Gene expression profile of patients affected by Metachromatic Leukodystrophy (MLD)
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE23350 | GEO | 2013/11/21
SECONDARY ACCESSION(S): PRJNA131461
REPOSITORIES: GEO
Similar Datasets
Project description:The genome-wide analysis of T lympocytes from 24 MLD patients and a corresponding number of matched controls reveals a list of differentially expressed genes that can be used to build classifiers that correctly classify not only patients and controls with high sensitivity and specificity, but also MLD patients into early- and late-onset clinical classes. Among the most differentially expressed genes, the class of metallothioneins shows a consistent and significant over-expression, confirmed also in affected tissues of patients and mouse models of MLD and other Lysosomal Storage Disorders, indicating metallothioneins a novel dynamic biomarker of disease progression and treatment response in this class of diseases. Moreover, gene onthology analysis performed on the microarray data suggest an involvement of stress response- and autophagy-related pathways as pathological mechanisms in MLD. Total RNA extracted from T-lymphocytes of 24 MLD patients and 24 age-and sex-matched controls
2013-11-21 | E-GEOD-23350 | 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. 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: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.
2011-12-24 | GSE34714 | 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.
2011-12-21 | GSE34577 | GEO
Project description:By WHO 2008, CEBPA-mutated AML became a provisional subentity, but it remains to be clarified how CEBPAmut AML with multilineage dysplasia (MLD; ≥50% dysplastic cells in 2-3 lineages) but no other MDS-related feature should be classified. We investigated 108 CEBPAmut AML (15.7-87.6 years) for the impact of MLD and genetic features. MLD-positive patients differed from MLD-negative only by lower mean WBC counts (p=0.004), but not by other blood values, biologic characteristics, cytogenetic risk profiles, or additional molecular markers (NPM1mut, FLT3-ITD/TKD, RUNX1, MLL-PTD, IDH1/2). Biallelic CEBPAmut differed from wild-type-cases by differential expression of 213 genes, but did not differ significantly between MLD-positive/-negative patients. Survival outcomes were improved for females and those <60 years, intermediate versus adverse karyotypes (p=0.021), and for biallelic versus monoallelic/homozygous CEBPAmut (p=0.060) in case of FLT3-ITD-negativity. In contrast, 2-year OS (MLD+: 56.5%; MLD-: 65.5%) and 2-year EFS (MLD+: 13.8 months; MLD-: 16.3 months) did not differ significantly between MLD-positive/-negative patients. By univariable Cox regression analysis, gender, age, WBC count and MRC-cytogenetic risk category only were prognostically relevant for OS, while MLD was irrelevant. Therefore, CEBPAmut AML patients should be characterized only according to mut-status, cytogenetic risk groups, or additional mutations, whereas dysplasia is not relevant for this subtype.
2012-06-15 | GSE33223 | GEO
Project description:Full Title: Multilineage Dysplasia (MLD) in AML correlates with MDS-related cytogenetic abnormalities and a prior history of MDS or MDS/MPN but has no independent prognostic relevance: A comparison of 408 cases classified as “AML not otherwise specified” or “AML with myelodysplasia-related changes” The WHO classification of acute myeloid leukemia (AML) is hierarchically structured and integrates genetic information, data on patients’ history, and multilineage dysplasia (MLD). The category “AML with MDS-related changes” (AML-MRC) is separated from AML not otherwise specified (AML-NOS) by the presence of either MLD, MDS-related cytogenetics or MDS history. To clarify whether MLD alone is clinically relevant, we analyzed 408 adult patients categorized as AML-MRC or AML-NOS. EFS (Median 13.8 months vs. 16.0 months) and 3-year-OS (45.8% vs. 53.9%) did not differ significantly between patients with MLD and without. However, MLD correlated with pre-existing MDS (p<0.001) and MDS-related cytogenetics (p=0.035). Patients with MLD as sole AML-MRC criterion (AML-MLD-sole; n=90) had less frequently FLT3-ITD (p=0.032), and a lower median age than AML-NOS (n=232), but EFS, OS, and WBC did not differ significantly. Contrarily, patients with AML-NOS plus AML-MLD-sole (n=323) versus patients with MDS history or MDS-related cytogenetics (n=85) had better EFS (16.9 vs. 10.7 months; p=0.005) and 3-year-OS (55.8% vs. 32.5%; p=0.001). Gene expression profiles were measured for a subset of 96 patients. Analysis of the expression data showed distinct clusters for AML-MLD-sole and AML-NOS versus AML with MDS-related cytogenetics or MDS history. Thus, MLD demonstrated no independent clinical impact, while cytogenetics and MDS history were of prognostic relevance. This data suggest modifications in a revised WHO proposal.
