scholarly journals Proteomics in Pediatric Acute Myeloid and T-Cell Lymphoblastic Leukemia: Shared Individual Protein Expression Patterns Co-Cluster into Overall Distinct Combinations

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 35-36
Author(s):  
Fieke W Hoff ◽  
Anneke D. van Dijk ◽  
Yihua Qiu ◽  
Eveline S. de Bont ◽  
Steven M. Kornblau ◽  
...  
Keyword(s):  
Protein Expression ◽  
Clinical Utility ◽  
Lymphoblastic Leukemia ◽  
Expression Patterns ◽  
The Other ◽  
Differentially Expressed ◽  
Protein Clusters ◽  
Cd34 Cells ◽  
Pediatric Aml ◽  
Acute Myeloid

INTRODUCTION: Pediatric acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) are heterogeneous diseases mediated by changes in protein expression. As most chemotherapeutic agents target proteins, and because overall survival of pediatric AML is far inferior to both pre-B and T-ALL, we aimed to compare the proteomic landscape of pediatric T-ALL and AML, with the goal of determining common AML-T-ALL pathways that are potentially targetable with novel agents. METHODS: Reverse phase protein arrays (RPPA) analysis was used to measure protein expression in 858 acute leukemia samples (358 T-ALL and 500 AML, 723 pediatric (< 18 yrs.), 135 adults (≥18 yrs.)) and 61 normal CD34+ samples using 270 validated antibodies. Expression levels were normalized against CD34+ cells. Proteins were allocated into 30 functionally related subgroups (Protein Functional Group (PFG)). A progeny clustering algorithm was applied to each PFG to search for strong correlations between proteins and to identify an optimal number of Protein Clusters (PC). Block clustering identified PC that recurrently co-occurred together (Protein Constellation (CON)) and patients that expressed similar combination of CON were defined as Protein Signature (SIG). Proteins that were differentially expressed were identified using the Student's t-test or ANOVA, with a Bonferroni adjusted p-value (0.05/ 270 = 0.00019)). RESULTS: Of the 270 analyzed proteins, 131 proteins (49%) were differentially expressed between T-ALL and AML; 60 were higher in T-ALL, 71 in AML. Similar to our previous analysis in adult AML and ALL, cell cycle regulators (CDKN1A, CDKN1B) and 2 of the 5 histone marks (H3K36Me3 & H3K4Me3) were higher expressed in T-ALL compared to AML. Heat shock proteins (HSP90AA1_B1, HSPA1A_L, HSPB1 and HSPB1-pSer82) were higher in AML as well as translation proteins EIF2S1, EIF4E and EIF4EBP1 and ribosomal proteins RPS6-pSer235_236 and RPS6KB1, while expression of the translation inhibitory proteins EIF2S1-pSer51 and EIF2AK2-pThr451 was lower in AML compared to T-ALL. Next, cluster analysis in the context of 30 PFG resulted in 133 PC. The majority (n=102) of PC were expressed in both diseases, 30 PC (22.6%) were AML-specific, and only one PC was specific to T-ALL (characterized by high CDKN1A, CDKN1B and CCND1, but low WEE1, CCNB1 and RB1-pSer). Co-clustering of the 133 PC identified 14 CON that formed 17 SIG. Three CON (5, 9, 10) were associated with AML, 2 with T-ALL (2, 13) and 8 CON were observed in both diseases. In contrast, 15 of SIG were associated with either T-ALL or AML, and two SIG (9, 10) included a mixture of both diseases (P < 0.001, annotation bar Figure 1 "Disease")). SIG were associated with gender (P < 0.001), but not with CNS-status and ethnicity (Hispanic vs. non-Hispanic). No age-specific (kids vs. adults) signatures were observed. For each SIG and CON, proteins significantly higher or lower expressed compared to the normal CD34+ cells were identified. CONCLUSIONS: This study provides support for our previous hypothesis that pediatric T-ALL and AML can be characterized by recurrent protein expression patterns. While most PC and CON were found in both diseases, SIG (i.e. combinations of protein expression patterns) were specific to either T-ALL or AML. We found similar results when comparing B-ALL to AML in adults. Shared CON indicate that there are common protein expression patterns between pediatric T-ALL and AML. Proteins or pathways with similar utilization (e.g. CON3, 5) in both diseases may allow for information on clinical utility from one disease to be applicable to the other. Those with differential utilization are likely to be uninformative with respect to clinical utility in the other disease. Figure. "MetaGalaxy" analysis for pediatric AML and T-ALL. Each row represents one protein clusters (n = 133), each column represents one patient (n = 858). Blue indicates membership for that particular protein cluster. Annotation bar shows strong correlation with disease (yellow = T-ALL, blue = AML). No associations were seen for age (blue = adult, pink = pediatric) or Ethnicity (blue = Hispanic, yellow = non-Hispanic). Figure 1 Disclosures No relevant conflicts of interest to declare.

scholarly journals Proteomic Landscape of Acute Leukemia: A Comparison between ALL and AML in Adults

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1461-1461
Author(s):  
Fieke W Hoff ◽  
Yihua Qiu ◽  
Wendy Hu ◽  
Amina A Qutub ◽  
Eveline S. de Bont ◽  
...  
Keyword(s):  
Protein Expression ◽  
Finite Number ◽  
Clinical Utility ◽  
Expression Patterns ◽  
Optimal Number ◽  
The Other ◽  
Differential Proteomics ◽  
Data Set ◽  
Functional Relationships ◽  
Protein Clusters

Background: Acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) are both heterogeneous diseases. The underlying changes that results in leukemia are due to developmental, genetic, or environmental effects, and are mostly mediated by changes in protein expression or modification. We hypothesize that there is a finite number of patterns of protein expression and protein pathway utilization, whose perturbations result in the hallmarks of cancer. In this study we performed differential proteomics of ALL and AML, with the goal to understand the underlying (disease-specific) cellular changes of AML and ALL, as well as to identify protein utilizations that are shared between AML and ALL. Method: Reverse phase protein arrays (RPPA) was generated for 230 strictly validated antibodies using samples from 130 ALL and 241 AML patient samples, and 10 CD34+ samples from healthy controls. Expression levels were normalized relative to the normal CD34+ cells. Due to some inherent considerations of the traditional hierarchical clustering (HC) (e.g. HC weighs all proteins equally in all situations, HC is agnostic to all known functional relationships between proteins, HC requires that all data be considered and placed into a group), expression data was analyzed using the MetaGalaxyanalysis. This approach starts with the allocation of proteins into 31 protein functional groups (PFG). Progeny clustering was applied to identify an optimal number of protein clusters within each PFG. Block clustering identified protein clusters that recurrently co-occurred (protein constellation (CON)), and for each subgroup of patients that expressed similar combinations of protein constellations (patient signature (SIG)). Proteins that were differentially expressed were identified using the student's T-test or ANOVA, and a Bonferroni adjusted P-value (0.05/ 230 = 0.00021739). Results: The MetaGalaxy approach identified a substantial amount of structure across the data set (Figure 1), with an optimal number of 12 CON (horizontally) and 13 SIG (vertically). The majority of SIG were correlated with either ALL (SIG 1, 3, 4, 5) or AML (SIG7, 8, 9, 10, 11, 12) (annotation bar Figure 1), although SIG2, 6, and 13 contained a mixture of both (P< 0.001). Similarly, CON1, 2, 3 were mostly associated with ALL, CON6, 8, 9 and 11 with AML, while CON4, 5, 7 and 10 were observed in both diseases. To understand more about the protein signaling utilizations deregulated proteins were identified for each CON and SIG. For example, CON1 was associated with PFG Apoptosis Occurring (e.g. CASP9-cl330, PARP1), autophagy (e.g. PRKAA1_2, PRKAA1_2-pTyr172), and apoptosis BH3 (e.g. BCL2, BAD-pSer112). In ALL, signature membership of CON5 was associated with a superior overall survival and complete remission duration (P= 0.016; P= 0.035). CON4 was associated with a high rate of early deaths (P = 0.041), but not with a higher frequency of relapses (P = 0.520). In AML, signatures were predictive for OS and CR, with SIG7, 10, 12 as being favorable vs SIG2, 6, 9, 11, 13 as being unfavorable. CON4 was predictive of late relapses (≥ 2 yr.). Interestingly, CON5 was associated with a trend toward inferior CR duration in AML (P= 0.093), whereas this CON5 was favorably prognostic in ALL. Conclusion: In this study we confirmed our original hypothesis that there is a finite number of SIG in ALL and AML. Although, ALL and AML are both hematological diseases that share many molecular events, SIG and CON membership were significantly correlated with ALL and AML, confirming that protein expression patterns for the majority of cases of ALL ≠ AML. However, given that some CON were associated with both disease, this indicates that common features between both also exist. Proteins or pathways with similar utilization in both diseases may allow for information on clinical utility from one disease to be transitive to the other, while those with differential utilization are likely to be uninformative with respect to clinical utility in the other disease. Figure 1. MetaGalaxy analysis for AML and ALL. Each row represents one protein clusters (n = 123), each column represents one patient (n = 371). Blue indicates membership for that particular protein cluster. Annotation bar shows strong correlation with disease (yellow = B-ALL, pink = T ALL, blue = AML). Figure Disclosures No relevant conflicts of interest to declare.

