Chromatin Accessibility Identifies Regulatory Elements Predictive of Oncogene Expression in Multiple Myeloma

Introduction Extensive genomic characterization of multiple myeloma has identified subtypes with prognostic and therapeutic implications. In contrast, less is known about the myeloma epigenome. One challenge that has hindered epigenetic studies are assays amenable to biobanked specimens. Here, we sought to determine whether ATAC-seq and RNA-seq of myeloma cells from cryopreserved bone marrow aspirates recapitulated those from fresh samples and used this approach to investigate enhancers of myeloma oncogenes. Methods Consent and collection of specimens followed approved Institutional Review Board protocols. Mononuclear cells were enrichment by Ficoll gradient centrifugation and were either cryopreserved in 10% DMSO and RPMI media with 10% FBS or used to isolate viable CD138+CD38+ myeloma cells. RNA-seq used the mRNA HyperPrep kit (Kapa Biosystems) with RNA from 50,000 cells. ATAC-seq used the Tn5 transposase (Illumina) on 20,000 cells. Sequencing was performed on an HiSeq 4000 (Illumina). Sequencing data were quality and adapter trimmed using Trim Galore! And mapped to the GRCh37 genome using STAR (RNA-seq) or bowtie2 (ATAC-seq). MACS2 was used to determine chromatin accessible regions and R was used for downstream analyses. H3K27ac ChIP-seq from Jin et al. (Blood, 2018) were downloaded from the European nucleotide archive (PRJEB25605). RNA-seq from CoMMpass (NCT01454297) were downloaded from dbGaP phs000748.v7.p4. Enhancer RNAs were interrogated in intergenic regions excluding 500 bp upstream of TSSs and 5 kb downstream of transcription termination sites to avoid contamination from exonic mRNAs or intronic pre-mRNAs. Results We compared RNA-seq and ATAC-seq data from myeloma cells isolated from fresh bone marrow aspirates to those cryopreserved for up to 6 months from the same aspirate. RNA-seq and ATAC-seq data from fresh and frozen samples from the same aspirate were highly correlated with each other but distinct from other samples as depicted by principal component analysis (Fig. A,B). Inspection of CCND1 showed high levels of RNA in two patients and this was consistent in both fresh and frozen specimens as well as with FISH results indicating a t(11;14) translocation in these samples (Fig. C). Similarly, fresh and frozen specimens from the same patient showed consistent expression for CCND2 and MYC and these data corresponded with chromatin accessibility found near these genes (Fig. D, see regions shaded in gray). Based on these results we expanded our analysis to include 8 biobanked specimens, which identified 91,632 regions of chromatin accessibility that were enriched around plasma cell lineage genes such as IRF4, CD38, SLAMF7, and IGH. Chromatin accessibility often predicted proximal gene expression and this was especially pronounced for regions enriched for histone 3 lysine 27 acetylation (H3K27ac) - a mark of enhancers. Active enhancers are sometimes demarcated by enhancer RNAs (eRNAs) observable in RNA-seq data, thus we queried intergenic regions marked by chromatin accessibility and H3K27ac for eRNAs using RNA-seq data on 768 myeloma specimens from the CoMMpass study. This identified transcription at 4,729 of 13,452 potential regions. eRNA expression was highly correlated with proximal gene expression. To illustrate this point, we performed t-SNE clustering based on mRNA and eRNA expression and color-coded each sample by myeloma gene expression subtype (Fig. E). Interestingly, this identified several regions highly correlated with oncogene expression between myeloma subtypes. For example, an enhancer ~154 kb upstream of CCND2 was uniquely transcribed in the MAF subtype (Fig. F) and this was highly correlated with CCND2 expression (Fig. G). Conclusions Cryopreservation of myeloma bone marrow aspirates allows isolation and analysis of biobanked samples that produce RNA-seq and ATAC-seq data that are highly congruent with those obtained from fresh samples and this represents a strategy for retrospective genomic and epigenomic studies. Chromatin accessibility analysis identified distinct enhancer elements regulating oncogenes in myeloma subtypes providing mechanistic insight into myeloma pathology. Figure 1 Disclosures Lin: Amgen: Current Employment, Current equity holder in publicly-traded company. Hofmeister:Bristol Myers Squibb: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Nektar: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; Oncopeptides: Honoraria; Oncolytics Biotech: Research Funding; Imbrium: Honoraria; Karyopharm: Honoraria, Research Funding. Nooka:Celgene: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Honoraria; Adaptive Technologies: Consultancy, Honoraria; Spectrum Pharmaceuticals: Consultancy; Takeda: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Other: Personal Fees: Travel/accomodations/expenses, Research Funding; Karyopharm Therapeutics, Adaptive technologies: Consultancy, Honoraria, Research Funding; Oncopeptides: Consultancy, Honoraria. Lonial:GSK: Consultancy, Honoraria, Other: Personal fees; BMS: Consultancy, Honoraria, Other: Personal fees, Research Funding; Takeda: Consultancy, Other: Personal fees, Research Funding; Novartis: Consultancy, Honoraria, Other: Personal fees; Janssen: Consultancy, Honoraria, Other: Personal fees, Research Funding; Merck: Consultancy, Honoraria, Other: Personal fees; JUNO Therapeutics: Consultancy; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Honoraria; Onyx: Honoraria; Genentech: Consultancy; Karyopharm: Consultancy; Amgen: Consultancy, Honoraria, Other: Personal fees; Sanofi: Consultancy; Abbvie: Consultancy. Boise:AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genetech: Membership on an entity's Board of Directors or advisory committees.

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Dynamic Epigenetic Landscapes Define Multiple Myeloma Progression and Drug Resistance

Blood ◽

10.1182/blood-2020-142872 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 32-33

Author(s):

Rafael Renatino-Canevarolo ◽

Mark B. Meads ◽

Maria Silva ◽

Praneeth Reddy Sudalagunta ◽

Christopher Cubitt ◽

...

Keyword(s):

Gene Expression ◽

Drug Resistance ◽

Disease Progression ◽

Board Of Directors ◽

Research Funding ◽

Chromatin Accessibility ◽

Refractory Disease ◽

Advisory Committees ◽

Cytogenetic Abnormalities ◽

Genome Wide

Multiple myeloma (MM) is an incurable cancer of bone marrow-resident plasma cells, which evolves from a premalignant state, MGUS, to a form of active disease characterized by an initial response to therapy, followed by cycles of therapeutic successes and failures, culminating in a fatal multi-drug resistant cancer. The molecular mechanisms leading to disease progression and refractory disease in MM remain poorly understood. To address this question, we have generated a new database, consisting of 1,123 MM biopsies from patients treated at the H. Lee Moffitt Cancer Center. These samples ranged from MGUS to late relapsed/refractory (LR) disease, and were comprehensively characterized genetically (844 RNAseq, 870 WES, 7 scRNAseq), epigenetically (10 single-cell chromatin accessibility, scATAC-seq) and phenotypically (537 samples assessed for ex vivo drug resistance). Mutational analysis identified putative driver genes (e.g. NRAS, KRAS) among the highest frequent mutations, as well as a steady increase in mutational load across progression from MGUS to LR samples. However, with the exception of KRAS, these genes did not reach statistical significance according to FISHER's exact test between different disease stages, suggesting that no single mutation is necessary or sufficient to drive MM progression or refractory disease, but rather a common "driver" biology is critical. Pathway analysis of differentially expressed genes identified cell adhesion, inflammatory cytokines and hematopoietic cell identify as under-expressed in active MM vs. MGUS, while cell cycle, metabolism, DNA repair, protein/RNA synthesis and degradation were over-expressed in LR. Using an unsupervised systems biology approach, we reconstructed a gene expression map to identify transcriptomic reprogramming events associated with disease progression and evolution of drug resistance. At an epigenetic regulatory level, these genes were enriched for histone modifications (e.g. H3k27me3 and H3k27ac). Furthermore, scATAC-seq confirmed genome-wide alterations in chromatin accessibility across MM progression, involving shifts in chromatin accessibility of the binding motifs of epigenetic regulator complexes, known to mediate formation of 3D structures (CTCF/YY1) of super enhancers (SE) and cell identity reprograming (POU5F1/SOX2). Additionally, we have identified SE-regulated genes under- (EBF1, RB1, SPI1, KLF6) and over-expressed (PRDM1, IRF4) in MM progression, as well as over-expressed in LR (RFX5, YY1, NBN, CTCF, BCOR). We have found a correlation between cytogenetic abnormalities and mutations with differential gene expression observed in MM progression, suggesting groups of genetic events with equivalent transcriptomic effect: e.g. NRAS, KRAS, DIS3 and del13q are associated with transcriptomic changes observed during MGUS/SMOL=>active MM transition (Figure 1). Taken together, our preliminary data suggests that multiple independent combinations of genetic and epigenetic events (e.g. mutations, cytogenetics, SE dysregulation) alter the balance of master epigenetic regulatory circuitry, leading to genome-wide transcriptional reprogramming, facilitating disease progression and emergence of drug resistance. Figure 1: Topology of transcriptional regulation in MM depicts 16,738 genes whose expression is increased (red) or decreased (green) in presence of genetic abnormality. Differential expression associated with (A) hotspot mutations and (B) cytogenetic abnormalities confirms equivalence of expected pairs (e.g. NRAS and KRAS, BRAF and RAF1), but also proposes novel transcriptomic dysregulation effect of clinically relevant cytogenetic abnormalities, with yet uncharacterized molecular role in MM. Figure 1 Disclosures Kulkarni: M2GEN: Current Employment. Zhang:M2GEN: Current Employment. Hampton:M2GEN: Current Employment. Shain:GlaxoSmithKline: Speakers Bureau; Amgen: Speakers Bureau; Karyopharm: Research Funding, Speakers Bureau; AbbVie: Research Funding; Takeda: Honoraria, Speakers Bureau; Sanofi/Genzyme: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Adaptive: Consultancy, Honoraria; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Siqueira Silva:AbbVie: Research Funding; Karyopharm: Research Funding; NIH/NCI: Research Funding.

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A Comprehensive Analysis of Single-Cell Chromatin Accessibility and Gene Expression Identifies Intra-Tumor Heterogeneity and Molecular Treatment Responses in Relapsed/Refractory Multiple Myeloma

Blood ◽

10.1182/blood-2019-130051 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 575-575

Author(s):

Alexandra M Poos ◽

Jan-Philipp Mallm ◽

Stephan M Tirier ◽

Nicola Casiraghi ◽

Hana Susak ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Board Of Directors ◽

Research Funding ◽

Tumor Heterogeneity ◽

Mek Inhibitor ◽

Chromatin Accessibility ◽

University Research ◽

Sample Processing ◽

Advisory Committees

Introduction: Multiple myeloma (MM) is a heterogeneous malignancy of clonal plasma cells that accumulate in the bone marrow (BM). Despite new treatment approaches, in most patients resistant subclones are selected by therapy, resulting in the development of refractory disease. While the subclonal architecture in newly diagnosed patients has been investigated in great detail, intra-tumor heterogeneity in relapsed/refractory (RR) MM is poorly characterized. Recent technological and computational advances provide the opportunity to systematically analyze tumor samples at single-cell (sc) level with high accuracy and througput. Here, we present a pilot study for an integrative analysis of sc Assay for Transposase-Accessible Chromatin with high-throughput sequencing (scATAC-seq) and scRNA-seq with the aim to comprehensively study the regulatory landscape, gene expression, and evolution of individual subclones in RRMM patients. Methods: We have included 20 RRMM patients with longitudinally collected paired BM samples. scATAC- and scRNA-seq data were generated using the 10X Genomics platform. Pre-processing of the sc-seq data was performed with the CellRanger software (reference genome GRCh38). For downstream analyses the R-packages Seurat and Signac (Satija Lab) as well as Cicero (Trapnell Lab) were used. For all patients bulk whole genome sequencing (WGS) data was available, which we used for confirmatory studies of intra-tumor heterogeneity. Results: A comprehensive study at the sc level requires extensive quality controls (QC). All scATAC-seq files passed the QC, including the detected number of cells, number of fragments in peaks or the ratio of mononucleosomal to nucleosome-free fragments. Yet, unsupervised clustering of the differentially accessible regions resulted in two main clusters, strongly associated with sample processing time. Delay of sample processing by 1-2 days, e.g. due to shipment from participating centers, resulted in global change of chromatin accessibility with more than 10,000 regions showing differences compared to directly processed samples. The corresponding scRNA-seq files also consistently failed QC, including detectable genes per cell and the percentage of mitochondrial RNA. We excluded these samples from the study. Analysing scATAC-seq data, we observed distinct clusters before and after treatment of RRMM, indicating clonal adaptation or selection in all samples. Treatment with carfilzomib resulted in highly increased co-accessibility and >100 genes were differentially accessible upon treatment. These genes are related to the activation of immune cells (including T-, and B-cells), cell-cell adhesion, apoptosis and signaling pathways (e.g. NFκB) and include several chaperone proteins (e.g. HSPH1) which were upregulated in the scRNA-seq data upon proteasome inhibition. The power of our comprehensive approach for detection of individual subclones and their evolution is exemplarily illustrated in a patient who was treated with a MEK inhibitor and achieved complete remission. This patient showed two main clusters in the scATAC-seq data before treatment, suggesting presence of two subclones. Using copy number profiles based on WGS and scRNA-seq data and performing a trajectory analysis based on scATAC-seq data, we could confirm two different subclones. At relapse, a seemingly independent dominant clone emerged. Upon comprehensive integration of the datasets, one of the initial subclones could be identified as the precursor of this dominant clone. We observed increased accessibility for 108 regions (e.g. JUND, HSPA5, EGR1, FOSB, ETS1, FOXP2) upon MEK inhibition. The most significant differentially accessible region in this clone and its precursor included the gene coding for krüppel-like factor 2 (KLF2). scRNA-seq data showed overexpression of KLF2 in the MEK-inhibitor resistant clone, confirming KLF2 scATAC-seq data. KLF2 has been reported to play an essential role together with KDM3A and IRF1 for MM cell survival and adhesion to stromal cells in the BM. Conclusions: Our data strongly suggest to use only immediately processed samples for single cell technologies. Integrating scATAC- and scRNA-seq together with bulk WGS data showed that detection of individual clones and longitudinal changes in the activity of cis-regulatory regions and gene expression is feasible and informative in RRMM. Disclosures Goldschmidt: John-Hopkins University: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; MSD: Research Funding; Molecular Partners: Research Funding; Dietmar-Hopp-Stiftung: Research Funding; Janssen: Consultancy, Research Funding; Chugai: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees.