2011-01-01 | GSE21261 | GEO
Project description:Multilineage dysplasia (MLD) has no impact on biological, clinico-pathological and prognostic features of AML with mutated nucleophosmin (NPM1) NPM1-mutated AML is a provisional entity in the WHO-2008 classification of myeloid neoplasms. The significance of concomitant multilineage dysplasia (MLD) in NPM1-mutated AML is unclear. Thus, in the WHO-2008 classification, NPM1-mutated AML with MLD is classified as AML with myelodysplasia(MD)-related changes. We evaluated the MLD impact in 378 NPM1-mutated AML patients. MLD was found in about 25% cases. Except for a lower WBC and FLT3-ITD incidence in MLD+ group, no significant differences were observed in age, sex, cytogenetics and FLT3-TKD between NPM1-mutated AML with and without MLD. Notably, NPM1-mutated AML with/without MLD showed overlapping immunophenotype (CD34-negativity) and GEP (CD34 downregulation and HOX genes upregulation). Moreover, OS and EFS did not differ among NPM1-mutated AML patients, independently of whether they carried or not MLD, the NPM1-mutated/FLT3-ITD negative cases showing the better prognosis. Lack of MLD impact on survival was confirmed by multivariate analysis that highlighted FLT3-ITD as the most significant prognostic parameter in NPM1-mutated AML. Our findings indicate that NPM1 mutations rather than MLD dictate the distinctive features of NPM1-mutated AML. Thus, irrespective of MLD, NPM1-mutated AML should be considered as one disease entity clearly distinct from AML with MD-related changes. These findings have important diagnostic and prognostic implications in AML.
2010-07-01 | GSE18018 | GEO
Project description:To analyse gene expression patterns and to define a specific gene expression signature in patients with the severe end of the spectrum of cryopyrin-associated periodic syndromes (CAPS). The molecular consequences of interleukin 1 inhibition were examined by comparing gene expression patterns in 16 CAPS patients before and after treatment with anakinra. Many DEG include transcripts related to the regulation of innate and adaptive immune responses, oxidative stress, cell death, cell adhesion and motility. A set of gene expression-based models comprising the CAPS-specific gene expression signature correctly classified all 17 samples from an independent dataset. This classifier also correctly identified 15 of 16 post-anakinra CAPS samples despite the fact that these CAPS patients were in clinical remission. We identified a gene expression signature that clearly distinguished CAPS patients from controls. A number of DEG were in common with other systemic inflammatory diseases such as systemic onset juvenile idiopathic arthritis. The CAPS-specific gene expression classifiers also suggest incomplete suppression of inflammation at low doses of anakinra. We collected peripheral blood mononuclear cells from 23 CAPS patients with active disease and from 14 healthy children. Transcripts that passed stringent filtering criteria (p values ≤ false discovery rate 1%) were considered as differentially expressed genes (DEG). A set of DEG was validated by quantitative reverse transcription PCR and functional studies with primary cells from CAPS patients and healthy controls. We used 17 CAPS and 43 non-CAPS patient samples to create a set of gene expression models that differentiates CAPS patients from controls and from patients with other autoinflammatory conditions.
2013-01-16 | E-GEOD-43553 | biostudies-arrayexpress
Project description:To analyse gene expression patterns and to define a specific gene expression signature in patients with the severe end of the spectrum of cryopyrin-associated periodic syndromes (CAPS). The molecular consequences of interleukin 1 inhibition were examined by comparing gene expression patterns in 16 CAPS patients before and after treatment with anakinra. Many DEG include transcripts related to the regulation of innate and adaptive immune responses, oxidative stress, cell death, cell adhesion and motility. A set of gene expression-based models comprising the CAPS-specific gene expression signature correctly classified all 17 samples from an independent dataset. This classifier also correctly identified 15 of 16 post-anakinra CAPS samples despite the fact that these CAPS patients were in clinical remission. We identified a gene expression signature that clearly distinguished CAPS patients from controls. A number of DEG were in common with other systemic inflammatory diseases such as systemic onset juvenile idiopathic arthritis. The CAPS-specific gene expression classifiers also suggest incomplete suppression of inflammation at low doses of anakinra.
2013-01-16 | GSE43553 | GEO