scholarly journals Cite-Seq Reveals Distinct Patterns and Potential Mechanisms of Relapse in Pediatric AML

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3458-3458
Author(s):  
Tsz-Kwong Man ◽  
Mohammad Javad Najaf Panah ◽  
Jessica L. Elswood ◽  
Pavel Sumazin ◽  
Michele S. Redell
Keyword(s):  
Protein Expression ◽  
Single Cell ◽  
Actin Polymerization ◽  
Conflicts Of Interest ◽  
Limit Of Detection ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Cell Types ◽  
Differentially Expressed ◽  
Pediatric Aml

Abstract Introduction - Acute myeloid leukemia (AML) is an aggressive disease with a relapse rate of approximately 40% in children. Progress in improving cure rates has been slow, in part because AML is very heterogeneous. Molecular studies consistently show that most cases are comprised of distinct subclones that diminish or expand over the course of therapy. Single-cell profiling methods now allow parsing of the leukemic population into subsets based on gene and/or protein expression patterns. We hypothesized that comparing the features of the subsets that are dominant at relapse with those that are dominant at diagnosis would reveal mechanisms of treatment failure. Methods - We profiled diagnosis-relapse pairs from 6 pediatric AML patients by Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq). All patients were treated at Texas Children's Cancer Center and consented to banking of tissue for research. CITE-Seq was performed by Immunai (New York, NY) using a customized panel of 65 oligonucleotide-tagged antibodies, the 10x Genomics Chromium system for single-cell RNA library generation, and the Novaseq 6000 for sequencing. After data cleanup and normalization, clustering by scRNA-seq was done using the Seurat package. Cell-type identification of clusters was facilitated by published healthy bone marrow scRNA-seq datasets (van Galen et al, Cell 2019). Differentially expressed genes (DEGs) and proteins (DEPs) between diagnosis and relapse were determined using Wilcoxin ranked sum tests. Results - We generated single-cell transcriptomes for a total of 28,486 cells from 12 samples, with a mean of 2373 cells and 1416 genes per sample. Samples were integrated with batch effect correction, producing 30 distinct clusters (cell types) in total (Figure 1A). Cell types with expression profiles consistent with lymphocytes and erythroid precursors were identified in multiple patients, whereas AML cell types tended to be specific to individual patients (Figure 1B). For patients TCH1, TCH2 and TCH3, the most abundant cell types at diagnosis were rare at relapse, and cell types that were rare at diagnosis became dominant at relapse. For these 3 cases, we identified DEGs between the dominant diagnosis cell types and dominant relapse cell types. We found 18 genes that were upregulated at relapse in at least 2 of the cases. Several genes related to actin polymerization were enriched (ARPC1B, ACTB, PFN1), possibly reflecting an enhanced capacity for adhesion and migration. Also of note, macrophage migration inhibitory factor (MIF) and its receptor CD74 were upregulated at relapse, suggesting a role in chemoresistance. For patients TCH4, TCH5 and TCH6, the same cell types that were abundant at diagnosis were also abundant at relapse, and few genes were significantly altered between diagnosis and relapse in multiple cases. Only SRGN, which encodes the proteoglycan serglycin, and GAPDH were altered in 2 of these 3 cases, and both were downregulated at relapse. We performed similar comparisons to identify proteins that were differentially expressed between diagnosis and relapse pairs. The number of DEPs between the dominant diagnosis and relapse cell types ranged from 0 (TCH1 and TCH6) to 5 (TCH2). The only protein altered in more than one case was CD7, which was enriched at relapse in TCH2, TCH3 and TCH4. Conclusions - From CITE-Seq profiling of 6 pediatric AML cases we identified two distinct patterns of relapse. For 3 cases, relapse occurred by expansion of a subset that was small but present at diagnosis. Enrichment of genes associated with adhesion and survival signaling suggests that these cells survived because they were well-equipped to take advantage of interactions with the microenvironment. For 3 other cases, the population that was dominant at diagnosis persisted and expanded at relapse with few substantial changes in gene or protein expression profiles. This pattern suggests that these AML cells were a priori equipped to survive chemotherapy, even though bulk disease levels were transiently reduced below the limit of detection. Most profiled proteins did not change substantially between diagnosis and relapse. An exception is CD7, which was enriched at relapse in 50% of our cases and represents a potential therapeutic target. Analysis of more cases will refine these relapse patterns, reveal potential mechanisms of chemoresistance and inform the development of novel therapies. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

