Acute Leukemia Classification Using Transcriptional Profiles From Low-Cost Nanopore mRNA Sequencing

Most cases of pediatric acute leukemia occur in low- and middle-income countries, where health centers lack the tools required for accurate diagnosis and disease classification. Recent research shows the robustness of using unbiased short-read RNA sequencing to classify genomic subtypes of acute leukemia. Compared with short-read sequencing, nanopore sequencing has low capital and consumable costs, making it suitable for use in locations with limited health infrastructure.

We show the feasibility of nanopore mRNA sequencing on 134 cryopreserved acute leukemia specimens (26 acute myeloid leukemia [AML], 73 B-lineage acute lymphoblastic leukemia [B-ALL], 34 T-lineage acute lymphoblastic leukemia, and one acute undifferentiated leukemia). Using multiple library preparation approaches, we generated long-read transcripts for each sample. We developed a novel composite classification approach to predict acute leukemia lineage and major B-ALL and AML molecular subtypes directly from gene expression profiles.

We demonstrate accurate classification of acute leukemia samples into AML, B-ALL, or T-lineage acute lymphoblastic leukemia (96.2% of cases are classifiable with a probability of > 0.8, with 100% accuracy) and further classification into clinically actionable genomic subtypes using shallow RNA nanopore sequencing, with 96.2% accuracy for major AML subtypes and 94.1% accuracy for major B-lineage acute lymphoblastic leukemia subtypes.

Transcriptional profiling of acute leukemia samples using nanopore technology for diagnostic classification is feasible and accurate, which has the potential to improve the accuracy of cancer diagnosis in low-resource settings.