CaArray_willm-00090: Gene Expression Profiling Reveals Novel Genes for Improved Risk Classification and Outcome Prediction in Pediatric Acute Lymphoblastic Leukemia: Identification, Cloning, and Validation of OPAL1
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE92708 | GEO | 2016/12/22
SECONDARY ACCESSION(S): PRJNA358405
REPOSITORIES: GEO
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Project description:RNA was extracted from the diagnostic bone marrow specimens of 50 T-cell acute lymphoblastic leukemia pediatric patients and analysed by Affymetrix microarray to model gene classifiers predictive of clinical outcome
2012-07-06 | E-MTAB-1205 | biostudies-arrayexpress
Project description:BackgroundAcute lymphoblastic leukemia (ALL) contains cytogenetically distinct subtypes that respond differently to cytotoxic drugs. Therefore, subtype classification is important and indispensable in ALL diagnosis. In our previous study, we identified some marker genes in childhood ALL by means of microarray technology and, furthermore, detected the relative expression levels of 57 marker genes and built a comparatively convenient and cost-effective classifier with a prediction accuracy as high as 94% based on the advanced fragment analysis (AFA) technique.MethodsA more convenient improved AFA (iAFA) technique with one-step multiplex RT-PCR and an anti-contamination system was developed to detect 57 marker genes for ALL.ResultsThe iAFA assay is much easier and more convenient to perform than the previous AFA assay and has a prediction accuracy of 95.29% in ALL subtypes. The anti-contamination system could effectively prevent the occurrence of lab DNA contamination. We also showed that marker gene expression profiles in pediatric ALL revealed 2 subgroups with different outcomes. Most ALL patients (95.8%) had a good-risk genetic profile, and only 4.2% of ALL patients had a poor-risk genetic profile, which predicted an event-free survival (EFS) of 93.6 ± 1.3% vs 18.8 ± 9.8% at 5 years, respectively (P < 0.001).ConclusionsCompared to the previous AFA assay, the iAFA technique is more functional, time-saving and labor-saving. It could be a valuable clinical tool for the classification and risk stratification of pediatric ALL patients.
| S-EPMC6482565 | biostudies-literature
Project description:To determine whether gene expression profiling could improve outcome prediction in children with acute lymphoblastic leukemia (ALL) at high risk for relapse, we profiled pretreatment leukemic cells in 207 uniformly treated children with high-risk B-precursor ALL. A 38-gene expression classifier predictive of relapse-free survival (RFS) could distinguish 2 groups with differing relapse risks: low (4-year RFS, 81%, n = 109) versus high (4-year RFS, 50%, n = 98; P < .001). In multivariate analysis, the gene expression classifier (P = .001) and flow cytometric measures of minimal residual disease (MRD; P = .001) each provided independent prognostic information. Together, they could be used to classify children with high-risk ALL into low- (87% RFS), intermediate- (62% RFS), or high- (29% RFS) risk groups (P < .001). A 21-gene expression classifier predictive of end-induction MRD effectively substituted for flow MRD, yielding a combined classifier that could distinguish these 3 risk groups at diagnosis (P < .001). These classifiers were further validated on an independent high-risk ALL cohort (P = .006) and retainedindependent prognostic significance (P < .001) in the presence of other recently described poor prognostic factors (IKAROS/IKZF1 deletions, JAK mutations, and kinase expression signatures). Thus, gene expression classifiers improve ALL risk classification and allow prospective identification of children who respond or fail current treatment regimens. These trials were registered at http://clinicaltrials.gov under NCT00005603.
