Dataset Information

MicroRNA profiling in pediatric AML

ABSTRACT: MicroRNAs (miRNAs) play a pivotal role in the regulation of hematopoiesis and development of leukemia. Great interest emerged in modulating miRNA expression for therapeutic purposes. In order to identify miRNAs, which specifically suppress leukemic growth of AML with t(8;21), inv(16) or MLL-rearrangement by inducing differentiation, we conducted a miRNA expression profiling in a cohort of 90 cytogenetically characterized, de novo pediatric AML cases. Four miRNAs, specifically downregulated in MLL-rearranged, t(8;21) or inv(16) AMLs, were characterized by their tumor suppressive properties in cell lines representing those respective cytogenetic groups. Among those, forced expression of miR-9 reduced leukemic growth and induced monocytic differentiation of t(8;21) AML cell lines in vitro and in vivo. The tumor suppressive functions of miR-9 were specifically restricted to AML cell lines and primary leukemic blasts with t(8;21). On the other hand, these functions were not evident in AML blasts from patients with MLL-rearrangements. We showed that miR-9 exerts its effects through the cooperation with let-7 to repress the oncogenic LIN28B/HMGA2 axis. Thus, miR-9 is a tumor suppressor-miR which acts in a stringent cell context-dependent manner.

ORGANISM(S): Homo sapiens

PROVIDER: GSE52541 | GEO | 2014/01/01

SECONDARY ACCESSION(S): PRJNA229391

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

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).

Project description:Contemporary treatment of pediatric acute myeloid leukemia (AML) requires the assignment of patients to specific risk groups. To explore whether expression profiling of leukemic blasts could accurately distinguish between the known risk groups of AML, we analyzed 130 pediatric and 20 adult AML diagnostic bone marrow or peripheral blood samples using the Affymetrix U133A microarray. Class discriminating genes were identified for each of the major prognostic subtypes of pediatric AML, including t(15;17)[PML-RARalpha], t(8;21)[AML1-ETO], inv(16) [CBFbeta-MYH11], MLL chimeric fusion genes, and cases classified as FAB-M7. When subsets of these genes were used in supervised learning algorithms, an overall classification accuracy of more than 93% was achieved. Moreover, we were able to use the expression signatures generated from the pediatric samples to accurately classify adult de novo AMLs with the same genetic lesions. The class discriminating genes also provided novel insights into the molecular pathobiology of these leukemias. Finally, using a combined pediatric data set of 130 AMLs and 137 acute lymphoblastic leukemias, we identified an expression signature for cases with MLL chimeric fusion genes irrespective of lineage. Surprisingly, AMLs containing partial tandem duplications of MLL failed to cluster with MLL chimeric fusion gene cases, suggesting a significant difference in their underlying mechanism of transformation. All the gene expression arrays are available through http://www.stjuderesearch.org/site/data/AML1/ in the original study (PMID:15226186). To study the RAS gene expression in the human AML patients, a total of 104 AML cases with known KRAS and NRAS status (including 72 gene expression arrays in the original study and 32 additional arrays acquired later on), as well as 4 CD34+ normal bone marrow cases deposited in GEO GSE33315, were including in this depository. Gene expression profiling was performed on 104 single diagnosis tumor samples and 4 CD34+ normal bone marrow samples

Dataset Information

MicroRNA profiling in pediatric AML

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure