methylation profile; pediatric low grade glioma; pleomorphic xanthoastrocytoma.

Fig. 1

Stratification of PLGG into clusters based on epigenetic profiling. (A) Unsupervised hierarchical clustering of methylation data of 152 PLGGs with 5000 probes showing highest median absolute deviation. The tumors are divided into 3 clusters termed cluster 1, 2, and 3. The following information is indicated below the heatmap: tumor location, molecular status, pathology, CDKN2A status, and the Heidelberg classifier result. (B) Prevalence of tumors attaching to the midline among hemispheric tumors in cluster 3. (C–G) Representative MRI of hemispheric PLGGs in cluster 3A and (H –I) those in cluster 3B.

Fig. 2

Molecular and clinical analysis of PLGG in cluster 2. (A) Bar chart of the distribution of LUMP score in PLGGs depending on BRAF V600E status. (B) PFS of BRAF V600E mutant PLGGs stratified by LUMP score. (C) PFS of BRAF V600E mutant PLGGs depending on degree of lymphocytic infiltration by pathology.

Fig. 3

Epigenetic, clinical, and molecular features of study cohort. (A) Oncoprint of the cases. The Heidelberg classifier is indicated in the top column. The following information is indicated below the figure: confidence of the classifier showing the calibrated score more than 0.9 or not, Institution, tumor location, pathology, and molecular status, including BRAF V600E mutation, BRAF fusions, FGFR1-TACC1 fusion, FGFR2 fusions, MYBL1 alterations, ALK fusions, and CDKN2A status. (B) OS of the cases diagnosed as “PXA like” by the classifier. Green dot line indicates a survival curve of all of the cases. Red and blue lines are indicated survival curves depending on histological grade. (C) OS of pediatric gliomas with both BRAF-V600E mutation and CDKN2A homozygous deletion stratified by the histological grade. (D) Survival of the same cohort as stratified by the classifier.