Concomitant <i>AID</i> Expression and <i>BCL7A</i> Loss Associates With Accelerated Phase Progression and Imatinib Resistance in Chronic Myeloid Leukemia

Yu, Nae; Shin, Miyoung; Choi, Jong Rak; Kim, Yoonjung; Lee, Kyung A.

doi:10.3343/alm.2017.37.2.177

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…Topic 2 has multiple DMRs near PTH2R, parathyroid hormone receptor, w hich w as show n to be the most upregulated gene in MDS and AML 79 and differentially expressed in patients w ith IDH2 mutations 80 . Topic 8 has DMRs in close proximity to KDM2B, a key lysine demethylase in AML; BCL7A, a BAF remodeling tumor suppressor 81 , and TCF7L2, a WNT pathw ay transcription factor implicated in regeneration of hematopoietic stem cells 82 . Topic 12 has DMRs near immunoglobulin heavy constant gamma as w ell as T-Cell Receptor Beta locus.…”

Section: Acknowledgementmentioning

confidence: 99%

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

Gurun

Tyner

Demir

et al. 2023

Preprint

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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.

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