Intraoperative DNA methylation classification of brain tumors impacts neurosurgical strategy

Abstract

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 six 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 was 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 minutes (range 91-161 minutes). 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.

An Integrated Epigenomic and Genomic View on Phyllodes and Phyllodes-like Breast Tumors

“…In recent years, combined genome-wide DNA methylation and chromosomal copy number analysis by microarrays has gained considerable interest as a precise tool to classify benign and malignant tumors based on their individual, often lineage-reflecting methylation patterns [5][6][7][8]. Most prominently, the brain tumor methylation classifier has become a mainstay in neuropathological tumor diagnostics worldwide [5] and has already influenced several entity definitions in the 2016 WHO classification [9].…”

Detecting cell-of-origin and cancer-specific methylation features of cell-free DNA from Nanopore sequencing

“…Despite the limitations discussed above, the simplicity of native ONT sequencing and the number of features that can be extracted from a single run, combined with the low cost and portability of sequencer, make it an interesting proposition for clinical settings. Fast sample prep and sequencing times can allow a complete methylation analysis from sample preparation to computational classification in as little as 1-3 hours, enabling real-time medical applications in cancer (11,12). Because DNA methylation can differentiate non-cancer cell types as well, Nanopore liquid biopsy could be used to monitor collateral damage to adjacent tissue in cancer (23), or urgent conditions in other areas of medicine such as myocardial infarction, sepsis, and COVID-19 (4)(5)(6)41).…”

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“…In regards to rapid data output, technology developed by Oxford Nanopore may speed up the diagnostic process. [89][90][91] Irrespective of the technical approach, a straightforward question addresses the potential for methylation-based classification of tumors from other origins. Would such approach be feasible for hematopoietic tumors?…”

“…Chip‐based methylation analyses may be replaced by methylation sequencing if issues of reproducibility are solved. In regards to rapid data output, technology developed by Oxford Nanopore may speed up the diagnostic process 89–91 …”