Rapid DNA methylation-based classification of pediatric brain tumors from ultrasonic aspirate specimens.

BACKGROUND: Although cavitating ultrasonic aspirators are commonly used in neurosurgical procedures, the suitability of ultrasonic aspirator-derived tumor material for diagnostic procedures is still controversial. Here, we explore the feasibility of using ultrasonic aspirator-resected tumor tissue to classify otherwise discarded sample material by fast DNA methylation-based analysis using low pass nanopore whole genome sequencing. METHODS: Ultrasonic aspirator-derived specimens from pediatric patients undergoing brain tumor resection were subjected to low-pass nanopore whole genome sequencing. DNA methylation-based classification using a neural network classifier and copy number variation analysis were performed. Tumor purity was estimated from copy number profiles. Results were compared to microarray (EPIC)-based routine neuropathological histomorphological and molecular evaluation. RESULTS: 19 samples with confirmed neuropathological diagnosis were evaluated. All samples were successfully sequenced and passed quality control for further analysis. DNA and sequencing characteristics from ultrasonic aspirator-derived specimens were comparable to routinely processed tumor tissue. Classification of both methods was concordant regarding methylation class in 17/19 (89%) cases. Application of a platform-specific threshold for nanopore-based classification ensured a specificity of 100%, whereas sensitivity was 79%. Copy number variation profiles were generated for all cases and matched EPIC results in 18/19 (95%) samples, even allowing the identification of diagnostically or therapeutically relevant genomic alterations. CONCLUSION: Methylation-based classification of pediatric CNS tumors based on ultrasonic aspirator-reduced and otherwise discarded tissue is feasible using time- and cost-efficient nanopore sequencing.

Robust methylation-based classification of brain tumours using nanopore sequencing.

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.

Intraoperative DNA methylation classification of brain tumors impacts neurosurgical strategy.

BACKGROUND: Brain tumor surgery must balance the benefit of maximal resection against the risk of inflicting severe damage. The impact of increased resection is diagnosis-specific. However, the precise diagnosis is typically uncertain at surgery due to limitations of imaging and intraoperative histomorphological methods. Novel and accurate strategies for brain tumor classification are necessary to support personalized intraoperative neurosurgical treatment decisions. Here, we describe a fast and cost-efficient workflow for intraoperative classification of brain tumors based on DNA methylation profiles generated by low coverage nanopore sequencing and machine learning algorithms. METHODS: We evaluated 6 independent cohorts containing 105 patients, including 50 pediatric and 55 adult patients. Ultra-low coverage whole-genome sequencing was performed on nanopore flow cells. Data were analyzed using copy number variation and ad hoc random forest classifier for the genome-wide methylation-based classification of the tumor. RESULTS: Concordant classification was obtained between nanopore DNA methylation analysis and a full neuropathological evaluation in 93 of 105 (89%) cases. The analysis demonstrated correct diagnosis in 6/6 cases where frozen section evaluation was inconclusive. Results could be returned to the operating room at a median of 97 min (range 91-161 min). Precise classification of the tumor entity and subtype would have supported modification of the surgical strategy in 12 out of 20 patients evaluated intraoperatively. CONCLUSION: Intraoperative nanopore sequencing combined with machine learning diagnostics was robust, sensitive, and rapid. This strategy allowed DNA methylation-based classification of the tumor to be returned to the surgeon within a timeframe that supports intraoperative decision making.

Enhancer hijacking determines extrachromosomal circular MYCN amplicon architecture in neuroblastoma.

MYCN amplification drives one in six cases of neuroblastoma. The supernumerary gene copies are commonly found on highly rearranged, extrachromosomal circular DNA (ecDNA). The exact amplicon structure has not been described thus far and the functional relevance of its rearrangements is unknown. Here, we analyze the MYCN amplicon structure using short-read and Nanopore sequencing and its chromatin landscape using ChIP-seq, ATAC-seq and Hi-C. This reveals two distinct classes of amplicons which explain the regulatory requirements for MYCN overexpression. The first class always co-amplifies a proximal enhancer driven by the noradrenergic core regulatory circuit (CRC). The second class of MYCN amplicons is characterized by high structural complexity, lacks key local enhancers, and instead contains distal chromosomal fragments harboring CRC-driven enhancers. Thus, ectopic enhancer hijacking can compensate for the loss of local gene regulatory elements and explains a large component of the structural diversity observed in MYCN amplification.