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Promoter-Wide Transcriptional Deregulation In Waldenstrom Macroglobulinemia

Blood ◽

10.1182/blood.v116.21.3620.3620 ◽

2010 ◽

Vol 116 (21) ◽

pp. 3620-3620

Author(s):

Yang Liu ◽

Min Ni ◽

Aldo M. Roccaro ◽

Xavier Leleu ◽

Yong Zhang ◽

...

Keyword(s):

Gene Expression ◽

Bone Marrow ◽

Board Of Directors ◽

Binding Sites ◽

Research Funding ◽

Enrichment Analysis ◽

Promoter Regions ◽

Advisory Committees ◽

Pol Ii ◽

Normal Controls

Abstract Abstract 3620 Introduction: Waldenstrom macroglobulinemia (WM) is a rare indolent non-Hodgkin lymphoma, characterized by bone marrow infiltration of clonal lymphoplasmacytic cells. Despite recent advances in understanding the pathogenesis of this disease, the molecular basis of WM etiology has not been clearly defined. We therefore performed genome-wide analysis of RNA polymerase II (pol II) binding sites and gene expression profiling in primary WM cells in order to comprehensively define the aberrant transcriptional regulation and related genes in WM. Methods: Primary CD19+ bone marrow derived WM cells and normal primary bone marrow were used. Genomic DNA was extracted using genome isolation kit (QIAGEN) after cross linking. All the DNA samples were sent for Chip assay and human promoter 1.0R array (Genepathway Inc.) which comprised of over 4.6 million probes tiled through over 25.500 human promoter regions. Each promoter region covers approximately 7.6kb upstream through 2.45kb downstream of the transcription start sites. For over 1,300 cancer associated genes, coverage of promoter regions was expanded to additional genomic content; for selected genes total coverage spans from 10kb upstream through 2.45kb downstream of transcription start sites. The published gene expression datasets (GDS2643) which included 10 CD19+ B cell from bone marrow of 10 WM patients and 8 normal controls was analyzed by d-chip software and normalized to normal control. The motif analysis was performed using Cistrome online tools from the Dana Farber Cancer Institute. The gene sets enrichment analysis (GSEA) was performed using GSEA online software from Broad institute. Results: A total of 13,546 high-confidence pol II sites were identified in WM samples and share a small percentage of overlap (11.5%) with the binding sites identified in normal controls. Combining the expression microarray data of WM patient samples and normal controls, we demonstrated a significant correlation between high levels of gene expression and enriched promoter binding of pol II. Notably, we also observed that the WM-unique pol II binding sites are localized in the promoters of 5,556 genes which are involved in important signaling pathways, such as Jak/STAT and MAPK pathways by applying gene set enrichment analysis (GSEA). Interestingly, we found that STAT, FOXO and IRF family binding sites motifs were enriched in the pol II-bound promoter region of IL-6 which plays a crucial role in cell proliferation and survival of WM cells. Moreover, the CpG island associated c-fos promoter was enriched for Pol II binding as compared to the normal control. Conclusion: The presence of increased Pol II binding and the identification of transcription factor motifs in the promoters of key oncogenes may lead to a better understanding of WM. Our findings suggest that altered transcriptional regulation may play an important role in the pathogenesis of WM. In addition, this study will provide novel insights into the molecular mechanism of WM etiology, and may lead to discovery of novel diagnostic molecular biomarkers and therapeutic targets for WM. Disclosures: Leleu: Celgene: Consultancy, Research Funding; Janssen Cilag: Consultancy, Research Funding; Leo Pharma: Consultancy; Amgen: Consultancy; Chugai: Research Funding; Roche: Consultancy, Research Funding; Novartis: Consultancy, Research Funding. Ghobrial:Celgene: Membership on an entity's Board of Directors or advisory committees; Millennium: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees.

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Deregulation of TNFRSF18 (GITR) Through Promoter CpG Island Methylation Induces Tumor Proliferation in Multiple Myeloma

Blood ◽

10.1182/blood.v118.21.2424.2424 ◽

2011 ◽

Vol 118 (21) ◽

pp. 2424-2424

Author(s):

Yang Liu ◽

Yong Zhang ◽

Phong Quang ◽

Hai T Ngo ◽

Feda Azab ◽

...

