Deciphering the Epigenetic Landscape of Relapsed Pediatric Acute Lymphoblastic Leukemia

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 612-612
Author(s):  
Teena Bhatla ◽  
Roy Blum ◽  
Jinhua Wang ◽  
Courtney L Jones ◽  
Debra J. Morrison ◽  
...  

Abstract Introduction: Given the dismal outcome of relapsed pediatric ALL, there is an urgent need to identify underlying drug resistance mechanisms. We have previously discovered that chemosensitivity can be restored by epigenetic reprogramming (Bhatla et al, Blood 2012). Based on our prior work, we hypothesize that epigenetic changes play a major role in mediating chemoresistance and relapse in pediatric ALL. To develop a comprehensive map of relapse specific epigenetic alterations and to understand the impact of epigenetic alterations on the relapse specific gene expression signature, we have embarked on an unbiased genome-wide approach to map the location of key histone marks by chromatin immunoprecipitation sequencing (ChIP-seq) in diagnosis-relapse patient pairs with B-lymphoblastic leukemia. Methods: To date, we have performed ChIP-seq on 13 matched diagnosis/relapse cryopreserved bone marrow samples from patients enrolled on Children’s Oncology Group protocols. We assessed histone marks associated with promoters (H3K4me3, H3K9ac), enhancers (H3K27Ac) and those which are rather widely distributed in euchromatin and heterochromatin (H3K9me3, H3K27me3). 51-cycle single-end sequencing was performed using the Illumina HiSeq2000 Analyzer. Reads were aligned to the Human reference genome (assembly hg19) using the Burrows-Wheeler Alignment tool (BWA, v0.7.7) and post-preocessed using Samtools (v0.1.18). Enriched binding sites (“peaks”) were determined by the peak-calling algorithm, MACS2 (v2.0.10.20131216) using a q-value of 0.01 to define significance. Histone peak deposition on the promoters and enhancer regions were correlated with gene expression data from microarrays obtained from NCI’s TARGET initiative (Therapeutically Applicable Research to Generate Effective Treatment) on the same patients. Promoter regions were defined as 3 kb upstream and downstream of Transcription Start Site (TSS). Super-enhancers were identified by executing the ROSE algorithm (Hnisz et al, Cell 2013). Results: Promoter and enhancer region analysis was carried out only on activating histone marks (H3K4me3, H3K9ac and H3K27Ac) due to their expected uniform and enriched deposition in these regions. We observed that approximately 50% of the genes exhibited transcriptional activation or repression with respective concordant gain or loss of activating histone marks in the majority of patients, while the regulation of rest of the genes seemed independent of histone modification. Next, we sought to determine the impact of histone modification from diagnosis to relapse on the gene expression signature previously established in a cohort of 49 diagnosis-relapse patient pairs (Hogan et al, Blood 2011). Of 60 genes, 46 genes had one or more activating histone marks differentially deposited in the 6 kb promoter region in one or more of the patient samples analyzed and showed concordant expression. Furthermore, differentially up-regulated relapse specific genes such as FOXM1, FANCD2, PRMT7, CENPM and PTBP1 showed concordant deposition of activating histone marks in approximately 50% of relapse samples. Likewise, 5 down-regulated genes including SMEK2 and FOXP1 had concordant loss of these marks in approximately 50% of relapse samples. In order to identify the compendium of distal regulatory enhancers that may govern transcription, we generated chromatin state maps based on the histone modification H3K27ac, which depicts active enhancers. This analysis suggested that the super-enhancers deposited adjacent to genes having higher expression at diagnosis relative to relapse (eg. JARID2, TLE4, ETS1, EBF1 and CIITA), are implicated in transcriptional regulation. Likewise, genes involved in DNA replication and repair such as PHB and TOP3B and those involved in immune regulation such as CD34, IGLL1 and LMO2 were up-regulated with concordant gain of super-enhancers at the time of relapse. Conclusions: In a pilot ChIP-seq analysis of 13 ALL diagnosis/relapse pairs, we have identified several candidate genes, whose transcription appear to be epigenetically regulated and are markers of aggressive disease. Our study further implicates a potential use for epigenetic therapy for the treatment of relapsed ALL. Disclosures No relevant conflicts of interest to declare.

