How nanopore sequencing has the future potential for rapid brain tumour classification
In the first ever clinical and biopharma day at London Calling 2024, we heard from not one, but two neuropathologists discussing the future potential of nanopore sequencing for the classification and diagnosis of central nervous system (CNS) tumours during surgery* — paving the way for a paradigm shift in the way that CNS tumours are classified.
Why is CNS tumour classification important?
CNS tumours are responsible for substantial morbidity and mortality worldwide1. A major challenge in the treatment of CNS tumours is that taking a biopsy is often not possible, and therefore, treatment usually starts with a surgical resection before the precise tumour type is known.
The neurosurgeon must make a choice that balances the benefit of maximal resection against the risk of inflicting severe damage to the patient. The impact of increased resection varies between tumour types and is therefore limited to those tumours where it is really needed.
Current standard practice relies on preoperative imaging and intraoperative histological analysis to determine the surgical strategy, but these are not always conclusive and are occasionally wrong. The importance of molecular and genetic features in tumour classification has been increasingly recognised, and the 2021 World Health Organization (WHO) Classification of Tumours of the Central Nervous System now recommends molecular, genetic, and epigenetic analyses for a substantial proportion of tumour types2.
What is the potential for nanopore sequencing in CNS tumour classification?
Researchers have turned to nanopore technology for a possible new method to classify CNS tumours and potentially support personalised neurosurgical treatment decisions during operations. Methylome profiling plays a crucial role in distinguishing between different types of CNS tumours that appear histologically similar, and PCR-free nanopore sequencing enables direct detection of DNA modifications, such as methylation, without any additional sample or library prep.
In London, Prof. Dr Pieter Wesseling (Princess Máxima Center for Pediatric Oncology & Amsterdam University Medical Centers, The Netherlands) spoke about the team’s recently published ultra-fast, deep-learned model, Sturgeon, that uses methylation data generated from rapid nanopore sequencing to classify CNS tumours in an intraoperative timescale3. Pieter presented that for most of the 47 CNS tumour research samples, the model delivered an accurate classification during surgery, and when answering questions from the audience, he highlighted that ‘from the start of the sequencing until the answer, it’s about 45 minutes’.
The ability to detect methylation, rapidly and directly, is not the only reason why researchers are choosing nanopore sequencing for this application. Dr Simon Paine (Nottingham University Hospitals NHS Trust, UK) explained how an ultra-fast nanopore sequencing pipeline, developed by his team, directly detects the ‘methylation fingerprint’ of CNS tumour research samples within two hours, but also provides all the potentially diagnostically relevant genetic information within 24 hours.
Long, highly accurate reads generated by nanopore sequencing technology can detect single nucleotide variants, copy number variants, indels, gene fusions, and structural variants — all of which have been identified in various CNS tumours2.
How can nanopore sequencing shape the future of CNS tumour classification?
Simon spoke about how the current model of molecular analysis for CNS tumours is suboptimal and ‘give[s] results when the system delivers them, and not when the patient needs them’, leading to situations where patient outcomes are impacted. The standard-of-care diagnosis obtains molecular information through several different analyses that are performed separately with ‘a timeline … that ends in 28 days [or] often longer’.
Intraoperative nanopore sequencing analysis has the potential to remove the need for multiple analyses, and Pieter stated that it ‘can drastically speed-up the multidisciplinary decision-making process’. Therefore, nanopore technology has the potential to provide rapid CNS tumour classification and diagnostic information during surgery all on a single platform.
*Oxford Nanopore Technologies products are for research use only and are not approved to diagnose any disease or condition.
1. GBD 2016 Brain and Other CNS Cancer Collaborators. Global, regional, and national burden of brain and other CNS cancer, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 18(4):376–393 (2019). DOI: https://doi.org/10.1016/s1474-4422(18)30468-x
2. Louis, D.N. et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 23(8):1231–1251 (2021). DOI: https://doi.org/10.1093/neuonc/noab106
3. Vermeulen, C. et al. Ultra-fast deep-learned CNS tumour classification during surgery. Nature 622(7984):842–849. DOI: https://doi.org/10.1038/s41586-023-06615-2