Prevalence and prognostic implications of CEBPA mutations in pediatric acute myeloid leukemia (AML): a report from the Children's Oncology Group.
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ABSTRACT:
SUBMITTER: Ho PA
PROVIDER: S-EPMC2943755 | biostudies-literature | 2009 Jun
REPOSITORIES: biostudies-literature
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Prevalence and prognostic implications of CEBPA mutations in pediatric acute myeloid leukemia (AML): a report from the Children's Oncology Group.
Ho Phoenix A PA Alonzo Todd A TA Gerbing Robert B RB Pollard Jessica J Stirewalt Derek L DL Hurwitz Craig C Heerema Nyla A NA Hirsch Betsy B Raimondi Susana C SC Lange Beverly B Franklin Janet L JL Radich Jerald P JP Meshinchi Soheil S
Blood 20090320 26
CEBPA mutations have been associated with improved outcome in adult acute myeloid leukemia (AML). We evaluated the prevalence and prognostic significance of CEBPA mutations in 847 children with AML treated on 3 consecutive pediatric trials. Two types of CEBPA mutations-N-terminal truncating mutations and in-frame bZip-domain mutations-were detected in 38 (4.5%) of 847 patients tested; 31 (82%) of 38 patients with mutations harbored both mutation types. Mutation status was correlated with laborat ...[more]
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Project description:PAPER 1:"Identification of novel subgroups of high-risk pediatric precursor B acute lymphoblastic leukemia (B-ALL) by unsupervised microarray analysis: clinical correlates and therapeutic implications. A Children's Oncology Group (COG) study." ABSTRACT We examined gene expression profiles of pre-treatment specimens from 207 patients from the COG P9906 study to identify signatures of children with high risk B-precursor acute lymphoblastic leukemia (ALL) and to determine whether the resulting clusters are associated with either specific clinical features or treatment response characteristics. Four unsupervised clustering methods were utilized to classify patients into similar groups. The different clustering algorithms showed significant overlap in cluster membership. Two clusters contained all cases with either t(1;19)(q23;p13) translocations or MLL rearrangements. The other six clusters were novel and had no recurring chromosomal abnormalities or distinctive clinical features. Members of two of these novel clusters had significant survival differences when compared to the overall 4-year relapse-free survival (RFS) of 61%. These included clusters of patients with either significantly better (94.7%) or worse (21.0%) RFS at 4 years. Children of Hispanic/Latino ethnicity were disproportionately present in the poor outcome cluster. The poor outcome cluster represents a novel biologically distinctive subset of B-precursor ALL that may occur at least as frequently as BCR/ABL. Further molecular characterization of this cluster may lead to the discovery of genomic abnormalities that can be targeted to improve the currently dismal outcome for children with this gene signature. The Sample data have also been used in another study: PAPER 2: "Gene expression classifiers for minimal residual disease and relapse free survival improve outcome prediction and risk classification in children with high risk acute lymphoblastic leukemia. A Children's Oncology Group study". ABSTRACT Background. Nearly 25% of children with B-precursor ALL present with "high-risk" disease (HR-ALL) that is resistant to current therapies. Gene expression profiling may yield molecular classifiers for outcome prediction that can be used to improve risk classification and therapeutic targeting. Methods. Expression profiles were obtained in pre-treatment leukemic samples from 207 uniformly treated children with HR-ALL. Relapse free survival (RFS) was 61% at 4 years and flow cytometric measures of minimal residual disease (MRD) at the end of induction (day 29) were predictive of outcome (P<0.001). Molecular classifiers predictive of RFS and MRD were developed using extensive cross-validation procedures. Results. A 38 gene molecular risk classifier predictive of RFS (MRC-RFS) distinguished two groups in HR-ALL with different relapse risks: low (4 yr RFS: 81%, n=109) vs. high (4 yr RFS: 50%, n=98) (P<0.0001). In multivariate analysis, the best predictor combined MRC-RFS and day 29 flow MRD data, classifying children into low (87% RFS), intermediate (62% RFS), or high risk (29% RFS) groups (P<0.0001). A 21 gene molecular classifier predictive of MRD could effectively substitute for day 29 flow MRD, yielding a combined classifier that similarly distinguished three risk groups at pre-treatment (low: 82% RFS; intermediate: 63% RFS; and high risk: 45% RFS) (P<0.0001). This combined molecular classifier was further validated on an independent cohort of 84 children with HR-ALL (P = 0.006). Conclusions. Molecular classifiers predictive of RFS and MRD can be used to distinguish distinct prognostic groups within HR-ALL, significantly improving risk classification schemes and the ability to prospectively identify children at diagnosis who will respond to or fail current treatment regimens. NOTE: Due to Children's Oncology Group (COG) restrictions, outcome and MRD data cannot be provided as part of the covariate data for this dataset at the present time. If you would like to arrange individual access to this data, please contact COG or the PI of this study, Dr. Cheryl Willman, at the University of New Mexico Cancer Center (cwillman@unm.edu) to arrange a collaboration.
