Robust methylation-based classification of brain tumours using nanopore sequencing

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.

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.

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.