Rapid epigenomic classification of acute leukemia

Acute leukemia requires precise molecular classification and urgent treatment. However, standard-of-care diagnostic tests are time-intensive and do not capture the full spectrum of acute leukemia heterogeneity. Here, we developed a framework to classify acute leukemia using genome-wide DNA methylation profiling. We first assembled a comprehensive reference cohort (n = 2,540 samples) and defined 38 methylation classes. Methylation-based classification matched standard-pathology lineage classification in most cases and revealed heterogeneity in addition to that captured by genetic categories. Using this reference, we developed a neural network (MARLIN; methylation- and AI-guided rapid leukemia subtype inference) for acute leukemia classification from sparse DNA methylation profiles. In retrospective cohorts profiled by nanopore sequencing, high-confidence predictions were concordant with conventional diagnoses in 25 out of 26 cases. Real-time MARLIN classification in patients with suspected acute leukemia provided accurate predictions in five out of five cases, which were typically generated within 2 h of sample receipt. In summary, we present a framework for rapid acute leukemia classification that complements and enhances standard-of-care diagnostics. The authors present a molecular classification of acute leukemia using 5-methylcytosine signatures, together with a neural network-based classifier for clinical use.