Dissecting the Epigenetic Landscape of Smoldering, Newly Diagnosed and Relapsed Multiple Myeloma Revealed IRAK3 As a Marker of Disease Progression

Abstract Introduction. Multiple myeloma (MM) is a complex and heterogeneous malignancy of plasma cells that has two precursor states: monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). MGUS and SMM are asymptomatic states that eventually give rise to overt MM, with some patients progressing, while others do not. Recent studies in MM pathobiology have highlighted epigenetic alterations that contribute to the onset, progression and heterogeneity of MM. Global hypomethylation of DNA, including tumor suppressor genes, and hypermethylation of B-cell specific enhancers, abnormal histone methylation patterns due to the overexpression of histone methyltransferases such as MMSET, and deregulation of non-coding RNAs along with mutations in different classes of chromatin modulators underline a potential for epigenetic biomarkers in disease prognosis and treatment. This study aimed to define epigenetic pathways that lead to the dynamic regulation of gene expression in MM pathogenesis. Methods. We performed ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) and RNA-seq on 10 MM cell lines and CD138+ plasma cells isolated from bone marrow aspirates of 3 healthy donors, 9 SMM, 8 newly diagnosed MM (NDMM) and 9 relapsed (RRMM) patients. ATAC-seq reads were trimmed of adapters, aligned to hg19 using bowtie2, and filtered for mapping quality >=Q30 using the ENCODE ATAC-seq pipeline. Reads mapping to promoter regions, defined as -400 to +250 bases from a refseq transcription start site, were counted using bedtools for each sample. Promoter read counts were then normalized by the total number of reads in promoters in the sample, scaled to 1 million total reads, and converted to log10(x+1) space. Results. To characterize the epigenetic contribution to disease progression in MM, we first identified accessible promoter regions in normal plasma cells (NPC), SMM, NDMM and RRMM patients and found regions displaying differential accessibility in MM progression. Next, we intersected the list of differential accessible regions (DARs) with matched transcriptome data and observed two main clusters: genes with unaltered transcription profiles and genes in which the dynamics of open chromatin regions (OCRs) correlated with gene expression. Transcriptomic analysis revealed that a large portion of the differentially expressed (DE) genes in SMM remain DE in NDMM as compared to NPCs (882 genes out of 1642 and 1150 DE genes in SMM and NDMM, respectively). Those genes were significantly enriched for pathways like epithelial mesenchymal transition, cell cycle checkpoints and mitosis, KRAS signaling and interleukin-JAK-STAT pathways. To investigate the genes that behaved differently among the stages of disease, we looked at differential accessibility and expression in NDMM and SMM samples, and integrated them with Whole-Genome Bisulfite-Sequencing and 450K DNA-methylation data from MM patients and healthy donors (BLUEPRINT). This analysis led to the identification of novel genes in MM progression, such as the transcriptional repressor ZNF254 and IRAK3, a negative regulator of the TLR/IL1R signaling pathway. Although gene expression data for these genes showed comparable mRNA levels in SMM and NPCs, followed by a significant decrease in NDMM/ RRMM, ATAC-seq revealed a striking drop in promoter accessibility in SMM, NDMM and RRMM cases. Comparison of ATAC-seq peaks to DNA methylation and ChIP-seq data revealed that the altered OCR of IRAK3 is actually hypermethylated in MM patients and marked by H3K4me3, a marker of active promoters, in MM cell lines. Hypermethylation of IRAK3 has been described in hepatocellular carcinoma, where it is associated with poor prognosis. Together, our data suggest that the identified IRAK3 OCR may act as a bivalent domain that loses accessibility in the precursor states and gains DNA methylation in MM progression. Hence, IRAK3 methylation could be a novel prognostic marker in MM. Conclusion. We have generated a global epigenetic map of primary tumors from patients at the smoldering, newly diagnosed and relapsed/refractory stage of multiple myeloma. Integrative analysis of ATAC-seq data with DNA methylome, transcriptome and whole-genome map of active and repressive histone marks in our study led to the identification of IRAK3 as a novel epigenetic biomarker of disease progression. Disclosures Licht: Celgene: Research Funding. Ghobrial:Takeda: Consultancy; BMS: Consultancy; Celgene: Consultancy; Janssen: Consultancy.

