Impact of the methylation classifier and ancillary methods on CNS tumor diagnostics.
DNA methylation profile
brain tumor classification
deconvolution
neuropathology
tumor purity
Journal
Neuro-oncology
ISSN: 1523-5866
Titre abrégé: Neuro Oncol
Pays: England
ID NLM: 100887420
Informations de publication
Date de publication:
01 04 2022
01 04 2022
Historique:
pubmed:
24
9
2021
medline:
5
4
2022
entrez:
23
9
2021
Statut:
ppublish
Résumé
Accurate CNS tumor diagnosis can be challenging, and methylation profiling can serve as an adjunct to classify diagnostically difficult cases. An integrated diagnostic approach was employed for a consecutive series of 1258 surgical neuropathology samples obtained primarily in a consultation practice over 2-year period. DNA methylation profiling and classification using the DKFZ/Heidelberg CNS tumor classifier was performed, as well as unsupervised analyses of methylation data. Ancillary testing, where relevant, was performed. Among the received cases in consultation, a high-confidence methylation classifier score (>0.84) was reached in 66.4% of cases. The classifier impacted the diagnosis in 46.7% of these high-confidence classifier score cases, including a substantially new diagnosis in 26.9% cases. Among the 289 cases received with only a descriptive diagnosis, methylation was able to resolve approximately half (144, 49.8%) with high-confidence scores. Additional methods were able to resolve diagnostic uncertainty in 41.6% of the low-score cases. Tumor purity was significantly associated with classifier score (P = 1.15e-11). Deconvolution demonstrated that suspected glioblastomas (GBMs) matching as control/inflammatory brain tissue could be resolved into GBM methylation profiles, which provided a proof-of-concept approach to resolve tumor classification in the setting of low tumor purity. This work assesses the impact of a methylation classifier and additional methods in a consultative practice by defining the proportions with concordant vs change in diagnosis in a set of diagnostically challenging CNS tumors. We address approaches to low-confidence scores and confounding issues of low tumor purity.
Sections du résumé
BACKGROUND
Accurate CNS tumor diagnosis can be challenging, and methylation profiling can serve as an adjunct to classify diagnostically difficult cases.
METHODS
An integrated diagnostic approach was employed for a consecutive series of 1258 surgical neuropathology samples obtained primarily in a consultation practice over 2-year period. DNA methylation profiling and classification using the DKFZ/Heidelberg CNS tumor classifier was performed, as well as unsupervised analyses of methylation data. Ancillary testing, where relevant, was performed.
RESULTS
Among the received cases in consultation, a high-confidence methylation classifier score (>0.84) was reached in 66.4% of cases. The classifier impacted the diagnosis in 46.7% of these high-confidence classifier score cases, including a substantially new diagnosis in 26.9% cases. Among the 289 cases received with only a descriptive diagnosis, methylation was able to resolve approximately half (144, 49.8%) with high-confidence scores. Additional methods were able to resolve diagnostic uncertainty in 41.6% of the low-score cases. Tumor purity was significantly associated with classifier score (P = 1.15e-11). Deconvolution demonstrated that suspected glioblastomas (GBMs) matching as control/inflammatory brain tissue could be resolved into GBM methylation profiles, which provided a proof-of-concept approach to resolve tumor classification in the setting of low tumor purity.
CONCLUSIONS
This work assesses the impact of a methylation classifier and additional methods in a consultative practice by defining the proportions with concordant vs change in diagnosis in a set of diagnostically challenging CNS tumors. We address approaches to low-confidence scores and confounding issues of low tumor purity.
Identifiants
pubmed: 34555175
pii: 6374546
doi: 10.1093/neuonc/noab227
pmc: PMC8972234
doi:
Types de publication
Journal Article
Research Support, N.I.H., Intramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
571-581Commentaires et corrections
Type : CommentIn
Informations de copyright
Published by Oxford University Press on behalf of the Society for Neuro-Oncology 2021.
Références
Acta Neuropathol. 2018 Aug;136(2):181-210
pubmed: 29967940
Epigenetics Chromatin. 2011 May 05;4:7
pubmed: 21545704
Cancer Cell. 2010 May 18;17(5):510-22
pubmed: 20399149
Acta Neuropathol Commun. 2019 Feb 20;7(1):24
pubmed: 30786920
Nature. 1983 Jan 6;301(5895):89-92
pubmed: 6185846
Acta Neuropathol. 2021 Jul;142(1):179-189
pubmed: 33876327
Acta Neuropathol. 2016 Jun;131(6):803-20
pubmed: 27157931
J Neuropathol Exp Neurol. 2012 Jan;71(1):83-9
pubmed: 22157621
J Neuropathol Exp Neurol. 2019 Sep 1;78(9):791-797
pubmed: 31373367
Brain Pathol. 2011 Nov;21(6):645-51
pubmed: 21470325
Nat Commun. 2015 Dec 04;6:8971
pubmed: 26634437
Clin Epigenetics. 2019 Dec 5;11(1):185
pubmed: 31806041
Genome Biol. 2017 Jan 25;18(1):17
pubmed: 28122605
Lancet Child Adolesc Health. 2020 Feb;4(2):121-130
pubmed: 31786093
Nat Commun. 2018 Aug 13;9(1):3220
pubmed: 30104673
Acta Neuropathol Commun. 2020 Jul 8;8(1):101
pubmed: 32641156
Nature. 2018 Mar 22;555(7697):469-474
pubmed: 29539639
Neuropathol Appl Neurobiol. 2020 Aug;46(5):478-492
pubmed: 32072658
Nat Commun. 2019 Jul 31;10(1):3417
pubmed: 31366909
Hum Mol Genet. 2009 Dec 15;18(24):4808-17
pubmed: 19776032
Cancer Res. 1986 Jun;46(6):2917-22
pubmed: 3009002
Hum Mol Genet. 2011 Jul 15;20(14):2710-21
pubmed: 21505077
Genome Biol. 2017 Mar 24;18(1):55
pubmed: 28340624
Genome Res. 2012 Feb;22(2):407-19
pubmed: 21613409
BMC Bioinformatics. 2019 Aug 16;20(1):428
pubmed: 31419933
Acta Neuropathol. 2019 Dec;138(6):1075-1089
pubmed: 31414211