Decades before its clinical onset, epigenetic changes start to accumulate in the progenitor cells of Acute Myelogenous Leukemia– caused by tumor intrinsic driver mutations and tumor extrinsic environmental factors. There are millions of cancer-associated epigenetic changes that are persistent and stable throughout the leukemogenesis and at relapse. By grouping correlated loci into “epigenetic signatures”, we can gain insights on the AML etiology, dynamics as well as mechanisms around the methylation pathways of driver events. We approached this problem in two parts: First, we computationally constructed and analyzed methylation signatures that are associated with DNMT3A and TET2 mutations. Both of them are frequently mutated in AML and they are major causes of changes in the methylation landscape of healthy individuals, conferring a broad risk of transformation to a myeloid malignancy with the serial acquisition of an additional cooperating mutation. We built highly accurate ridge regression models that can differentiate the wild type vs mutant genotypes for DNMT3A and TET2 based on their downstream epigenetic impacts (AUROC 0.9 and 0.8, respectively). It suggests that despite the complicated genetic and epigenetic make-up of post-diagnosis samples, epigenetic regulator mutations’ signatures are distinguishable, and these distinctive marks remain stable over time. We further evaluated methylation loci selected by the models and showed that known downstream targets of DNMT3A such as EVI1/MECOM and AEBP2 (polycomb repressive complex member) were frequently selected by the models. We also identified multiple novel recurrent sites that were previously unassociated with DNMT3A and TET2. Second, we modeled different sources of variations in epigenetic patterns with Topic Modeling. Topic Modeling maps the sparse and high dimensional space of methylation profiles to a small and non-sparse latent space of small number of factors (topics). Each sample is represented as a combination of few topics in that latent space. We annotated these topics with biological and clinical features such as mutation status, prior malignancy, age and gender to shed light on the prevalence and combination of the mutations and other extrinsic factors present at the time of diagnosis. In the future, we aim to identify the etiologies of new signatures to inform detection, monitoring, risk stratifying and drug response studies for AML patients and track these changes throughout leukemogenesis in early detection cohorts.

Citation Format: Burcu Gurun, Jeffrey W. Tyner, Emek Demir, Brian J. Druker, Paul T. Spellman. Computational modeling of methylation impact of AML driver events [abstract]. In: Proceedings of the AACR Special Conference: Acute Myeloid Leukemia and Myelodysplastic Syndrome; 2023 Jan 23-25; Austin, TX. Philadelphia (PA): AACR; Blood Cancer Discov 2023;4(3_Suppl):Abstract nr A15.