Medulloblastoma with extensive nodularity (MBEN) is one of the few central nervous system (CNS) tumor entities occurring in infants which is traditionally associated with good to excellent prognosis. Some MBEN, however, have been reported with an unfavorable clinical course. We performed an integrated DNA/RNA-based molecular analysis of a multi-institutional MBEN cohort (n = 41) to identify molecular events which might be responsible for variability in patients’ clinical outcomes. RNA sequencing analysis of this MBEN cohort disclosed two clear transcriptome clusters (TCL) of these CNS tumors: “TCL1 MBEN” and “TCL2 MBEN” which were associated with various gene expression signatures, mutational landscapes and, importantly, prognosis. Thus, the clinically unfavorable “TCL1 MBEN” subset revealed transcriptome signatures composed of cancer-associated signaling pathways and disclosed a high frequency of clinically relevant germline PTCH1/SUFU alterations. In contrast, gene expression profiles of tumors from the clinically favorable “TCL2 MBEN” subgroup were associated with activation of various neurometabolic and neurotransmission signaling pathways, and germline SHH-pathway gene mutations were extremely rare in this transcriptome cluster. “TCL2 MBEN” also revealed strong and ubiquitous expression of VSNL1 (visinin-like protein 1) both at the mRNA and protein level, which was correlated with a favorable clinical course. Thus, combining mutational and epigenetic profiling with transcriptome analysis including VSNL1 immunohistochemistry, MBEN patients could be stratified into clinical risk groups of potential value for subsequent treatment planning.
Transcriptional profiling of medulloblastoma with extensive nodularity (MBEN) reveals two clinically relevant tumor subsets with VSNL1 as potent prognostic marker / Korshunov, A.; Okonechnikov, K.; Sahm, F.; Ryzhova, M.; Stichel, D.; Schrimpf, D.; Ghasemi, D. R.; Pajtler, K. W.; Antonelli, M.; Donofrio, V.; Mastronuzzi, A.; Rossi, S.; Camassei, F. D.; Buccoliero, A. M.; Haberler, C.; Slavc, I.; Dahiya, S.; Casalini, B.; Sievers, P.; Meyer, J.; Kumirova, E.; Zheludkova, O.; Golanov, A.; Jones, D. T. W.; Pfister, S. M.; Kool, M.; von Deimling, A.. - In: ACTA NEUROPATHOLOGICA. - ISSN 0001-6322. - 139:3(2020), pp. 583-596. [10.1007/s00401-019-02102-z]
Transcriptional profiling of medulloblastoma with extensive nodularity (MBEN) reveals two clinically relevant tumor subsets with VSNL1 as potent prognostic marker
Antonelli M.;Mastronuzzi A.;
2020
Abstract
Medulloblastoma with extensive nodularity (MBEN) is one of the few central nervous system (CNS) tumor entities occurring in infants which is traditionally associated with good to excellent prognosis. Some MBEN, however, have been reported with an unfavorable clinical course. We performed an integrated DNA/RNA-based molecular analysis of a multi-institutional MBEN cohort (n = 41) to identify molecular events which might be responsible for variability in patients’ clinical outcomes. RNA sequencing analysis of this MBEN cohort disclosed two clear transcriptome clusters (TCL) of these CNS tumors: “TCL1 MBEN” and “TCL2 MBEN” which were associated with various gene expression signatures, mutational landscapes and, importantly, prognosis. Thus, the clinically unfavorable “TCL1 MBEN” subset revealed transcriptome signatures composed of cancer-associated signaling pathways and disclosed a high frequency of clinically relevant germline PTCH1/SUFU alterations. In contrast, gene expression profiles of tumors from the clinically favorable “TCL2 MBEN” subgroup were associated with activation of various neurometabolic and neurotransmission signaling pathways, and germline SHH-pathway gene mutations were extremely rare in this transcriptome cluster. “TCL2 MBEN” also revealed strong and ubiquitous expression of VSNL1 (visinin-like protein 1) both at the mRNA and protein level, which was correlated with a favorable clinical course. Thus, combining mutational and epigenetic profiling with transcriptome analysis including VSNL1 immunohistochemistry, MBEN patients could be stratified into clinical risk groups of potential value for subsequent treatment planning.File | Dimensione | Formato | |
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