Clonotypic Bone Marrow-Derived Endothelial Progenitor Cells in Multiple Myeloma.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 3497-3497
Author(s):  
Marc J. Braunstein ◽  
Daniel R. Carrasco ◽  
David Kahn ◽  
Kumar Sukhdeo ◽  
Alexei Protopopov ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
High Resolution ◽  
Gene Expression Profiling ◽  
Progenitor Cells ◽  
Tumor Cells ◽  
Endothelial Progenitor Cells ◽  
Expression Profiling ◽  
Gains And Losses

Abstract In multiple myeloma (MM), bone marrow-derived endothelial progenitor cells (EPCs) contribute to tumor neoangiogenesis and their levels covary with tumor mass and prognosis. Recent X-chromosome inactivation studies in female patients showed that, similar to tumor cells, EPCs are clonally restricted in MM. Genomic profiling of MM using high-resolution array comparative genomic hybridization (aCGH) has been previously utilized to mine the genome and find clinical correlates in MM patients. In this study, clonotypic aspects of bone marrow-derived EPCs and MM cells were investigated using aCGH and expression profiling analysis. Confluent EPCs were outgrown from bone marrow aspirates by adherence to laminin. EPCs were >98% vWF/CD133/KDR+ and <1% CD38+. The laminin-nonadherent bone marrow fraction enriched for tumor cells was >50% CD38+. For aCGH and for gene expression profiling, genomic DNA and total RNA from EPCs and MM cells were hybridized to human oligonucleotide arrays (Agilent Technologies) and human cDNA microarrays (Affymetrix), respectively. High resolution aCGH with segmentation analysis showed that EPCs and MM cells in one of ten cases share identical patterns of chromosomal gains and losses, while another 5 cases shared multiple focal copy number alterations (CNAs) including gains and losses. The genomes of EPCs and MM cells additionally displayed exclusive CNAs, but these were far fewer in EPCs than in MM cells. In 3 patients, EPCs harbored a common 0.6Mb deletion at 1q21 not shared by MM cells. Pertinent genes in this region that could affect proliferation and tumor suppression include N2N, NBPF10, and TXNIP. Validation studies of aCGH findings by other methods are ongoing. Gene expression profiling showed decreased expression of 1q21 region genes (e.g., calgranulin C and lamin A/C). A genome-wide comparison of patients’ MM cells and EPCs, which is focused on their shared genetic characteristics, will be presented.

Genome-Wide Profiling of Endothelial Progenitor Cells in Multiple Myeloma: Overlaps with Myeloma Tumor Cells and Common Cancer Gene-Pathways.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 394-394
Author(s):  
Marc J. Braunstein ◽  
Daniel R. Carrasco ◽  
Fabien Campagne ◽  
Piali Mukherjee ◽  
Kumar Sukhdeo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
Progenitor Cells ◽  
Tumor Cells ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Biomarkers ◽  
Genome Wide

Abstract Background: In multiple myeloma (MM), bone-marrow-derived endothelial progenitor cells (EPCs) contribute to tumor neoangiogenesis, and their levels covary with tumor mass and prognosis. Recent X-chromosome inactivation studies showed that EPCs are clonally restricted in MM. In addition, high-resolution array comparative genomic hybridization (aCGH) found that the genomes of EPCs and MM cells display similar chromosomal gains and losses in the same patient. In this study, we performed an integrative analysis of EPCs and tumor cells by genome-wide expression profiling, and applied a bioinformatics approach that leverages gene expression data from cancer datasets to mine MM gene pathways common to multiple tumor tissues and likely involved in MM pathogenesis. Methods: Confluent EPCs (&gt;98% vWF/CD133/KDR+ and CD38−) were outgrown from 22 untreated MM patients’ bone marrow aspirates by adherence to laminin. The fractions enriched for tumor cells were &gt;50% CD38+. For gene expression profiling, total RNA from EPCs, MM cells, and control HUVECs were hybridized to cDNA microarrays, and comparisons were made by analysis of variance. Results: Two sets of EPC gene profiles were of particular interest. The first contained genes that differ significantly between EPCs and HUVEC, but not between EPCs and tumor (Profile 1). We hypothesize that this profile is a consequence of the clonal identity previously reported between EPCs and tumor, and that a subset of these genes is largely responsible for MM progression. The second set of important EPC genes are differentially regulated compared both to HUVECs and to tumor cells (Profile 2). These genes may represent the profile of EPCs that are clonally diverse from tumor cells but nevertheless display common gene expression patterns with other cancers. Profile 2 genes may also represent genes that confer a predisposition to clonal transformation of EPCs. When genes in Profile 1 and Profile 2 were overlapped with published lists of cancer biomarkers, significant similarities (P&lt;.05) were apparent. The largest overlaps were observed with the HM200 gene list, a list composed of 200 genes most consistently differentially expressed in human/mouse cancers (Campagne and Skrabanek, BMC Bioinformatics 2006). More than 80% of genes in either EPC profile have not been previously characterized in MM, but have been identified as cancer biomarkers in other cancer studies. These genes will be presented and discussed in the context of MM. Current studies are aimed at integrating Profile 1 and Profile 2 genes in each patient with chromosomal copy number abnormalities (CNAs) found in EPCs, and also with clinical stage and disease severity, in order to elucidate the pathogenic information that the profiles hold. Conclusions: The genomes of EPCs display ranges of overlap with tumor cells in MM, evidenced by gene expression profiles with varying similarity to those found in MM tumor cells. More importantly, MM EPC gene expression profiles, in contrast to normal endothelial cells, contain cancer biomarker genes in tumors not yet associated with MM. Results strongly support the concept that EPCs are an integral part of the neoplastic process in MM.