Keyword(s):

Gene Expression ◽

Bone Marrow ◽

Flow Cytometry ◽

Board Of Directors ◽

Research Funding ◽

Plasma Cells ◽

Cpg Island ◽

Advisory Committees ◽

Brdu Assay

Abstract Abstract 2424 Introduction Tumor necrosis factor receptor super families (TNFRSFs) play an important role in activation of lymphocyte and cell apoptosis. However the function of TNFRSFs in multiple myeloma (MM) remains unknown. Loss of function mutation of Fas antigen (TNFRSF6) was identified in MM cells, thus suggesting the possible role of TNFRSFs in regulating MM pathogenesis. We therefore investigated the epigenetic mechanisms that may mediate inactivation of TNFRSFs and its functional role in MM. Methods Dchip software was utilized for analyzing gene expression dataset. DNA was extracted from both primary CD138+ MM plasma cells and MM cell lines using blood & tissue DNA isolation kit (Qiagen, Inc.). Expression of GITR in primary CD138+ plasma cells was detected by Imunohistochemistry (IHC) DNA methylation was analyzed by methylated DNA immunoprecipitation (Medip) assay and bisulfate sequencing. 5'azacytidine was used to demethylate genomic DNA. Gene expression was detected by qRT-PCR and confirmed at the protein level by flow cytometry and western-blot. Over-expression of GITR was obtained in MM1.S cells by using GITR recombinant plasmid and electroporation. Apoptosis was determined using Annexin/PI staining and flow cytometry analysis. Activation of apoptotic signaling was studied by western blot. Cell survival and proliferation were analyzed by MTT and BrdU assay, respectively. Recombinant GITR-lentivirus was obtained from the supernatant of culture medium after 72 hours transfection in 293 cells. GFP positive MM cells were sorted and analyzed by flow cytometry. In vivo effect of GITR on MM tumor growth was determined by injection of GITR over-expressing MM cells in null mice. Mice skull, femur and vertebrae were isolated after 4 weeks injection. Anti-human CD138+ mAb microbead was used to detect MM cells extracted from mice tissue by flow cytometry. Results Gene-expression profiling showed down-regulation of TNFRSFs, including TNFRSF11A, TNFRSF11B, TNFRSF8, TNFRSF10C, TNFRSF9, TNFRSF21, TNFRSF1B, TNFRSF1A and TNFRSF18, compared to normal plasma cells. Moreover, Our IHC results also showed that GITR expression was positive in primary CD138+ plasma cells from 9 normal bone marrow, but negative in 9 MM samples. Importantly, we found that low GITR expression significantly correlated with MM progression. Indeed, GITR gene levels were lower in smoldering and active MM patients compared to MGUS patients and normal donors. Promoter CpG island (CGI) methylation of GITR was indentified in 5 out of 7 MM primary bone marrow (BM)-derived CD138+ cells but not in normal BM-derived plasma cells. Bisulfate sequencing and Medip assay showed that methylation of GITR was significantly associated with GITR expression in 5 MM cell lines, including MM1.S, OPM1, U266, RPMI and INA6. Promoter CGI of GITR was highly methylated leading to complete silencing of GITR in MM1.S cell line. GITR expression was significantly up-regulated in MM cells upon treatment with the 5'azacytidine. MTT and BrdU assay revealed that the proliferation and survival of MM1.S cells was disrupted in the GITR over-expressing MM1.S cells, notably with inhibition of cell proliferation compared to control vector infected cells. Moreover induction of cytotoxicity in GITR over-expressing cells was confirmed by using GFP competition assay. GITR-induced apoptosis was supported by induction of caspase 8 and 3 cleavage. The inhibition of human CD138+ plasma cell growth in the bone marrow of SCID mice using a disseminated MM xenograft model was observed in the experimental group injected with GITR expressing cells compared to the control group after 4 weeks injection. Conclusion Our findings uncovered a novel epigenetic mechanism contributing to MM pathogenesis, showing the role of GITR methylation as a key regulator of MM cell survival. Disclosures: Roccaro: Roche:. Ghobrial:Novartis: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Research Funding; Noxxon: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding.

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Inhibition of SUV39H Methyltransferase As a Potent Therapeutic Target in Multiple Myeloma

Blood ◽

10.1182/blood.v126.23.1771.1771 ◽

2015 ◽

Vol 126 (23) ◽

pp. 1771-1771 ◽

Cited By ~ 3

Author(s):

Julie Devin ◽

Elena Viziteu ◽

Laurie Herviou ◽

Anja Seckinger ◽

Grandmougin Camille ◽

...

Keyword(s):

Bone Marrow ◽

Board Of Directors ◽

Research Funding ◽

Plasma Cells ◽

Advisory Committees ◽

Myeloma Cells ◽

Normal Bone ◽

Significant Toxicity ◽

Wide Range ◽

Travel Grant

Abstract Epigenetics is characterized by a wide range of changes that are reversible and orchestrate gene expression. Recent studies have shown that epigenetic modifications play a role in multiple myeloma (MM) by silencing various cancer-related genes. We investigated the epigenetic genes differentially expressed between normal bone marrow plasma cells (BMPC ; N=5) and MM plasma cells from patients (N=206). Using SAM (Significance Analysis of Microarrays) analysis, only 12 genes significantly differentially expressed between BMPC and MM cells (ratio > 2 and FDR (false discovery rate) < 5%) were identified, including the SUV39H1 histone methyltransferase. SUV39H1 and SUV39H2 are regulators of chromatin organization. SUV39H1-dependent trimethylation of H3K9 is essential for maintenance of both pericentromeric and telomeric heterochromatin. SUV39H1 deficiency reduced cell viability severely and is associated to heterochromatin decompaction, loss of silencing, genome instability, and a wide range of defects in cell cycle, cell growth, and meiosis. SUV39H1-mediated H3K9me has been linked to gene silencing of the tumor suppressor genes, such as p15INK4B and E-cadherin, in acute myeloid leukemia (AML). Therefore, it is highly possible that the default function of SUV39H1 is to maintain genome stability by limiting the acute activation of oncogenes while its dysregulation could cause tumor formation. We reported that high SUV39H1 expression, in MM cells, is associated with a poor prognosis in two independent cohorts of patients (Heidelberg-Montpellier cohort - N=206 and UAMS-TT2 cohort - N=345). SUV39H1 expression was downregulated by conditional shRNA expression through lentiviral delivery. SUV39H1 knock down significantly inhibits H3K9me3, growth of myeloma cells, induces apoptosis, cell cycle deregulation, reactive oxygen species production and spontaneous accumulation of DNA double strand breaks. According to these results, SUV39H1 depletion sensitizes myeloma cells to melphalan. Chaetocin is a selective inhibitor of SUV39H1. We identified that chaetocin has anti-myeloma effects at low nanomolar doses (range: 4 to 17 nM), on 11 different human myeloma cell lines, that are representative of the molecular heterogeneity of the patients, in association with H3K9 trimethylation inhibition. Furthermore, this significant toxicity of chaetocin in MM was confirmed on primary myeloma cells of 5 patients cocultured with their bone marrow microenvironment without significant toxicity on normal bone marrow cells and hematopoietic stem cells. Interestingly, the IC50 doses of chaetocin in MM were 50 fold lower compared to results published in AML, suggesting H3K9 histone methyltransferases could be a potent therapeutic target in MM. Disclosures Seckinger: EngMab AG: Research Funding; Takeda: Other: Travel grant. Goldschmidt:Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millenium: Honoraria, Research Funding, Speakers Bureau; Onyx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen-Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Chugai: Honoraria, Research Funding, Speakers Bureau. Hose:EngMab AG: Research Funding; Takeda: Other: Travel grant.

