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Detection of prognostic methylation markers by methylC-capture sequencing in acute myeloid leukemia.

ABSTRACT: Clinical and genetic features incompletely predict outcome in acute myeloid leukemia (AML). The value of clinical methylation assays for prognostic markers has not been extensively explored. We assess the prognostic implications of methylC-capture sequencing (MCC-Seq) in patients with de novo AML by integrating DNA methylation and genetic risk stratification. MCC-Seq assessed DNA methylation level in 44 samples. The differentially methylated regions associated with prognostic genetic information were identified. The selected prognostic DNA methylation markers were independently validated in two sets. MCC-Seq exhibited good performance in AML patients. A panel of 12 differentially methylated genes was identified with promoter hyper-differentially methylated regions associated with the outcome. Compared with a low M-value, a high M-value was associated with failure to achieve complete remission (p = 0.024), increased hazard for disease-free survival in the study set (p = 0.039) and poor overall survival in The Cancer Genome Atlas set (p = 0.038). Hematopoietic stem cell transplantation and survival outcomes were not adversely affected by a high M-value (p = 0.271). Our study establishes that MCC-Seq is a stable, reproducible, and cost-effective methylation assay in AML. A 12-gene M-value encompassing epigenetic and genetic prognostic information represented a valid prognostic marker for patients with AML.

SUBMITTER: Li Y

PROVIDER: S-EPMC5746395 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Aberrant DNA methylation is known to occur in patients with acute myeloid leukemia (AML), whereas methylation signatures and prognostic markers have been proposed. The objective of the current study was to evaluate all CpG sites of the genome and identify prognostic methylation markers for overall survival in patients with AML with normal karyotype (AML-NK). METHODS:AML-NK samples from 7 SWOG trials were analyzed using a novel genome-wide approach called "CHARMcox" (comprehensive high-throughput array-based relative methylation analysis combined with the Cox proportional hazards model) controlling for known clinical covariates. CHARMcox was applied to a phase 1 discovery cohort (72 patients) to identify survival-associated methylation regions (SAMRs). Subsequently, using bisulfite pyrosequencing, SAMRs were studied in phase 2 model-building (65 patients) and phase 3 validation (65 patients) cohorts. An independent external cohort from The Cancer Genome Atlas (TCGA) AML study (LAML) was used for further validation (93 patients). RESULTS:Two SAMRs, located at the CpG island shores of leucine zipper tumor suppressor 2 (LZTS2) and nuclear receptor subfamily 6 group a member 1 (NR6A1), respectively, were identified. Multivariable analyses demonstrated that hypomethylation of either LZTS2 or NR6A1 was associated with worse overall survival in the SWOG cohort (P<.001). The prognosis was validated in patients with AML-NK from the TCGA-LAML cohort. Methylation values below the median at both markers predicted worse overall survival (SWOG: hazard ratio, 1.89 [P<.001]; and TCGA-LAML: hazard ratio, 2.08 [P=.006]). The C-statistic was 0.71 for both cohorts, and the impact was independent of the Fms-related tyrosine kinase 3 internal tandem duplication (FLT3-ITD) status. CONCLUSIONS:The 2 methylation markers, measurable by clinically applicable assays such as bisulfite pyrosequencing, are promising for risk stratification among patients with AML-NK. Cancer 2017;123:2472-81. © 2017 American Cancer Society.

Project description:Background: Metabolic reprogramming is the core characteristic of tumors during the development of tumors, and cancer cells can rely on metabolic changes to support their rapid growth. Nevertheless, an overall analysis of metabolic markers in acute myeloid leukemia (AML) is absent and urgently needed. Methods: Within this work, genetic expression, mutation data and clinical data of AML were queried from Genotype-Tissue Expression (GTEx) database, The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The tumor samples of TCGA were randomly divided into a training group (64 samples) and an internal validation group (64 samples) at one time, and the tumor samples of GEO served as two external validation groups (99 samples, 374 samples). According to the expression levels of survival-associated metabolic genes, we divided all TCGA tumor samples into high, medium and low metabolism groups, and evaluated the immune cell activity in the tumor microenvironment of the three metabolism groups by single-sample gene set enrichment analysis (ssGSEA) algorithm. Finally, we examined the mutations and prognostic effects of each model gene. Results: Four metabolism-related genes were screened and applied to construct a prognostic model for AML, giving excellent results. As for the area under the curve (AUC) value of receiver operating characteristic (ROC) curve, the training group was up to 0.902 (1-year), 0.81 (3-year), and 0.877 (5-year); and the internal and external validation groups also met the expected standards, showing high potency in predicting patient outcome. Univariate and multivariate prognostic analyses indicated that the riskScore obtained from our prognostic model was an independent prognostic factor. ssGSEA analysis revealed the high metabolism group had higher immune activity. Single and multiple gene survival analysis validated that each model gene had significant effects on the overall survival of AML patients. Conclusions: In our study, a high-efficiency prognostic prediction model was built and validated for AML patients. The results showed that metabolism-related genes could become potential prognostic biomarkers for AML.

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Detection of prognostic methylation markers by methylC-capture sequencing in acute myeloid leukemia.

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