scholarly journals Proteomic Landscape of De Novo Pediatric Acute Myeloid Leukemia

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 294-294
Author(s):  
Fieke W Hoff ◽  
Yihua Qiu ◽  
Wendy Hu ◽  
Amina A Qutub ◽  
Alan S Gamis ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Protein Expression ◽  
Board Of Directors ◽  
Myeloid Leukemia ◽  
Expression Patterns ◽  
Advisory Committees ◽  
Pediatric Acute Myeloid Leukemia ◽  
Protein Clusters ◽  
Protein Signatures ◽  
Acute Myeloid

Abstract Background: Many genetic drivers that are implicated in disease pathology and risk stratification have been identified for pediatric acute myeloid leukemia (pedi-AML). However, only a minority have been exploited for therapeutic interventions and most of the identified genetic events currently lack targeted therapy to address the mutations. The combined consequences of genetic and epigenetic events culminate in a net effect manifested at the protein level and most of the chemotherapies target proteins. Yet little is known about the proteomic landscape of pedi-AML. Methods: Reverse Phase Protein Array (RPPA) was performed with 291 strictly validated antibodies to determine the protein expression levels of bulk leukemic cells from 505 pedi-AML patient samples that were collected prior to therapy. All patients participated on the COG AAML1031 Phase 3 clinical trial, that compared standard therapy (ADE) to ADE plus bortezomib (ADE+B). Proteins were allocated into 31 protein functional groups (PFG) (e.g. cell cycle, apoptosis) to analyze proteins in relation to related proteins. Progeny clustering was performed to identify patients with correlated protein expression patterns within each PFG (protein cluster). Block clustering searched for protein clusters that recurrently co-occurred (protein constellation), and for subgroups of patients that expressed similar combinations of protein constellations (protein signatures). Protein signatures were correlated with known cytogenetics and mutational state. Results: For each PFG, cluster analysis identified an optimal number of protein clusters, resulting in a total of 120. From this we constructed 11 protein constellations (PRCON) and 10 protein signatures (SIG) (Fig. 1A). A training set (n=334) and test set (n=171) showed high reproducibility (Pearson's X2; p < 0.001). SIG were prognostic for event-free survival (EFS) (p = 0.029), with a favorable EFS for SIG 1, 2, 3, 5, 7 & 9, and an unfavorable EFS for SIG 4, 6, 8, 10. Notably, patients that formed SIG 3 had a significant better EFS after receiving ADE+B than patients that received ADE (p = 0.039) (Fig. 1B). This SIG was highly enriched for CEBPA mutated cases; 43% vs. 9% overall (p < 0.001). SIG were associated with cytogenetic aberrations (p < 0.001) and mutational state, as well as with the traditional risk groups (p < 0.001). For example, t(8;21) was overrepresented in SIG 9 (39% vs. 16% overall) and MLL-rearrangement in SIG 6 (61% vs. 19% overall). Multivariate analysis was performed using variables definable at time of diagnosis and known prognostic factors. This resulted in a final model including unfavorable protein SIG together with low risk cytogenetics and NPM1 mutation state as independent prognostic factors, suggesting that proteins add to known prognostic factors. Proteomics could also identify aberrantly expressed proteins within each SIG compared to normal CD34+ cells. This recognized 31 proteins as universally down regulated (e.g. CDKN1A, PPP2R2A) and 13 as universally up regulated (e.g. PIK3CA, NCL). Many other proteins were different between SIG, and thus potentially targetable in particular patient groups: high KIT (SIG 3, 4 & 5), high BCL2 (SIG 3, 4, 5, 6, 9 & 10) and high BRD4 (SIG 6). SIG 1 & 2 both had a very characteristic expression pattern and were most distinct from the others. IGFR1, IGFR1.pY1135, AKT3, SMAD5.pS463-7 and RICTOR.pT1135 were all high, whereas they were low in most other SIG. Conversely, STAT3 was normal in SIG 1&2, but changed in the other SIG. SIG 6, 7 & 8 also had relatively similar expression patterns, but had different outcomes. Compared to SIG 6 & 8, SIG 7 had higher GAB2 and MCL1 and lower RAD50, ERCC1. Conclusions: Pedi-AML is characterized by a finite number (n=10) of recurrent protein signatures. SIG were only partially correlated with cytogenetics and mutation state, indicating that protein expression could add to genetics in the process of risk stratification. We identified SIG that did well vs. SIG that did not, independent of known risk factors, and identified a group of patients that could potentially benefit from ADE+B. Recognition of differentially expressed proteins suggest potential targets for combinational treatment (Hoff et al. ASH HSF1 abstract). Figure 1. A. Identification of 11 PRCON (horizontally) and 10 SIG (vertically). B. SIG were prognostic for EFS. C. SIG 3 significantly benefitted from ADE+B (green) compared to ADE alone (blue). Disclosures Kolb: Roche- Genentech: Membership on an entity's Board of Directors or advisory committees; Servier: Membership on an entity's Board of Directors or advisory committees.

