scholarly journals Transcriptional and Mutational Profiling of B-Other Acute Lymphoblastic Leukemia for Improved Diagnostics

Cancers ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 5653
Author(s):  
Philippe Chouvarine ◽  
Željko Antić ◽  
Jana Lentes ◽  
Charlotte Schröder ◽  
Julia Alten ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Diagnostic Procedures ◽  
Sample Collection ◽  
Driver Genes ◽  
Whole Transcriptome Sequencing ◽  
Pediatric All ◽  
Downstream Analysis

B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is the most common cancer in children, and significant progress has been made in diagnostics and the treatment of this disease based on the subtypes of BCP-ALL. However, in a large proportion of cases (B-other), recurrent BCP-ALL-associated genomic alterations remain unidentifiable by current diagnostic procedures. In this study, we performed RNA sequencing and analyzed gene fusions, expression profiles, and mutations in diagnostic samples of 185 children with BCP-ALL. Gene expression clustering showed that a subset of B-other samples partially clusters with some of the known subgroups, particularly DUX4-positive. Mutation analysis coupled with gene expression profiling revealed the presence of distinctive BCP-ALL subgroups, characterized by the presence of mutations in known ALL driver genes, e.g., PAX5 and IKZF1. Moreover, we identified novel fusion partners of lymphoid lineage transcriptional factors ETV6, IKZF1 and PAX5. In addition, we report on low blast count detection thresholds and show that the use of EDTA tubes for sample collection does not have adverse effects on sequencing and downstream analysis. Taken together, our findings demonstrate the applicability of whole-transcriptome sequencing for personalized diagnostics in pediatric ALL, including tentative classification of the B-other cases that are difficult to diagnose using conventional methods.

Rearranged Infant Acute Lymphoblastic Leukemia.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1913-1913 ◽  
Author(s):  
Ronald W. Stam ◽  
Monique L. Den Boer ◽  
Pauline Schneider ◽  
Jasper de Boer ◽  
Jill Hagelstein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Profiles ◽  
Glucocorticoid Resistance ◽  
Glucocorticoid Treatment ◽  
Pediatric All

Abstract MLL rearranged Acute Lymphoblastic Leukemia (ALL) represents an unfavorable and difficult to treat type of leukemia that often is highly resistant to glucocorticoids like prednisone and dexamethasone. As the response to prednisone largely determines the clinical outcome of pediatric ALL patients, overcoming resistance to these drugs may be an important step towards improved prognosis. Here we compared gene expression profiles between prednisone-resistant and prednisone-sensitive pediatric ALL patients to obtain gene expression signatures associated with prednisone resistance for both childhood (>1 year of age) and MLL rearranged infant (<1 year of age) ALL. Merging both signatures in search for overlapping genes associated with prednisone resistance in both patient groups we, found that elevated expression of MCL-1 (an anti-apoptotic member of the BCL-2 protein family) appeared to be characteristic for both prednisone-resistant ALL samples. To validate this observation, we determined MCL-1 expression using quantitative RT-PCR in a cohort of MLL rearranged infant ALL samples (n=23), and confirm that high-level MCL-1 expression significantly confers glucocorticoid resistance both in vitro and in vivo. Finally, down-regulation of MCL-1 in prednisone resistant MLL rearranged ALL cells by RNA interference (RNAi) markedly sensitized these cells to prednisone. Therefore we conclude that MCL-1 plays an important role in glucocorticoid resistance and that MCL- 1 suppressing agents co-administered during glucocorticoid treatment may be beneficial especially for MLL rearranged infant ALL patients.

