Dataset Information

DNA methylation-based subtype prediction for pediatric acute lymphoblastic leukemia.

ABSTRACT: We present a method that utilizes DNA methylation profiling for prediction of the cytogenetic subtypes of acute lymphoblastic leukemia (ALL) cells from pediatric ALL patients. The primary aim of our study was to improve risk stratification of ALL patients into treatment groups using DNA methylation as a complement to current diagnostic methods. A secondary aim was to gain insight into the functional role of DNA methylation in ALL.We used the methylation status of ~450,000 CpG sites in 546 well-characterized patients with T-ALL or seven recurrent B-cell precursor ALL subtypes to design and validate sensitive and accurate DNA methylation classifiers. After repeated cross-validation, a final classifier was derived that consisted of only 246 CpG sites. The mean sensitivity and specificity of the classifier across the known subtypes was 0.90 and 0.99, respectively. We then used DNA methylation classification to screen for subtype membership of 210 patients with undefined karyotype (normal or no result) or non-recurrent cytogenetic aberrations ('other' subtype). Nearly half (n?=?106) of the patients lacking cytogenetic subgrouping displayed highly similar methylation profiles as the patients in the known recurrent groups. We verified the subtype of 20% of the newly classified patients by examination of diagnostic karyotypes, array-based copy number analysis, and detection of fusion genes by quantitative polymerase chain reaction (PCR) and RNA-sequencing (RNA-seq). Using RNA-seq data from ALL patients where cytogenetic subtype and DNA methylation classification did not agree, we discovered several novel fusion genes involving ETV6, RUNX1, and PAX5.Our findings indicate that DNA methylation profiling contributes to the clarification of the heterogeneity in cytogenetically undefined ALL patient groups and could be implemented as a complementary method for diagnosis of ALL. The results of our study provide clues to the origin and development of leukemic transformation. The methylation status of the CpG sites constituting the classifiers also highlight relevant biological characteristics in otherwise unclassified ALL patients.

SUBMITTER: Nordlund J

PROVIDER: S-EPMC4343276 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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DNA methylation-based subtype prediction for pediatric acute lymphoblastic leukemia.

Nordlund Jessica J Bäcklin Christofer L CL Zachariadis Vasilios V Cavelier Lucia L Dahlberg Johan J Öfverholm Ingegerd I Barbany Gisela G Nordgren Ann A Övernäs Elin E Abrahamsson Jonas J Flaegstad Trond T Heyman Mats M MM Jónsson Ólafur G ÓG Kanerva Jukka J Larsson Rolf R Palle Josefine J Schmiegelow Kjeld K Gustafsson Mats G MG Lönnerholm Gudmar G Forestier Erik E Syvänen Ann-Christine AC

Clinical epigenetics 20150217

<h4>Background</h4>We present a method that utilizes DNA methylation profiling for prediction of the cytogenetic subtypes of acute lymphoblastic leukemia (ALL) cells from pediatric ALL patients. The primary aim of our study was to improve risk stratification of ALL patients into treatment groups using DNA methylation as a complement to current diagnostic methods. A secondary aim was to gain insight into the functional role of DNA methylation in ALL.<h4>Results</h4>We used the methylation status ...[more]

PMID: 25729447

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Project description:Samples from 20 children with B-cell precursor ALL, comprising ten with high hyperdiploidy and ten with the ETV6/RUNX1 fusion, were included in the study. Four Âµg of methylated and input fraction DNA were analyzed with a classic MeDIP assay and subsequent microarray hybridization using the NimbleGen CpG Island-Plus-Promoter Array (Roche NimbleGen, Madison, WI).<br><br>This array platform covers all 28,226 UCSC-annotated CpG islands and promoter regions for all RefSeq genes, including 385,000 probes of 50-75mer length per array, with an upstream and downstream promoter tiling of 800 bp and 200 bp, respectively. Signal intensity data were extracted from the scanned images. Each feature on the array has a corresponding log2 ratio, which is the ratio of the input signals for the methylated DNA and the input fraction DNA. The log2 ratio was scaled in order to center the ratio data around zero by subtracting the bi-weight mean for the log2 ratio values for all features on the array from each log2 ratio value. A modified ACME algorithm, where a fixed-length window (750 bp) is placed around each consecutive probe, and the one-sided Kolmogorov-Smirnov test were then applied on the scaled log2 ratio data to determine whether the probes were drawn from a significantly more positive distribution (peak) of intensity log2 ratios than the other probes in the array. The resulting score for each probe is the -log10 p-value from the windowed Kolmogorov-Smirnov test around that probe. Peak data were generated by searching for probes above a p-value minimum cut-off (-log10) of 2.<br><br>Peaks within 500 bp of each other were merged. Each annotated gene was subsequently searched for peaks appearing in a specified promoter region around the transcription start site. The region searched was design-specific, spanning from 5 kb upstream to 1 kb downstream of the transcription start site. Genes that had at least two probes with peaks above the p-value minimum cut-off were considered to have significant CpG island methylation scores for hypermethylation.

Dataset Information

DNA methylation-based subtype prediction for pediatric acute lymphoblastic leukemia.

Publications

DNA methylation-based subtype prediction for pediatric acute lymphoblastic leukemia.

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