Exploring the future of rapid leukaemia diagnosis with a single-platform workflow

Haematological malignancies are some of the most common cancers worldwide¹, with acute leukaemias being the most common blood cancer in children². Acute leukaemias progress quickly, and improved survival rates are predicated on rapid and accurate treatment selection. However, current diagnostic methods are lengthy — taking anywhere from five days to several weeks — which delays treatment plans and impacts patient outcomes.

Discover how researchers are using Oxford Nanopore sequencing to rapidly generate comprehensive data from a single platform, with the hope of shortening the diagnosis and classification of acute leukaemia to within hours — bringing the field close to ending the diagnostic odyssey patients often face.

‘Moving acute leukaemia diagnostics from several days to a few hours’ with Salvatore Benfatto

Acute myeloid leukaemia (AML) is a heterogeneous cancer that currently requires a complex series of genetic tests to achieve a comprehensive diagnosis and to identify the most effective treatment plan for each patient². This complexity is due to the vast array of genomic abnormalities associated with the disease, which influence both risk and therapeutic response³. However, diagnosis can take several weeks, and specialised expertise is required for these tests. This diagnostic odyssey sparked a question for Dr Salvatore Benfatto (Dana-Farber Cancer Institute, USA): ‘how can we make acute leukaemia diagnostics rapid?’.

At the London Calling 2025 conference, Salvatore presented preliminary data demonstrating a potential alternative method for AML classification using Oxford Nanopore sequencing. He was inspired by others in the Nanopore Community who have published ground-breaking research using machine learning models for rapid classification of central nervous system tumours based on direct DNA methylation detection from Oxford Nanopore data^4,5. Salvatore developed a method to classify leukaemias by creating the machine learning model MARLIN⁶: methylation and AI-guided rapid leukaemia subtype inference.

Utilising Oxford Nanopore methylation data, the model predicted tumour classification with 95% concordance to conventional diagnostic methods. Furthermore, Salvatore harnessed the live data-streaming capabilities of nanopore sequencing to generate real-time results, with predictions available within just ten minutes of sequencing. In one analysis, the team accurately predicted a TP53 aneuploidy-enriched AML subtype within 100 minutes. Four days later, the team were thrilled when the results from expedited conventional diagnostic testing confirmed their predicted classification.

Not only were the results accurate and rapid, but Salvatore also highlighted that ‘this framework is very easy to implement anywhere in the world’. The entire workflow is simple and can be performed using a benchtop PromethION 2 device, providing any lab with affordable access to rapid, comprehensive data, with the potential to simplify AML classification.

Towards achieving low-cost, comprehensive diagnosis and classification

Other approaches have also been developed to streamline acute leukaemia classification while keeping costs low. Dr Javeria Aijaz (Indus Hospital and Health Network, Pakistan), in collaboration with St. Jude Children’s Research Hospital, has established a whole-transcriptome workflow leveraging Oxford Nanopore sequencing.

Presenting the workflow and preliminary data, Javeria explained that low-coverage whole-transcriptome sequencing on a MinION can classify AML at a cost of just $80–100 per sample. Nonetheless, the team are working to improve resolution by adding new parameters to their assay and transitioning to high-depth transcriptomic sequencing using a PromethION 2 device to achieve up to 200x coverage.

While increasing sequencing depth will raise the cost to approximately $600 per sample, Javeria explained that this is ‘much less than the cost that we have for all of the other tests combined’. The team hopes that, in the future, this high-depth whole-transcriptome sequencing workflow could offer a rapid, comprehensive, and cost-effective diagnosis solution for any lab across the globe.

Implementing streamlined acute leukaemia sequencing worldwide

Cancer impacts people globally, but in low- and middle-income countries (LMICs), paediatric cancer remains significantly underdiagnosed — leaving thousands of children without a diagnosis⁷. To address this, diagnostic and classification methods must be simplified so that they are not limited to highly specialised, centralised labs with access to complex genetic testing.

At London Calling 2025, Dr Thomas Alexander (University of North Carolina, USA) explained that diagnostic tests are typically developed and validated in centralised labs in high-income countries before deployment in LMICs. However, this approach is not always suitable for labs in LMICs because the tests are expensive, time-consuming, and require specialist equipment and expertise, which are not always available in low-resource labs.

Instead, Thomas is collaborating with St. Jude Children’s Research Hospital and researchers across multiple low-resource countries, including Javeria, to develop and validate a single-platform rapid diagnostic method suitable for any lab setting. Using the same transcriptomics workflow as Javeria, Thomas explained that this simple approach is highly accessible, requiring minimal expertise and only a low-cost, portable MinION sequencing device.

Achieving accurate results in first-time runs

Thomas also shared preliminary data demonstrating that this nanopore sequencing workflow can be successfully implemented across multiple labs with only remote training. Notably, from the first run in Malawi, where none of the lab staff had prior experience sequencing RNA with Oxford Nanopore devices, all but two sequenced samples out of the 52 cases were classified correctly. Reflecting on the results, Thomas noted that he was ‘really surprised to see this level of accuracy in a team in their first time running nanopore RNA in a low-income country’, exalting the ease of using Oxford Nanopore workflows.

To effectively tackle haematological malignancies and improve patient outcomes, rapid diagnostic methods are required to deliver comprehensive data to clinicians within hours of suspected diagnosis. Current approaches are costly, complex, and time-consuming — leaving patients on a diagnostic odyssey that can stretch over many weeks.

Now the team is taking the next step towards developing a new AML testing methodology by utilising DNA and adaptive sampling to capture copy number variation (CNV), translocations, and single nucleotide variation (SNV), providing more comprehensive insights (Figure 1). In this recent publication from Geyer et al.⁸, the team demonstrates how harnessing Oxford Nanopore sequencing holds promise as a rapid, single-platform alternative that could potentially transform cancer diagnosis and classification in the future.

Taking AML classification to the next level

Now the team is taking the next step towards developing a new AML testing methodology by utilising DNA and adaptive sampling to capture copy number variation (CNV), translocations, and single nucleotide variation (SNV), providing more comprehensive insights (Figure 1). In this recent publication from Geyer et al.⁸, the team demonstrates how harnessing Oxford Nanopore sequencing holds promise as a rapid, single-platform alternative that could potentially transform cancer diagnosis and classification in the future.

Figure 1. Oxford Nanopore sequencing generates data that consolidates multiple tests onto a single platform for assessment of multiple genetic abnormalities, including A) large- and B) small-scale CNV, C) SNV, and D) fusion genes. Figure from Geyer et al.⁸ and available under Creative Commons license (https://creativecommons.org/licenses/by/4.0).