Oncotarget ◽  
2015 ◽  
Vol 6 (18) ◽  
pp. 16527-16542 ◽  
Author(s):  
Jin Wang ◽  
Jian-Qing Mi ◽  
Alexandra Debernardi ◽  
Anne-Laure Vitte ◽  
Anouk Emadali ◽  
...  

PLoS ONE ◽  
2012 ◽  
Vol 7 (7) ◽  
pp. e40934 ◽  
Author(s):  
Ilaria Iacobucci ◽  
Nunzio Iraci ◽  
Monica Messina ◽  
Annalisa Lonetti ◽  
Sabina Chiaretti ◽  
...  

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 1828-1828
Author(s):  
Claudia D. Baldus ◽  
Michael D. Radmacher ◽  
Clara D. Bloomfield ◽  
Peter Martus ◽  
Stefan Schwartz ◽  
...  

Abstract The ETS transcription factor ERG is expressed during early, normal T-cell development and shut off once T-cell commitment is complete. In addition, ERG is involved in various chromosomal translocations and implicated in oncogenic pathways in Ewing sarcoma, prostate cancer, and acute myeloid leukemia. Due to the specific regulation during T-cell development and its oncogenic potential we investigated the prognostic impact of ERG expression in T-lymphoblastic leukemia (T-ALL). We have shown in a cohort of 105 adult patients (pts) with newly diagnosed T-ALL that high ERG expression is an independent risk factor predicting inferior relapse-free survival (RFS, P=0.003; 5-year RFS: high ERG 34% vs. low ERG 72%; Baldus et al., JCO. Vol. 24 10/2006). Gene expression profiling (Affymetrix U133 plus 2.0) was performed on diagnostic bone marrow samples of 31 adult T-ALL pts to define an ERG related gene expression signature indicative of its pathogenic role in T-ALL. ERG mRNA expression levels were determined by quantitative real-time RT-PCR and pts were dichotomized at ERG′s median expression level into low ERG (n=16) and high ERG expressers (n=15). After a filtering step that reduced the number of probe sets to just over 10,000, we identified 39 probe sets (representing 35 known genes) differentially expressed (P<0.001) between low ERG and high ERG expressers. Compared to the low ERG group, 23 probe sets had significantly higher expression levels in the high ERG group, and 16 probe sets had significantly lower expression levels in the high ERG group. This signature contained three probe sets for ERG, each one showing about a 2-fold higher expression in the high ERG group as compared to the low ERG group; supporting our RT-PCR results. Of the overexpressed genes in the high ERG group, 5 genes were involved in DNA binding (MYO18A, DNAJC1) or transcription factor activity (ERG, ETV6, DIP). ERG and ETV6 are both ETS transcription factors and are frequently rearranged resulting in oncogenic fusion genes involved in leukemia. Moreover, ETV6 has been shown to be essential for the establishment of early hematopoiesis. In addition, genes with ATPase and ATP binding activity (ATP2B4, ATP2C1, MYO18A), potentially involved in signal transduction pathways were related to high ERG expression. Up-regulation of AKR1C1, a member of the aldo-keto reductase superfamily, was demonstrated in the high ERG group. Interestingly, overexpression of AKR1C1 has been found in various types of cancers and was correlated with tumor progression and resistance to chemotherapy. In conclusion, an ERG associated gene expression signature was identified in T-ALL and provides insights into ERG co-regulated genes. The delineation of these ERG related pathways may pinpoint mechanisms responsible for the more malignant phenotype resulting in the inferior outcome of T-ALL with high ERG expression.


Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2523-2523
Author(s):  
Katalin Gyurina ◽  
Bettina Kárai ◽  
Anikó Ujfalusi ◽  
Zsuzsanna Hevessy ◽  
Gábor Barna ◽  
...  