Gene Expression Profiling Identifies Distinct Subclasses in Core Binding Factor Acute Myeloid Leukemia.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 673-673
Author(s):  
Lars Bullinger ◽  
Stephan Kurz ◽  
Konstanze Dohner ◽  
Claudia Scholl ◽  
Stefan Frohling ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Expression Patterns ◽  
The Other ◽  
Rank Test ◽  
Core Binding Factor ◽  
Log Rank Test ◽  
Binding Factor ◽  
Acute Myeloid

Abstract Recurrent cytogenetic aberrations have been shown to constitute markers of diagnostic and prognostic value in acute myeloid leukemia (AML). However, even within well-defined cytogenetic AML subgroups with an inv(16) or a t(8;21) we see substantial biological and clinical heterogeneity which is not fully reflected by the current classification system. Therefore, we profiled gene expression in a large series of adult AML patients with core binding factor (CBF) leukemia [inv(16) n=55, t(8;21) n=38] using a whole genome DNA microarray platform in order to better characterize this disease on the molecular level. By unsupervised hierarchical clustering based on 8556 filtered genes we observed that our CBF leukemia samples separated primarily into three different subgroups. While two of the subgroups were characterized by inv(16) and t(8;21) cases, respectively, the third subgroup contained a mixture of both cytogenetic groups. There was no obvious correlation with known secondary aberrations or molecular marker like FLT3-ITD, NRAS and KIT mutations between the cases in the mixed subgroup and the others. However, the newly defined inv(16)/t(8;21)-subgroup (n=35) was characterized by distinct clinical behavior with shorter overall survival times (P=0.029; log rank test) compared to the other two groups. Unsupervised analyses within the inv(16) and t(8;21) cases also revealed corresponding inv(16) and t(8;21) subgroups with a strong trend towards inferior outcome (P=0.11 and P=0.09, respectively; log rank test). Since the primary translocation/inversion events themselves are not sufficient for leukemogenesis, distinct patterns of gene expression found within each of these cytogenetic groups may suggest alternative cooperating mutations and deregulated pathways leading to transformation. Therefore, we performed a supervised analysis to determine the characteristic gene expression patterns underlying the cluster-defined subgroups. We identified 528 genes significantly differentially expressed between the newly defined inv(16)/t(8;21)-subgroup and the other CBF cases (significance analysis of microarrays, false discovery rate &lt; 0.001). Potential candidates for cooperating pathways characterizing the mixed inv(16)/t(8;21)-subgroup included e.g. AVO3, a member of the mTOR pathway, oncogene homologs like LYN and BRAF, as well as FOXO1A and IL6ST which have been previously identified to correlate with outcome in AML (Bullinger et al., N Engl J Med350:1605, 2004). In conclusion, while the observed signatures remain to be validated for their functional relevance, both supervised and unsupervised methods provide numerous insights into the pathogenesis of CBF AML, identifying clinically significant patterns of gene expression, as well as candidate target genes involved in leukemogenesis.

