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Long-term epilepsy-associated tumors: transcriptional signatures reflect clinical course.


ABSTRACT: Long-term epilepsy-associated tumors (LEATs) represent mostly benign brain tumors associated with drug-resistant epilepsy. The aim of the study was to investigate the specific transcriptional signatures of those tumors and characterize their underlying oncogenic drivers. A cluster analysis of 65 transcriptome profiles from three independent datasets resulted in four distinct transcriptional subgroups. The first subgroup revealed transcriptional activation of STAT3 and TGF-signaling pathways and contained predominantly dysembryoplastic neuroepithelial tumors (DNTs). The second subgroup was characterized by alterations in the MAPK-pathway and up-stream cascades including FGFR and EGFR-mediated signaling. This tumor cluster exclusively contained neoplasms with somatic BRAFV600E mutations and abundance of gangliogliomas (GGs) with a significantly higher recurrence rate (42%). This finding was validated by examining recurrent tumors from the local database exhibiting BRAFV600E in 90% of the cases. The third cluster included younger patients with neuropathologically diagnosed GGs and abundance of the NOTCH- and mTOR-signaling pathways. The transcript signature of the fourth cluster (including both DNTs and GGs) was related to impaired neural function. Our analysis suggests distinct oncological pathomechanisms in long-term epilepsy-associated tumors. Transcriptional activation of MAPK-pathway and BRAFV600E mutation are associated with an increased risk for tumor recurrence and malignant progression, therefore the treatment of these tumors should integrate both epileptological and oncological aspects.

SUBMITTER: Delev D 

PROVIDER: S-EPMC6952384 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Long-term epilepsy-associated tumors (LEATs) represent mostly benign brain tumors associated with drug-resistant epilepsy. The aim of the study was to investigate the specific transcriptional signatures of those tumors and characterize their underlying oncogenic drivers. A cluster analysis of 65 transcriptome profiles from three independent datasets resulted in four distinct transcriptional subgroups. The first subgroup revealed transcriptional activation of STAT3 and TGF-signaling pathways and  ...[more]

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