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Whole Genome DNA Methylation Analysis of Commpass Identifies Biomarkers of Multiple Myeloma Survival

Blood ◽

10.1182/blood-2018-99-116875 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 3174-3174

Author(s):

Benjamin G Barwick ◽

Daniel Auclair ◽

Alex Blanski ◽

Meghan Kirchhoff ◽

Brianne Docter ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Multiple Myeloma ◽

B Cells ◽

B Cell ◽

Prognostic Value ◽

Bisulfite Sequencing ◽

Plasma Cells ◽

Whole Genome ◽

Dna Methylome

Abstract Multiple myeloma is a malignancy of terminally differentiated, antibody secreting B cells known as plasma cells. Normal B cell differentiation and cell fate are coupled to epigenetic and transcriptional reprogramming, including a proliferation-dependent global loss of DNA methylation (Barwick et al., 2016, 2018). However, relatively little is known about the epigenetic changes that underlie myelomagenesis and how these may contribute to pathogenesis. To this end, we are analyzing the DNA methylome of myeloma specimens from the MMRF CoMMpass trial (NCT01454297), which has already characterized the mutational, structural, and transcriptional landscape of nearly 1,000 myelomas from newly diagnosed patients. CoMMpass specimens were obtained from a centralized biobank with approval from the CoMMpass Tissue Use Committee and Emory IRB. DNA isolated from CD138+ myeloma specimens was subjected to reduced representation bisulfite sequencing (RRBS) or whole genome bisulfite sequencing (WGBS). In total, DNA methylation was derived for over 24 million CpGs with an average of 18x coverage. WGBS data from normal B cells and plasma cells was obtained with permission from the BluePrint project (Agirre et al., 2015) via the European Genome Archive. DNA methylation levels were associated with PFS and OS using a cox proportional regression. We have determined the DNA methylome for 36 primary myeloma specimens and an additional 84 specimens are currently being sequenced. Relative to normal B cells that had an average DNA methylation level of 89.1%, plasma cells and myelomas exhibited a progressive demethylation with mean levels of 71.3% and 43.7%, respectively. While this is consistent with previous observations (Agirre et al., 2015; Salhia et al., 2010), WGBS revealed that myeloma in particular was characterized by large hypomethylated domains. These large hypomethylated domains encompassed genes that were devoid of gene expression whereas DNA methylation remained unchanged in the bodies of genes that were highly expressed. Although the majority of these hypomethylated domains were common across myelomas, there existed many regions where methylation levels varied between myelomas and these differences commonly corresponded with local gene expression differences. To understand if these specific patterns of DNA methylation were indicative of disease pathogenesis, DNA methylation levels were compared to PFS and OS. This identified 2,594 regions where the level of DNA methylation was prognostic of outcome (P≤0.001). Reduced DNA methylation corresponded with poor outcome at 88.5% (N=2,298) of these regions, which included loci proximal to cell cycle genes such as MYC, E2F3, CCND1, and CCNE1. Only 11.5% (N=296) of regions associated with outcome had higher levels of DNA methylation associated with poor prognosis. These regions tended to be proximal to genes involved in B cell receptor signaling, such as PLCG2 and VAV2. Although the expression of several of these genes was also prognostic of survival, the majority were not, indicating that the epigenetic state contains a unique prognostic value. These data indicate that myeloma undergoes profound epigenetic remodeling that is co-ordinate with changes in gene expression. Perhaps the most striking feature were megabase domains of hypomethylation. That DNA methylation was preferentially retained in the bodies of expressed genes suggests that a molecular mechanism and/or cellular selection occurs to maintain methylation at genes whose expression is required for myeloma cell survival. Despite the small number (N=36) of myeloma specimens analyzed thus far, the large number of regions associated with survival indicates the potential prognostic value of DNA methylation in myeloma. Furthermore, DNA methylation indicative of outcome only partially overlapped with the prognostic value of gene expression, indicating DNA methylation has independent value as a biomarker of outcome in myeloma. This may be due, in part, to the fact that DNA methylation is a very stable modification that not only reflects the current gene expression program, but is also indicative of the cell history and potential. Integrative genetic, epigenetic, and transcriptional analysis from WGBS of 120 CoMMpass myeloma specimens will be presented, including matched baseline and relapsed specimens from 25 patients. Disclosures Lonial: Amgen: Research Funding. Boise:Abbvie: Consultancy; AstraZeneca: Honoraria.