A Novel Subtype-Classifier Of Pediatric Acute Lymphoblastic Leukemia Using Gexp Multiplexed System

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 2589-2589
Author(s):  
Han Zhang ◽  
Xianping Zeng ◽  
Yanfen Chen ◽  
Qingqing Wang ◽  
Hao Cheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Conflicts Of Interest ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Malignant Neoplasm ◽  
Marker Genes ◽  
Minimal Amount ◽  
Pediatric Leukemia ◽  
Pediatric All

Abstract Objective Acute lymphoblastic leukemia (ALL) is the most frequent malignant neoplasm in children. The lack of reasonable and accurate classification has becomes the main obstructive factor, which makes normative treatment more difficult to carry out. Morphology, immunology, cytogenetics and molecular biology (MICM) classification is widely used clinically for pediatric leukemia. However, it is a time-consuming and expensive process and only available in a few major medical centers in some developing countries, which heavily impact the accurate classification. Our previous microarray analysis of gene expression profiles in 100 pediatric ALL bone marrow samples screened out 62 classification markers (mapped to 61 ENTREZ genes), which could classify pediatric ALL into 6 major ALL subtypes. In this study, the GenomeLab Gene Expression Profiler Genetic Analysis System (GeXP) was applied here to custom design a multiplex of 57-gene classifier to validate the feasibility of these genes as classification markers, and then establish a new and practical method for ALL classification, which will guide the risk classification and stratified treatment of leukemia. Methods Sixty-two classification markers were divided into 3 panels with each panel containing 20∼21 genes. Each primer is a chimeric primer consists of a gene-specific sequence fused to a universal tag sequence at the 5' terminal. The GeXP technology was used to measure the expression levels of these genes. The peak areas of each target gene and endogenous reference controls were normalized, from which, the relative quantification of each gene in a sample was determined. For each sample, we ranked the gene expression values from low to high and normalized these rank values by Z-score. Super k-means method was used to do the cluster by genes and samples. Results A novel gene expression-based ALL subtype classification using GeXP multiplexed assay was optimized and developed, which led to a rapid, reliable, and cost-effective classifier. Using a subset of 61 genes, we totally got 57 marker genes' expression data in 85 pediatric ALL samples. From the cluster results, 1) we got 4 clusters that mainly represented by 4 subtypes of ALL, including TEL-AML1+ ALL, BCR-ABL+ ALL, E2A-PBX1+ ALL and T-cell ALL, which obtained a high accuracy (98.4%, 60/61) with MICM classification (Fig 1); 2) we found the marker genes for each subtype and especially for those samples without any subtype, which provided the prediction according to which cluster it located in; 3) this classifier could take a single sample and made a prediction based solely on the relative expression ranks among the marker genes. Conclusions We develop a novel multiplexed 57-gene classifier to identify the major subtypes of pediatric ALL. This promising molecular test allows for a high-throughput, robust and reproducible assessment of multiplexed gene expression analysis. It offers a powerful diagnostic tool for the rapid classification using a minimal amount of samples. It may be usefully applied in the future clinical work and assist the physician in a more rapid and accurate classification of the subtypes in pediatric ALL. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Identification and Validation of Novel Reference Genes in Acute Lymphoblastic Leukemia for Droplet Digital PCR

Genes ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 376 ◽  
Author(s):  
Vanessa Villegas-Ruíz ◽  
Karina Olmos-Valdez ◽  
Kattia Alejandra Castro-López ◽  
Victoria Estefanía Saucedo-Tepanecatl ◽  
Josselen Carina Ramírez-Chiquito ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Reference Genes ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Profiles ◽  
Housekeeping Genes ◽  
Digital Pcr ◽  
Droplet Digital Pcr ◽  
Cellular Processes

Droplet digital PCR is the most robust method for absolute nucleic acid quantification. However, RNA is a very versatile molecule and its abundance is tissue-dependent. RNA quantification is dependent on a reference control to estimate the abundance. Additionally, in cancer, many cellular processes are deregulated which consequently affects the gene expression profiles. In this work, we performed microarray data mining of different childhood cancers and healthy controls. We selected four genes that showed no gene expression variations (PSMB6, PGGT1B, UBQLN2 and UQCR2) and four classical reference genes (ACTB, GAPDH, RPL4 and RPS18). Gene expression was validated in 40 acute lymphoblastic leukemia samples by means of droplet digital PCR. We observed that PSMB6, PGGT1B, UBQLN2 and UQCR2 were expressed ~100 times less than ACTB, GAPDH, RPL4 and RPS18. However, we observed excellent correlations among the new reference genes (p < 0.0001). We propose that PSMB6, PGGT1B, UBQLN2 and UQCR2 are housekeeping genes with low expression in childhood cancer.

