Long-read epigenomic diagnosis and prognosis of Acute Myeloid Leukemia

Abstract

Acute Myeloid Leukemia (AML) is an aggressive cancer with dismal outcomes, vast subtype heterogeneity, and suboptimal risk stratification. In this study, we harmonized DNA methylation data from 3,314 patients across 11 cohorts to develop the Acute Leukemia Methylome Atlas (ALMA) of diagnostic relevance that predicted 27 WHO 2022 acute leukemia subtypes with an overall accuracy of 96.3% in discovery and 90.1% in validation cohorts. Specifically, for AML, we also developed AML Epigenomic Risk, a prognostic classifier of overall survival (OS) (HR=4.40; 95% CI=3.45–5.61; P<0.0001), and a targeted 38CpG AML signature using a stepwise EWAS-CoxPH-LASSO model predictive of OS (HR=3.84; 95% CI=3.01–4.91; P<0.0001). Finally, we developed a specimen-to-result protocol for simultaneous whole-genome and epigenome sequencing that accurately predicted diagnoses and prognoses from twelve prospectively collected patient samples using long-read sequencing. Our study unveils a new paradigm in acute leukemia management by leveraging DNA methylation for diagnostic and prognostic applications.