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Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients

Blood ◽

10.1182/blood-2009-12-261032 ◽

2010 ◽

Vol 116 (14) ◽

pp. 2543-2553 ◽

Cited By ~ 171

Author(s):

Annemiek Broyl ◽

Dirk Hose ◽

Henk Lokhorst ◽

Yvonne de Knegt ◽

Justine Peeters ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Plasma Cells ◽

Medical Science ◽

Protein Tyrosine Phosphatases ◽

Molecular Classification ◽

Newly Diagnosed ◽

Expression Of Genes

Abstract To identify molecularly defined subgroups in multiple myeloma, gene expression profiling was performed on purified CD138+ plasma cells of 320 newly diagnosed myeloma patients included in the Dutch-Belgian/German HOVON-65/GMMG-HD4 trial. Hierarchical clustering identified 10 subgroups; 6 corresponded to clusters described in the University of Arkansas for Medical Science (UAMS) classification, CD-1 (n = 13, 4.1%), CD-2 (n = 34, 1.6%), MF (n = 32, 1.0%), MS (n = 33, 1.3%), proliferation-associated genes (n = 15, 4.7%), and hyperdiploid (n = 77, 24.1%). Moreover, the UAMS low percentage of bone disease cluster was identified as a subcluster of the MF cluster (n = 15, 4.7%). One subgroup (n = 39, 12.2%) showed a myeloid signature. Three novel subgroups were defined, including a subgroup of 37 patients (11.6%) characterized by high expression of genes involved in the nuclear factor kappa light-chain-enhancer of activated B cells pathway, which include TNFAIP3 and CD40. Another subgroup of 22 patients (6.9%) was characterized by distinct overexpression of cancer testis antigens without overexpression of proliferation genes. The third novel cluster of 9 patients (2.8%) showed up-regulation of protein tyrosine phosphatases PRL-3 and PTPRZ1 as well as SOCS3. To conclude, in addition to 7 clusters described in the UAMS classification, we identified 3 novel subsets of multiple myeloma that may represent unique diagnostic entities.

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Epigenetic Deregulation of Telomere-Related Genes in Newly Diagnosed Multiple Myeloma Patients

Cancers ◽

10.3390/cancers13246348 ◽

2021 ◽

Vol 13 (24) ◽

pp. 6348

Author(s):

Samrat Roy Choudhury ◽

Cody Ashby ◽

Fenghuang Zhan ◽

Frits van Rhee

Keyword(s):

Dna Methylation ◽

Multiple Myeloma ◽

Dna Sequences ◽

Critical Length ◽

Newly Diagnosed ◽

Genome Wide ◽

Healthy Donors ◽

Binding Factor ◽

G Quadruplex

High-risk Multiple Myeloma (MM) patients were found to maintain telomere length (TL), below the margin of short critical length, consistent with proactive overexpression of telomerase. Previously, DNA methylation has been shown as a determinant of telomere-related gene (TRG) expression and TL to assess risk in different types of cancer. We mapped genome-wide DNA methylation in a cohort of newly diagnosed MM (NDMM; n = 53) patients of major molecular subgroups, compared to age-matched healthy donors (n = 4). Differential methylation and expression at TRG-loci were analyzed in combination with overlapping chromatin marks and underlying DNA-sequences. We observed a strong correlation (R2 ≥ 0.5) between DNA methylation and expression amongst selective TRGs, such that demethylation at the promoters of DDX1 and TERF1 were associated to their oncogenic upregulation, while demethylation at the bodies of two key tumor suppressors ZNF208 and RAP1A led to downregulation of the genes. We demonstrated that TRG expression may be controlled by DNA methylation alone or in cooperation with chromatin modifications or CCCTC-binding factor at the regulatory regions. Additionally, we showed that hypomethylated DMRs of TRGs in NDMM are stabilized with G-quadruplex forming sequences, suggesting a crucial role of these epigenetically vulnerable loci in MM pathogenesis. We have identified a panel of five TRGs, which are epigenetically deregulated in NDMM patients and may serve as early detection biomarkers or therapeutic targets in the disease.

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Modelization of Blood-Borne Hypercoagulability in Myeloma: A Tissue-Factor-Bearing Microparticle-Driven Process

TH Open ◽

10.1055/s-0039-1700885 ◽

2019 ◽

Vol 03 (04) ◽

pp. e340-e347

Author(s):