Differential Expression of ERG Isoforms in Acute Myeloid and T-Lymphoblastic Leukemia Is Regulated by DNA-Methylation.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 2126-2126
Author(s):  
Claudia D. Baldus ◽  
Cornelia Schlee ◽  
Julia Thibaut ◽  
Sandra Heesch ◽  
Arend Bohne ◽  
...  
Keyword(s):  
Differential Expression ◽  
Cpg Island ◽  
Lymphoblastic Leukemia ◽  
Prognostic Significance ◽  
Rt Pcr ◽  
Acute Leukemias ◽  
Aberrant Expression ◽  
Cd34 Cells ◽  
Exon 4 ◽  
Acute Myeloid

Abstract The oncogenic ETS transcription factor ERG is involved in various cellular pathways including developmental regulation, proliferation, and differentiation. In hematopoiesis ERG plays a specific role during normal T-cell differentiation showing high expression levels in stem cells and down regulation in the progenitor compartment. In this regard, it is intriguing that aberrant expression of ERG was found in a subset of patients with acute T-lymphoblastic leukemia (T-ALL) and was associated with an inferior outcome. Furthermore, high level ERG expression was of adverse prognostic significance in patients with newly diagnosed acute myeloid leukemia (AML), thus highlighting ERG’s potential role in myeloid as well as T-lineage leukemogenesis. ERG3 (NM_182918) and ERG2 (NM_004449) represent the main isoforms and show abundant expression in myeloid and lymphoid hematopoietic progenitor cells. The expression pattern of specific ERG isoforms in acute leukemias has yet to be investigated. To further elucidate the nature of aberrant ERG expression we have determined the existence and transcriptional regulation of ERG isoforms in pretreatment bone marrow samples of adult T-ALL (n=21) and AML (n=20) patients as well as in normal CD34+ hematopoietic cells of healthy volunteers (n=5). 5′RACE revealed the presence of a new ERG isoform (ERG3Δex12) characterized by expression of exon 5 and absence of exon 12. Expression of ERG3Δex12 was verified by RT-PCR in AML, T-ALL, and CD34+ cells. In addition, real-time RT-PCR showed concomitant expression of the two main isoforms ERG2 and ERG3 in AML and normal CD34+ cells. In contrast, T-ALL patients lacked expression of ERG isoforms harboring exon 4 (ERG2). Promoter analyses of ERG2 and ERG3 revealed the presence of two CpG islands in the ERG2 promoter region, whereas no CpG island was predicted in the ERG3 promoter. Bisulfit conversion of genomic DNA and sequencing of cloned PCR products revealed a significantly higher degree of methylation of CpG island 2 in T-ALL samples (median: 86.4%, range: 16.0 – 98.8%) as compared to AML (median: 38.1%, range: 10.9 – 60.7%; P-value=0.0002 - two sided T-test). As for CpG island 1, CD34+ cells had the lowest rate of methylation in CpG island 2 (median: 7.7%, range: 2.4 – 20.7%). Thus, the differential expression of ERG isoforms is mediated by epigenetic silencing of exon 4 containing transcripts in T-ALL. In conclusion, the identification of the new ERG isoform (ERG3Δex12) suggests the association with different partners as the central exons, including exon 12, guide the interaction with different proteins. Furthermore, the distinct expression of specific ERG transcripts controlled by methylation adds to the complexity of ERG directed downstream pathways in different leukemic subtypes.

Mirnas and Gene Expression Profiles in CD34+ Cells Are Dependent On the Source of Progenitor Cells Employed in Transplantation.

