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Methylome profiling enhances risk stratification and reveals context-dependent chemo-resistance mechanisms in acute myeloid leukemia

ABSTRACT: Acute Myeloid Leukemia (AML) is the second most lethal hematologic malignancy, with approximately 30% of patients being refractory to first-line induction therapy. While current risk stratification systems, such as European LeukemiaNet (ELN), use cytogenetic and molecular markers to guide treatment decisions, their predictive accuracy remains suboptimal, particularly in forecasting treatment response. In this study, we performed integrated methylome and transcriptome analysis of diagnostic samples from 145 AML patients, including methylome data on 25 patients, to investigate the molecular basis underlying the discrepancy between predicted and actual treatment outcomes. Unsupervised analysis revealed that while genomic backgrounds strongly influence molecular profiles, treatment response patterns frequently diverge from predictions based on cytogenetic and mutational risk classifications. We identified PTX4 as a novel tumor suppressor gene (TSG) silenced by DNA hypermethylation in patients with adverse-risk AML. Within individual AML subtypes, comparison of refractory/relapsed (RR) cases versus those achieving complete remission (CR) uncovered distinct resistance mechanisms. Furthermore, analysis of hematopoietic developmental trajectories revealed that RR cases exhibit altered epigenetic programming, characterized by preferential methylation changes in regulatory regions, such as polycomb-repressed chromatin states. These results represent both context-dependent and unifying mechanism of treatment resistance that is independent of genetic background. These findings highlight the limitations of current genetic-based risk assessments and underscore the potential of incorporating epigenetic profiling, such as methylome analysis, to more accurately predict treatment response and guide therapeutic strategies in AML.

ORGANISM(S): Homo sapiens

PROVIDER: GSE300773 | GEO | 2026/06/01

REPOSITORIES: GEO

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Project description:PURPOSE: To evaluate frequency, biological features and clinical relevance of RUNX1 mutations in acute myeloid leukemia (AML). PATIENTS AND METHODS: Diagnostic samples from 945 patients (18 to 60 years) were analyzed for RUNX1 mutations. In a subset of cases (n=269) microarray gene expression analysis was performed. RESULTS: Fifty-nine RUNX1 mutations were identified in 53 of 945 (5.6%) cases, predominantly in exons 3 (n=11), 4 (n=10) and 8 (n=23). RUNX1 mutations clustered in the intermediate-risk cytogenetic group (46/640, 7.2%; cytogenetically normal, 34/538, 6.3%), they were less frequent in adverse-risk cytogenetics (5/109, 4.6%), and absent in core-binding-factor AML (0/77) and acute promyelocytic leukemia (0/61); RUNX1 mutations were associated with MLL-partial tandem duplications (p=0.0007) and IDH1/IDH2 mutations (p=0.03), inversely correlated with NPM1 (p<0.0001) and in trend with CEBPA (p=0.10) mutations. RUNX1 mutations were characterized by a distinct gene expression pattern; this RUNX1 mutation-derived signature was not exclusive for the mutation, but also included mostly adverse-risk AML [eg, 7q-, -7, inv(3) or t(3;3)]. RUNX1 mutations predicted for resistance to chemotherapy (rates of refractory disease 30% and 19%, p=0.047, for RUNX1-mutated and wildtype patients, respectively), as well as inferior event-free survival (EFS; p<0.0001), relapse-free survival (RFS, p=0.022), and overall survival (p=0.051). In multivariable analysis, RUNX1 mutations were an independent prognostic marker for shorter EFS (p=0.007). Explorative subgroup analysis revealed that allogeneic hematopoietic stem cell transplantation had a favorable impact on RFS in RUNX1-mutated patients (p<0.0001). CONCLUSION: AML with RUNX1 mutations exhibit distinct biological properties and are associated with resistance to therapy and inferior outcome. A total of 269 RNA samples derived from adult AML patients were provided by the German Austrian AML Study Group (AMLSG) [AMLSG trial AML HD98A (ClinicalTrials.gov Identifier: NCT00146120)]. Conventional cytogenetic banding, and FLT3, CEBPA and NPM1 mutational analysis were performed as previously described (Schlenk et al., N Engl J Med 2008). Detailed clinical, cytogenetic and molecular cytogenetic information are also provided in the supplementary information. Following enrichment, all samples contained at least 80% leukemic cells. Gene expression profiling (GEP) was performed as previously described (Bullinger et al., N Engl J Med 2004).

Project description:Advances in transcriptomics have impacted the way of looking atimproved our understanding of leukemic development and helped enhancing the stratification of patients. Analyses of individual cohortsTranscriptomics studies often combined AML samples regardless of cytogenetic abnormalities. , which would lead to bias in Hence, deregulated genesdifferential gene expression analysis that may result from the differential representation of in less frequent AML subgroups have low weight, contrarily to those in abundant subgroups. We, thusHence, we performed a horizontal meta-analysis that integrated transcriptomic data of AML from multicentric multiple studies to enrich the less frequent cytogenetic subgroups and to uncover common genes involved in AML development and response to therapy. A total of 28 Affymetrix microarray datasets harboring 3940 AML samples were downloaded from the GEO database. After stringent quality control, transcriptomics data of 1523 samples from 11 datasets, covering 10 AML cytogenetic subgroups, were retained and merged with the transcriptomic data of 198 healthy bone marrow samples. Differentially expressed genes between each cytogenetic subgroup and normal samples were extracted, allowing the unbiased identification of 330 commonly deregulated genes (CODEGs)., which CODEGs showed enriched expression profiles in myeloid differentiation, leukemic stem cell status and relapse. Most of these genes were downregulated, in accordance with DNA hypermethylation. CODEGS were then used to create a prognostic score based on the weighted sum of expression of 22 core genes (CODEG22). The score was validated in on microarray data of five independent cohorts, and by qRT-PCR in a cohort of 142 samples, by qRT-PCR. CODEG22 stratified patients globally, as well as in subpopulations of cytologically normal and elderly patients. , and hence, couldSo, it may complement the European LeukemiaNet classification for a more accurate prediction of AML outcomes.

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Methylome profiling enhances risk stratification and reveals context-dependent chemo-resistance mechanisms in acute myeloid leukemia

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