2009-06-25 | GSE11877 | GEO
Project description:PAPER 1:"Identification of novel subgroups of high-risk pediatric precursor B acute lymphoblastic leukemia (B-ALL) by unsupervised microarray analysis: clinical correlates and therapeutic implications. A Children's Oncology Group (COG) study." ABSTRACT We examined gene expression profiles of pre-treatment specimens from 207 patients from the COG P9906 study to identify signatures of children with high risk B-precursor acute lymphoblastic leukemia (ALL) and to determine whether the resulting clusters are associated with either specific clinical features or treatment response characteristics. Four unsupervised clustering methods were utilized to classify patients into similar groups. The different clustering algorithms showed significant overlap in cluster membership. Two clusters contained all cases with either t(1;19)(q23;p13) translocations or MLL rearrangements. The other six clusters were novel and had no recurring chromosomal abnormalities or distinctive clinical features. Members of two of these novel clusters had significant survival differences when compared to the overall 4-year relapse-free survival (RFS) of 61%. These included clusters of patients with either significantly better (94.7%) or worse (21.0%) RFS at 4 years. Children of Hispanic/Latino ethnicity were disproportionately present in the poor outcome cluster. The poor outcome cluster represents a novel biologically distinctive subset of B-precursor ALL that may occur at least as frequently as BCR/ABL. Further molecular characterization of this cluster may lead to the discovery of genomic abnormalities that can be targeted to improve the currently dismal outcome for children with this gene signature. The Sample data have also been used in another study: PAPER 2: "Gene expression classifiers for minimal residual disease and relapse free survival improve outcome prediction and risk classification in children with high risk acute lymphoblastic leukemia. A Children's Oncology Group study". ABSTRACT Background. Nearly 25% of children with B-precursor ALL present with "high-risk" disease (HR-ALL) that is resistant to current therapies. Gene expression profiling may yield molecular classifiers for outcome prediction that can be used to improve risk classification and therapeutic targeting. Methods. Expression profiles were obtained in pre-treatment leukemic samples from 207 uniformly treated children with HR-ALL. Relapse free survival (RFS) was 61% at 4 years and flow cytometric measures of minimal residual disease (MRD) at the end of induction (day 29) were predictive of outcome (P<0.001). Molecular classifiers predictive of RFS and MRD were developed using extensive cross-validation procedures. Results. A 38 gene molecular risk classifier predictive of RFS (MRC-RFS) distinguished two groups in HR-ALL with different relapse risks: low (4 yr RFS: 81%, n=109) vs. high (4 yr RFS: 50%, n=98) (P<0.0001). In multivariate analysis, the best predictor combined MRC-RFS and day 29 flow MRD data, classifying children into low (87% RFS), intermediate (62% RFS), or high risk (29% RFS) groups (P<0.0001). A 21 gene molecular classifier predictive of MRD could effectively substitute for day 29 flow MRD, yielding a combined classifier that similarly distinguished three risk groups at pre-treatment (low: 82% RFS; intermediate: 63% RFS; and high risk: 45% RFS) (P<0.0001). This combined molecular classifier was further validated on an independent cohort of 84 children with HR-ALL (P = 0.006). Conclusions. Molecular classifiers predictive of RFS and MRD can be used to distinguish distinct prognostic groups within HR-ALL, significantly improving risk classification schemes and the ability to prospectively identify children at diagnosis who will respond to or fail current treatment regimens. NOTE: Due to Children's Oncology Group (COG) restrictions, outcome and MRD data cannot be provided as part of the covariate data for this dataset at the present time. If you would like to arrange individual access to this data, please contact COG or the PI of this study, Dr. Cheryl Willman, at the University of New Mexico Cancer Center (cwillman@unm.edu) to arrange a collaboration. Unsupervised clustering and supervised risk classification analyses of 207 diagnostic samples and associated clinical covariate data. See the Summary for greater details. The data were analyzed using Microarray Suite version 5.0 (MAS 5.0) in the Affymetrix Gene Chip Operating Software Version 1.4. Probe masking was used (see 9906_TT207_Affymetrix_probe_mask.msk, linked below as a supplementary file). Otherwise all Affymetrix default parameter settings were used. Global scaling as the normalization method, with the default target intensity of 500, was used.
