Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of bone marrow samples of 31 children with acute lymphoblastic leukemia to identify changes in gene expression that are associated with the current risk assignment, irrespective of the genetic subtype

ABSTRACT: We analyzed bone marrow samples of 31 children with acute lymphoblastic leukemia to identify changes in gene expression that are associated with the current risk assignment, irrespective of the genetic subtype

ORGANISM(S): Homo sapiens

SUBMITTER: Beat Schafer

PROVIDER: E-MEXP-120 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Gene expression profiles and risk stratification in childhood acute lymphoblastic leukemia.

Teuffel Oliver O Dettling Marcel M Cario Gunnar G Stanulla Martin M Schrappe Martin M Bühlmann Peter P Niggli Felix K FK Schäfer Beat W BW

Haematologica 20040701 7

<h4>Background and objectives</h4>Childhood acute lymphoblastic leukemia (ALL) is a heterogeneous disease. There are several distinct genetic subtypes, characterized by typical changes in gene expression pattern. In addition to cytogenetic markers, the in vivo response to treatment is an emerging prognostic marker for risk stratification. However, it has not yet been reported whether gene expression profiles can predict risk group stratification already at the time of diagnosis.<h4>Design and me ...[more]

PMID: 15257931

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Project description:Background: Gene expression profiling has shown its ability to identify with high accuracy low cytogenetic risk acute myeloid leukemia such as acute promyelocytic leukemia and leukemias with t(8;21) or inv(16). The aim of this gene expression profiling study was to evaluate to what extent suboptimal samples with low leukemic blast load (range, 2-59%) and/or poor quality control criteria could also be correctly identified. Methods: Specific signatures were first defined so that all 71 acute promyelocytic leukemia, leukemia with t(8;21) or inv(16)-AML as well as cytogenetically normal acute myeloid leukemia samples with at least 60% blasts and good quality control criteria were correctly classified (training set). The classifiers were then evaluated for their ability to assign to the expected class 111 samples considered as suboptimal because of a low leukemic blast load (n=101) and/or poor quality control criteria (n=10) (test set). Results: With 10-marker classifiers, all training set samples as well as 97 of the 101 test samples with a low blast load, and all 10 samples with poor quality control criteria were correctly classified. Regarding test set samples, the overall error rate of the class prediction was below 4 percent, even though the leukemic blast load was as low as 2%. Sensitivity, specificity, negative and positive predictive values of the class assignments ranged from 91% to 100%. Of note, for acute promyelocytic leukemia and leukemias with t(8;21) or inv(16), the confidence level of the class assignment was influenced by the leukemic blast load. Conclusion: Gene expression profiling and a supervised method requiring 10-marker classifiers enable the identification of favorable cytogenetic risk acute myeloid leukemia even when samples contain low leukemic blast loads or display poor quality control criterion. Specific signatures were first defined so that all 71 acute promyelocytic leukemia, leukemia with t(8;21) or inv(16)-AML as well as cytogenetically normal acute myeloid leukemia samples with at least 60% blasts and good quality control criteria were correctly classified (training set). The classifiers were then evaluated for their ability to assign to the expected class 111 samples considered as suboptimal because of a low leukemic blast load (n=101) and/or poor quality control criteria (n=10) (test set).

Dataset Information

Transcription profiling of bone marrow samples of 31 children with acute lymphoblastic leukemia to identify changes in gene expression that are associated with the current risk assignment, irrespective of the genetic subtype

Publications

Gene expression profiles and risk stratification in childhood acute lymphoblastic leukemia.

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