8e95e7e5-d478-4b9f-b896-716afe1f70d4 - samples
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ABSTRACT:
PROVIDER: EGAD00001007940 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Whole exome sequencing (WES) data of paired (germline and leukemic) samples of 100 adult patients affected by acute myeloid leukemia.
Targeted sequencing data of myeloid-related genes of 21 leukemia (not paired) samples from adult patients affected by acute myeloid leukemia.
| EGAD00001007941 | EGA
Project description:Searching for new strategies of acute myeloid leukemia (AML) treatment is of particular interest. Cell lines, e. g. HL-60 and NB4, represent model systems to study molecular features of leukemic cells. The all-trans-retinoic acid (ATRA) has proven itself to be an effective treatment for one of AML subtypes, i.e., acute promyelocytic leukemia (APL). At the same time, ATRA causes granulocytic differentiation of non-APL leukemic cells in vitro. Combination of new therapeutics with ATRA could improve efficiency of treatment. Studying the proteome perturbation in leukemic cells under the ATRA treatment allows to determine potential regulatory molecules that could be affected pharmacologically. Thus, the TMT-based proteomic profiles of HL-60, NB4, and K562 cell lines under the ATRA treatment were obtained at 0, 3, 12, 24, and 72 h after the ATRA treatment.
2024-06-23 | PXD050317 | Pride
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:Most cases of adult myeloid neoplasms are routinely assumed to be sporadic. Here, we describe an adult familial acute myeloid leukemia (AML) syndrome caused by germline mutations in the DEAD/H-Box helicase gene DDX41. DDX41 was also found to be affected by somatic mutations in sporadic cases of myeloid neoplasms as well as in a biallelic fashion in 50% of patients with germline DDX41 mutations. Moreover, corresponding deletions on 5q35.3 present in 6% of cases lead to haploinsufficient DDX41 expression. DDX41 lesions caused altered pre-mRNA splicing and RNA processing. DDX41 is exemplary of other RNA helicase genes also affected by somatic mutations, suggesting that they constitute a family of tumor suppressor genes. Affymetrix SNP arrays were performed according to the manufacturer's directions on DNA extracted from cryopreserved diagnostic bone marrow or peripheral blood samples.
2015-04-01 | E-GEOD-66668 | biostudies-arrayexpress
Project description:Most cases of adult myeloid neoplasms are routinely assumed to be sporadic. Here, we describe an adult familial acute myeloid leukemia (AML) syndrome caused by germline mutations in the DEAD/H-Box helicase gene DDX41. DDX41 was also found to be affected by somatic mutations in sporadic cases of myeloid neoplasms as well as in a biallelic fashion in 50% of patients with germline DDX41 mutations. Moreover, corresponding deletions on 5q35.3 present in 6% of cases lead to haploinsufficient DDX41 expression. DDX41 lesions caused altered pre-mRNA splicing and RNA processing. DDX41 is exemplary of other RNA helicase genes also affected by somatic mutations, suggesting that they constitute a family of tumor suppressor genes.
2015-04-01 | GSE66668 | GEO
Project description:Transcriptome profiling of Acute Myeloid Leukemia samples. This dataset includes patients with diagnosis of de novo or secondary AML who experienced non-HLA loss disease relapse after allo-HCT, and for whom paired pre- and post-transplant viable leukemic samples were available.
2019-01-29 | E-MTAB-7456 | biostudies-arrayexpress
Project description:Transcriptome profiling of Acute Myeloid Leukemia samples. This dataset includes patients with diagnosis of de novo or secondary AML who experienced non-HLA loss disease relapse after allo-HCT, and for whom paired pre- and post-transplant viable leukemic samples were available.
2019-01-31 | E-MTAB-7628 | biostudies-arrayexpress
Project description:Transcriptome profiling of Acute Myeloid Leukemia samples. This dataset includes patients with diagnosis of de novo or secondary AML who experienced non-HLA loss disease relapse after allo-HCT, and for whom paired pre- and post-transplant viable leukemic samples were available.
2019-01-31 | E-MTAB-7630 | biostudies-arrayexpress
Project description:Genotype profiling of Acute Myeloid Leukemia samples. This dataset includes patients with diagnosis of de novo or secondary AML who experienced non-HLA loss disease relapse after allo-HCT, and for whom paired pre- and post-transplant viable leukemic samples were available.
2019-01-30 | E-MTAB-7631 | 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