Children's group identity is related to their assessment of fair and unfair advantages.
Ontology highlight
ABSTRACT:
SUBMITTER: D'Esterre AP
PROVIDER: S-EPMC8608754 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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: Not available
| S-EPMC6754384 | biostudies-literature
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:Background: Improvements in pediatric cancer survival are attributed to cooperative clinical trials. Under-representation of specific demographic groups has been described in adult and pediatric cancer trials and poses a threat to the generalizability of results. An evaluation of data provided by the Children's Oncology Group (COG) of upfront trial enrollment for US patients 0 to 29 years old between 2004 and 2015 was performed.Methods: US cancer cases were estimated using incidence data and US population estimates from the Surveillance, Epidemiology, and End Results Program and compared to observed COG cases. Percent enrollment and standardized ratios of enrollment were calculated across demographic, disease, and socioeconomic groups. The COG website was utilized to quantify available trials and assess age eligibility.Results: 19.9% of estimated US cancer patients age 0 to 19 years enrolled on COG trials. Younger patients were more represented across diseases and races/ethnicities. Patients with hematologic malignancies were more represented compared to solid and central nervous system (CNS) tumors.Conclusion: COG trial enrollment rates are declining when compared to previously published data, potentially from challenges in pediatric drug development, difficulty designing feasible trials for highly curable diagnoses, and issues ensuring trial availability for the heterogeneous group of solid and CNS tumors. Though racial/ethnic groups and county-level socioeconomic factors were proportionally represented, under representation of the adolescent/young adult (AYA) population and younger patients with solid and CNS tumors remains a concern. Targeted efforts should focus on these subgroups and further research should evaluate AYA enrollment rates across all available trials.
| S-EPMC7179840 | biostudies-literature
Project description:BackgroundOntology matching should contribute to the interoperability aspect of FAIR data (Findable, Accessible, Interoperable, and Reusable). Multiple data sources can use different ontologies for annotating their data and, thus, creating the need for dynamic ontology matching services. In this experimental study, we assessed the performance of ontology matching systems in the context of a real-life application from the rare disease domain. Additionally, we present a method for analyzing top-level classes to improve precision.ResultsWe included three ontologies (NCIt, SNOMED CT, ORDO) and three matching systems (AgreementMakerLight 2.0, FCA-Map, LogMap 2.0). We evaluated the performance of the matching systems against reference alignments from BioPortal and the Unified Medical Language System Metathesaurus (UMLS). Then, we analyzed the top-level ancestors of matched classes, to detect incorrect mappings without consulting a reference alignment. To detect such incorrect mappings, we manually matched semantically equivalent top-level classes of ontology pairs. AgreementMakerLight 2.0, FCA-Map, and LogMap 2.0 had F1-scores of 0.55, 0.46, 0.55 for BioPortal and 0.66, 0.53, 0.58 for the UMLS respectively. Using vote-based consensus alignments increased performance across the board. Evaluation with manually created top-level hierarchy mappings revealed that on average 90% of the mappings' classes belonged to top-level classes that matched.ConclusionsOur findings show that the included ontology matching systems automatically produced mappings that were modestly accurate according to our evaluation. The hierarchical analysis of mappings seems promising when no reference alignments are available. All in all, the systems show potential to be implemented as part of an ontology matching service for querying FAIR data. Future research should focus on developing methods for the evaluation of mappings used in such mapping services, leading to their implementation in a FAIR data ecosystem.
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Project description:PurposeInterindividual variability in the dose-dependent association between anthracyclines and cardiomyopathy suggests that genetic susceptibility could play a role. The current study uses an agnostic approach to identify genetic variants that could modify cardiomyopathy risk.MethodsA genome-wide association study was conducted in childhood cancer survivors with and without cardiomyopathy (cases and controls, respectively). Single-nucleotide polymorphisms (SNPs) that surpassed a prespecified threshold for statistical significance were independently replicated. The possible mechanistic significance of validated SNP(s) was sought by using healthy heart samples.ResultsNo SNP was marginally associated with cardiomyopathy. However, SNP rs1786814 on the CELF4 gene passed the significance cutoff for gene-environment interaction (Pge = 1.14 × 10(-5)). Multivariable analyses adjusted for age at cancer diagnosis, sex, anthracycline dose, and chest radiation revealed that, among patients with the A allele, cardiomyopathy was infrequent and not dose related. However, among those exposed to greater than 300 mg/m(2) of anthracyclines, the rs1786814 GG genotype conferred a 10.2-fold (95% CI, 3.8- to 27.3-fold; P < .001) increased risk of cardiomyopathy compared with those who had GA/AA genotypes and anthracycline exposure of 300 mg/m(2) or less. This gene-environment interaction was successfully replicated in an independent set of anthracycline-related cardiomyopathy. CUG-BP and ETR-3-like factor proteins control developmentally regulated splicing of TNNT2, the gene that encodes for cardiac troponin T (cTnT), a biomarker of myocardial injury. Coexistence of more than one cTnT variant results in a temporally split myofilament response to calcium, which causes decreased contractility. Analysis of TNNT2 splicing variants in healthy human hearts suggested an association between the rs1786814 GG genotype and coexistence of more than one TNNT2 splicing variant (90.5% GG v 41.7% GA/AA; P = .005).ConclusionWe report a modifying effect of a polymorphism of CELF4 (rs1786814) on the dose-dependent association between anthracyclines and cardiomyopathy, which possibly occurs through a pathway that involves the expression of abnormally spliced TNNT2 variants.
| S-EPMC5070560 | biostudies-literature