Transcription profiling of human acute myeloid leukemia patient samples
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Roel Verhaak
PROVIDER: E-GEOD-1159 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Publications
Identification of genes with abnormal expression changes in acute myeloid leukemia.
Stirewalt Derek L DL Meshinchi Soheil S Kopecky Kenneth J KJ Fan Wenhong W Pogosova-Agadjanyan Era L EL Engel Julia H JH Cronk Michelle R MR Dorcy Kathleen Shannon KS McQuary Amy R AR Hockenbery David D Wood Brent B Heimfeld Shelly S Radich Jerald P JP
Genes, chromosomes & cancer 20080101 1
Acute myeloid leukemia (AML) is one of the most common and deadly forms of hematopoietic malignancies. We hypothesized that microarray studies could identify previously unrecognized expression changes that occur only in AML blasts. We were particularly interested in those genes with increased expression in AML, believing that these genes may be potential therapeutic targets. To test this hypothesis, we compared gene expression profiles between normal hematopoietic cells from 38 healthy donors an ...[more]
Similar Datasets
Project description:Expression profiles of acute myeloid leukemia patient samples. Blasts and mononuclear cells were purified from bone marrow or peripheral blood aspirates of acute myeloid patients. Samples contained 80-100 percent blast cells. Total RNA was extracted by lyses with guanidium isothiocyanate followed by cesium chloride gradient purification Keywords: other
2004-04-15 | GSE1159 | GEO
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).
2011-12-24 | E-GEOD-34714 | biostudies-arrayexpress
Project description:The pretreatment karyotype of leukemic blasts is currently the key determinant in therapy decision-making in acute myeloid leukemia (AML). However, approximately fifty percent of AML patients, often carrying a normal karyotype, are currently unclassifiable based these established methods. Gene expression profiling has proven to be valuable for risk stratification of AML. This is a repository of 662 adult AML cases characterized on gene expression microarrays and used for different studies investigating the mechanisms underlying leukemogenesis. Bone marrow aspirates or peripheral blood samples of three independent representative cohorts of de novo AML patients, comprising 277, 256 and 129 cases respectively, were collected at diagnosis. Gene expression profiling was performed on 662 adult AML patients who have been treated according to Dutch-Belgian Hemato-Oncology Cooperative Group and the Swiss Group for Clinical Cancer Research (HOVON/SAKK) AML-04, -04A, -29, -32, -42, -42A, -43 and -92 protocols (available at http://www.hovon.nl). All patients provided written informed consent in accordance with the Declaration of Helsinki, and the study was approved by all participating institutional review boards. Blast cell purification and RNA isolation were performed as previously described (Valk et al., Prognostically Useful Gene-Expression Profiles in Acute Myeloid Leukemia, New England Journal of Medicine, 2004).
2016-06-23 | E-MTAB-3444 | biostudies-arrayexpress
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. Training set.
2011-12-21 | E-GEOD-34577 | biostudies-arrayexpress
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.
2011-12-21 | GSE34577 | GEO
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.