2009-06-25 | E-GEOD-11877 | biostudies-arrayexpress
Project description:Biallelic CEBPA mutations are associated with favorable outcomes in acute myeloid leukemia (AML). We evaluated the clinical and biologic implications of CEBPA-basic leucine zipper (CEBPA-bZip) mutations in children and young adults with newly diagnosed AML. CEBPA-bZip mutation status was determined in 2958 patients with AML enrolled on Children's Oncology Group trials (NCT00003790, NCT0007174, NCT00372593, NCT01379181). Next-generation sequencing (NGS) was performed in 1863 patients (107 with CEBPA mutations) to characterize the co-occurring mutations. CEBPA mutational status was correlated with disease characteristics and clinical outcomes. CEBPA-bZip mutations were identified in 160 (5.4%) of 2958 patients, with 132 (82.5%) harboring a second CEBPA mutation (CEBPA-double-mutated [CEBPA-dm]) and 28 (17.5%) had a single CEBPA-bZip only mutation. The clinical and laboratory features of the 2 CEBPA cohorts were very similar. Patients with CEBPA-dm and CEBPA-bZip experienced identical event-free survival (EFS) of 64% and similar overall survival (OS) of 81% and 89%, respectively (P = .259); this compared favorably to EFS of 46% and OS of 61% in patients with CEBPA-wild-type (CEBPA-WT) (both P < .001). Transcriptome analysis demonstrated similar expression profiles for patients with CEBPA-bZip and CEBPA-dm. Comprehensive NGS of patients with CEBPA mutations identified co-occurring CSF3R mutations in 13.1% of patients and GATA2 mutations in 21.5% of patients. Patients with dual CEBPA and CSF3R mutations had an EFS of 17% vs 63% for patients with CEBPA-mutant or CSF3R-WT (P < .001) with a corresponding relapse rate (RR) of 83% vs 22%, respectively (P < .001); GATA2 co-occurrence did not have an impact on outcome. CEBPA-bZip domain mutations are associated with favorable clinical outcomes, regardless of monoallelic or biallelic status. Co-occurring CSF3R and CEBPA mutations are associated with a high RR that nullifies the favorable prognostic impact of CEBPA mutations.
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Project description:BackgroundIn patients with acute myeloid leukemia (AML), CD56 expression has been associated with adverse clinical outcome. We reported on a phenotype associated with very poor prognosis (RAM) in children enrolled in the Children's Oncology Group trial AAML0531 (Brodersen et al. Leukemia 30 (2016) 2077-2080). RAM is also characterized in part by high-intensity expression of the CD56 antigen. Herein, we investigate underlying biological and clinical differences among CD56-positive AMLs for patients in AAML0531.MethodsFor 769 newly diagnosed pediatric patients with de novo AML enrolled in AAML0531, bone marrow specimens were submitted for flow cytometric analysis. For each patient, an immunophenotypic expression profile (IEP) was defined by mean fluorescent intensities of assayed surface antigens. Unsupervised hierarchical clustering analysis (HCA) was completed to group patients with similar immunophenotypes. Clusters were then evaluated for CD56 expression. Principal component analysis (PCA) was subsequently applied to determine whether CD56-positive patient groups were nonoverlapping.ResultsHCA of IEPs revealed three unique phenotypic clusters of patients with CD56-positive AML, and PCA showed that these three cohorts are distinct. Cohort 1 (N = 77) showed a prevalence of t(8;21) patients (72%), Cohort 2 (N = 52) a prevalence of 11q23 patients (69%), and Cohort 3 (RAM) (N = 16) a prevalence of patients with co-occurrence of the CBFA2T3-GLIS2 fusion transcript (63%). The 5-year event-free survival (EFS) for Cohorts 1, 2, and 3 were 69, 39, and 19%, respectively.ConclusionsWhen leukemia is considered by its multidimensional immunophenotype and not by the expression of a single antigen, correlations are seen between genotype and there are significant differences in patient outcomes. © 2019 International Clinical Cytometry Society.
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