Dataset Information

Integrative meta-analysis of differential gene expression in acute myeloid leukemia.

ABSTRACT: Acute myeloid leukemia (AML) is a heterogeneous disease with an overall poor prognosis. Gene expression profiling studies of patients with AML has provided key insights into disease pathogenesis while exposing potential diagnostic and prognostic markers and therapeutic targets. A systematic comparison of the large body of gene expression profiling studies in AML has the potential to test the extensibility of conclusions based on single studies and provide further insights into AML.In this study, we systematically compared 25 published reports of gene expression profiling in AML. There were a total of 4,918 reported genes of which one third were reported in more than one study. We found that only a minority of reported prognostically-associated genes (9.6%) were replicated in at least one other study. In a combined analysis, we comprehensively identified both gene sets and functional gene categories and pathways that exhibited significant differential regulation in distinct prognostic categories, including many previously unreported associations.We developed a novel approach for granular, cross-study analysis of gene-by-gene data and their relationships with established prognostic features and patient outcome. We identified many robust novel prognostic molecular features in AML that were undetected in prior studies, and which provide insights into AML pathogenesis with potential diagnostic, prognostic, and therapeutic implications. Our database and integrative analysis are available online (http://gat.stamlab.org).

SUBMITTER: Miller BG

PROVIDER: S-EPMC2830886 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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Integrative meta-analysis of differential gene expression in acute myeloid leukemia.

Miller Brady G BG Stamatoyannopoulos John A JA

PloS one 20100301 3

<h4>Background</h4>Acute myeloid leukemia (AML) is a heterogeneous disease with an overall poor prognosis. Gene expression profiling studies of patients with AML has provided key insights into disease pathogenesis while exposing potential diagnostic and prognostic markers and therapeutic targets. A systematic comparison of the large body of gene expression profiling studies in AML has the potential to test the extensibility of conclusions based on single studies and provide further insights into ...[more]

PMID: 20209125

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Project description:Gene expression is a combinatorial function of genetic/epigenetic factors such as copy number variation (CNV), DNA methylation (DM), transcription factors (TF) occupancy, and microRNA (miRNA) post-transcriptional regulation. At the maturity of microarray/sequencing technologies, large amounts of data measuring the genome-wide signals of those factors became available from Encyclopedia of DNA Elements (ENCODE) and The Cancer Genome Atlas (TCGA). However, there is a lack of an integrative model to take full advantage of these rich yet heterogeneous data. To this end, we developed RACER (Regression Analysis of Combined Expression Regulation), which fits the mRNA expression as response using as explanatory variables, the TF data from ENCODE, and CNV, DM, miRNA expression signals from TCGA. Briefly, RACER first infers the sample-specific regulatory activities by TFs and miRNAs, which are then used as inputs to infer specific TF/miRNA-gene interactions. Such a two-stage regression framework circumvents a common difficulty in integrating ENCODE data measured in generic cell-line with the sample-specific TCGA measurements. As a case study, we integrated Acute Myeloid Leukemia (AML) data from TCGA and the related TF binding data measured in K562 from ENCODE. As a proof-of-concept, we first verified our model formalism by 10-fold cross-validation on predicting gene expression. We next evaluated RACER on recovering known regulatory interactions, and demonstrated its superior statistical power over existing methods in detecting known miRNA/TF targets. Additionally, we developed a feature selection procedure, which identified 18 regulators, whose activities clustered consistently with cytogenetic risk groups. One of the selected regulators is miR-548p, whose inferred targets were significantly enriched for leukemia-related pathway, implicating its novel role in AML pathogenesis. Moreover, survival analysis using the inferred activities identified C-Fos as a potential AML prognostic marker. Together, we provided a novel framework that successfully integrated the TCGA and ENCODE data in revealing AML-specific regulatory program at global level.

Project description:Relapsed pediatric B-acute lymphoblastic leukemia (B-ALL) remains as the leading cause of cancer death among children. Other than stem cell transplantation and intensified chemotherapy, no other improved treatment strategies have been approved clinically. Gene expression profiling represents a powerful approach to identify potential biomarkers and new therapeutic targets for various diseases including leukemias. However, inadequate sample size in many individual experiments has failed to provide adequate study power to yield translatable findings. With the hope of getting new insights into the biological mechanisms underpinning relapsed ALL and identifying more promising biomarkers or therapeutic targets, we conducted a meta-analysis of gene expression studies involving ALL from 3 separate studies.By using the keywords "acute lymphoblastic leukemia", and "microarray", a total of 280 and 275 microarray datasets were found listed in Gene Expression Omnibus database GEO and ArrayExpress database respectively. Further manual inspection found that only three studies (GSE18497, GSE28460, GSE3910) were focused on gene expression profiling of paired diagnosis-relapsed pediatric B-ALL. These three datasets which comprised of a total of 108 matched diagnosis-relapsed pediatric B-ALL samples were then included for this meta-analysis using RankProd approach.Our analysis identified a total of 1795 upregulated probes which corresponded to 1527 genes (pfp?<?0.01; FC?>?1), and 1493 downregulated probes which corresponded to 1214 genes (pfp?<?0.01; FC?<?1) respectively. S100A8 appeared as the top most overexpressed gene (pfp?<?0.01, FC?=?1.8) and is a potential target for further validation. Based on gene ontology biological process annotation, the upregulated genes were most enriched in cell cycle processes (enrichment score?=?15.3), whilst the downregulated genes were clustered in transcription regulation (enrichment score?=?12.6). Elevated expression of cell cycle regulators (e.g kinesins, AURKA, CDKs) was the key genetic defect implicated in relapsed ALL, and serve as attractive targets for therapeutic intervention.We identified S100A8 as the most overexpressed gene, and the cell cycle pathway as the most promising biomarker and therapeutic target for relapsed childhood B-ALL. The validity of the results warrants further investigation.

Dataset Information

Integrative meta-analysis of differential gene expression in acute myeloid leukemia.

Publications

Integrative meta-analysis of differential gene expression in acute myeloid leukemia.

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