DNA methylation analysis improves the prognostication of acute myeloid leukemia

Abstract

Integration of orthogonal data could provide new opportunities to pinpoint the underlying molecular mechanisms of hematologic disorders. Using a novel gene network approach, we integrated DNA methylation data from The Cancer Genome Atlas (n = 194 cases) with the corresponding gene expression profile. Our integrated gene network analysis classified AML patients into low-, intermediate-, and high-risk groups.The identified high-risk group had significantly shorter overall survival compared to the low-risk group (p-value ≤10 −11 ). Specifically, our approach identified a particular subgroup of nine high-risk AML cases that died within 2 years after diagnosis.These high-risk cases otherwise would be incorrectly classified as intermediate-risk solely based on cytogenetics, mutation profiles, and common molecular characteristics of AML. We confirmed the prognostic value of our integrative gene network approach using two independent datasets, as well as through comparison with European LeukemiaNet and LSC17 criteria. Our approach could be useful in the prognostication of a subset of borderline AML cases. These cases would not be classified into appropriate risk groups by other approaches that use gene expression, but not DNA methylation data. Our findings highlight the significance of epigenomic data, and they indicate integrating DNA methylation data with gene coexpression networks can have a synergistic effect. 1 INTRODUCTION Acute myeloid leukemia (AML), which is the most common acute leukemia among adults [1], has several subtypes that have various prognoses [2] depending on age, cytogenetic abnormalities [3], specific mutations [4], and other unknown risk factors (Note S1). Timely diag-This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

The pathogenesis and development of targeted drugs in acute T lymphoblastic leukaemia

“…By competitively inhibiting the ATP binding site on the catalytic subunit of JAK1/2 kinase domain, it inhibits the activity of JAK kinase and the activation of JAK–STAT signalling pathway. Thus it inhibits the expression of p‐JAK2 in tumour tissues and human erythroleukaemia cell line (HEL cells) of myeloproliferative neoplasms (MPN) patients and interferes with the binding of downstream target genes to matrix metalloprotein, MMP2 and MMP9 promoter genes (Cheng et al, 2017) and blocks the unlimited proliferation and migration of tumour cells (Samimi et al, 2021). In addition, pre‐clinical studies indicated that the JAK/TYK2 inhibitor (JAKi), cerdulatinib, as a promising therapeutic against TYK2‐rearranged acute lymphoblastic leukaemia, attenuating the constitutive JAK–STAT signalling resulting from the Tyk2 fusion protein (Tavakoli Shirazi et al, 2021).…”

“…Thus it inhibits the expression of p-JAK2 in tumour tissues F I G U R E 4 IL7-IL7R signalling pathway. IL7 is a basic cytokine that maintains T-cell proliferation and differentiation, regulates cell cycle, maintains cell homeostasis and mediates apoptosis and IL7R dimer can induce the phosphorylation of JAK1 and JAK3, and further play the physiological function of regulating T-cell differentiation, maturation and apoptosis.and human erythroleukaemia cell line (HEL cells) of myeloproliferative neoplasms (MPN) patients and interferes with the binding of downstream target genes to matrix metalloprotein, MMP2 and MMP9 promoter genes(Cheng et al, 2017) and blocks the unlimited proliferation and migration of tumour cells(Samimi et al, 2021). In addition, pre-clinical studies indicated that the JAK/TYK2 inhibitor (JAKi), cerdulatinib, as a promising therapeutic against TYK2-rearranged acute lymphoblastic leukaemia, attenuating the constitutive JAK-STAT signalling resulting from the Tyk2 fusion protein(Tavakoli Shirazi et al, 2021).…”

Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package

“…Orthogonal data types, and specifically genomic and epigenomic profiles, can potentially provide new opportunities to pinpoint underlying molecular mechanisms of diseases 1 .…”

“…Complex multi-omics data, including transcriptomics, epigenomics, and proteomics data, can be integrated using a network analysis approach 1 – 7 .…”

“…( 2 ), ( 3 ), ( 4 ) in “ Methods ”, respectively). Eigengenes are robust biological features useful for downstream data mining analyses e.g., classification 18 , survival analysis 21 , and prognostication 1 . Here, we illustrate the application of eigengenes in determining risk groups in different diseases and show the advantage of integrating DNA methylation data in a gene co-expression network.…”