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 3020-3020
Author(s):  
Alicia Báez ◽  
Beatriz Martin-Antonio ◽  
Concepción Prats-Martín ◽  
Isabel Álvarez-Laderas ◽  
María Victoria Barbado ◽  
...  
Keyword(s):  
Gene Expression ◽  
Conflicts Of Interest ◽  
Reference Group ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Differentially Expressed ◽  
Expression Levels ◽  
Cd34 Cells ◽  
Different Sources

Abstract Abstract 3020 Introduction: Hematopoietic progenitors cells (HPCs) used in allogenic transplantation (allo-HSCT) may have different biological properties depending on their source of origin: mobilized peripheral blood (PB), bone marrow (BM) or umbilical cord (UC), which may be reflected in miRNAs or gene expression. The identification of different patterns of expression could have clinical implications. The aim of this study was to determine differences in miRNAs and gene expression patterns in the different sources of HPCs used in allo-HSCT. Materials and Method: CD34 + cells were isolated by immunomagnetic separation and sorting from 5 healthy donors per type of source: UC, BM and PB mobilized with G-CSF. A pool of samples from PB not mobilized was used as reference group. We analyzed the expression of 375 miRNAs using TaqMan MicroRNA Arrays Human v2.0 (Applied Biosystems), and gene expression using Whole Human Genome Oligo microarray kit 4×44K (Agilent). The expression levels of genes and miRNAs were obtained by the 2-ΔΔCTmethod. From expression data hierarchical clustering was performed using the Euclidean distance. To identify genes and miRNAs differentially expressed between the different sources of HPCs statistical Kruskal Wallis test was applied. All analysis were performed using the Multiexperiment Viewer 4.7.1. The function of the miRNAs and genes of interest was determined from the various databases available online (TAM database, Gene Ontology and TargetScan Human). Results: Forty-two miRNAs differentially expressed between the different sources were identified. As compared to BM or UC, in mobilized PB most miRNAs were overexpressed, including the miRNA family of miR515, which is characteristic of embryonic stem cells. On the other hand, 47 genes differentially expressed between the different sources were identified. Interestingly, a similar pattern of expression was observed between movilized PB and UC as compared to BM. Interestingly, 13 of these genes are targets of the miRNAs also identified in this study, which suggests that their expression might be regulated by these miRNAs. Conclusion: There are significant differences in miRNAs and gene expression levels between the different sources of HPCs Disclosures: No relevant conflicts of interest to declare.

scholarly journals Clinical relevance of proteomic profiling in de novo pediatric acute myeloid leukemia: a Children’s Oncology Group study

Haematologica ◽  
2022 ◽  
Author(s):  
Fieke W. Hoff ◽  
Anneke D. Van Dijk ◽  
Yihua Qiu ◽  
Chenyue W. Hu ◽  
Rhonda E. Ries ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Protein Expression ◽  
Myeloid Leukemia ◽  
De Novo ◽  
Expression Patterns ◽  
Chemotherapeutic Agents ◽  
Classification Systems ◽  
Proteomic Profiling ◽  
Pediatric Acute Myeloid Leukemia ◽  
Acute Myeloid

Pediatric acute myeloid leukemia (AML) remains a fatal disease for at least 30% of patients, stressing the need for improved therapies and better risk stratification. As proteins are the unifying feature of (epi)genetic and environmental alterations, and are often targeted by novel chemotherapeutic agents, we studied the proteomic landscape of pediatric AML. Protein expression and activation levels were measured in 500 bulk leukemic patient samples and 30 control CD34+ samples, using the reverse phase protein arrays with 296 strictly validated antibodies. The multi-step “MetaGalaxy” analysis methodology was applied and identified nine protein expression signatures (PrSIG), based on strong recurrent protein expression patterns. PrSIGs were associated with cytogenetics and mutational state, and with both favorable or unfavorable prognosis. Analysis based on treatment (i.e., ADE vs. ADE plus bortezomib (ADEB)) identified three PrSIGs that did better with ADEB vs. ADE. When PrSIGs were studied in the context of genetic subgroups, PrSIGs were independently prognostic after multivariate analysis, suggesting a potential value for proteomics in combination with current classification systems. Proteins with universally increased (n=7) or decreased (n=17) expression were observed across PrSIGs. Expression of certain proteins significantly differentially expressed from normal could be identified, forming a hypothetical platform for personalized medicine.

scholarly journals A Strong Expression of CD44-6v Correlates With Shorter Survival of Patients With Acute Myeloid Leukemia