Loula Papageorgiou ◽

Kutaiba Alhaj Hussen ◽

Sandrine Thouroude ◽

Elisabeth Mbemba ◽

Héléne Cost ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Tissue Factor ◽

Thrombin Generation ◽

Plasma Cells ◽

Annexin V ◽

Factor Vii ◽

Newly Diagnosed ◽

Normal Human ◽

Tf Gene

Abstract Introduction Hypercoagulability is a common blood alteration in newly diagnosed chemotherapy naïve patients with multiple myeloma. The identification of the procoagulant potential of cancer cells, which is principally related to tissue factor (TF) expression, attracts particular interest. The mechanisms by which myeloma plasma cells (MPCs) activate blood coagulation have been poorly investigated. Aim To identify the principal actors related with MPCs that boost thrombin generation (TG). Methods TF and annexin V expression by MPCs and MPC-derived microparticles (MPC-dMPs) was analyzed by flow cytometry. TF activity (TFa) and TF gene expression were also determined. TG in the presence of MPCs or MPC-dMPs was assessed with the calibrated automated thrombogram assay (CAT) in normal human PPP and in plasma depleted of factor VII or XII. TG was also assessed in plasma spiked with MPCs and MPC-dMPs. Results MPC-dMPs expressed approximately twofold higher levels of TF as compared with MPCs. The TFa expressed by MPC-dMPs was significantly higher compared with that expressed by MPCs. MPCs and MPC-dMPs enhanced TG of human plasma. TG was significantly higher with MPC-dMPs compared with MPCs. Conclusion MPCs indirectly induce blood-borne hypercoagulability through the release of MPC-dMPs rich in TF. Since MPCs, expressing low TFa, represent a weak procoagulant stimulus, the hypercoagulability at the microenvironment could be the resultant of MPC-dMPs rich in TF.

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Gene Expression Profiling of Myeloma Cells To Predict Response to Primary Therapy with Thalidomide-Dexamethasone for Newly Diagnosed Multilple Myeloma.

Blood ◽

10.1182/blood.v110.11.1494.1494 ◽

2007 ◽

Vol 110 (11) ◽

pp. 1494-1494

Author(s):

Abderrahman Abdelkefi ◽

John de Vos ◽

Said Assou ◽

Tarek Ben Othman ◽

Jean-Francois Rossi ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Gene Expression Profiling ◽

Treatment Strategy ◽

Expression Profiling ◽

Plasma Cells ◽

Gene List ◽

Microarray Platform ◽

Primary Therapy ◽

Newly Diagnosed

Abstract Background: Thalidomide which represents an effective treatment strategy for relapsed/refractory multiple myeloma, actually represents a standard of care also for newly diagnosed multiple myeloma patients. Methods: In the present study, we adopted a gene expression profiling (GEP) strategy in an attempt to predict response (> 50% reduction in serum M protein) to primary therapy with thalidomide-dexamethasone for newly diagnosed multiple myeloma. Plasma cells (CD138+) were purified from bone marrow aspirates from 17 patients at diagnosis, before initiation of treatment with thalidomide-dexamethasone. GEP was performed using the Affymetrix U133 Plus_2 microarray platform. The Affymetrix output (CEL files) was imported into Genespring 7.3 (Agilent technologies) microarray analysis software, where data files were normalized across chips using GCRMA and to the 50th percentile, followed by per gene normalization to median. Criteria of response were those established by Bladè et al. Results: After sufficient follow-up, responders (n=9) and nonresponders (n=8) were identified, and gene expression differences in baselines samples were examined. Of the 11000 genes surveyed, Wilcoxon rank sum test identified 149 genes that distinguished response from non response. A multivariate step-wise discriminant analysis (MSDA) revealed that 14 of the 149 genes could be used in a response predictor model (see table). Of interest, the gene list encompasses WXSC1, known to be involved in the chromosomal translocation t(4;14) (p16.3;q32.3) in multiple myeloma. Conclusion: These results could be the first step to adopt microfluidic cards, in an attempt to select at diagnosis patients who will respond favourably to a particular treatment strategy. List of 14 genes able to predict response to primary therapy with thalidomide-dexamethasone for newly diagnosed multiple myeloma. Gene ID Gene Name Chromosomal location 212771_at C10orf38 10p13 229874_x_at LOC400741 1p36.13 219690_at U2AF1L4 19q13.12 202207_at ARL7 2q37.1 243819_at GNG2 14q21 203753_at TCF4 18q21.1 235400_at FCRLM1 1q23.3 211474_s_at SERPINB6 6p25 226785_at ATP11C Xq27.1 215440_s_at BEXL1 Xq22.1–q22.3 209054_s_at WXSC1 4p16.3 227168_at FLJ25967 22p12.1 213355_at ST3GAL6 3q12.1 223218_s_at NFKBIZ 3p12–q12

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Transcriptome Alterations of Isolated Primary Bone Marrow Endothelial Cells Provide Insigt to MGUS and Multiple Myeloma Pathobiology

Blood ◽

10.1182/blood-2019-128024 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 4373-4373

Author(s):

