Abstract
Posterior fossa ependymoma comprise three distinct molecular variants, termed PF-EPN-A (PFA), PF-EPN-B (PFB), and PF-EPN-SE (subependymoma). Clinically, they are very disparate and PFB tumors are currently being considered for a trial of radiation avoidance. However, to move forward, unraveling the heterogeneity within PFB would be highly desirable. To discern the molecular heterogeneity within PFB, we performed an integrated analysis consisting of DNA methylation profiling, copy-number profiling, gene expression profiling, and clinical correlation across a cohort of 212 primary posterior fossa PFB tumors. Unsupervised spectral clustering and t-SNE analysis of genome-wide methylation data revealed five distinct subtypes of PFB tumors, termed PFB1-5, with distinct demographics, copy-number alterations, and gene expression profiles. All PFB subtypes were distinct from PFA and posterior fossa subependymomas. Of the five subtypes, PFB4 and PFB5 are more discrete, consisting of younger and older patients, respectively, with a strong female-gender enrichment in PFB5 (age: p = 0.011, gender: p = 0.04). Broad copy-number aberrations were common; however, many events such as chromosome 2 loss, 5 gain, and 17 loss were enriched in specific subtypes and 1q gain was enriched in PFB1. Late relapses were common across all five subtypes, but deaths were uncommon and present in only two subtypes (PFB1 and PFB3). Unlike the case in PFA ependymoma, 1q gain was not a robust marker of poor progression-free survival; however, chromosome 13q loss may represent a novel marker for risk stratification across the spectrum of PFB subtypes. Similar to PFA ependymoma, there exists a significant intertumoral heterogeneity within PFB, with distinct molecular subtypes identified. Even when accounting for this heterogeneity, extent of resection remains the strongest predictor of poor outcome. However, this biological heterogeneity must be accounted for in future preclinical modeling and personalized therapies.
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Acknowledgements
VR is supported by operating funds from the Canadian Institutes of Health Research, American Brain Tumor Association, the Garron Family Cancer Center, Meagan’s Walk, b.r.a.i.n.child, the Brain Tumor Foundation of Canada and a Collaborative Ependymoma Research Network (CERN) basic science fellowship. FMGC is supported by the Stephen Buttrum Brain Tumor Research Fellowship, granted by Brain Tumor Foundation of Canada. MZ is supported by Garron Family Cancer Center Fellowship, Meagan’s Walk Neuro-Oncology Fellowship and Restracomp from Research Training Center at The Hospital for Sick Children. MDT is supported by funds from the Garron Family Chair in Childhood Cancer Research at The Hospital for Sick Children and The University of Toronto, and operating funds from the National Institutes of Health (R01CA159859 and R01CA148699), and the Pediatric Brain Tumor Foundation. EB is supported by funds from the Garron Family Chair in Childhood Cancer Research at The Hospital for Sick Children and The University of Toronto. This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748 to Memorial Sloan Kettering Cancer Center. KWP is supported by a CERN Research Fellowship.
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Florence M. G. Cavalli, Jens-Martin Hübner, and Tanvi Sharma are co-first authors.
Marcel Kool and Vijay Ramaswamy are co-senior authors.
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Supplemental Fig. 1A–F: Spectral clustering of PFB at k = 2–7. Heatmap of the similarity matrix obtained by methylation data on the 212 primary PFB samples. Yellow represents more similar and red represents dissimilar. Color bar at the top represents the final PFB1–5 cluster assignation of the samples (EPS 38612 kb)
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Supplemental Fig. 2: Genomic comparison of PFB subtypes. A) Comparison of subtype assignments between spectral clustering and t-SNE analysis. Relationships (indicated by the grey bars between columns) between the groups of samples obtained by t-SNE analysis (3 to 5 clusters) and the groups obtained by spectral clustering (SpC, 2–5 clusters). B) Density plot of overall promoter methylation levels across the five PFB subtypes (EPS 2173 kb)
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Supplemental Fig. 3: List of differentially regulated pathways across PFB subtypes. Enrichment of specific pathways within the PFB subtypes using significantly differentially expressed genes (adjusted p < 0.05). No pathway were identified for PFB2 and 3, since those two subtypes had a low number of significantly expressed genes (EPS 1839 kb)
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Supplemental Fig. 4: Unsupervised clustering of broad copy-number events across PFB subtypes. A) t-SNE analysis of broad copy-number profiles across 212 PFB ependymomas with corresponding methylation t-SNE subtype assigned. B) Unsupervised consensus clustering of broad copy-number events using negative matrix factorization (NMF) from k = 2 to k = 6 and a plot of the cophenetic coefficient (EPS 31557 kb)
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Supplemental Fig. 5: Arm-level cytogenetic events at k = 4 PFB subtypes from t-SNE analysis. A) Frequency and significance of arm-level gains and losses across the 4 PFB subtypes. Darker bars show significant arm-level events (q value ≤ 0.1, Chi-squared test). B) Frequency and significance of whole chromosome gain and losses across the four PFB subtypes. Darker bars show significant arm-level events (q value ≤ 0.1, Chi-squared test) (EPS 1472 kb)
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Supplemental Fig. 6: Clinical characteristics of PFB subtypes. A) Progression-free and B) overall survival across PFB subtypes. P values determined using the log-rank test. C) Pie chart of the administration of upfront radiation across PFB subtypes and D) Pie chart of the incidence of gross total resection across PFB subtypes. P values determined using the Fisher’s exact test. Overall survival stratified by E) 1q gain and F) 13q status. p values determined using the log-rank test (EPS 2421 kb)
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Cavalli, F.M.G., Hübner, JM., Sharma, T. et al. Heterogeneity within the PF-EPN-B ependymoma subgroup. Acta Neuropathol 136, 227–237 (2018). https://doi.org/10.1007/s00401-018-1888-x
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DOI: https://doi.org/10.1007/s00401-018-1888-x