Blood ◽  
1998 ◽  
Vol 91 (9) ◽  
pp. 3401-3413 ◽  
Author(s):  
S. Legras ◽  
U. Günthert ◽  
R. Stauder ◽  
F. Curt ◽  
S. Oliferenko ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Protein Expression ◽  
Myeloid Leukemia ◽  
Surface Glycoprotein ◽  
Leukemic Cells ◽  
Cell Surface Glycoprotein ◽  
Variant Isoforms ◽  
Cd34 Cells ◽  
First Time ◽  
Acute Myeloid

CD44 is a ubiquitous cell-surface glycoprotein that displays many variant isoforms (CD44v) generated by alternative splicing of exons 2v to 10v. The expression of variant isoforms is highly restricted and correlated with specific processes, such as leukocyte activation and malignant transformation. We have herein studied CD44v expression in acute myeloid leukemia (AML) and, for comparison, in normal myelopoiesis. Protein expression of total CD44 and of CD44-3v, -6v, and -9v isoforms has been measured using specific monoclonal antibodies and flow cytometry. The composition of variant exon transcripts has been analyzed by semi-quantitative reverse transcriptase-polymerase chain reaction followed by Southern hybridization with exon-specific probes. Our data show that (1) CD44-6v isoforms are expressed on 12.0% ± 2.5% of normal CD34+ cells; this expression is sharply upregulated through monopoiesis and, inversely, downregulated during granulopoiesis. Also, CD44-3v and CD44-9v isoforms are detected on 10% and 14% of normal monocytes, respectively. (2) Sixty-nine from a total of 95 AML patients display a variable proportion (range, 5% to 80%) of CD44-6v+ leukemic cells. (3) A shorter overall survival characterizes the group of AML patients displaying more than 20% of CD44-6v+ leukemic cells (8 months v 18 months, P < .02). These data suggest, for the first time, that the protein expression of CD44-6v containing isoforms may serve as a new prognostic factor in AML.

scholarly journals Immunoexpression of CD34, CD117, and p53 in Hypocellular Bone Marrow Disorders

2021 ◽  
Author(s):  
Pooja Sharma ◽  
Anshu Palta ◽  
Anita Tahlan ◽  
Manveen Kaur ◽  
Ram Singh
Keyword(s):  
Bone Marrow ◽  
Myeloid Leukemia ◽  
The Other ◽  
Immunohistochemical Expression ◽  
Cd34 Cells ◽  
Significant Difference ◽  
Blast Count ◽  
Bone Marrow Disorders ◽  
Acute Myeloid

Abstract Objectives Hypocellular bone marrow (BM) disorders comprise heterogeneous entities associated with peripheral cytopenias and decreased production of hematopoietic cells in BM. This study was undertaken to analyze immunohistochemical expression of CD34, CD117, and p53 in morphologically diagnosed patients of hypocellular BM (aplastic anemia [AA], hypocellular myelodysplastic syndrome [h-MDS], and hypocellular acute myeloid leukemia [h-AML]). Materials and Methods BM specimens were obtained from patients presenting with pancytopenia/bicytopenia. On 30 patients diagnosed as hypocellular BM, immunohistochemistry (IHC) for CD34, CD117, and p53 was performed. Results BM cellularity was < 30% in all (100%) patients. Blast count was increased in h-MDS and h-AML. Features of dysplasia were noted in six (20%) patients. Out of these, three patients were diagnosed as h-MDS having bilineage/trilineage dysplasia, and the other three patients were of AA (11.5% patients) displaying only dyserythropoiesis. On IHC, percentage of BM CD34+ cells was increased in h-MDS+ h-AML (3.87 ± 0.86) as compared with AA (0.19 ± 0.15) and controls (0.81 ± 0.21), p = 0.01. Percentage of BM p53+ cells was also increased in h-MDS+ h-AML (2.9 ± 2.07) as compared with AA and controls, which did not show any p53+ cells, p = 0.0. No statistically significant difference was observed in the expression of CD117 in h-MDS+ h-AML (4.95 ± 3.40) compared with AA (4.49 ± 1.07), p = 0.99. Conclusion The study demonstrates the usefulness of CD34 and p53 immunoexpression as an important ancillary method in distinguishing various hypocellular BM disorders, especially h-MDS and AA. However, the role of CD117 remains unclear and needs to be evaluated further by larger studies.

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