21 research outputs found
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Two Different EVI1 Expressing Poor-Risk AML Subgroups with Distinct Epigenetic Signatures Uncovered by Genome Wide DNA Methylation Profiling
Abstract
Although the clinical outcome for patients with acute myeloid leukemia (AML) has improved over the years, failure to maintain complete remission remains a major problem with current standard treatments. The development of individually tailored and patient-specific therapy could potentially significantly improve therapeutic efficacy. In particular we are interested in better understanding the biological features associated with aberrant expression of the EVI1 oncogene, which we previously showed is associated with a poor prognosis. Two different EVI1 transcripts have been identified, i.e. a short form (E) and a long form called MDS1-EVI1 (ME) encoding respectively, a 140 kDa and 170 kDa protein. In EVI1 positive AMLs a distinction can be made between patients that express both EVI1 transcripts (E+/ME+) and cases that express the short form solely (E+), since the latter group is exclusively associated with 3q26 chromosomal abnormalities. EVI1 is a nuclear zinc-finger transcriptional repressor oncoprotein that is known to interact with several epigenetic regulators, e.g. HDACs, CtBPs, histone methyl transferases and MBD3. Since EVI1 presumably mediates its effects through aberrant transcriptional repression, we hypothesize that its aberrant expression results in aberrant epigenetic programming of leukemia cells, which might provide an opportunity for epigenetic-targeted therapy in these patients. In order to determine whether EVI1 over-expressing (EVI1+) AMLs display aberrant epigenetic programming we performed HELP (HpaII tiny fragment enrichment by ligation-mediated PCR) DNA methylation assays in 26 EVI1+ AMLs and 8 CD34+ normal bone marrow controls (NBM). Our HELP assay measured the abundance of DNA methylation at ~50,000 CpG sites covering ~13,000 promoter regions. Single locus validation assays using Sequenom Epityping showed that HELP was >95% accurate in quantifying CpG methylation. We found that unsupervised analysis using hierarchical clustering (Pearson correlation distance with Ward’s clustering method) readily separated the EVI1+ AMLs from NBMs. Supervised analysis comparing EVI1+ to NBM identified 303 promoter sequences as being differently methylated (P1.5). Remarkably, 80% of these genes were hypermethylated in EVI1+ patients, while only 20% of genes were hypomethylated. The hypermethylated profile included genes associated with cell death (Caspase-2, MAD1L1) and cell cycle (TNF, JARID1B). The 26 EVI1+ leukemias further segregated into two distinct subgroups in unsupervised analysis: one cluster (n=14) was highly enriched for E+ AML cases carrying 3q26 abnormalities (n=7) while the other one (n=12) mainly harbored the E+/ME+ AMLs (n=10). Supervised analysis of these two EVI1+ clusters revealed that the 3q26-enriched group featured 122-gene signature (P1.5) consisting entirely of hypermethylated genes. When each of the individual EVI1 clusters was independently compared to the NBM samples using supervised analysis we found that the 3q26-enriched group contained a significantly more methylated gene signature containing 429 hypermethylated and 47 hypomethylated HpaII fragments (P1.5). Pathway analysis of the promoter regions differentially methylated in the 3q26-enriched AML group included genes involved in protein degradation and cellular response to therapeutics. In contrast, the E+/ME+ enriched group showed a more balanced distribution of differential methylation when compared to the NBMs (226 hypermethylated and 158 hypomethylated genes). Taken together, our data show that EVI1 overexpression is associated with specific alterations in epigenetic programming vs. normal CD34+ cells. Even more remarkably, we showed that EVI1+ AMLs form two epigenetically distinct AML subtypes. Specifically, the 3q26 subgroup, short EVI1+ isoform AMLs display marked hypermethylation vs. the MDS1-EVI1 expressing patients, involving aberrant methylation of different pathways. This shows that the two forms of EVI1+ AMLs become aberrantly programmed in different ways and are biologically distinct entities, and further suggest distinct mechanisms of action for the different EVI1 isoforms. The marked hypermethylation profile of the short EVI1 isoform AMLs suggests that these patients might benefit from treatment with DNA methyltransferase inhibitors