Background Leukemic B-cell precursor (BCP) lymphoblasts were identified as a novel expression site for coagulation factor XIII subunit A (FXIII-A). FXIII-A expression determined by flow cytometry (FC) exhibited characteristically distinct expression patterns in subgroups of lymphoblasts. The FXIII-A negative subgroup was significantly associated with the 'B-other' genetic category and had an unfavorable disease outcome. Methods RNA was extracted from bone marrow lymphoblasts of 42 pediatric patients with BCP-acute lymphoblastic leukemia (ALL). FXIII-A expression was determined by multiparameter FC. Genetic diagnosis was based on conventional cytogenetic method and fluorescence in situ hybridization. Affymetrix GeneChip Human Primeview array was used to analyze global expression pattern of 28869 well-annotated genes. Microarray data were analyzed by Genespring GX14.9.1 software. Gene Ontology (GO) analysis was performed using Cytoscape 3.4.0 software with ClueGO application. Selected differentially expressed (DE) genes were validated by RT-Q-PCR. Results We demonstrated, for the first time, the general expression of F13A1 gene in pediatric BCP-ALL samples. The intensity of F13A1 expression corresponded to the expression of FXIII-A protein, as determined by FC. We observed three well-defined categories of FXIII-A protein expression: FXIII-A negative (<20% FXIII-A positive blasts), FXIII-A dim (20-80% FXIII-A positive blasts), and FXIII-A bright (>80% FXIII-A positive blasts). The intensity of the FXIII-A expression increased continuously, which excluded the existence of a distinct FXIII-A negative and a FXIII-A bright subpopulation within the FXIII-A dim subgroup (Image 1/A). These three subgroups defined three characteristic and distinct gene expression signatures detected by Affymetrix oligonucleotide microarrays. There were 26 DE genes found when comparing the FXIII-A negative with the FXIII-A bright subgroup. The FXIII-A dim vs. bright comparison resulted in 155 DE genes and there were 88 DE genes identified between the FXIII-A negative and dim subgroups (Image 1/B). Expression of F13A1 gene was detected and readily validated by RT-Q-PCR in every sample. Intensity of gene expression; however, was characteristically different among samples of the three different FXIII-A protein expression subgroups with an increasing intensity in terms of relative fold-changes measured by RT-Q-PCR from the FXIII-A negative, through dim to bright subgroups. We selected 13/45 genes based on fold-change results detected by microarray, whereas an additional 32/45 genes were selected according to enriched functional categories of potential interest as defined by the GO analysis. Relative gene expression intensity of ANGPTL2, EHMT1 FOXO1, HAP1, NUCKS1, NUP43, PIK3CG, RAPGEF5, SEMA6A, SPIN1, TRH, and WASF2, validated by RT-Q-PCR according to the FXIII-A status, followed the pattern of intensity of the expression of the F13A1 gene. Of these genes, ANGPTL2, EHMT1 FOXO1, HAP1, NUCKS1, PIK3CG, RAPGEF5, SEMA6A, SPIN1, TRH, and WASF2 appear to have a role in leukemia and other forms of cancer. NUP43 has not yet been shown to be associated with any forms of human cancer in contrast to other members of the NUP gene family. PLAC8 which is a trophoblast lineage marker was most intensively expressed in the FXIII-A dim subgroup. This gene has been shown to be aberrantly activated in various types of cancer arising in mammals and mammalian cancer cell lines, but not in any subtype of human ALL (Image 1/C). Gene expression signature of the FXIII-A negative subgroup showed an overlap with the signature of 'B-other' samples. We identified 14 DE genes by microarray overlapping with DE genes of the 'B-other' subgroup defined by the COALL Group. DFFA, GIGYF1, GIGYF2, and INTS3 were upregulated and CD3G was downregulated in the 'B-other' subgroup. Conclusions We described differential expression of genes not shown previously to be associated with pediatric BCP-ALL. Gene expression signature according to FXIII-A protein expression status defined three novel subgroups of pediatric BCP-ALL. Validated genes proved biologically and clinically relevant. Multiparameter FC appears to be an easy-to-use and affordable method to help in selecting FXIII-A negative patients who require a more elaborate and expensive molecular genetic investigation to design precision treatment. Figure Disclosures No relevant conflicts of interest to declare.


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