Distinct gene expression profiles determine molecular treatment response in childhood acute lymphoblastic leukemia

Blood ◽  
2005 ◽  
Vol 105 (2) ◽  
pp. 821-826 ◽  
Author(s):  
Gunnar Cario ◽  
Martin Stanulla ◽  
Bernard M. Fine ◽  
Oliver Teuffel ◽  
Nils v. Neuhoff ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Treatment Response ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Treatment Resistance ◽  
Childhood Acute Lymphoblastic Leukemia ◽  
Childhood All ◽  
Resistance To Chemotherapy ◽  
Intrinsic Feature

AbstractTreatment resistance, as indicated by the presence of high levels of minimal residual disease (MRD) after induction therapy and induction consolidation, is associated with a poor prognosis in childhood acute lymphoblastic leukemia (ALL). We hypothesized that treatment resistance is an intrinsic feature of ALL cells reflected in the gene expression pattern and that resistance to chemotherapy can be predicted before treatment. To test these hypotheses, gene expression signatures of ALL samples with high MRD load were compared with those of samples without measurable MRD during treatment. We identified 54 genes that clearly distinguished resistant from sensitive ALL samples. Genes with low expression in resistant samples were predominantly associated with cell-cycle progression and apoptosis, suggesting that impaired cell proliferation and apoptosis are involved in treatment resistance. Prediction analysis using randomly selected samples as a training set and the remaining samples as a test set revealed an accuracy of 84%. We conclude that resistance to chemotherapy seems at least in part to be an intrinsic feature of ALL cells. Because treatment response could be predicted with high accuracy, gene expression profiling could become a clinically relevant tool for treatment stratification in the early course of childhood ALL.

Identification of Genomic Classifiers That Distinguish Induction Failure in T-Lineage Acute Lymphoblastic Leukemia in COG Study 9404.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 1826-1826
Author(s):  
Stuart S. Winter ◽  
Hadya Khawaja ◽  
Zeyu Jiang ◽  
Timothy Griffin ◽  
Barbara Asselin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Resistance ◽  
Acute Lymphoblastic Leukemia ◽  
Cell Lines ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Profiles ◽  
Resistant Cell ◽  
Acquired Drug Resistance ◽  
Induction Failure

Abstract The clinical features of age, white count, and presence of extramedullary disease cannot predict risk for induction failure (IF) in patients who present with T-cell acute lymphoblastic leukemia (T-ALL). On the basis of recent observations that gene expression profiles can distinguish clinicopathologic cohorts of patients with acute leukemia, we hypothesized that microarray analyses performed on diagnostic T-ALL bone marrow samples might identify a genomic classifier for IF patients. Using a case-control study design for children and young adults treated for T-ALL on Children’s Oncology Group Study 9404, we analyzed 50 cryopreserved T-ALL samples using Affymetrix U133A Plus 2 genechips, which have 54,000 genes, ESTs and genomic classifiers. Following RMA normalization, we used Prognostic Multi-array Analysis (PAM) to identify a 116-member genomic classifier that could accurately identify all 6 IF cases from the 44 patients who achieved remission. Within the IF cohort, 37 genes were up-regulated and 79 were down-regulated in comparison to other outcome groups. To further investigate the genetic mechanisms governing IF, we developed four cell lines with acquired drug resistance: Jurkat and Sup T1; each having resistance to daunorubicin (DNR) and asparaginase (ASP). Using a comparative analysis for fold-change in gene expression among 6 IF patients and the T-ALL DNR and ASP-resistant cell lines, we identified seven genes that were up-regulated, and another set of seven genes that were commonly down-regulated. To validate the potential use of our 116-member gene set in predicting IF in T-ALL, we tested our genomic classifier in 42 cases which were treated on COG study 8704 and hybridized to the Affymetrix U133Av.2 chip. Because only 85 probes were shared between U133A Plus 2 and U133Av. 2 chips, we employed shrunken class centroids to constrain our classifier to 25 rank-ordered probes. This smaller classifier correctly identified the single IF case in 8704, as well as another patient who was an early treatment failure, indicating that similar genomic classifiers may identify IF patients in different clinical trials. These results indicate that genetic profiling may be useful in prospectively identifying IF patients in T-ALL. In addition, we identified genes that were commonly upregulated in IF patients and T-ALL cell lines with intrinsic drug resistance.

