dc.contributor.author
Kuschel, Luis P.
dc.contributor.author
Hench, Jürgen
dc.contributor.author
Frank, Stephan
dc.contributor.author
Hench, Ivana Bratic
dc.contributor.author
Girard, Elodie
dc.contributor.author
Blanluet, Maud
dc.contributor.author
Masliah‐Planchon, Julien
dc.contributor.author
Misch, Martin
dc.contributor.author
Onken, Julia
dc.contributor.author
Czabanka, Marcus
dc.contributor.author
Yuan, Dongsheng
dc.contributor.author
Lukassen, Sören
dc.contributor.author
Karau, Philipp
dc.contributor.author
Ishaque, Naveed
dc.contributor.author
Hain, Elisabeth G.
dc.contributor.author
Heppner, Frank
dc.contributor.author
Idbaih, Ahmed
dc.contributor.author
Behr, Nikolaus
dc.contributor.author
Harms, Christoph
dc.contributor.author
Capper, David
dc.contributor.author
Euskirchen, Philipp
dc.date.accessioned
2025-03-28T15:44:18Z
dc.date.available
2025-03-28T15:44:18Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/47080
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-46797
dc.description.abstract
Background
DNA methylation-based classification of cancer provides a comprehensive molecular approach to diagnose tumours. In fact, DNA methylation profiling of human brain tumours already profoundly impacts clinical neuro-oncology. However, current implementation using hybridisation microarrays is time consuming and costly. We recently reported on shallow nanopore whole-genome sequencing for rapid and cost-effective generation of genome-wide 5-methylcytosine profiles as input to supervised classification. Here, we demonstrate that this approach allows us to discriminate a wide spectrum of primary brain tumours.
Results
Using public reference data of 82 distinct tumour entities, we performed nanopore genome sequencing on 382 tissue samples covering 46 brain tumour (sub)types. Using bootstrap sampling in a cohort of 55 cases, we found that a minimum set of 1000 random CpG features is sufficient for high-confidence classification by ad hoc random forests. We implemented score recalibration as a confidence measure for interpretation in a clinical context and empirically determined a platform-specific threshold in a randomly sampled discovery cohort (N = 185). Applying this cut-off to an independent validation series (n = 184) yielded 148 classifiable cases (sensitivity 80.4%) and demonstrated 100% specificity. Cross-lab validation demonstrated robustness with concordant results across four laboratories in 10/11 (90.9%) cases. In a prospective benchmarking (N = 15), the median time to results was 21.1 h.
Conclusions
In conclusion, nanopore sequencing allows robust and rapid methylation-based classification across the full spectrum of brain tumours. Platform-specific confidence scores facilitate clinical implementation for which prospective evaluation is warranted and ongoing.
en
dc.rights.uri
https://creativecommons.org/licenses/by-nc/4.0/
dc.subject
brain tumour
en
dc.subject
machine learning
en
dc.subject
molecular pathology
en
dc.subject
nanopore sequencing
en
dc.subject
whole-genome sequencing
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
Robust methylation‐based classification of brain tumours using nanopore sequencing
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
e12856
dcterms.bibliographicCitation.doi
10.1111/nan.12856
dcterms.bibliographicCitation.journaltitle
Neuropathology and Applied Neurobiology
dcterms.bibliographicCitation.number
1
dcterms.bibliographicCitation.originalpublishername
Wiley
dcterms.bibliographicCitation.volume
49
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.funding
DEAL Wiley
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
36269599
dcterms.isPartOf.issn
0305-1846
dcterms.isPartOf.eissn
1365-2990