| S-EPMC2826761 | biostudies-literature
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:BackgroundContinuous complete clinical remission in T-cell acute lymphoblastic leukemia (T-ALL) is now approaching 80% due to the implementation of aggressive chemotherapy protocols but patients that relapse continue to have a poor prognosis. Such patients could benefit from augmented therapy if their clinical outcome could be more accurately predicted at the time of diagnosis. Gene expression profiling offers the potential to identify additional prognostic markers but has had limited success in generating robust signatures that predict outcome across multiple patient cohorts. This study aimed to identify robust gene classifiers that could be used for the accurate prediction of relapse in independent cohorts and across different experimental platforms.ResultsUsing HG-U133Plus2 microarrays we modeled a five-gene classifier (5-GC) that accurately predicted clinical outcome in a cohort of 50 T-ALL patients. The 5-GC was further tested against three independent cohorts of T-ALL patients, using either qRT-PCR or microarray gene expression, and could predict patients with significantly adverse clinical outcome in each. The 5-GC featured the interleukin-7 receptor (IL-7R), low-expression of which was independently predictive of relapse in T-ALL patients. In T-ALL cell lines, low IL-7R expression was correlated with diminished growth response to IL-7 and enhanced glucocorticoid resistance. Analysis of biological pathways identified the NF-kappaB and Wnt pathways, and the cell adhesion receptor family (particularly integrins) as being predictive of relapse. Outcome modeling using genes from these pathways identified patients with significantly worse relapse-free survival in each T-ALL cohort.ConclusionsWe have used two different approaches to identify, for the first time, robust gene signatures that can successfully discriminate relapse and CCR patients at the time of diagnosis across multiple patient cohorts and platforms. Such genes and pathways represent markers for improved patient risk stratification and potential targets for novel T-ALL therapies.
| S-EPMC2879253 | biostudies-literature
Project description:We present a method that utilizes DNA methylation profiling for prediction of the cytogenetic subtypes of acute lymphoblastic leukemia (ALL) cells from pediatric ALL patients. The primary aim of our study was to improve risk stratification of ALL patients into treatment groups using DNA methylation as a complement to current diagnostic methods. A secondary aim was to gain insight into the functional role of DNA methylation in ALL.We used the methylation status of ~450,000 CpG sites in 546 well-characterized patients with T-ALL or seven recurrent B-cell precursor ALL subtypes to design and validate sensitive and accurate DNA methylation classifiers. After repeated cross-validation, a final classifier was derived that consisted of only 246 CpG sites. The mean sensitivity and specificity of the classifier across the known subtypes was 0.90 and 0.99, respectively. We then used DNA methylation classification to screen for subtype membership of 210 patients with undefined karyotype (normal or no result) or non-recurrent cytogenetic aberrations ('other' subtype). Nearly half (n?=?106) of the patients lacking cytogenetic subgrouping displayed highly similar methylation profiles as the patients in the known recurrent groups. We verified the subtype of 20% of the newly classified patients by examination of diagnostic karyotypes, array-based copy number analysis, and detection of fusion genes by quantitative polymerase chain reaction (PCR) and RNA-sequencing (RNA-seq). Using RNA-seq data from ALL patients where cytogenetic subtype and DNA methylation classification did not agree, we discovered several novel fusion genes involving ETV6, RUNX1, and PAX5.Our findings indicate that DNA methylation profiling contributes to the clarification of the heterogeneity in cytogenetically undefined ALL patient groups and could be implemented as a complementary method for diagnosis of ALL. The results of our study provide clues to the origin and development of leukemic transformation. The methylation status of the CpG sites constituting the classifiers also highlight relevant biological characteristics in otherwise unclassified ALL patients.