The Heterogeneous Fusion Gene Landscape in Pediatric Acute Lymphoblastic Leukemia

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4081-4081
Author(s):  
Yanara Marincevic-Zuniga ◽  
Johan Dahlberg ◽  
Sara Nilsson ◽  
Amanda Raine ◽  
Jonas Abrahamsson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Acute Lymphoblastic Leukemia ◽  
Gene Fusion ◽  
Conflicts Of Interest ◽  
Fusion Gene ◽  
Lymphoblastic Leukemia ◽  
Fusion Genes ◽  
Fusion Transcripts ◽  
Pediatric All

Abstract Background: Next generation sequencing allows for the detection of expressed fusion transcripts across the transcriptome and has spurred the discovery of many novel chimeric transcripts in various cancers. Structural chromosomal rearrangements that lead to fusion transcripts are a hallmark of acute lymphoblastic leukemia (ALL) and serve as markers for diagnosis and stratification of pediatric ALL patients into prognostically relevant subgroups. Improved delineation of structural alterations in ALL could provide additional information for prognosis in ALL and for improved stratification of patients into treatment groups. Methods: To identify novel fusion transcripts in primary pediatric ALL cells we performed whole transcriptome sequencing of 134 BCP and T-ALL patient samples collected at diagnosis. Our study include samples from patients with the well-known ALL subtypes t(12;21)ETV6-RUNX1, high hyperdiploid (51-67 chromosomes), t(9;22)BCR-ABL1, 11q23/MLL and dic(9;20), in addition to patients with undefined karyotype or non-recurrent cytogenetic aberrations ("undefined" and "other") (n=58). FusionCatcher was used for the detection of somatic fusion genes, followed by a stringent filtering pipeline including gene fusion validation by Sanger sequencing in order to reduce the number of false positives. Principal component analysis (PCA) of patients with fusion genes was performed using genome wide gene expression levels and DNA methylation levels (Infinium HumanMethylation450 bead array). Results: We identified and validated 60 unique fusion events in almost half of the analyzed patients (n=69). Of the identified fusion genes, 60% have not previously been reported in ALL or other forms of cancer. The majority of the fusion genes were found in a single patient, but 23% were recurrent, including known ALL fusion genes (n=10) and novel fusion genes (n=7). We found that BCP-ALL samples displayed a higher number of validated fusion genes (54%) compared to the T-ALL samples (28%) moreover in BCP-ALL patients with "other" and "undefined" karyotypes, we detected fusion genes in 71% and 61% of the samples, respectively. High hyperdiploid patients had the lowest rate of validated fusion genes (24%) compared to the other well-known subtypes, where we detected subtype-associated fusion genes in 97% of cases. We also identified promiscuous fusion gene partners, such as ETV6, RUNX1, PAX5 and ZNF384 that fused with up to five different genes. Interestingly, PCA revealed molecularly distinct gene expression and DNA methylation signatures associated with these fusion partners. Conclusion: RNA-sequencing of pediatric ALL cells revealed a detailed view of the heterogeneous fusion gene landscape, identifying both known and novel fusion genes. By grouping samples based on recurrent gene fusion partners we are able to find shared gene expression and DNA methylation patterns compared to other subtypes of ALL, suggesting a shared molecular etiology within these distinct subgroups, offering novel insights into the delineation of fusion genes in ALL. Disclosures No relevant conflicts of interest to declare.

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