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Targeting EZH2 in Multiple Myeloma Could be Promising for a Subgroup of MM Patients in Combination with IMiDs

Blood ◽

10.1182/blood.v128.22.311.311 ◽

2016 ◽

Vol 128 (22) ◽

pp. 311-311 ◽

Cited By ~ 4

Author(s):

Laurie Herviou ◽

Alboukadel Kassambara ◽

Stephanie Boireau ◽

Nicolas Robert ◽

Guilhem Requirand ◽

...

Keyword(s):

Multiple Myeloma ◽

Bone Marrow ◽

Board Of Directors ◽

Research Funding ◽

Plasma Cells ◽

Newly Diagnosed ◽

Advisory Committees ◽

Myeloma Cells ◽

Ezh2 Expression ◽

Bone Marrow Plasma

Abstract Multiple Myeloma is a B cell neoplasia characterized by the accumulation of clonal plasma cells within the bone marrow.Epigenetics is characterized by a wide range of changes that are reversible and orchestrate gene expression. Recent studies have shown that epigenetic modifications play a role in multiple myeloma (MM) by silencing various cancer-related genes. We investigated the epigenetic genes differentially expressed between normal bone marrow plasma cells (BMPC ; N=5) and MM plasma cells from patients (N=206). Using SAM (Significance Analysis of Microarrays) analysis, only 12 genes significantly differentially expressed between BMPC and MM cells (ratio > 2 and FDR (false discovery rate) < 5%) were identified, including the EZH2 histone methyltransferase. EZH2, the enzymatic subunit of Polycomb Repressive Complex 2, is a histone methyltransferases able to repress gene expression by catalyzing H3K27me3 histone mark. EZH2 overexpression has been associated with numerous hematological malignancies, including MM. We thus studied EZH2 role in MM physiopathology and drug resistance. EZH2 expression was analyzed in normal bone marrow plasma cells (BMPCs; N=5), primary myeloma cells from newly diagnosed patients (MMCs; N=206) and human myeloma cell lines (HMCLs; N=40) using Affymetrix microarrays. EZH2 gene is significantly overexpressed in MMCs of patients (median 574, range 105 - 4562) compared to normal BMPCs (median = 432; range: 314 - 563) (P < 0.01). The expression is even higher in HMCLs (median 4481, range 581 - 8455) compared to primary MMCs or BMPCs (P < 0.001). High EZH2 expression is associated with a poor prognosis in 3 independent cohorts of newly diagnosed patients (Heidelberg-Montpellier cohort - N=206, UAMS-TT2 cohort - N=345 and UAMS-TT3 cohort - N =158). Furthermore, GSEA analysis of patients with high EZH2 expression highlighted a significant enrichment of genes involved in cell cycle, downregulated in mature plasma cells vs plasmablasts, and EZH2 targets. Specific EZH2 inhibition by EPZ-6438 EZH2 inhibitor induced a significant decrease of global H3K27me3 in all the HMCLs tested (P < 0.01) and inhibited MM cell growth in 5 out of the 6 HMCLs tested. The inhibitory effect of EZH2 inhibitor on MM cell growth appeared at day 6 suggesting that it is mediated by epigenetic reprogramming. To confirm that EZH2 is also required for the survival of primary MMCs from patients, primary MM cells (n = 17 patients) co-cultured with their bone marrow microenvironment and recombinant IL-6 were treated with EPZ-6438. As identified in HMCLs, EZH2 inhibition significantly reduced the median number of viable myeloma cells by 35% (P = 0.004) from a subset of patients (n=9) while the other group (n=8) was resistant. Of interest, EPZ-6438 induced a significant global H3K27me3 decrease in both groups of patient. RNA sequencing of 6 HMCLs treated with EPZ-6438 combined with H3K27me3 ChIP analyses allowed us to create an EZ GEP-based score able to predict HMCLs and primary MM cells sensitivity to EZH2 inhibitors. We also observed a synergy between EPZ-6438 and Lenalidomide, a conventional drug used for MM treatment. More interestingly, pretreatment of myeloma cells with EPZ-6438 significantly re-sensitize drug-resistant MM cells to Lenalidomide. Investigating the effect of EPZ-6438/Lenalidomide combination in MMC, we identified that IKZF1, IRF4 and MYC protein levels were significantly more inhibited by the combination treatment (65.5%, 63.9% and 14.8% respectively) compared with Lenalidomide (51.5%, 43% and 2.2%) or EPZ-6438 (45.2%, 38.7% and 6.2%) alone. Clinical trials are ongoing with EZH2 inhibitors in lymphoma and could be promising for a subgroup of MM patients in combination with IMiDs. Furthermore, the EZ score enables identification of MM patients with an adverse prognosis and who could benefit from treatment with EZH2 inhibitors. Disclosures Goldschmidt: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Onyx: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium: Membership on an entity's Board of Directors or advisory committees, Research Funding; Chugai: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Hose:EngMab: Research Funding; Takeda: Other: Travel grant; Sanofi: Research Funding.

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Dysregulation of Splicing in Multiple Myeloma: The Splicing Factor SRSF1 Supports MM Cell Proliferation Via Splicing Control

Blood ◽

10.1182/blood-2018-99-118845 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 4500-4500

Author(s):

Mariateresa Fulciniti ◽

Michael A Lopez ◽

Anil Aktas Samur ◽

Eugenio Morelli ◽

Hervé Avet-Loiseau ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Alternative Splicing ◽

