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DNA methylation-based classification of MCD in the human brain

ABSTRACT: DNA methylation-based classification of MCD in the human brain

PROVIDER: PRJNA767634 | ENA |

REPOSITORIES: ENA

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DNA methylation-based classification of MCD in the human brain

Project description:Genome wide DNA methylation profiling of human normal and epileptic brain tissue. The Illumina Infinium 850K Human DNA methylation Beadchip was used to obtain DNA methylation profiles across approximately 850.000 CpGs in formalin-fixed paraffin-embedded surgical brain samples. Samples included 316 cases diagnosed with malformations of cortical development (MCD), non-MCD epilepsy or no-epilepsy autopsy controls.

2021-11-30 | GSE185090 | GEO

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DNA methylation-based classification of sinonasal tumors

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DNA methylation-based classification of sinonasal tumors [test set]

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DNA methylation-based classification of sinonasal tumors [reference set]

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Robust methylation based classification of brain tumors using nanopore sequencing

Project description:Using a public reference data set of 82 unique entities, 382 nanopore-sequenced brain tumor samples were classified based on their methylation status through an ad hoc random forest algorithm. As a measure of confidence, score recalibration was performed and platform-specific thresholds were defined.

2022-07-30 | GSE209865 | GEO

Robust methylation-based classification of brain tumours using nanopore sequencing

Project description: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 implementations using hybridization microarrays are 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 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 N=382 tissue samples covering 46 brain tumour (sub)types. Using bootstrap sampling in a cohort of N = 56 cases, we find that a minimum set of 1,000 random CpG features is sufficient for high-confidence classification by ad hoc random forests. We implemented score recalibration as 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), median time to results was 21.1 hours. 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.

| EGAS00001006540 | EGA

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