2011-12-24 | GSE34714 | GEO
Project description:Acute lymphoblastic leukemia (ALL) is characterized by the accumulation of somatic genetic alterations in B- or T-lymphoid precursor cells eventually resulting in overt leukemia. Contrary to paediatric ALL, its adult counterpart is rather scarcely researched. Early studies demonstrated that gene expression profiling (GEP) is a valuable tool for subgroup identification and risk stratification. This submission of 192 ALL (137 B-cell ALL [B-ALL] and 55 T-cell ALL [T-ALL]) cases characterized on gene expression microarrays serves as a repository for multiple studies examining the leukemogenic mechanisms underlying overt ALL establishment. After informed consent, bone marrow aspirates or peripheral blood samples of a representative cohort of 192 adult ALL patients were collected. Eligible patients had a diagnosis of primary ALL confirmed by cytological examination and immunophenotyping of blood and bone marrow. The majority of these cases were treated following the HOVON (Dutch-Belgian Hematology-Oncology Co-operative group) protocols (http://www.hovon.nl). All studies were approved through the institutional ethics review board and all patients provided written informed consent in accordance to the declaration of Helsinki. Survival data for these patients are captured but not shared publicly for data protection purposes, and will only be available on a case-by-case basis upon specific request, which are likely to be made in a collaborative structure. Blasts and mononuclear cells were purified by Ficoll-Hypaque (Nygaard, Oslo, Norway) centrifugation and cryopreserved. All samples contained 80-to-100 percent blast cells after thawing, regardless of the blast count at diagnosis. RNA isolation was performed as previously described (Valk et al., Prognostically Useful Gene-Expression Profiles in Acute Myeloid Leukemia, New England Journal of Medicine, 2004). <br>Complementary genotyping data from the same study have also been deposited at ArrayExpress, under accession number E-MTAB-5036 ( http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-5036/ ). A supplementary file 'DNA_RNA_link_numbers.xlsx' is also provided allowing RNA samples in this submission to be mapped against equivalent DNA sample in E-MTAB-5036.</br>
2017-01-31 | E-MTAB-5035 | biostudies-arrayexpress
Project description:Differentiating agents have been proposed to overcome the impaired cellular differentiation in acute myeloid leukemia (AML). However, only the combinations of all-trans retinoic acid or arsenic trioxide with chemotherapy have been successful, and only in treating acute promyelocytic leukemia (also called AML3). Here we showed that iron homeostasis is an effective target in the treatment of AML. Iron chelating therapy induces the differentiation of leukemia blasts and normal bone marrow precursors into monocytes/macrophages in a manner involving modulation of reactive oxygen species expression and the activation of mitogen-activated protein kinases (MAPK). Thirty percent of the genes most strongly induced by iron deprivation are also targeted by vitamin D3 (VD), a well-known differentiating agent. Iron chelating agents induce expression and phosphorylation of the VD receptor, and iron deprivation and VD act synergistically. VD magnifies activation of MAPK JNK and the induction of VD receptor target genes. When used to treat one AML patient refractory to chemotherapy, the combination of iron chelating agents and VD resulted in reversal of pancytopenia and blast differentiation. We propose that iron availability modulates myeloid cell commitment, and that targeting this cellular differentiation pathway together with conventional differentiating agents provides new therapeutic modalities for AML. This study presents 8 arrays 4x44K Agilent.
2010-03-23 | E-TABM-925 | biostudies-arrayexpress
Project description:Single-cell RNA sequencing was performed on bone marrow mononuclear of a patient with acute myeloid leukemia with erythroid differentiation of the blasts and on peripheral blood mononuclear cells of a patient with acute myeloid leukemia with megakaryocytic differentiation of the blasts. Raw data for this dataset can be found at the EGA under accession EGAS00001006819.
2022-12-05 | E-MTAB-12607 | biostudies-arrayexpress
Project description:The leukemia stem cell (LSC) populations of acute myeloid leukemia (AML) exhibit phenotypic, genetic and functional heterogeneity that contribute to therapy failure and relapse. Progress towards understanding the mechanistic basis for therapy resistance in LSCs has been hampered by difficulties in isolating cell fractions that enrich for the entire heterogeneous population of LSCs within individual AML samples. We previously reported that CD200 gene expression is upregulated in LSC-containing AML fractions. We demonstrate that CD200 is present on a greater proportion of CD45dim blasts compared to more differentiated CD45high cells in AML patient samples. In 75% of AML cases examined, CD200 was expressed on 10% of CD45dim blasts; of these, CD200 identified LSCs within the blast population in 90% of samples tested in xenotransplantation assays. Notably, CD200 expression captured both CD34- and CD34- LSCs within individual AML samples. Highly purified CD45dimCD200+ LSC-containing blasts were enriched in primitive HSC/progenitor-like signatures, while CD45dimCD200- nonengrafting blasts were enriched in myeloid-like signature. Moreover, analysis of CD45dimCD200+ blasts from NPM1-mutated AMLs also demonstrated an enrichment of primitive gene expression signatures compared to unfractionated (bulk) cells.
2020-10-10 | GSE159321 | GEO