Interview: Rapid leukemia classification using nanopore sequencing

Mélanie Sagniez is a PhD student in Bioinformatics at CHU Sainte-Justine Research Center & Department of Biochemistry and Molecular Medicine, Université de Montréal in Canada, where she focusses on the future potential of rapid diagnosis and stratification of pediatric leukemias using nanopore sequencing.

We caught up with Mélanie to talk about how nanopore sequencing is helping to classify childhood leukemias, what inspired her work, and what’s next for her research.

You can also watch her recent talk below, where she covers her work in more detail.

What are your current research interests?

My goal is to generate comprehensive and precise transcriptomic profiles for diagnosis, classification, and treatment selection of acute lymphoblastic leukemia (ALL) in precision medicine. For that I'm interested in the potential of transcriptomics, direct RNA sequencing and machine learning algorithms.

What first ignited your interest in genomics and bioinformatics?

I used to work for clinical studies promoters in lymphoma and leukemia and was outraged at how much time it could take for some patients to obtain a diagnosis of their cancer. I then proceeded to look for a lab that would allow me to research precision medicine and help reduce diagnosis turnaround time for these people.

How is nanopore sequencing helping in our understanding of the transcriptome in cancer? How has it benefitted your work?

As of today, we work 100% with nanopore technologies in our research, using its real-time features and direct RNA sequencing to build precise ALL molecular profiles. As nanopore sequencing technology is capable of generating high-resolution transcriptomic data in real time and at low cost, this could herald new opportunities for molecular medicine.

What impact could real-time transcriptomic profiling have on the classification and potential treatment of leukemia?

Our work showed that ALL molecular profiles can be distinguished after only 5 minutes of sequencing which greatly outperforms conventional cytogenetics turnaround times. Nanopore sequencing also allows multiple features to be accurately detected using a single dataset, such as fusion genes and RNA modifications, that may have the potential to refine diagnosis and prognosis profiles. We think that nanopore sequencing could be highly valuable within current clinical workflows by reducing turnaround times and costs on multiple levels.

What have been the main challenges in your research and how have you approached them?

The main challenge we faced with our ALL molecular classifier using nanopore sequencing is profiling genomic expression levels based on isoforms. As the precise reconstruction of the transcriptome is diffused due to RNA degradation, RT-PCR bias for cDNA, alignment, etc., we are now putting great effort into converting our gene-level expression-based classifier into a transcript-level expression classifier.

What's next for your research?

Since obtaining very good results with our classifier, we can now focus on optimising it, replacing the gene-base expression to a transcript-base expression processing looking for potential new biomarkers. We also want to characterise fusion genes and generate epitranscriptomic data to further improve and refine our ALL profiling.

To learn more about other applications of nanopore sequencing in cancer research, click here.