Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of acute lymphoblastic leukaemia patient samples that represent six different subgroups defined by cytogenetic features and immunophenotype

ABSTRACT: We examined published microarray data from 104 acute lymphoblastic leukaemia patient specimens, that represent six different subgroups defined by cytogenetic features and immunophenotypes. Using the decision-tree based supervised learning algorithm Random Forest (RF), we determined a small set of genes for optimal subgroup distinction and subsequently validated their predictive power in an independent cohort of 68 specimens that were assessed using Affymetrix HG-U133A arrays.

ORGANISM(S): Homo sapiens

SUBMITTER: Katrin Hoffmann

PROVIDER: E-TABM-125 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Translating microarray data for diagnostic testing in childhood leukaemia.

Hoffmann Katrin K Firth Martin J MJ Beesley Alex H AH de Klerk Nicholas H NH Kees Ursula R UR

BMC cancer 20060926

<h4>Background</h4>Recent findings from microarray studies have raised the prospect of a standardized diagnostic gene expression platform to enhance accurate diagnosis and risk stratification in paediatric acute lymphoblastic leukaemia (ALL). However, the robustness as well as the format for such a diagnostic test remains to be determined. As a step towards clinical application of these findings, we have systematically analyzed a published ALL microarray data set using Robust Multi-array Analysi ...[more]

PMID: 17002788

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Project description:Advances in transcriptomics have impacted the way of looking atimproved our understanding of leukemic development and helped enhancing the stratification of patients. Analyses of individual cohortsTranscriptomics studies often combined AML samples regardless of cytogenetic abnormalities. , which would lead to bias in Hence, deregulated genesdifferential gene expression analysis that may result from the differential representation of in less frequent AML subgroups have low weight, contrarily to those in abundant subgroups. We, thusHence, we performed a horizontal meta-analysis that integrated transcriptomic data of AML from multicentric multiple studies to enrich the less frequent cytogenetic subgroups and to uncover common genes involved in AML development and response to therapy. A total of 28 Affymetrix microarray datasets harboring 3940 AML samples were downloaded from the GEO database. After stringent quality control, transcriptomics data of 1523 samples from 11 datasets, covering 10 AML cytogenetic subgroups, were retained and merged with the transcriptomic data of 198 healthy bone marrow samples. Differentially expressed genes between each cytogenetic subgroup and normal samples were extracted, allowing the unbiased identification of 330 commonly deregulated genes (CODEGs)., which CODEGs showed enriched expression profiles in myeloid differentiation, leukemic stem cell status and relapse. Most of these genes were downregulated, in accordance with DNA hypermethylation. CODEGS were then used to create a prognostic score based on the weighted sum of expression of 22 core genes (CODEG22). The score was validated in on microarray data of five independent cohorts, and by qRT-PCR in a cohort of 142 samples, by qRT-PCR. CODEG22 stratified patients globally, as well as in subpopulations of cytologically normal and elderly patients. , and hence, couldSo, it may complement the European LeukemiaNet classification for a more accurate prediction of AML outcomes.

Dataset Information

Transcription profiling of acute lymphoblastic leukaemia patient samples that represent six different subgroups defined by cytogenetic features and immunophenotype

Publications

Translating microarray data for diagnostic testing in childhood leukaemia.

Similar Datasets

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