Rapid epigenomic classification of acute leukemia
Til L. Steinicke, Salvatore Benfatto, Maria Capilla-Guerra, Andre B. Monteleone, Jonathan H. Young, Subha Shankar, Phillip Michaels, Harrison Tsai, Jonathan Good, Antonia Kreso, Peter van Galen, Christoph Schliemann, Evan C. Chen, Gabriel K. Griffin, Volker Hovestadt
Abstract
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.