Board Of Directors ◽

Research Funding ◽

Splicing Factor ◽

Splicing Factors ◽

Rna Seq ◽

Advisory Committees ◽

Disease Biology

Abstract Gene expression profile has provided interesting insights into the disease biology, helped develop new risk stratification, and identify novel druggable targets in multiple myeloma (MM). However, there is significant impact of alternative pre-mRNA splicing (AS) as one of the key transcriptome modifier. These spliced variants increases the transcriptomic complexity and its misregulation affect disease behavior impacting therapeutic consideration in various disease processes including cancer. Our large well annotated deep RNA sequencing data from purified MM cells data from 420 newly-diagnosed patients treated homogeneously have identified 1534 genes with one or more splicing events observed in at least 10% or more patients. Median alternative splicing event per patient was 595 (range 223 - 2735). These observed global alternative splicing events in MM involves aberrant splicing of critical growth and survival genes affects the disease biology as well as overall survival. Moreover, the decrease of cell viability observed in a large panel of MM cell lines after inhibition of splicing at the pre-mRNA complex and stalling at the A complex confirmed that MM cells are exquisitely sensitive to pharmacological inhibition of splicing. Based on these data, we further focused on understanding the molecular mechanisms driving aberrant alternative splicing in MM. An increasing body of evidence indicates that altered expression of regulatory splicing factors (SF) can have oncogenic properties by impacting AS of cancer-associated genes. We used our large RNA-seq dataset to create a genome wide global alterations map of SF and identified several splicing factors significantly dysregulated in MM compared to normal plasma cells with impact on clinical outcome. The splicing factor Serine and Arginine Rich Splicing Factor 1 (SRSF1), regulating initiation of spliceosome assembly, was selected for further evaluation, as its impact on clinical outcome was confirmed in two additional independent myeloma datasets. In gain-of (GOF) studies enforced expression of SRSF1 in MM cells significantly increased proliferation, especially in the presence of bone marrow stromal cells; and conversely, in loss-of function (LOF) studies, downregulation of SRSF1, using stable or doxy-inducible shRNA systems significantly inhibited MM cell proliferation and survival over time. We utilized SRSF1 mutants to dissect the mechanisms involved in the SRSF1-mediated MM growth induction, and observed that the growth promoting effect of SRSF1 in MM cells was mainly due to its splicing activity. We next investigated the impact of SRSF1 on allelic isoforms of specific gene targets by RNA-seq in LOF and confirmed in GOF studies. Splicing profiles showed widespread changes in AS induced by SRSF1 knock down. The most recurrent splicing events were skipped exon (SE) and alternative first (AF) exon splicing as compared to control cells. SE splice events were primarily upregulated and AF splice events were evenly upregulated and downregulated. Genes in which splicing events in these categories occurred mostly did not show significant difference in overall gene expression level when compared to control, following SRSF1 depletion. When analyzing cellular functions of SRSF1-regulated splicing events, we found that SRSF1 knock down affects genes in the RNA processing pathway as well as genes involved in cancer-related functions such as mTOR and MYC-related pathways. Splicing analysis was corroborated with immunoprecipitation (IP) followed by mass spectrometry (MS) analysis of T7-tagged SRSF1 MM cells. We have observed increased levels of SRSF phosphorylation, which regulates it's subcellular localization and activity, in MM cell lines and primary patient MM cells compared to normal donor PBMCs. Moreover, we evaluated the chemical compound TG003, an inhibitor of Cdc2-like kinase (CLK) 1 and 4 that regulate splicing by fine-tuning the phosphorylation of SR proteins. Treatment with TG003 decreased SRSF1 phosphorylation preventing the spliceosome assembly and inducing a dose dependent inhibition of MM cell viability. In conclusions, here we provide mechanistic insights into myeloma-related splicing dysregulation and establish SRSF1 as a tumor promoting gene with therapeutic potential. Disclosures Avet-Loiseau: Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding. Munshi:OncoPep: Other: Board of director.

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Baseline and on-Treatment Bone Marrow Microenvironments Predict Myeloma Patient Outcomes and Inform Potential Intervention Strategies

Blood ◽

10.1182/blood-2018-99-113169 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 1882-1882 ◽

Cited By ~ 1

Author(s):

Samuel A Danziger ◽

Mark McConnell ◽

Jake Gockley ◽

Mary Young ◽

Adam Rosenthal ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Bone Marrow ◽

Board Of Directors ◽

Research Funding ◽

Expression Profiles ◽

Cell Types ◽

Employment Equity ◽

Advisory Committees ◽

Equity Ownership

Abstract Introduction The multiple myeloma (MM) tumor microenvironment (TME) strongly influences patient outcomes as evidenced by the success of immunomodulatory therapies. To develop precision immunotherapeutic approaches, it is essential to identify and enumerate TME cell types and understand their dynamics. Methods We estimated the population of immune and other non-tumor cell types during the course of MM treatment at a single institution using gene expression of paired CD138-selected bone marrow aspirates and whole bone marrow (WBM) core biopsies from 867 samples of 436 newly diagnosed MM patients collected at 5 time points: pre-treatment (N=354), post-induction (N=245), post-transplant (N=83), post-consolidation (N=51), and post-maintenance (N=134). Expression profiles from the aspirates were used to infer the transcriptome contribution of immune and stromal cells in the WBM array data. Unsupervised clustering of these non-tumor gene expression profiles across all time points was performed using the R package ConsensusClusterPlus with Bayesian Information Criterion (BIC) to select the number of clusters. Individual cell types in these TMEs were estimated using the DCQ algorithm and a gene expression signature matrix based on the published LM22 leukocyte matrix (Newman et al., 2015) augmented with 5 bone marrow- and myeloma-specific cell types. Results Our deconvolution approach accurately estimated percent tumor cells in the paired samples compared to estimates from microscopy and flow cytometry (PCC = 0.63, RMSE = 9.99%). TME clusters built on gene expression data from all 867 samples resulted in 5 unsupervised clusters covering 91% of samples. While the fraction of patients in each cluster changed during treatment, no new TME clusters emerged as treatment progressed. These clusters were associated with progression free survival (PFS) (p-Val = 0.020) and overall survival (OS) (p-Val = 0.067) when measured in pre-transplant samples. The most striking outcomes were represented by Cluster 5 (N = 106) characterized by a low innate to adaptive cell ratio and shortened patient survival (Figure 1, 2). This cluster had worse outcomes than others (estimated mean PFS = 58 months compared to 71+ months for other clusters, p-Val = 0.002; estimate mean OS = 105 months compared with 113+ months for other clusters, p-Val = 0.040). Compared to other immune clusters, the adaptive-skewed TME of Cluster 5 is characterized by low granulocyte populations and high antigen-presenting, CD8 T, and B cell populations. As might be expected, this cluster was also significantly enriched for ISS3 and GEP70 high risk patients, as well as Del1p, Del1q, t12;14, and t14:16. Importantly, this TME persisted even when the induction therapy significantly reduced the tumor load (Table 1). At post-induction, outcomes for the 69 / 245 patients in Cluster 5 remain significantly worse (estimate mean PFS = 56 months compared to 71+ months for other clusters, p-Val = 0.004; estimate mean OS = 100 months compared to 121+ months for other clusters, p-Val = 0.002). The analysis of on-treatment samples showed that the number of patients in Cluster 5 decreases from 30% before treatment to 12% after transplant, and of the 63 patients for whom we have both pre-treatment and post-transplant samples, 18/20 of the Cluster 5 patients moved into other immune clusters; 13 into Cluster 4. The non-5 clusters (with better PFS and OS overall) had higher amounts of granulocytes and lower amounts of CD8 T cells. Some clusters (1 and 4) had increased natural killer (NK) cells and decreased dendritic cells, while other clusters (2 and 3) had increased adipocytes and increases in M2 macrophages (Cluster 2) or NK cells (Cluster 3). Taken together, the gain of granulocytes and adipocytes was associated with improved outcome, while increases in the adaptive immune compartment was associated with poorer outcome. Conclusions We identified distinct clusters of patient TMEs from bulk transcriptome profiles by computationally estimating the CD138- fraction of TMEs. Our findings identified differential immune and stromal compositions in patient clusters with opposing clinical outcomes and tracked membership in those clusters during treatment. Adding this layer of TME to the analysis of myeloma patient baseline and on-treatment samples enables us to formulate biological hypotheses and may eventually guide therapeutic interventions to improve outcomes for patients. Disclosures Danziger: Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment. Gockley:Celgene Corporation: Employment. Young:Celgene Corporation: Employment, Equity Ownership. Schmitz:Celgene Corporation: Employment, Equity Ownership. Reiss:Celgene Corporation: Employment, Equity Ownership. Davies:MMRF: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; TRM Oncology: Honoraria; Abbvie: Consultancy; ASH: Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria. Copeland:Celgene Corporation: Employment, Equity Ownership. Fox:Celgene Corporation: Employment, Equity Ownership. Fitch:Celgene Corporation: Employment, Equity Ownership. Newhall:Celgene Corporation: Employment, Equity Ownership. Barlogie:Celgene: Consultancy, Research Funding; Dana Farber Cancer Institute: Other: travel stipend; Multiple Myeloma Research Foundation: Other: travel stipend; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend; Millenium: Consultancy, Research Funding; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; Myeloma Health, LLC: Patents & Royalties: : Co-inventor of patents and patent applications related to use of GEP in cancer medicine licensed to Myeloma Health, LLC. Trotter:Celgene Research SL (Spain), part of Celgene Corporation: Employment, Equity Ownership. Hershberg:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Dervan:Celgene Corporation: Employment, Equity Ownership. Ratushny:Celgene Corporation: Employment, Equity Ownership. Morgan:Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding.

