NANOPORE-BASED INTRA-OPERATIVE CLASSIfiCATION OF CENTRAL NERVOUS SYSTEM TUMOURS IN THE NHS

Abstract

Abstract AIMS The 2021 WHO classification of CNS tumours necessitates the use of molecular testing in order to reach a confident diagnosis of many tumour types. At present, this is achieved using array-based technology delivered by a centralised model. This approach has inherent delays that mean it is often several weeks before the treating clinical team is informed of diagnosis, delaying the start of adjuvant therapy and causing significant distress for patients. As such, there is an urgent need to rethink this pathway and improve time to diagnosis. Nanopore longread sequencing technology has the potential to enable a paradigm shift in diagnostic and therapeutic pathways, offering rapid, comprehensive molecular diagnosis that can be performed in the intra-operative setting. We aimed to test the hypothesis that intra-operative methylation-based classification can be performed using nanopore sequencing and thereby expediently inform surgical and oncological decision-making. METHOD Oxford Nanopore Technology (ONT) is a native-strand, long-read sequencing platform that can perform copy- number profiling along with parallel single-gene methylation, structural variant, mutational and methylation analyses. Capital and consumable costs are relatively low, and testing can be performed in the local setting on single samples. We have optimised DNA extraction and library preparation for rapid tissue processing. We have developed a robust bioinformatic pipeline which is able to produce intraoperative classification within minutes of sequencing and full molecular profiling after 24 hours. RESULTS We demonstrate the feasibility of using ONT to deliver intra-operative methylation-based classification of CNS tumours in ‘realtime’ within an NHS hospital setting and will discuss the challenges of implementing this testing and the opportunities to inform neuro-oncological practice. CONCLUSION By giving a summary of our novel protocol of rapid DNA extraction, library preparation and bioinformatic analysis, we will show that this approach can revolutionise the diagnosis of brain tumours and has the potential for rapid adoption by non-expert laboratories.