| S-EPMC4343276 | biostudies-literature
Project description:This series represents expression profiles of 34 non-seminoma germ cell tumors (NSGCTs) from patients who received cisplatin based chemotherarpy for treatment of their disease for whom full clinical follow-up information was available. These specimens were used as a validation set to test outcome prediction models using a subset of previously profiled GCT specimens (see GEO accession #GSE3218). Keywords: Disease state analysis (good vs. poor patient outcome)
2008-09-30 | GSE10783 | GEO
Project description:BackgroundAlthough the WIND (Weaning according to a New Definition) classification based on duration of ventilation after the first separation attempt has been proposed, this new classification has not been tested in clinical practice. The objective of this cohort study was to evaluate the clinical relevance of WIND classification and its association with hospital mortality compared to the International Consensus Conference (ICC) classification.MethodsAll consecutive medical ICU patients who were mechanically ventilated for more than 24 h between July 2010 and September 2013 were prospectively registered. Patients were classified into simple, difficult, or prolonged weaning group according to ICC classification and Groups 1, 2, 3, or no weaning (NW) according to WIND classification.ResultsDuring the study period, a total of 1600 patients were eligible. These patients were classified by the WIND classification as follows: Group NW?=?580 (36.3%), Group 1?=?617 (38.6%), Group 2?=?186 (11.6%), and Group 3?=?217 (13.6%). However, only 735 (45.9%) patients were classified by ICC classification as follows: simple weaning?=?503 (68.4%), difficult weaning?=?145 (19.7%), and prolonged weaning?=?87 (11.8%). Clinical outcomes were significantly different across weaning groups by ICC classification and WIND classification. However, there were no statistical differences in successful weaning rate (96.6% vs. 95.2%) or hospital mortality (22.5% vs. 25.5%) between simple and difficult weaning groups by the ICC. Conversely, there were statistically significant differences in successful weaning rate (98.5% vs. 76.9%) and hospital mortality (21.2% vs. 33.9%) between Group 1 and Group 2 by WIND.ConclusionsThe WIND classification could be a better tool for predicting weaning outcomes than the ICC classification.
| S-EPMC6265356 | biostudies-literature
Project description:Pediatric acute lymphoblastic leukemia (ALL) is the most common neoplasm and one of the primary causes of death in children. Its treatment is highly dependent on the correct classification of subtype. Previously, we developed a microarray-based subtype classifier based on the relative expression levels of 62 marker genes, which can predict 7 different ALL subtypes with an accuracy as high as 97% in completely independent samples. Because the classifier is based on gene expression rank values rather than actual values, the classifier enables an individualized diagnosis, without the need to reference the background distribution of the marker genes in a large number of other samples, and also enables cross platform application. Here, we demonstrate that the classifier can be extended from a microarray-based technology to a multiplex qPCR-based technology using the same set of marker genes as the advanced fragment analysis (AFA). Compared to microarray assays, the new assay system makes the convenient, low cost and individualized subtype diagnosis of pediatric ALL a reality and is clinically applicable, particularly in developing countries.
| S-EPMC4508914 | biostudies-literature
Project description:ETV6-RUNX1 fusion is the most common genetic aberration in childhood acute lymphoblastic leukemia (ALL). To evaluate whether outcomes for this drug-sensitive leukemia are improved by contemporary risk-directed therapy, we studied clinical features, response and adverse events of 168 children with newly diagnosed ETV6-RUNX1-positive ALL on St Jude Total Therapy studies XIIIA (N=36), XIIIB (N=38) and XV (N=94). Results were compared with 494 ETV6-RUNX1-negative B-precursor ALL patients. ETV6-RUNX1 was associated with age 1-9 years, pre-treatment classification as low risk and lower levels of minimal residual disease (MRD) on day 19 of therapy (P<0.001). Event-free survival (EFS) or overall survival (OS) did not differ between patients with or without ETV6-RUNX1 in Total XIIIA or XIIIB. By contrast, in Total XV, patients with ETV6-RUNX1 had significantly better EFS (P=0.04; 5-year estimate, 96.8±2.4% versus 88.3±2.5%) and OS (P=0.04; 98.9±1.4% versus 93.7±1.8%) than those without ETV6-RUNX1. Within the ETV6-RUNX1 group, the only significant prognostic factor associated with higher OS was the treatment protocol Total XV (versus XIIIA or XIIIB) (P=0.01). Thus, the MRD-guided treatment schema including intensive asparaginase and high-dose methotrexate in the Total XV study produced significantly better outcomes than previous regimens and demonstrated that nearly all children with ETV6-RUNX1 ALL can be cured.
| S-EPMC3345278 | biostudies-literature