Computational modeling of methylation impact of AML drivers reveals new pathways and refines AML risk-stratification

Abstract

Decades before its clinical onset, epigenetic changes start to accumulate in the progenitor cells of Acute Myelogenous Leukemia (AML). Delineating these changes can improve risk-stratification for patients and shed insights into AML etiology, dynamics and mechanisms. Towards this goal, we extracted "epigenetic signatures" through two parallel machine learning approaches: a supervised regression model using frequently mutated genes as labels and an unsupervised topic modeling approach to factorize covarying epigenetic changes into a small number of "topics". First, we created regression models for DNMT3A and TET2, the two most frequently mutated epigenetic drivers in AML. Our model differentiated wild-type vs. mutant genotypes based on their downstream epigenetic impacts with very high accuracy: AUROC 0.9 and 0.8, respectively. Methylation loci frequently selected by the models recapitulated known downstream pathways and identified several novel recurrent targets. Second, we used topic modeling to systematically factorize the high dimensional methylation profiles to a latent space of 15 topics. We annotated identified topics with biological and clinical features such as mutation status, prior malignancy and ELN criteria. Topic modeling successfully deconvoluted the combined effects of multiple upstream epigenetic drivers into individual topics including relatively infrequent cytogenetic events, improving the methylation-based subtyping of AML. Furthermore, they revealed complimentary and synergistic interactions between drivers, grouped them based on the similarity of their downstream methylation impact and linked them to prognostic criteria. Our models identify new signatures and methylation pathways, refine risk-stratification and inform detection and drug response studies for AML patients. KEY POINTSO_LISupervised and unsupervised models reveal new methylation pathways of AML driver events and validate previously known associations. C_LIO_LIIndividual DNMT3A and TET2 signatures are accurate and robust, despite the complex genetic and epigenetic make-up of samples at diagnosis. C_LIO_LIUnsupervised topic modeling factorizes covarying methylation changes and isolates methylation signatures caused by rare mutations. C_LIO_LITopic modeling reveals a group of mutations with similar downstream methylation impacts and mapped to adverse-risk class by ELN. C_LIO_LITopic modeling uncovers methylation signatures of infrequent cytogenetic events, significantly improving methylation-based subtyping. C_LIO_LIOur models can be leveraged to build predictive models for AML-risk. C_LIO_LIOur models show that cytogenetic events, such as t(15;17) have widespread trans downstream methylation impacts. C_LI