Genomic Analysis of Endothelial Progenitor Cells In Multiple Myeloma Reveals Aberrant Gene Pathways Common to Tumor Cells

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 3016-3016
Author(s):  
Marc J Braunstein ◽  
Sadeaqua Scott ◽  
Eric LP Smith ◽  
Danielle F Joseph ◽  
Jason Gonsky ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Human Genome ◽  
Progenitor Cells ◽  
Tumor Cells ◽  
Endothelial Progenitor Cells ◽  
Copy Number ◽  
Gene Expression Level ◽  
Expression Level ◽  
Gains And Losses

Abstract Abstract 3016 Background: Multiple myeloma (MM) is an incurable disease characterized by genetically transformed clonal plasma cells that develop a proliferative advantage within the supportive bone marrow (BM) microenvironment. Recent findings from our and other laboratories have shown genetically unstable endothelial progenitor cells (EPCs) to be a key component of the MM microenvironment, integral to tumor neovascularization. However, the contribution and characterization of genomic alterations in the tumor microenvironment in the progression of MM has not been established. Using array-comparative genomic hybridization (aCGH), the present study examined genome-wide copy number alterations (CNAs) within the EPC genome and compared them to tumor cells and control endothelial cells (ECs). Published human copy number variations (CNVs) were excluded from the analyses. Methods: Informed consent was obtained from all subjects. EPCs (>98% vWF/CD133/KDR+/CD38–) from BM aspirates of 16 untreated MM patients were outgrown on laminin-coated flasks. Controls included EPCs from healthy subjects and human umbilical vein ECs (HUVECs). For microarray analysis, genomic DNA from paired EPCs and tumor cells from MM patients enriched for CD38+ cells, as well as control cells, were hybridized to Agilent 244A Human Genome CGH Microarrays with differentially labeled control peripheral blood mononuclear cells, and feature intensities and ratios were extracted in Agilent CGH Analytics Software. The aberration detection method-1 algorithm was used to assess intervals in which the average log2 ratio of the MM cells and EPCs to control cells and ECs exceeded 0.3 (at least 1.23 fold-change). Human Genome Structural Variation Project (humanparalogy.gs.washington.edu) and the Database of Genomic Variants (projects.tcag.ca/variation) served as control CNVs. Affymetrix U133 plus 2.0 GeneChips confirmed gene expression using GeneSpring software (Agilent), and group comparisons were made by ANOVA. Results: Extensive chromosomal CNAs were found in MM EPCs; gains and losses were found to approximately the same extent in matched tumor cells. Germline CNVs accounted for less than 10% of MM EPC CNAs. The greatest number of CNA gains in EPCs were found on chr 7q, followed by 2p and 22q; the most recurrent sequences with CNA gains were on chr 7. The greatest CNAs losses in MM EPCs were found equivalently on chr 1q, 11q, and 15q. Consistent with their clonal expression in MM, immunoglobulin genes were found to be dysregulated in MM EPCs (e.g., 14q32 gains), which were confirmed at the gene expression level (e.g., over-expression of IGHG1 mRNA compared to control ECs). When comparing CNAs in MM EPCs to those in corresponding tumor cells, 15 of 16 patients (94%) shared identical CNAs at 2 or more loci, with greater than 48% similarity in CNAs between EPCs and tumor cells. Control EPCs and HUVECs did not show significant baseline alterations compared to control normal lymphocyte DNA, whereas identical CNAs were found in MM EPCs and tumor cells throughout their genomes. The most recurrent CNAs in both EPCs and tumors were found on chr 1 and 14, which are known to be highly dysregulated in MM. The clinical relevance of our aCGH data is suggested by the finding that more CNA gains and losses were found both in EPCs and in tumor cells from MM patients with treatment-resistant, progressive MM than in patients in remission (P<.01). The consequences of CNAs at the gene expression level in EPCs showed the highest level of dysregulation among the extracellular matrix genes. Discussion: aCGH results presented here are an extension of our previous findings of clonality within EPCs, including allelic X-chromosome inactivation and idiotypic IgH rearrangement, and further elucidate the genomic alterations responsible for increased angiogenesis in MM. The finding that MM-specific CNAs within EPCs correlate with resistant disease and poor survival may enhance existing criteria for prediction of aggressive MM, and also improve individualization of anti-myeloma strategies. Conclusions: Our results strongly indicate that EPCs are an integral part of the neoplastic process in MM. Their altered genomic profile compared to control ECs indicates pathogenic functions critical for MM evolution. The high degree of commonly dysregulated genes among EPCs and MM cells permits prioritization of candidate MM-endothelial biomarkers not yet defined in this disease. Disclosures: No relevant conflicts of interest to declare.

