ArticleClinical chemistry2015
Validation of DNA methylation to predict outcome in acute myeloid leukemia by use of xMELP.
Article in Clinical chemistry, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Machine Learning in Laboratory Medicine: Recommendations of the IFCC Working Group.Clinical chemistry · 2023Article
- Article
- Detection of prognostic methylation markers by methylC-capture sequencing in acute myeloid leukemia.Oncotarget · 2017Article
- Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach.Scientific reports · 2017Article
- Clinical implications of genome-wide DNA methylation studies in acute myeloid leukemia.Journal of hematology & oncology · 2017Review
- Validation of a clinical assay of multi-locus DNA methylation for prognosis of newly diagnosed AML.American journal of hematology · 2017Article
- A clinical measure of DNA methylation predicts outcome in de novo acute myeloid leukemia.JCI insight · 2016Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundEpigenetic dysregulation involving alterations in DNA methylation is a hallmark of various types of cancer, including acute myeloid leukemia (AML). Although specific cancer types and clinical aggressiveness of tumors can be determined by DNA methylation status, the assessment of DNA methylation at multiple loci is not routinely performed in the clinical laboratory.
methodsWe recently described a novel microsphere-based assay for multiplex evaluation of DNA methylation. In the current study, we validated and used an improved assay [termed expedited microsphere HpaII small fragment Enrichment by Ligation-mediated PCR (xMELP)] that can be performed with appropriate clinical turnaround time.
resultsUsing the xMELP assay in conjunction with a new 17-locus random forest classifier that has been trained using 344 AML samples, we were able to segregate an independent cohort of 70 primary AML patients into methylation-determined subgroups with significantly distinct mortality risk (P = 0.009). We also evaluated precision, QC parameters, and preanalytic variables of the xMELP assay and determined the sensitivity of the random forest classifier score to failure at 1 or more loci.
conclusionsOur results demonstrate that xMELP performance is suitable for implementation in the clinical laboratory and predicts AML outcome in an independent patient cohort.
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Registered trials
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