Sandro Bräunig ◽

Dimitra Zacharaki ◽

Hongzhe Li ◽

Hooi Ching Lim ◽

Stefan Lang ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Bone Marrow ◽

Endothelial Cells ◽

Disease Progression ◽

Plasma Cell ◽

Gene Expression Analysis ◽

Bone Marrow Microenvironment ◽

Healthy Controls ◽

Healthy Donors

Multiple myeloma (MM) is characterized by an abnormal clonal expansion of plasma cells in the bone marrow, production of monoclonal immunoglobulins and finally organ damage (CRAB). The premalignant precursor of MM is Monoclonal gammopathy of undetermined significance (MGUS) and one percent of all MGUS patients progress to MM yearly. The bone marrow microenvironment is thought to play an important role in plasma cell growth, migration, and survival mainly via cytokine secretion and cell-cell interactions. Endothelial cells (ECs) are a major component in the bone marrow microenvironment, they regulate trafficking and homing of hematopoietic progenitor and stem cells. In MM increased bone marrow angiogenesis and recruitment of endothelial progenitors to the bone marrow niche has been reported. However, the specific EC contribution to myelomagenesis is not yet known. This study therefore aimed to investigate transcriptome alterations in prospectively isolated bone marrow ECs from MGUS and MM patients to identify possible disease-stage related changes. We isolated primary ECs from MGUS and MM patients undergoing diagnostic bone marrow aspirations and age-matched healthy donors by FACS. RNA from Lin- CD45- CD71- CD235a- CD271- CD31+ cells of MGUS (n=4) and MM (n=7) patients and healthy donors (n=6) was extracted. Sequencing was done using the Illumina® NextSeq 500/550 High Output Kit v2.5 (300 cycles). Gene expression analysis was performed in R. Differential gene expression analysis (DEseq2) identified 1,507 genes with p adjusted values below 1e-2 that were significantly differentially expressed between the three groups. Hierarchical clustering was done following Ward's method (ward.D2). Unsupervised clustering on the data showed that one MGUS-EC sample clustered with the healthy controls, and that one healthy control sample clustered with the MGUS samples. We therefore decided to restrict the analysis to those samples that clearly clustered separately, to be able to better depict the MGUS-, MM- and healthy EC specific profiles. Further clustering of differential expressed genes into 8 clusters revealed two especially interesting expression patterns. One cluster (#4) contained 102 genes that where higher expressed in the healthy controls with lower expression in MGUS and lowest expression in MM Samples. These genes thus reflect the downregulation during progression from a healthy bone marrow microenvironment to a reduced expression MGUS and further downregulation in MM. Another cluster (#6) showed the opposite pattern, with 105 genes being low or not expressed in healthy controls while the expression was higher in MGUS and highest in MM. Gene sets where further analyzed in the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8. Cluster 4 showed a high number of downregulated transmembrane genes. Six genes of the major histocompatibility complex conserved site where identified might indicate a possible immunomodulating effect in disease progression. Furthermore, within cluster 4 we identified a cluster of genes involved in cell adhesion and receptor binding. Cluster 6 most strikingly showed a group of 6 genes of the melanoma-associated antigen (MAGE) gene family that were upregulated with disease progression. MAGE genes which belong to the cancer-testis group of germline genes have previously been reported in MM, as being involved in tumorigenesis, and plasma cell MAGE expression has been associated with chemotherapy resistance. Furthermore, cluster 6 contained a high number of extracellular matrix genes, and genes for proteins having an extracellular region, respectively, hinting towards a differential microenvironment composition upon MM development. Taken together RNA sequencing analysis of prospectively isolated bone marrow endothelial cells identified genes that were specifically upregulated/suppressed in MM-ECs compared to MGUS-ECs and healthy donor-ECs. These genes thus represent potential gene candidates involved in the disruption of normal microenvironment function, thus leading to disease development and progression. Accordingly, studies are underway to investigate selected transcriptional deregulation EC-MM microenvironmental functions in the context of the disease. Disclosures No relevant conflicts of interest to declare.

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Crowdsourced High-Risk Classifiers for Multiple Myeloma Patients Commonly Identify PHF19 As a Robust Progression Biomarker

Blood ◽

10.1182/blood-2019-127362 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 4370-4370

Author(s):

