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

ACCESS DATA

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

Similar Datasets

Project description:ImportanceNumerous classification systems for morphea subtypes exist, but none have been systematically evaluated for their ability to categorize patients with morphea into demographically and clinically coherent groups. Although some subtypes, such as linear morphea, are present across all the classification schemes, others list unique subtypes. This creates confusion among investigators and practitioners and impairs accurate categorization of patients for study and clinical evaluation.ObjectiveTo evaluate how frequently the commonly used morphea classification systems categorize patients with morphea into clinically relevant subtypes using cross-sectional analysis of 2 large patient cohorts.Design, setting, and participantsThis cross-sectional study comprised 944 adults and children from 2 prospective cohorts-the Morphea in Adults and Children at the University of Texas Southwestern Medical Center (Dallas, Texas), which enrolled participants from July 20, 2007, to September 21, 2018, and the National Registry for Childhood-Onset Scleroderma at the University of Pittsburgh (Pittsburgh, Pennsylvania), which enrolled participants from October 23, 2002, to November 13, 2018.Main outcomes and measuresPatient demographic characteristics, morphea subtype, quality-of-life measures, disease activity, and damage as measured by Localized Scleroderma Cutaneous Assessment Tool scores during initial visits.ResultsA total of 944 participants (444 patients with adult-onset morphea and 500 patients with pediatric-onset morphea; 741 female participants [78%]; median age at onset, 16 years [interquartile range, 8-44 years]) were included in this study. Most participants were White (723 [77%]) and had the linear (474 [50%]) or generalized subtype of morphea (244 [26%]). With the use of the previously published Padua criteria, most patients were classified to have linear morphea (474 [50%]), followed by generalized morphea (244 [26%]), plaque morphea (141 [15%]), mixed morphea (38 [4%]), and pansclerotic morphea (3 [0.3%]). Overall, the Padua criteria successfully classified 900 patients (95%) in comparison with the Peterson criteria (533 [56%]) and the European Dermatology Forum classification (487 [52%]).Conclusions and relevanceIn this cross-sectional study of morphea subtype classification systems, the Padua criteria performed best in classifying patients into subgroups with cohesive demographic and clinical features, supporting its widespread use. However, they have ambiguities that might lead to misclassification, particularly in terms of generalized and pansclerotic morphea and descriptors such as morphea profunda. Consensus-based approaches are needed to address these ambiguities and develop a unified classification scheme.

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.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets