|Ahead of print
Microarray analysis reveals distinct immune signatures in BCR-ABL positive and negative myeloproliferative neoplasms
Cecil Ross1, Mugdha Sharma1, John Paul1, Sweta Srivastava2
1 Department of Medicine, St. John's Medical College and Hospital, Bengaluru, Karnataka, India
2 Department of Transfusion Medicine and Immunohematology, St. John's Medical College and Hospital, Bengaluru, Karnataka, India
|Date of Submission||09-Jan-2020|
|Date of Decision||10-Jan-2020|
|Date of Acceptance||31-Mar-2020|
|Date of Web Publication||27-Jan-2021|
Department of Transfusion Medicine and Immunohematology, St. John's Medical College and Hospital, Bengaluru, Karnataka
Source of Support: None, Conflict of Interest: None
Background: BCR-ABL mutation on the Philadelphia chromosome is the key driver of chronic myeloid leukemia (CML) pathogenesis. However, there are certain cases of myeloproliferative neoplasms (MPN) wherein no inherent driver mutation is detected resulting in clinical phenotype. It is important to identify key genes and pathways in driving the disease. The aim of the study was to use a gene-based omics approach to molecularly characterize these mutation-positive and negative cases to further strengthen diagnostics and precision medicine.
Methods: A microarray profiling was done on CD34 positive cells isolated from two BCR-ABL positive and five BCR-ABL negative samples. JAK2V617F mutation testing was also done to rule out the presence of any other mutation in the latter group. The fold change cut-off was taken as ±1.5 with p≤0.5 for significant genes. The gene network and pathway analysis were done using DAVID and STRING software.
Results: The genes upregulated in BCR-ABL negative samples were shown to be involved in immune regulation, signal transduction and T- and B-cell signalling. The protein-protein interaction network of upregulated genes in these samples were enriched for various immunomodulatory genes such as HLADP, HLADQ, IL7R, CCR7, CD3 subtypes. These genes further formed a network with signal transduction genes such as LCK, FYN, RAG1, DOCK1, AKT3, SMAD3, LEF1.
Conclusion: The results suggested a modulation of immune response genes and its subsequent effect on oncogenic signalling in BCR-ABL negative samples as compared to BCR-ABL positive samples. The protein network analysis was enriched for genes involved in Src, TGF-beta and PI3K-AKT pathway contributing to the proliferation of neoplastic clone.
Keywords: BCR-ABL, CD34, immune, JAK2V617F, microarray, myeloproliferative neoplasmsKey Message Microarray analysis of CD34(+) cells highlights the existence of altered T-cell mediated immune signaling in BCR-ABL negative myeloproliferative neoplasms as compared to BCR-ABL positive neoplasms.
|How to cite this URL:|
Ross C, Sharma M, Paul J, Srivastava S. Microarray analysis reveals distinct immune signatures in BCR-ABL positive and negative myeloproliferative neoplasms. Indian J Cancer [Epub ahead of print] [cited 2022 May 19]. Available from: https://www.indianjcancer.com/preprintarticle.asp?id=308038
| » Introduction|| |
Myeloproliferative neoplasms (MPN) are clonal disorders characterized by the expansion of one of the multiple differentiated lineage of myeloid cells. The classical MPNs include chronic myeloid leukemia (CML), polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). CML is molecularly characterized by the Philadelphia chromosome, t(9;22)(q34;q11.2) resulting in the formation of a BCR-ABL fusion protein which is a dysregulated tyrosine kinase leading to the enhanced mitotic ability of the clone. The gain of function JAK2V617F mutation is the driver mutation in the majority of PV patients (~90%) and ~40–60% ET and PMF patients.,,, The mutation occurs in the JAK homology 2 (JH2) negative regulatory domain, increases JAK2 kinase activity leading to the downstream activation of multiple signaling cascades such as the STAT proteins, phosphatidylinositol 3-kinase-AKT pathway and mitogen-activated protein kinases. The acquisition of this mutation causes cytokine-independent growth of cell lines and cultured bone marrow cells. Mutant JAK2 transfected into murine bone marrow cells produces erythrocytosis and subsequent myelofibrosis in recipient animals, suggesting a causal role for the mutation.,, Among the cases of PV-negative for the V617F mutation, a mutation in exon 12 is found in a significant number of cases. A subset of cases of ET and PMF but not PV were found to have mutations in the MPL gene which is the receptor for thrombopoietin. Two parallel discoveries described somatic, recurrent insertions/deletions exclusively affecting exon 9 of the calreticulin (CALR) gene. Affecting the same driver pathway, CALR mutations