Michael J Mason ◽

Carolina D. Schinke ◽

Christine Eng ◽

Fadi Towfic ◽

Fred Gruber ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

High Risk ◽

Disease Progression ◽

Board Of Directors ◽

Research Funding ◽

Plasma Cells ◽

Employment Equity ◽

Advisory Committees ◽

Equity Ownership

Multiple myeloma (MM) is a hematological malignancy of terminally differentiated plasma cells residing within the bone marrow with 25,000-30,000 patients diagnosed in the United States each year. The disease's clinical course depends on a complex interplay chromosomal abnormalities and mutations within plasma cells and patient socio-demographic factors. Novel treatments extended the time to disease progression and overall survival for the majority of patients. However, a subset of 15%-20% of MM patients exhibit an aggressive disease course with rapid disease progression and poor overall survival regardless of treatment. Accurately predicting which patients are at high-risk is critical to designing studies with a better understanding of myeloma progression and enabling the discovery of novel therapeutics that extend the progression free period of these patients. To date, most MM risk models use patient demographic data, clinical laboratory results and cytogenetic assays to predict clinical outcome. High-risk associated cytogenetic alterations include deletion of 17p or gain of 1q as well as t(14;16), t(14;20), and most commonly t(4,14), which leads to juxtaposition of MMSET with the immunoglobulin heavy chain locus promoter, resulting in overexpression of the MMSET oncogene. While cytogenetic assays, in particular fluorescence in situ hybridization (FISH), are widely available, their risk prediction is sub-optimal and recently developed gene expression based classifiers predict more accurately rapid progression. To investigate possible improvements to models of myeloma risk, we organized the Multiple Myeloma DREAM Challenge, focusing on predicting high-risk, defined as disease progression or death prior to 18 months from diagnosis. This effort combined 4 discovery datasets providing participants with clinical, cytogenetic, demographic and gene expression data to facilitate model development while retaining 4 additional datasets, whose clinical outcome was not publicly available, in order to benchmark submitted models. This crowd-sourced effort resulted in the unbiased assessment of 171 predictive algorithms on the validation dataset (N = 823 unique patient samples). Analysis of top performing methods identified high expression of PHF19, a histone methyltransferase, as the gene most strongly associated with disease progression, showing greater predictive power than the expression level of the putative high-risk gene MMSET. We show that a simple 4 feature model composed of age, stage and the gene expression of PHF19 and MMSET is as accurate as much larger published models composed of over 50 genes combined with ISS and age. Results from this work suggest that combination of gene expression and clinical data increases accuracy of high risk models which would improve patient selection in the clinic. Disclosures Towfic: Celgene Corporation: Employment, Equity Ownership. Dalton:MILLENNIUM PHARMACEUTICALS, INC.: Honoraria. Goldschmidt:Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Research Funding; Amgen: Consultancy, Research Funding; Chugai: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Molecular Partners: Research Funding; MSD: Research Funding; Sanofi: Honoraria, 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; Novartis: 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; Janssen: Consultancy, Research Funding; Dietmar-Hopp-Stiftung: Research Funding; John-Hopkins University: Research Funding. Avet-Loiseau:takeda: Consultancy, Other: travel fees, lecture fees, Research Funding; celgene: Consultancy, Other: travel fees, lecture fees, Research Funding. Ortiz:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Dervan:Celgene: Employment. Flynt:Celgene Corporation: Employment, Equity Ownership. Dai:M2Gen: Employment. Bassett:Celgene: Employment, Equity Ownership. Sonneveld:SkylineDx: Research Funding; Takeda: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Honoraria; Amgen: Honoraria, Research Funding. Shain:Amgen: 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; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy. Munshi:Abbvie: Consultancy; Takeda: Consultancy; Oncopep: Consultancy; Celgene: Consultancy; Adaptive: Consultancy; Amgen: Consultancy; Janssen: Consultancy. Morgan:Bristol-Myers Squibb, Celgene Corporation, Takeda: Consultancy, Honoraria; Celgene Corporation, Janssen: Research Funding; Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses. Walker:Celgene: Research Funding. Thakurta:Celgene: Employment, Equity Ownership.

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Imprinting of Mesenchymal Stromal Cell Transcriptome Persists even after Treatment in Patients with Multiple Myeloma

International Journal of Molecular Sciences ◽

10.3390/ijms21113854 ◽

2020 ◽

Vol 21 (11) ◽

pp. 3854

Author(s):

Léa Lemaitre ◽

Laura Do Souto Ferreira ◽

Marie-Véronique Joubert ◽

Hervé Avet-Loiseau ◽

Ludovic Martinet ◽

...

Keyword(s):

Multiple Myeloma ◽

Stromal Cells ◽

Plasma Cells ◽

Complete Response ◽

Response To Treatment ◽

Local Context ◽

Newly Diagnosed ◽

Healthy Donors ◽

Cell Neoplasm ◽

Cell Transcriptome

Introduction. Multiple myeloma (MM) is a B-cell neoplasm characterized by clonal expansion of malignant plasma cells (MM cells) in the bone-marrow (BM) compartment. BM mesenchymal stromal cells (MSC) from newly diagnosed MM patients were shown to be involved in MM pathogenesis and chemoresistance. The patients displayed a distinct transcriptome and were functionally different from healthy donors’ (HD) MSC. Our aim was to determine whether MM–MSC also contributed to relapse. Methods. We obtained and characterized patients’ MSC samples at diagnosis, two years after intensive treatment, without relapse and at relapse. Results. Transcriptomic analysis revealed differences in gene expression between HD and MM-MSC, whatever the stage of the disease. An easier differentiation towards adipogenesis at the expense of osteoblatogeneis was observed, even in patients displaying a complete response to treatment. Although their transcriptome was similar, we found that MSC from relapsed patients had an increased immunosuppressive ability, compared to those from patients in remission. Conclusion. We demonstrated that imprinting of MSC transcriptome demonstrated at diagnosis of MM, persisted even after the apparent disappearance of MM cells induced by treatment, suggesting the maintenance of a local context favorable to relapse.

