Multimodal classification of molecular subtypes in pediatric acute lymphoblastic leukemia
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ABSTRACT:
PROVIDER: PRJNA947154 | ENA |
REPOSITORIES: ENA
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Project description:Genomic analyses have redefined the molecular subgrouping of pediatric acute lymphoblastic leukemia (ALL). Molecular subgroups guide risk-stratification and targeted therapies, but outcomes of recently identified subtypes are often unclear, owing to limited cases with comprehensive profiling and cross-protocol studies. We developed a machine learning tool (ALLIUM) for the molecular subclassification of ALL in retrospective cohorts as well as for up-front diagnostics. ALLIUM uses DNA methylation and gene expression data from 1131 Nordic ALL patients to predict 17 ALL subtypes with high accuracy. ALLIUM was used to revise and verify the molecular subtype of 281 B-cell precursor ALL (BCP-ALL) cases with previously undefined molecular phenotype, resulting in a single revised subtype for 81.5% of these cases. Our study shows the power of combining DNA methylation and gene expression data for resolving ALL subtypes and provides a comprehensive population-based retrospective cohort study of molecular subtype frequencies in the Nordic countries.
2023-10-17 | GSE227832 | GEO
Project description:Pediatric acute lymphoblastic leukemia (ALL) contains cytogenetically distinct subtypes that respond differently to cytotoxic drugs. Subtype classification can be also achieved through gene expression profiling. However, how to apply such classifiers to a single patient and correctly diagnose the disease subtype in an independent patient group has not been addressed. Furthermore, the underlying regulatory mechanisms responsible for the subtype-specific gene expression patterns are still largely unknown. Here, by combining three published microarray datasets (PMIDs: 12086872, 12730115, 17002788) on 535 Caucasian samples and generating a new dataset on 100 Chinese children ALL samples, we were able to 1) identify a 62-gene classifier with 97.6% accuracy from the Caucasian samples and validated it on the completely independent set of 100 Chinese samples, 2) to uncover potential regulatory networks of ALL subtypes. The classifier we identified was so far the only one that could be applied directly to a single sample and sustained validation in a large independent patient group. Our results also suggest that the etiology of ALL is largely the same among different ethnic groups, and that the transcription factor hubs in the predicted regulatory network might play important roles in regulating gene expression and development of ALL. A total of 100 Chinese pediatric acute lymphoblastic leukemia bone marrow (BM) samples were analyzed, together with five non-ALL BM samples as negative control (the five samples were combined to generate one control, see details in Li et al. Blood, 2009). The diagnosis of ALL was based on morphology, immunology, cytogenetic and molecular (MICM) classification. Cytogenetic ALL subtypes were identified experimentally by G-banding karyotype and multiplex nested RT-PCR. Among the 100 ALL patients, eleven relapsed within five years. All the samples, including those relapsed afterward, were from patients treated on BCH-2003 protocol and were extracted at their initial diagnosis. The five non-ALL BM samples were taken from the removed bones of patients who had plastic surgery for their bone deformity in Beijing Children’s Hospital. And the informed consent was obtained from parents, guardians, or patients (as appropriate). More details are available in Li et al. Gene expression-based classification and regulatory networks of pediatric acute lymphoblastic leukemia, Blood, 2009. We have generated these ALL and control samples to validate that a high accuracy subtype classifier that we identified could be applied directly to a single sample and sustained validation in a large independent patient group.
2009-08-19 | E-GEOD-17703 | biostudies-arrayexpress
Project description:Genomic analyses have redefined the molecular subgrouping of pediatric acute lymphoblastic leukemia (ALL). Molecular subgroups guide risk-stratification and targeted therapies, but outcomes of recently identified subtypes are often unclear, owing to limited cases with comprehensive profiling and cross-protocol studies. We developed a machine learning tool (ALLIUM) for the molecular subclassification of ALL in retrospective cohorts as well as for up-front diagnostics. ALLIUM uses DNA methylation and gene expression data from 1131 Nordic ALL patients to predict 17 ALL subtypes with high accuracy. ALLIUM was used to revise and verify the molecular subtype of 281 B-cell precursor ALL (BCP-ALL) cases with previously undefined molecular phenotype, resulting in a single revised subtype for 81.5% of these cases. Our study shows the power of combining DNA methylation and gene expression data for resolving ALL subtypes and provides a comprehensive population-based retrospective cohort study of molecular subtype frequencies in the Nordic countries.
| S-EPMC10709574 | biostudies-literature
Project description:We surveyed the genome-wide DNA methylation levels and gene expression patterns in patients with pediatric acute lymphoblastic leukemia. Using Affymetrix U133 Plus 2.0 GeneChips, we identified a relatively small set of CpG sites that are highly correlated with gene expression.
2013-09-18 | GSE47051 | GEO
Project description:We surveyed the genome-wide DNA methylation levels and gene expression patterns in patients with pediatric acute lymphoblastic leukemia. Using Affymetrix U133 Plus 2.0 GeneChips, we identified a relatively small set of CpG sites that are highly correlated with gene expression. Genome-wide expression analysis of 108 primary ALL samples by Affymetrix Human Genome U133 Plus 2.0 GeneChips used to investigate relationships between subtype-specific DNA methylation patterns and gene expression in 93 patients with both gene expression and DNA methylation data.
2013-09-18 | E-GEOD-47051 | biostudies-arrayexpress
Project description:MicroRNA-sequencing of the bone marrow samples from Brazilian pediatric patients with B-cell acute lymphoblastic leukemia (B-ALL) and T-cell acute lymphoblastic leukemia (T-ALL).
2022-04-13 | E-MTAB-11621 | biostudies-arrayexpress
Project description:Array comparative genomic hybridization data for 372 diagnosis pediatric acute lymphoblastic leukemia samples.
2023-03-13 | GSE184692 | GEO
Project description:Pediatric acute lymphoblastic leukemia (ALL) contains cytogenetically distinct subtypes that respond differently to cytotoxic drugs. Subtype classification can be also achieved through gene expression profiling. However, how to apply such classifiers to a single patient and correctly diagnose the disease subtype in an independent patient group has not been addressed. Furthermore, the underlying regulatory mechanisms responsible for the subtype-specific gene expression patterns are still largely unknown. Here, by combining three published microarray datasets (PMIDs: 12086872, 12730115, 17002788) on 535 Caucasian samples and generating a new dataset on 100 Chinese children ALL samples, we were able to 1) identify a 62-gene classifier with 97.6% accuracy from the Caucasian samples and validated it on the completely independent set of 100 Chinese samples, 2) to uncover potential regulatory networks of ALL subtypes. The classifier we identified was so far the only one that could be applied directly to a single sample and sustained validation in a large independent patient group. Our results also suggest that the etiology of ALL is largely the same among different ethnic groups, and that the transcription factor hubs in the predicted regulatory network might play important roles in regulating gene expression and development of ALL.
2009-08-19 | GSE17703 | GEO
Project description:The overall goal of this study was to profile pediatric acute lymphoblastic leukemia bone marrow tissue based on their transcriptome to characterize clinically relevant subtypes. This project has received funding from Cancer Society of Finland, Jane and Aatos Erkko Foundation and ERAPerMed (Reference Number: ERAPERMED2018-209) under the ERA-NET Cofund scheme of the Horizon 2020 Research and Innovation Framework Programme of the European Commission Research Directorate-General Grant Agreement No. 779282.
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Project description:miRNA profiling has been performed on primary tumor samples collected at diagnosis from pediatric patients affected by T-cell lymphoblastic lymphoma.
2014-06-10 | GSE55846 | GEO