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Transcriptome Profiling of Pediatric Myeloproliferative Neoplasms Demonstrates Dysregulation of Platelet-Relevant Genes and Enrichment of Inflammatory and JAK2 Mediated Complement Pathways

Blood ◽

10.1182/blood-2019-125103 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 4203-4203

Author(s):

Nicole Kucine ◽

Amanda R. Leonti ◽

Aishwarya Krishnan ◽

Rhonda E. Ries ◽

Ross L. Levine ◽

...

Keyword(s):

Bone Marrow ◽

Board Of Directors ◽

Platelet Activation ◽

Research Funding ◽

Mononuclear Cells ◽

Myeloproliferative Neoplasms ◽

Differentially Expressed ◽

Rna Seq ◽

Advisory Committees ◽

Normal Controls

Introduction : Myeloproliferative neoplasms (MPNs) are rare clonal bone marrow disorders in children characterized by high blood counts, predisposition to clotting events, and the potential to transform to myelofibrosis or acute myeloid leukemia (AML). Children with MPNs have lower rates of the known driver mutations (in JAK2, MPL, and CALR) than adult patients, and the underlying pathways and molecular derangements in young patients remain unknown. Given the lack of knowledge about pediatric MPNs, it is critical that we gain a better understanding of the dysregulated pathways in these diseases, which is necessary for improving disease understanding and broadening treatment options in children. Therefore, the objective of this work was to identify differentially expressed genes and pathways between children with MPNs and healthy controls, as well as children with AML, to guide further study. Methods : Mononuclear cells were extracted from peripheral blood of pediatric MPN patients (n=20) and pediatric and young adult AML patients (n=1410), and bone marrow of normal controls (NC, n=68). AML patient samples were being evaluated as part of a Children's Oncology Group planned analysis. To identify an expression profile unique to MPNs, transcriptome data from MPN patients was contrasted against NC and AML patients. All samples were ribodepleted and underwent Illumina RNA-Seq to generate transcriptome expression data. All analyses were performed in R. Differentially expressed genes were identified using the voom function from the limma package (v. 3.38.3), and enriched pathways were identified using the pathfindR package (v. 1.3.1). Unsupervised hierarchical clustering and heatmap generation was performed using the ComplexHeatmap package (v. 1.20.0). Results : MPN patient samples showed a unique expression signature, distinct from both AML patients and normal controls. Unsupervised PCA plot (Figure 1A) and heatmaps (Figure 1B) show that MPN samples cluster together. There were 4,012 differentially expressed (DE) genes in MPNs compared to NC and 6,743 DE genes in MPNs compared to AML patients. There were 2,493 shared genes between the 2 groups (Figure 1C.) Significantly DE genes between MPNs and other groups included multiple platelet-relevant genes including PF4 (CXCL4), PF4V1, P2RY12, and PPBP (CXCL7). Interestingly, PF4V1 was the most DE gene in MPNs compared to AML, and third highest versus NC. Dysregulation of some of these genes has been seen in adult MPNs, as well as thrombosis. Further comparison of transcriptome profiles between children with (n=13) and without (n=7)JAK2 mutations showed upregulation of three genes, CFB, C2, and SERPING1, which are all known complement genes, implicating complement activation in JAK2-mutated MPN patients. Complement activation has previously been reported in adult MPNs. Pathway enrichment analysis shows a number of immune and inflammatory pathways as enriched in MPN patients compared to both AML and NC. There were 179 enriched pathways in MPNs compared to AML and 142 compared to NC, with 134 common pathways (Figure 1D.) The systemic lupus erythematosus pathway was the most heavily enriched pathway in MPNs compared to both AML and NC. Additional pathways with significant enrichment include hematopoietic cell lineage, cytokine-cytokine interactions, DNA replication, and various infection-relevant pathways. The JAK-STAT signaling pathway was also enriched in MPNs compared to both AML and NC, as was the platelet activation pathway. Conclusion: Transcriptome evaluation of childhood MPNs shows enrichment of numerous inflammatory and immune pathways, highlighting that, as in adult MPNs, inflammation is implicated in pediatric MPNs. Furthermore, specific complement genes were upregulated in JAK2-mutant MPN. Upregulation of platelet-specific genes implies potential insights into disease mechanisms and warrants more study. Variations in the cell populations may account for some of the differences seen, however all samples were largely mononuclear cells, making their comparisons reasonable. Further analysis of this early data is needed to better assess inflammatory changes and platelet activation in pediatric MPNs, as are larger sample sizes. Individual cells may have differential expression of various genes, and future experiments with single-cell RNA-seq would be helpful to further elucidate differences. Disclosures Levine: Novartis: Consultancy; Loxo: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Research Funding; Gilead: Consultancy; Roche: Consultancy, Research Funding; Lilly: Honoraria; Amgen: Honoraria; Qiagen: Membership on an entity's Board of Directors or advisory committees; Imago Biosciences: Membership on an entity's Board of Directors or advisory committees; C4 Therapeutics: Membership on an entity's Board of Directors or advisory committees; Prelude Therapeutics: Research Funding; Isoplexis: Membership on an entity's Board of Directors or advisory committees.

