Microarray analyses of induction failure in T-ALL (COG study 9404)
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE14615 | GEO | 2009/01/29
SECONDARY ACCESSION(S): PRJNA114443
REPOSITORIES: GEO
Similar Datasets
Project description:The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure. Based on the hypothesis that microarrays might identify patients who fail therapy, we used the Affymetrix U133 Plus 2.0 chip and prediction analysis of microarrays (PAM) to profile 50 newly diagnosed patients who were treated in the Children's Oncology Group (COG) T-ALL Study 9404. We identified a 116-member genomic classifier that could accurately distinguish all 6 induction failure (IF) cases from 44 patients who achieved remission; network analyses suggest a prominent role for genes mediating cellular quiescence. Seven genes were similarly upregulated in both the genomic classifier for IF patients and T-ALL cell lines having acquired resistance to neoplastic agents, identifying potential target genes for further study in drug resistance. We tested whether our classifier could predict IF within 42 patient samples obtained from COG 8704 and, using PAM to define a smaller classifier for the U133A chip, correctly identified the single IF case and patients with persistently circulating blasts. Genetic profiling may identify T-ALL patients who are likely to fail induction and for whom alternate treatment strategies might be beneficial. Experiment Overall Design: The was a case-controlled, retrospectively designed study. We performed expression profiles on 92 patients with T-ALL treated on Children's Oncology Group studies 8704 (42 patients) and 9404 (50 patients). Experiment Overall Design: Expression profiles were obtained from patients with newly diagnosed T-ALL. NR = no response (induction failure); F= failure (relapse); C= Complete Continous Remission. Experiment Overall Design: Note: Failure (relapse) samples F8,12 and F19 were removed for reasons of failure other than relapse.
2009-02-09 | E-GEOD-14613 | biostudies-arrayexpress
Project description:The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure. Based on the hypothesis that microarrays might identify patients who fail therapy, we used the Affymetrix U133 Plus 2.0 chip and prediction analysis of microarrays (PAM) to profile 50 newly diagnosed patients who were treated in the Children's Oncology Group (COG) T-ALL Study 9404. We identified a 116-member genomic classifier that could accurately distinguish all 6 induction failure (IF) cases from 44 patients who achieved remission; network analyses suggest a prominent role for genes mediating cellular quiescence. Seven genes were similarly upregulated in both the genomic classifier for IF patients and T-ALL cell lines having acquired resistance to neoplastic agents, identifying potential target genes for further study in drug resistance. We tested whether our classifier could predict IF within 42 patient samples obtained from COG 8704 and, using PAM to define a smaller classifier for the U133A chip, correctly identified the single IF case and patients with persistently circulating blasts. Genetic profiling may identify T-ALL patients who are likely to fail induction and for whom alternate treatment strategies might be beneficial. This SuperSeries is composed of the following subset Series:; GSE14613: Microarray analyses of induction failure in T-ALL (COG study 8704); GSE14615: Microarray analyses of induction failure in T-ALL (COG study 9404) Experiment Overall Design: Refer to individual Series Experiment Overall Design: This was a case-controlled, retrospectively designed study. We performed expression profiles on 92 patients with T-ALL treated on Children's Oncology Group studies 8704 (42 patients) and 9404 (50 patients). Experiment Overall Design: Expression profiles were obtained from patients with newly diagnosed T-ALL. NR = no response (induction failure); F= failure (relapse); C= Complete Continous Remission.
2009-02-07 | E-GEOD-14618 | biostudies-arrayexpress
Project description:The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure. Based on the hypothesis that microarrays might identify patients who fail therapy, we used the Affymetrix U133 Plus 2.0 chip and prediction analysis of microarrays (PAM) to profile 50 newly diagnosed patients who were treated in the Children's Oncology Group (COG) T-ALL Study 9404. We identified a 116-member genomic classifier that could accurately distinguish all 6 induction failure (IF) cases from 44 patients who achieved remission; network analyses suggest a prominent role for genes mediating cellular quiescence. Seven genes were similarly upregulated in both the genomic classifier for IF patients and T-ALL cell lines having acquired resistance to neoplastic agents, identifying potential target genes for further study in drug resistance. We tested whether our classifier could predict IF within 42 patient samples obtained from COG 8704 and, using PAM to define a smaller classifier for the U133A chip, correctly identified the single IF case and patients with persistently circulating blasts. Genetic profiling may identify T-ALL patients who are likely to fail induction and for whom alternate treatment strategies might be beneficial. Experiment Overall Design: This was a case-controlled, retrospectively designed study. We performed expression profiles on 92 patients with T-ALL treated on Children's Oncology Group studies 8704 (42 patients) and 9404 (50 patients). Experiment Overall Design: Expression profiles were obtained from patients with newly diagnosed T-ALL. NR = no response (induction failure); F= failure (relapse); C= Complete Continous Remission. Experiment Overall Design: NOTE: non-sequential numbers were removed for reasons other than relapse, or poor hybridization during chip preparation.
2009-02-09 | E-GEOD-14615 | biostudies-arrayexpress
Project description:The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure. Based on the hypothesis that microarrays might identify patients who fail therapy, we used the Affymetrix U133 Plus 2.0 chip and prediction analysis of microarrays (PAM) to profile 50 newly diagnosed patients who were treated in the Children's Oncology Group (COG) T-ALL Study 9404. We identified a 116-member genomic classifier that could accurately distinguish all 6 induction failure (IF) cases from 44 patients who achieved remission; network analyses suggest a prominent role for genes mediating cellular quiescence. Seven genes were similarly upregulated in both the genomic classifier for IF patients and T-ALL cell lines having acquired resistance to neoplastic agents, identifying potential target genes for further study in drug resistance. We tested whether our classifier could predict IF within 42 patient samples obtained from COG 8704 and, using PAM to define a smaller classifier for the U133A chip, correctly identified the single IF case and patients with persistently circulating blasts. Genetic profiling may identify T-ALL patients who are likely to fail induction and for whom alternate treatment strategies might be beneficial.