were mutually exclusive with JAK2V617F or MPL mutations., Based on the discovery of these new driver mutations, the World Health Organization (WHO) in 2016 included the testing of JAK2, MPL, and CALR mutations as one of the diagnostic criteria for MPN. For triple-negative PMF patients, 2016 WHO classification allows identification of the mutation in ASXL1, EZH2, TET2, IDH1/2, SRSF2, SF3B1 as a marker for clonality. These mutations are reported in ~ 50% of PMF patients. Apart from the discovery of many driver mutations, there are many unexplored areas in the initiation and development of MPNs. An important feature of all MPNs is clonal hematopoiesis which involves the expansion of a clonal population of cells harboring somatic mutation. This process forms an important basis for the development of preleukemic conditions progressing to cancer. Apart from somatic mutations, cues from various immune cells in the bone marrow microenvironment can lead to clonal expansion. A young patient without a history of smoking, the absence of known mutation or any family history of MPN poses a challenge because the investigative pathway is not clear. To understand the expression or signaling profile in stem-like cells of BCR-ABL positive and negative samples, we did a microarray-based transcriptomic analysis of CD34 positive cells. The BCR-ABL negative samples were also analyzed for any JAK2V617F mutations in the cells contributing to disease background. CD34 cells were isolated from the bone marrow of BCR-ABL positive and negative MPNs to identify unique signaling patterns differing with these subtypes of MPNs.
| » Subject and Methods|| |
Sample collection and sorting
Bone marrow samples were collected from seven MPN patients as per guidelines and approval by Institutional Ethics Committee. The patients were classified according to the WHO criteria for MPNs. CD34 cells were isolated from bone marrow samples using the CD34 Microbead kit from Miltenyi Biotec as per manufacturer's instructions.
RNA was isolated from CD34 cells using Trizol (Invitrogen). Whole transcriptome analysis of the seven samples was performed using WT PLUS Reagent Kit (Applied Biosystems).
The quality check of the CEL file was performed using Transcriptome Analysis Console (TAC) software and the files were normalized through Oligo- R based Bioconductor package. The processing was done using the RMA method without any CDF file for the R program.
The normalized RMA file was checked for any discrepancies across the housekeeping using the top 10 housekeeping gene and their expression value across the sample. Principal component analysis (PCA) was done to cluster the samples. The samples showing the nearest cluster for all the housekeeping genes were taken for further analysis.
The normalized text file was then subjected to Alt- analyze automated tool for analysis. The differential gene expression was calculated with ±1.5 as fold change cutoff with a statistical significance of < 0.05. Prune ontology was calculated using a z score (initial filtering of 1.96 cutoffs). The database used for analysis was Ensemble. The pathway and network cross-talk were generated automatically by the AltAnalyze tool.
Differentially expressed genes were calculated using ±1.5-fold change and P value ≤0.05. Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool v6.8 was used for gene ontology (GO), functional and pathway enrichment analyze. Protein-protein interaction (PPI) network was constructed using STRING database v11. Hierarchical clustering and heatmaps were created using Clustvis online tool.
| » Results|| |
Clustering and functional annotation of genes expressed in CD34+ cells isolated from BCR-ABL positive and negative samples
In order to understand the difference in molecular signatures between BCR-ABL positive and BCR-ABL negative MPNs, we isolated CD34+ cells from the bone marrow of seven MPN patients. [Table 1] represents the baseline characteristics of the patients involved in the study.
The microarray analyses of CD34 positive cells isolated from BCR-ABL positive and BCR-ABL and JAK2V617F negative samples resulted in 21,448 gene signatures in total. AltAnalyze program was used to calculate the differential gene expression between the samples. The gene expression profile was refined using a ±1.5-fold difference with P value ≤0.05 using one-way analysis of variance (ANOVA). The analysis resulted in 560 upregulated genes and 449 downregulated genes in negative versus positive samples. The maximally upregulated genes with more than 3-fold difference were DNTT, IL7R, IGLL5 while the most downregulated genes with less than 3-fold difference were MATN2, LOXHD1, and VIT between negative samples versus positive samples.
We performed hierarchical clustering of all the seven samples using Clustvis software which indicated distinct microarray expression signatures in negative samples and positive samples [Figure 1]. The two BCR-ABL positive samples were clustered closely while all five BCR-ABL and JAK2V617F negative samples were clustered together suggesting a homogenous expression pattern within the groups [Figure 1]. The red regions indicated higher log2 fold change while the blue regions indicated lower expression [Figure 1].
|Figure 1: Gene expression profile of BCR-ABL positive and negative CD34 sorted cells. A heat map representation of differential gene expression profiles of BCR-ABL positive and negative myeloproliferative neoplasm samples (Log2 fold change P < 0.05) using Clustvis analysis tool|
Click here to view
GO for upregulated genes in negative samples versus positive samples was performed using a functional annotation tool; DAVID [Figure 2]. The biological processes (BP) were enriched for pathways for T-cell receptor (TCR) signaling, T-cell co-stimulation, immune response, T-cell activation, and transmembrane receptor protein tyrosine kinase signaling pathway [Figure 2]a. The molecular functions (MF) correlated with BP with enrichment of processes such as major histocompatibility complex (MHC) class II receptor activity, protein binding, transmembrane signaling receptor activity, TCR binding, PI3K activity and transcription factor binding [Figure 2]b. The CC (Cellular Components) category also suggests the involvement of plasma membrane, TCR complex and cytosol for most of the cellular activities [Figure 2]c.
|Figure 2: A representation of gene ontology analysis terms using DAVID for enriched genes under three categories of (a) biological processes (BP), (b) molecular function (MF), and (c) cellular components (CC) with the corresponding P value scores and P significance (P < 0.05)|
Click here to view
Network analysis of upregulated genes in BCR-ABL negative samples
On the basis of GO signatures, we studied the protein-protein interactions between mutation-negative upregulated genes using the STRING database. An unbiased protein interaction map for all the upregulated genes is shown in [Figure 3]. The map displayed a dense network of protein interactions involved in immune system processes. The network correlated with the various BP listed in Figure 2a. Based on DAVID and STRING results, we refined the upregulated genes into fifty-nine genes listed in [Table 2]. The tight cluster of genes was enriched for genes such as GATA3, ZAP70, LCK, CD3G, HLADP1, CD79 and MALT1 indicated by red nodes that are involved in TCR signaling [Figure 4].
|Figure 3: Network of upregulated genes in BCR-ABL negative versus positive samples using String.db v11 at minimum confidence 0.4. Each node represents a protein|
Click here to view
|Figure 4: Tight cluster within the String network was enriched with TCR signaling pathway (red), regulation of immune response (blue) and signal transduction pathway (green) highlighted in the cluster|
Click here to view
|Table 2: List of upregulated genes in mutation-negative samples involved in immune response|
Click here to view
Genes such as LEF1, ETS1, CD27, RAG1, CD2, DDX58, and CCR2 among others mentioned above; shown as blue and green nodes are involved in the regulation of immune response and signal transduction respectively [Figure 4]. LEF1 and ETS1 encode transcription factors implicated in stem cell development, senescence, and apoptosis., A bar graph representing the significant increase in expression of selected few genes involved in immune response in BCR-ABL-negative samples above BCR-ABL-positive samples is shown in [Figure 5].
|Figure 5: Graphical representation of fold change difference of few selected significantly upregulated genes from the corresponding tight cluster in BCR-ABL negative samples over positive samples from microarray data|
Click here to view
| » Discussion|| |
The discovery of mutations such as BCR-ABL and JAK2V617F distinguished MPNs into three broad categories of BCR-ABL positive, JAK2 mutant positive and negative neoplasms. The role of BCR-ABL and JAK-STAT signaling has been well-characterized in the initiation and progression of MPNs. It is the prognosis of a mutation-negative subset of MPNs that is still ambiguous. In order to understand the differences in the stem cells from mutation-negative MPNs, we did a microarray-based expression profiling of CD34 cells from BCR-ABL positive and both BCR-ABL and JAK2V617F negative samples. The genes upregulated in mutation-negative samples were shown to be involved in immune regulation, signal transduction and T- and B-cell signaling. The protein-protein interaction network of unfiltered upregulated genes constructed using the STRING database was enriched for various immunomodulatory genes such as HLA-DP, HLA-DQ, IL7R, CD22, CCR7, IL2RB, CD3 subtypes, and TLR4. These genes further formed a network with signal transduction genes such as ZAP70, LCK, FYN, RAG1, DOCK1, AKT3, ITGB1, SMAD3, LEF1, ETS1, GATA3, DNMT3a, NFATC2 affecting downstream signaling regulating multiple targets. GATA3 is a transcription factor that is involved in T-cells development, proliferation and maintenance. It has been shown that DNMT3a mediated regulation of GATA3 results in the upregulation of HSC stemness and expansion. ZAP70 is a tyrosine kinase inhibitor playing a key role in TCR-mediated signal transduction. Upon stimulation, it interacts with other Src family kinases such as LCK and FYN involved in signal transduction. LCK, a protooncogene tyrosine kinase belonging to the Src family of the kinase. It is involved in migration and proliferation of T-cells along with PLC, PKC, and Rac1 via IL-2. MALT1 has been shown to drive the JAK/STAT signaling pathway and suppress type I IFN response and MHC class II expression. It is an integral component of the CBM (CARD11-BCL10-MALT1) complex triggering the canonical NF?B signaling upon antigen stimulation. CD79 and CD3 are key markers expressed on B-cells and TCR, respectively, forming an integral part of the adaptive immune response., In order to get a clear insight into the processes, we filtered the upregulated gene list to 59 key genes based on GO terms. The protein network was enriched for genes involved in TCR signaling. The TCR complex is composed of multiple subunits and is responsible for the activation of T-cells upon antigen presentation from MHC molecules (class I and II) present on antigen-presenting cells (APC). Co-stimulatory molecules such as CD2, CD28, CD4, CD8 aid in signal transduction. The upregulation of TCR signaling can trigger PI3K/AKT and NF?B pathways leading to an increased oncogenic potential in these cells. The role of activated TCR signaling has been shown in peripheral T-cell lymphomas wherein NF?B regulated the constitutive expression of IRF4 further driving MYC expression. The role of immune dysregulation and inflammation has been previously reported in MPNs. An increase in expression of inflammatory proteins is one of the main culprits of niche fibrosis affecting the survival and maintenance of HSC clones in bone marrow microenvironment. The correlation between high inflammatory burden on prognosis and treatment of MPN has been reported. Of late, a considerable interest has been drawn towards the role of immune dysregulation of tumor microenvironment affecting both innate and adaptive arms of immunity leading to an altered milieu. The relationship between hematopoietic stem cells and immunomodulatory cells has not been fully explored in MPNs. It would be of great interest to elucidate the roles of these immunomodulatory genes both in vitro and in vivo models to aid in the understanding of origin, survival, and progression of neoplastic clone in mutation-negative MPN.
Financial support and sponsorship
Advanced Research Wing, Rajiv Gandhi University of Health Sciences, Bengaluru.
Conflicts of interest
There are no conflicts of interest.
| » References|| |
Vardiman JW, Thiele J, Arber DA, Brunning RD, Borowitz MJ, Porwit A, et al
. The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: Rationale and important changes. Blood 2009;114:937-51.
Rowley JD. Letter. A new consistent chromosomal abnormality in chronic myelogenous leukaemia identified by quinacrine fluorescence and Giemsa staining. Nature 1973;243:290-3.
Baxter EJ, Scott LM, Campbell PJ, East C, Fourouclas N, Swanton S, et al
. Acquired mutation of the tyrosine kinase JAK2 in human myeloproliferative disorders. Lancet 2005;365:1054-61.
James C, Ugo V, Le Couedic JP, Staerk J, Delhommeau F, Lacout C, et al
. A unique clonal JAK2 mutation leading to constitutive signalling causes polycythaemia vera. Nature 2005;434:1144-8.
Kralovics R, Passamonti F, Buser AS, Teo SS, Tiedt R, Passweg JR, et al
. A gain-of-function mutation of JAK2 in myeloproliferative disorders. N Engl J Med 2005;352:1779-90.
Levine RL, Wadleigh M, Cools J, Ebert BL, Wernig G, Huntly BJ, et al
. Activating mutation in the tyrosine kinase JAK2 in polycythemia vera, essential thrombocythemia, and myeloid metaplasia with myelofibrosis. Cancer Cell 2005;7:387-97.
Scott LM, Tong W, Levine RL, Scott MA, Beer PA, Stratton MR, et al
. JAK2 exon 12 mutations in polycythemia vera and idiopathic erythrocytosis. N Engl J Med 2007;356:459-68.
Wernig G, Mercher T, Okabe R, Levine RL, Lee BH, Gilliland DG. Expression of Jak2V617F causes a polycythemia vera-like disease with associated myelofibrosis in a murine bone marrow transplant model. Blood 2006;107:4274-81.
Lacout C, Pisani DF, Tulliez M, Gachelin FM, Vainchenker W, Villeval JL. JAK2V617F expression in murine hematopoietic cells leads to MPD mimicking human PV with secondary myelofibrosis. Blood 2006;108:1652-60.
Pikman Y, Lee BH, Mercher T, McDowell E, Ebert BL, Gozo M, et al
. MPLW515L is a novel somatic activating mutation in myelofibrosis with myeloid metaplasia. PLoS Med 2006;3:e270.
Klampfl T, Gisslinger H, Harutyunyan AS, Nivarthi H, Rumi E, Milosevic JD, et al
. Somatic mutations of calreticulin in myeloproliferative neoplasms. N Engl J Med 2013;369:2379-90.
Nangalia J, Massie CE, Baxter EJ, Nice FL, Gundem G, Wedge DC, et al
. Somatic CALR mutations in myeloproliferative neoplasms with nonmutated JAK2. N Engl J Med 2013;369:2391-405.
Barbui T, Thiele J, Gisslinger H, Kvasnicka HM, Vannucchi AM, Guglielmelli P, et al
. The 2016 WHO classification and diagnostic criteria for myeloproliferative neoplasms: Document summary and in-depth discussion. Blood Cancer J 2018;8:15.
Vannucchi AM, Lasho TL, Guglielmelli P, Biamonte F, Pardanani A, Pereira A, et al
. Mutations and prognosis in primary myelofibrosis. Leukemia 2013;27:1861-9.
Reya T, Morrison SJ, Clarke MF, Weissman IL. Stem cells, cancer, and cancer stem cells. Nature 2001;414:105-11.
Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2009;37:1-13.
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al
. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019;47:D607-13.
Metsalu T, Vilo J. ClustVis: A web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res 2015;43:W566-70.
Nischal S, Bhattacharyya S, Christopeit M, Yu Y, Zhou L, Bhagat TD, et al
. Methylome profiling reveals distinct alterations in phenotypic and mutational subgroups of myeloproliferative neoplasms. Cancer Res 2013;73:1076-85.
Fu JF, Yen TH, Huang YJ, Shih LY. Ets1 plays a critical role in MLL/EB1-mediated leukemic transformation in a mouse bone marrow transplantation model. Neoplasia 2019;21:469-81.
Wan YY. GATA3: A master of many trades in immune regulation. Trends Immunol 2014;35:233-42.
Challen GA, Sun D, Jeong M, Luo M, Jelinek J, Berg JS, et al
. Dnmt3a is essential for hematopoietic stem cell differentiation. Nat Genet 2011;44:23-31.
Au-Yeung BB, Shah NH, Shen L, Weiss A. ZAP-70 in signaling, biology, and disease. Annu Rev Immunol 2018;36:127-56.
Llavero F, Artaso A, Lacerda HM, Parada LA, Zugaza JL. Lck/PLC? control migration and proliferation of interleukin (IL)-2-stimulated T cells via the Rac1 GTPase/glycogen phosphorylase pathway. Cell Signal 2016;28:1713-24.
Fontan L, Qiao Q, Hatcher JM, Casalena G, Us I, Teater M, et al
. Specific covalent inhibition of MALT1 paracaspase suppresses B cell lymphoma growth. J Clin Invest 2018;128:4397-412.
Meininger I, Krappmann D. Lymphocyte signaling and activation by the CARMA1-BCL10-MALT1 signalosome. Biol Chem 2016;397:1315-33.
Chu PG, Arber DA. CD79: A review. Appl Immunohistochem Mol Morphol 200?1;9:97-106.
Clevers H, Alarcon B, Wileman T, Terhorst C. The T cell receptor/CD3 complex: A dynamic protein ensemble. Annu Rev Immunol 1988;6:629-62.
Boddicker RL, Kip NS, Xing X, Zeng Y, Yang ZZ, Lee JH, et al
. The oncogenic transcription factor IRF4 is regulated by a novel CD30/NF-?B positive feedback loop in peripheral T-cell lymphoma. Blood 2015;125:3118-27.
Kristinsson SY, Landgren O, Samuelsson J, Bjorkholm M, Goldin LR. Autoimmunity and the risk of myeloproliferative neoplasms. Haematologica 2010;95:1216-20.
Sharma M, Ross C, Srivastava S. Ally to adversary: Mesenchymal stem cells and their transformation in leukaemia. Cancer Cell Int 2019;19:139.
Lussana F, Rambaldi A. Inflammation and myeloproliferative neoplasms. J Autoimmun 2017;85:58-63.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2]