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Epigenetic Signatures Identify New Clinically Relevant Subtypes and Define Gene Regulatory Patterns in Patients with Acute Myeloid Leukemia (AML)
Abstract
AML is heterogeneous group of diseases with variable clinical outcomes. While cytogenetics, molecular markers and gene expression profiling can help to classify these patients, they still cannot fully explain the biology and clinical outcomes of the disease. Epigenetic gene deregulation is a hallmark of cancer and our preliminary data suggest that epigenetic signatures are critical determinants of cellular phenotype in AML. Therefore, we hypothesized that aberrant epigenetic regulation of genes would provide critical insight into the biological complexity of AML and identify new and clinically relevant disease subtypes. We studied genome wide DNA methylation in a cohort of 295 patients from HOVON multicenter clinical trials using the HELP assay, which measures with >95% accuracy the abundance of DNA methylation at ~50,000 CpG sites covering ~13,000 promoter regions. Median follow-up was 18.2 months (range=0.1–214.5); median age: 48.1 years (range=15.8–75). Unsupervised analysis using hierarchical clustering (Pearson correlation distance with Ward’s clustering method) segregated the AMLs into 16 well-defined epigenetic clusters. Cluster 1 consisted 100% of patients with acute promyelocytic leukemia (n=6); 100% of cluster 4 harbored CEBPA mutations (n=14); 21/23 patients in cluster 6 carried an inv(16); clusters 7 and 9 were enriched for cases carrying the NPM1 mutation (#7: 80% NPM1+ and #9: 96%) and cluster 12 was enriched for t(8;21) AMLs (18/23). Most of the clusters however define previously unknown biological entities. Next we used a supervised analysis and identified the differentially methylated genes and gene networks that define each cluster, which revealed previously unknown biological differences among these patients. Moreover, Kaplan-Meier survival analysis revealed significant differences in event-free survival (EFS) and overall survival (OS) for the 8 clusters that consisted of >20 patients (clusters 5, 6, 7, 8, 9, 11, 12, and 14), which includes clusters that represent previously unidentified AML subtypes. The inv(16) and t(8;21) containing clusters (i.e. #6 and #12) demonstrated a 2-year EFS of 48% and 58%, respectively, compared to 2-year EFS ranging from 19%–44% for all other clusters (p=0.002 by log-rank test) and a 2-year OS of 70% and 61%, respectively, compared to 2-year OS ranging from 25%–50% for all other clusters (p=0.008 by log-rank test). After adjustment for age, cytogenetic risk, NPM1 mutation, and FLT3-itd status in a multivariate cox proportional hazards regression model, differences in EFS and OS remained between clusters i.e. multivariate analysis (utilizing cluster 12 as reference) showed that clusters 9, 5, 8 and 11 demonstrated hazard ratios for poor events of 3.2 (95% CI=1.0, 10.6; p=0.06), 3.2 (95% CI=1.1, 9.1; p=0.03), 3.4 (95% CI=1.2, 9.8; p=0.03) and 3.6 (95% CI=1.2, 10.3; p=0.02), respectively. Similarly, clusters 9, 8 and 11 demonstrated hazard ratios for mortality of 4.7 (95% CI=1.1, 19.8; p=0.03), 4.1 (95% CI=1.1, 15.2; p=0.03) and 3.7 (95% CI=1.0, 13.6; p=0.05), respectively. Interestingly none of these clusters could be entirely explained by any of the known molecular or cytogenetic markers. Clusters 9 and 5 consisted mainly of cases with normal karyotypes, while #8 and #11 grouped cases with a variety of karyotypes. Furthermore, cluster 9 was associated with a worse outcome despite the fact that 24/25 cases were NPM1+, only 11 of which also presented the poor risk association with FLT3-itd. An analysis restricted to the 125 cases with normal karyotype (NK-AML) segregated them into 2 main clusters, one enriched for NPM1+ cases (81.9%) and the other not (29.6% NPM1+) (Fisher exact test: p-value <2.6e-9). 13/14 cases with CEBPA mutations grouped in the NPM1 cluster, while the 52 FLT3-itd cases were equally distributed among the two clusters. A supervised analysis of NPM1+ NK-AMLs vs. cases without the mutation revealed a 167-gene signature of genes that were uniformly hypermethylated in NK-AML that did not carry the NPM1 mutation, suggesting that non-NPM1 NK-AML patients display common features indicative of a new AML subtype. These data show that rigorous analysis of epigenetic gene regulation in AML identifies novel and biologically relevant subgroups of AML with prognostic significance, and establishes the capture of epigenetic signatures as a new paradigm to improve understanding of disease pathogenesis and clinical behavior
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