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Association Study of Ploidy Category with Mitotic Disruption In Multiple Myeloma

Blood ◽

10.1182/blood.v116.21.2952.2952 ◽

2010 ◽

Vol 116 (21) ◽

pp. 2952-2952

Author(s):

Elena Dementyeva ◽

Fedor Kryukov ◽

Sabina Sevcikova ◽

Pavel Nemec ◽

Smetana Jan ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

B Cells ◽

Plasma Cells ◽

Centrosome Amplification ◽

Centrosome Duplication ◽

Newly Diagnosed ◽

Double Positive ◽

Myeloma Cells ◽

Qrt Pcr

Abstract Abstract 2952 Background Centrosome aberrations are common in many types of human malignancies and are associated with aneuploidy. Loss of centrosome duplication control will often create multipolar spindles that in turn could be responsible for incorrect segregation of whole chromosomes leading to aneuploidy. Hyperdiploidy (subtype of aneuploidy) is one of the most frequent cytogenetic abnormalities in multiple myeloma (MM), where molecular changes are among the primary genetic events in disease pathogenesis. But no correlation between centrosome aberrations and aneuploidy in MM has ever been found. Aims The objective of our study was to evaluate association of MM ploidy category with centrosome amplification in both B and plasma cells subpopulations and to investigate structural defects (gain/loss) and gene expression changes in genes controlling centrosome numbers. Methods Immunofluorescent labeling was used for evaluation of centrosome amplification (CA) in B-cells (CD19+) and PCs (CD138+) of MM patients. Centrin (centrosome protein) copy numbers were used to define presence of centrosome amplification (CA) in cells: cells with more than 4 signals of centrin were considered to be positive. Samples with ≥11% of B-cells or ≥10% of PCs with >4 fluorescence signals of centrin were considered as CA positive. A total of 140 patients were evaluated for CA in PCs and/or B-cells, including 50 patients where both cell types were analyzed. The patient population characteristics were as follows: males/females 67/73, median age of 66 years (range, 40–92 years). Newly diagnosed (52/140) and relapsed (88/140) patients were included in this study; most of them had advanced stage of MM (DS II/III n = 107; ISS II/III n = 92). Interphase FISH with cytoplasmic immunoglobulin light chain staining (cIg FISH), oligonucleotide-based arrayCGH (20 patients) and qRT-PCR (5 CA positive vs 5 CA negative patients) were performed on plasma cells. Hyperdiploidy analysis was done using Multi-Color Probe Panel (LSI D5S23/D5S721, CEP 9 and CEP 15) for chromosome 5, 9 and 15. Only cells with three or more signals from at least two of three investigated chromosomes were classified as hyperdiploid. ArrayCGH and qRT-PCR were focused on chosen list of mitotic genes, according to their role in normal centrosome duplication process. Results The frequency of MM cases positive for CA was 35% (35/100) and 39% (32/82), based on the analysis of PC samples and B-cell samples, respectively. Overall, 18% (9/50) of MM patients were double-positive. Presence of centrosome amplification in B-cells of MM patients was established in our previous study. Significant correlation of centrosome amplification in PCs with hyperdiploidy was not found. But association of CA in B-cells with PCs hyperdiploidy using phi 4-point correlation was proven (phi=0.358, p<0.05). In group of newly diagnosed patients (52/140), this correlation was much stronger (phi=0.555, p<0.05). ArrayCGH analysis of genes controlling centrosome duplication did not show correlation between their copy number defects and hyperdiploidy in myeloma cells. As for gene expression analysis, significant differences were found in levels of ARKA and PCNT genes (p<0.05). Relative quantification coefficient R of these genes was two times higher in CA positive patients when compared to CA negative patients. No significant correlation between amount of CA positive PCs and B-cells was found (p>0.05). But after splitting patients based on CA threshold, significant correlation was identified (r=-0,763, p=0.017) in double-positive group. Conclusion In our study, we show association of CA in B-cells with PCs hyperdiploidy. This finding relates to the role of B-cell mitotic disruption in MM aneuploidy and cell carcinogenesis. It gives us a possibility to suspect the impact of abnormal B cells in myeloma cells development. B-cells with CA probably enter mitosis but do not finish it properly resulting in aneuploid cells; these cells may represent an aneuploid pool of MM cells. Acknowledgments This study was supported by grants MSM 0021622434 and IGA 10207-3 from the Departments of Education and Health of the Czech Republic. Disclosures: No relevant conflicts of interest to declare.