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GFI1 Is a Downstream Target of EVI1 in Normal Hematopoiesis

Blood ◽

10.1182/blood-2020-134071 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 33-33

Author(s):

Akira Chiba ◽

Yosuke Masamoto ◽

Hideaki Mizuno ◽

Mineo Kurokawa

Keyword(s):

Bone Marrow ◽

Progenitor Cells ◽

Board Of Directors ◽

Research Funding ◽

Hematopoietic Progenitor Cells ◽

Rna Seq ◽

Hematopoietic Progenitor ◽

Advisory Committees ◽

Hematopoietic Stem ◽

Knock Out

Acute myeloid leukemia (AML) with high expression of a transcriptional factor, Ecotropic viral integration site 1 (EVI1), is associated with extremely poor prognosis. EVI1 is, however, also essential for maintaining normal hematopoietic stem cells (HSCs), rendering it potentially difficult to target this molecule. To overcome this therapeutic difficulty, it is important to comprehensively elucidate differentially regulated downstream targets between normal and leukemia cells. In this study, we searched downstream targets of EVI1 in normal hematopoiesis by combining a chromatin immunoprecipitation sequence (ChIP-seq) and RNA-sequence (RNA-seq) analysis using a mouse hematopoietic cell line 32D-cl3 with high EVI1 expression. We deleted Evi1 using CRISPR/Cas9 in 32D-cl3 cells. Evi1 knock-out (KO) 32D-cl3 cells showed comparable cell growth with parental cells in the presence of IL-3, which enables them to proliferate permanently without differentiation. When they are allowed to differentiate by adding G-CSF, the number of KO cells decreased sharply at day 5-6, compared with parental 32D-cl3 cells. Along with the decreased cell number, KO cells also demonstrated higher positive rate of Gr-1 at day 7, a typical marker of differentiation into granulocytes, indicating accelerated differentiation of KO cells. These results indicated that EVI1 is required to maintain undifferentiated status of 32D-cl3 cells in a differentiation-permissive conditions, which can model normal hematopoiesis. We knocked in 3×FLAG tag at the 3' end of the Evi1 gene to perform ChIP-seq using anti-FLAG antibody. By using these knock-in cells, ChIP-seq was performed on day 0 and day 3 of G-CSF treatment, when they had started to differentiate with still maintained EVI1 expression. The peaks observed in undifferentiated day 0 sample were considered to contain a group of genes involved in undifferentiated hematopoietic cells in cooperation with EVI1. Genes associated HDAC class I, RAC1 signaling were enriched in these genes. To investigate the functional implications of the result of ChIP-seq, RNA-seq data using two clones of KO cells and parental cells were combined. We found that 152 genes were significantly up-regulated, and 155 genes were down-regulated in the KO cells, with false discovery rate less than 0.05. Twenty-four genes were identified by extracting common genes between ChIP-seq and RNA-seq; namely, genes which had day 0-specific peaks in ChIP-seq, and whose expression were decreased in the KO cells. In order to further examine the physiological implications of 24 genes in vivo, we referred to the results of RNA-seq using murine bone marrow transplantation model, where murine hematopoietic progenitor cells retrovirally transduced with Evi1 were transplanted into irradiated syngeneic mice, finally leading to AML after a long latency. Samples obtained early after post transplantation and those after AML onset were compared to those of normal hematopoietic progenitor cells. Among the above 24 genes, the expression of 5 genes was increased early after transplantation and decreased after the onset of AML, that is, these genes were up-regulated by EVI1 but don't seem to be involved in AML maintenance. We functionally validated the role of these genes in 32D-cl3 cells. Of the above, CRISPR/Cas9-mediated knock-out of Gfi1(Growth Factor Independent 1 Transcriptional Repressor) and Mfsd2b (Major facilitator superfamily domain containing 2B) in 32D-cl3 cells led to high Gr-1 positivity at day 7 like Evi1-KO cells, suggesting that these genes are involved in the functions of EVI1 in the normal hematopoiesis. The mRNA expression of these genes was compared in LSK (Lineage- Sca1+ c-kit+) cells from the bone marrow of Evi1 conditional knockout (cKO) mice and control mice. The expression of Gfi1 and Mfsd2b was decreased in LSK cells from Evi1 cKO mice. Furthermore, retroviral expression of Gfi1 in LSK cells restored the reduced colony-forming ability of Evi1 cKO cells. These results collectively suggest that GFI1 is regulated by EVI1 and is involved in the function of EVI1 regulating the stemness of hematopoietic stem and progenitor cells in normal hematopoiesis. These findings provide us with the novel insights on EVI1-mediated HSC maintenance as well as on the therapeutic strategy that specifically targets leukemia-specific EVI1 effectors while preserving normal hematopoiesis. Disclosures Kurokawa: Shire Plc: Speakers Bureau; Jansen Pharmaceutical: Speakers Bureau; Ono: Research Funding, Speakers Bureau; Boehringer Ingelheim: Speakers Bureau; Bristol-Myers Squibb: Speakers Bureau; Eisai: Research Funding, Speakers Bureau; Sumitomo Dainippon Pharma: Research Funding, Speakers Bureau; Teijin: Research Funding; Takeda: Research Funding, Speakers Bureau; Kyowa Kirin: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Astellas: Research Funding, Speakers Bureau; Otsuka: Research Funding, Speakers Bureau; Pfizer: Research Funding; Sanwa-Kagaku: Consultancy; MSD: Consultancy, Research Funding, Speakers Bureau; Chugai: Consultancy, Research Funding, Speakers Bureau; Bioverativ Japan: Consultancy; Celgene: Consultancy, Speakers Bureau; Daiichi Sankyo: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Nippon Shinyaku: Research Funding, Speakers Bureau.

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