2009-01-29 | GSE14613 | GEO
Project description:The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure. Based on the hypothesis that microarrays might identify patients who fail therapy, we used the Affymetrix U133 Plus 2.0 chip and prediction analysis of microarrays (PAM) to profile 50 newly diagnosed patients who were treated in the Children's Oncology Group (COG) T-ALL Study 9404. We identified a 116-member genomic classifier that could accurately distinguish all 6 induction failure (IF) cases from 44 patients who achieved remission; network analyses suggest a prominent role for genes mediating cellular quiescence. Seven genes were similarly upregulated in both the genomic classifier for IF patients and T-ALL cell lines having acquired resistance to neoplastic agents, identifying potential target genes for further study in drug resistance. We tested whether our classifier could predict IF within 42 patient samples obtained from COG 8704 and, using PAM to define a smaller classifier for the U133A chip, correctly identified the single IF case and patients with persistently circulating blasts. Genetic profiling may identify T-ALL patients who are likely to fail induction and for whom alternate treatment strategies might be beneficial. This SuperSeries is composed of the SubSeries listed below.
2009-01-29 | GSE14618 | GEO
Project description:The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure or relapse. We used the Affymetrix U133 Plus 2.0 chip to profile 100 newly diagnosed patients who were treated in the Children's Oncology Group (COG) T-ALL AALL0434. We performed unsupervised hierarchical clustering of 25 HOXA probe sets within the cohort of 100 T-ALL cases. We identified a cluster of 20 cases (20%) characterized by increased expression of HOXA3, 5, 7, 9, and 10. In samples with HOXA9/10 deregulation, the presence of specific molecular lesions were confirmed through a systematic review of cytogenetic databases, FISH and PCR testing, and by RNA sequence analysis. Because MLL and AF10 genes rearrangements (MLL-R, AF10-R) are hallmarks of HOXA-deregulated leukemias, we sought to identify specific genes that are enriched with these genomic abnormalities.
2017-06-29 | GSE70536 | GEO
Project description:The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure or relapse. We investigated 213 newly diagnosed patients who were treated in the Children's Oncology Group (COG) T-ALL Studies 9404 and 0434 and identified a cluster cases characterized by increased expression of HOX9/10. In samples with >8-fold HOXA9/10 deregulation, the presence of specific molecular lesions were confirmed through a systematic review of cytogenetic databases, FISH and PCR testing. In cases with no identifiable lesions using the methods above, RNA sequencing was performed (Illumina and Ion Proton). Fifteen cases selected for RNA sequencing met three criteria: HOXA9/10 expression ≥8-fold above the median, sufficient RNA, and an incomplete or absent molecular explanation for each sample’s HOXA9/10 deregulation. Three cases were confirmed to have PICALM-AF10 fusions. Two additional AF10-R cases showed DDX3X-AF10 lesions, one of which harbored a novel CASK gene fragment in a complex CASK-DDX3X-AF10 translocation. Two cases harbored NUP98 fusions46 and one case each was identified for MLL-AF6, MLL-PICALM, HOXA10-(3’UTR)TRBC45, STAG2-LMO2, LOC338817-CCDC91. We could not identify fusion transcripts in 3 cases.
2017-06-29 | GSE71359 | 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:Conserved Oligomeric Golgi (COG) is an octameric protein complex that orchestrates intra-Golgi trafficking of glycosylation enzymes. Over a hundred individuals with 31 different COG mutations have been identified until now. The cellular phenotypes and clinical presentations of COG-CDGs are heterogeneous, and patients primarily represent neurological, skeletal, and hepatic abnormalities. The establishment of a cellular COG disease model will benefit the molecular study of the disease, explaining the detailed sequence of the interplay between the COG complex and the trafficking machinery. Moreover, patient fibroblasts are not a good representative of all the organ systems and cell types that are affected by COG mutations. We developed and characterized cellular models for human COG4 mutations specifically in RPE1 and HEK293T cell lines. Using a combination of CRISPR/Cas9 and lentiviral transduction technologies, both myc-tagged wild-type and mutant (G516R and R729W) COG4 proteins were expressed under the endogenous COG4 promoter. Constructed isogenic cell lines were comprehensively characterized using biochemical, microscopy (superresolution and electron), and proteomics approaches. The analysis revealed similar stability and localization of COG complex subunits, wild-type cell growth, and normal Golgi morphology in all three cell lines. Importantly, COG4-G516R cells demonstrated increased HPA-647 binding to the plasma membrane glycoconjugates, while COG4-R729W cells revealed high GNL-647 binding, indicating specific defects in O- and N-glycosylation. Both mutant cell lines express elevated level of heparin sulfate proteoglycans. Moreover, a quantitative mass-spectrometry analysis of proteins secreted by COG-deficient cell lines revealed abnormal secretion of SIL1 and ERGIC-53 proteins by COG4-G516R cells. Interestingly, the clinical phenotype of patients with congenital mutations in SIL1 gene (Marinesco-Sjogren syndrome) overlaps with the phenotype of COG4-G516R patients (Saul-Wilson syndrome). Our work is the first compressive study involving the creation of different COG mutations in different cell lines other than patient’s fibroblast. It may help to address the underlying cause of the phenotypic defects leading to the discovery of a proper treatment guideline for COG-CDGs.
2022-02-17 | PXD027202 | Pride
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