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Copy Number Variants Modulation and Their Clinical Impact in Multiple Myeloma

Blood ◽

10.1182/blood.v126.23.5304.5304 ◽

2015 ◽

Vol 126 (23) ◽

pp. 5304-5304

Author(s):

Zuzana Kufova ◽

Lucie Brozova ◽

Pavel Nemec ◽

Jan Smetana ◽

Elena Kryukova ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Overall Survival ◽

Copy Number ◽

Target Gene ◽

Plasma Cells ◽

Copy Number Variants ◽

Clinical Impact ◽

Newly Diagnosed ◽

Total N

Abstract Background: Multiple myeloma (MM) is a lymphoproliferative disease characterized by the clonal expansion of neoplastic plasma cells within the bone marrow. The genome of the malignant plasma cells is extremely unstable characterized by a complex combination of structure and numerical abnormalities. DNA copy number variants (CNV) affects target gene expression but such affectation is not compulsory and target gene expression can be modulated. We supposed that this modulation serves as a compensatory mechanism to keep genomic homeostasis. Further compensation mechanisms exhausting leads disease aggressiveness. Aims: The objective of our study was to define and describe influence of DNA copy number variants on gene expression level in multiple myeloma. Material and methods: 66 newly diagnosed patients with MM were evaluated for this study. The patients' baseline characteristics were as follows: males/females: 34/32 (52% /48%); median age of 68 years (range 49-83 years). Type of M protein IgG/IgA/LC/other (total n=58); 36/11/10/1 (62%/19%/17%/2%); most of the patients had advanced stage of MM DS II+III (total n=58) n =58 (100%); ISS II+III (total n=53) n=42 (79%). CD138+ plasma cells separated by MACS. Gene expression profiling was performed using Affymetrix GeneChip Human Gene ST 1.0 array (Affymetrix). DNA copy number variations was evaluated using Agilent Human Genome CGH Microarray (4x44K), Agilent SurePrint G3 Human Genome CGH+SNP Microarray Kit (4x180K), OGT CytoSure Haematological Cancer +SNP (8x60K), Agilent SurePrint G3 Human CGH Microarray (8x60K) with proper platforms aggregation (Agilent technologies). Results: Each patient had at least one CNV, the most often changes were at the level of entire chromosomes. Hyperdiploid/non-hyperdiploid patients (H-MM/NH-MM) represent 53 % (31/66) and 47 % (35/66), resp. CNV considered as uncompensated, if the value of target gene expression is lower than the 25th percentile of norm (gene expression of genes without loss or gain of DNA) for gene loss, or greater than the 75th percentile of norm for gene gain. Figure 1A shows that level of CNV modulation is determined by the number of changes that occur in a given patient. Further, ROC analysis was done to determine whether certain level of compensation is related to the overall survival and CNV compensation limit 20% (p=0.026) was established. Patients with ≥20% of decompensated CNV had significantly worse OS (survival median 5.2 month) compared to patients with <20% of decompensated CNV (survival median 23.5 month). Kaplan-Meier curves for given patients' subgroups are presented in Figure 1B. Conclusion: Copy number variants in MM can be compensated on gene expression level. Compensatory capacity of genome is associated with total number of CNV. Patients with ≥20% of decompensated CNV had significantly worse OS. Acknowledgment: This study was supported by grants no. MSK 02680/2014/RRC and MSK 02692/2014/RRC; MH CZ-DRO-FNOs/2014; SGS01/LF/2014-2015, SGS02/LF/2014-2015, SGS03/LF/2015-2016, NT14575, AZV 15-29508A and AZV 15-29667A Figure 1. Copy number variants (CNV) modulation and their clinical impact in multiple myeloma A. The overall rate of CNV occurrence in proportion to the gene expression affectation (loss of CNV compensation) B. Overall survival in newly diagnosed MM patients with different level of CNV compensation. Figure 1. Copy number variants (CNV) modulation and their clinical impact in multiple myeloma A. The overall rate of CNV occurrence in proportion to the gene expression affectation (loss of CNV compensation) B. Overall survival in newly diagnosed MM patients with different level of CNV compensation. Disclosures Hajek: Janssen-Cilag: Honoraria; Celgene, Amgen: Consultancy, Honoraria.

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