In:
Neuro-Oncology, Oxford University Press (OUP), Vol. 24, No. Supplement_1 ( 2022-06-03), p. i158-i159
Abstract:
The classification of human brain tumors by global DNA methylation profiling has become an essential part of modern integrated neuropathological diagnostics. It has proven to reliably identify known and novel brain tumor (sub-)types that are biologically and clinically distinct. Therefore, this technique has critically improved diagnostic accuracy and risk stratification of brain tumor patients. Although indispensable for the understanding of tumor biology and for preclinical drug trials, the comparison of genetically engineered mouse models to human brain tumors is still difficult. The assessment of tumor morphology only provides an approximate picture, and transcriptomic data from human brain tumors are sparse and suffer from platform-related technical incomparability. Here, we show array-based DNA methylation profiling of well-established murine brain tumors, such as Wnt and Shh medulloblastoma, YAP and RELA ependymoma, ETMR, and AT/RT. Similar to human brain tumors, unbiased clustering methods revealed distinct methylation profiles and mean methylation levels for mouse brain tumors types. Analyses were possible for fresh-frozen as well as for paraffin-embedded tissue, and copy number alterations could be inferred from methylation profiles. Most importantly, first results suggest that inter-species comparisons allow the classification of brain tumors from known or novel mouse models based on the constantly growing spectrum of human brain tumor types and subtypes with hundreds of thousands of available datasets. As an example, upon DNA methylation profiling, cerebellar tumors arising in Math1-cre::SmoM2Fl/+ mice display the highest similarity to human SHH medulloblastoma when compared to multiple human brain tumor entities including WNT and Non-WNT/Non-SHH medulloblastoma. These results suggest that global DNA methylation profiling may add another very important level of information to the characterization of genetically engineered mouse models.
Type of Medium:
Online Resource
ISSN:
1522-8517
,
1523-5866
DOI:
10.1093/neuonc/noac079.588
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2022
detail.hit.zdb_id:
2094060-9
