Robust methylation-based classification of brain tumours using nanopore sequencing
© 2022 The Authors. Neuropathology and Applied Neurobiology published by John Wiley & Sons Ltd on behalf of British Neuropathological Society..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:49 |
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| Enthalten in: |
Neuropathology and applied neurobiology - 49(2023), 1 vom: 21. Feb., Seite e12856 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Kuschel, Luis P [VerfasserIn] |
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| Links: |
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| Themen: |
Brain tumour |
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| Anmerkungen: |
Date Completed 28.02.2023 Date Revised 28.02.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1111/nan.12856 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM347842186 |
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| 520 | |a © 2022 The Authors. Neuropathology and Applied Neurobiology published by John Wiley & Sons Ltd on behalf of British Neuropathological Society. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a brain tumour | |
| 650 | 4 | |a epigenomics | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a molecular pathology | |
| 650 | 4 | |a nanopore sequencing | |
| 650 | 4 | |a whole-genome sequencing | |
| 700 | 1 | |a Hench, Jürgen |e verfasserin |4 aut | |
| 700 | 1 | |a Frank, Stephan |e verfasserin |4 aut | |
| 700 | 1 | |a Hench, Ivana Bratic |e verfasserin |4 aut | |
| 700 | 1 | |a Girard, Elodie |e verfasserin |4 aut | |
| 700 | 1 | |a Blanluet, Maud |e verfasserin |4 aut | |
| 700 | 1 | |a Masliah-Planchon, Julien |e verfasserin |4 aut | |
| 700 | 1 | |a Misch, Martin |e verfasserin |4 aut | |
| 700 | 1 | |a Onken, Julia |e verfasserin |4 aut | |
| 700 | 1 | |a Czabanka, Marcus |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Dongsheng |e verfasserin |4 aut | |
| 700 | 1 | |a Lukassen, Sören |e verfasserin |4 aut | |
| 700 | 1 | |a Karau, Philipp |e verfasserin |4 aut | |
| 700 | 1 | |a Ishaque, Naveed |e verfasserin |4 aut | |
| 700 | 1 | |a Hain, Elisabeth G |e verfasserin |4 aut | |
| 700 | 1 | |a Heppner, Frank |e verfasserin |4 aut | |
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| 700 | 1 | |a Capper, David |e verfasserin |4 aut | |
| 700 | 1 | |a Euskirchen, Philipp |e verfasserin |4 aut | |
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