Intra-Tumoral Heterogeneity In Multiple Myeloma Gleaned From Gene Expression Profiling Analysis Of Plasma Cells and Bone Marrow Biopsies: Comparison Of Random Bone Marrow Samples and CT-Guided Fine Needle Aspirations Of MRI-Defined Focal Lesions

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 3123-3123
Author(s):  
Bart Barlogie ◽  
Emily Hansen ◽  
Sarah Waheed ◽  
Jameel Muzaffar ◽  
Monica Grazziutti ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Plasma Cells ◽  
P Value ◽  
Focal Lesions ◽  
Molecular Subgroup

Abstract Intra-tumoral heterogeneity (ITH) is increasingly viewed as the Achilles heel of treatment failure in malignant disease including multiple myeloma (MM). Most MM patients harbor focal lesions (FL) that are recognized on MRI long before bone destruction is detectable by conventional X-ray examination. Serial MRI examinations show that eventually 60% of patients will achieve resolution of FL (MRI-CR). However, this will lag behind the onset of a clinical CR by 18 to 24 months, thus attesting to the biological differences between FL and diffuse MM growth patterns. Consequently, we performed concurrent gene expression profiling (GEP) analyses of plasma cells (PC) from both random bone marrow (RBM) via iliac crest and FL. Our primary aims were to first compare the molecular profiles of FL vs. RBM, second to determine if ITH existed (as defined molecular subgroup and risk), and finally to investigate if the bone marrow micro-environment (ME) contained a biologically interesting signature. A total of 176 patients were available for this study with a breakdown of: TT3 (n=23), TT4 for low-risk (n=131) and TT5 for high-risk MM (n=22). Regarding the molecular analyses of PCs, GEP-based risk (GEP-70, GEP-5) and molecular subgroup correspondence were examined for commonalties and differences between RBM and FL. A “filtering” approach for ME genes was also developed and bone marrow biopsy (BMBx) GEP data derived from this method is under analysis. PC risk correspondence between FL and RBM was 86% for GEP70 and 88% for the GEP5 model. Additionally, 82% had a molecular subgroup concordance, however, they did differ among subgroups (p=0.020) by Fisher's Exact Test. A lower concordance was noted in the CD2, LB, and PR subgroups (67%, 69%, 73%, respectively). GEP70 and GEP5 risk concordance between RBM and FL samples by molecular subgroup was also examined. The overall correlation coefficients were 0.619 (GEP70) and 0.597 (GEP5). The best correspondence was noted for CD1, MF and PR subgroups especially for the GEP5 model. HY, LB and MS showed intermediate correlations, while CD2 fared worst with values of only 0.322 for GEP70 and 0.267 for GEP5 model. Figure 1 portrays these data in more detail for the GEP70 and GEP5 models. Good correlations were noted between RBM and FL based risk scores in case of molecular subgroup concordance (left panels) in both GEP5 and GEP70 risk models, whereas considerable scatter existed in case of subgroup discordance (right panels). The clinical implications in TT4 regarding RBM and FL derived risk and molecular subgroup information, viewed in the context of standard prognostic baseline variables are portrayed in Table 1. High B2M levels at both cut-points imparted inferior OS and PFS as did low hemoglobin. Although present in 42% of patients, cytogenetic abnormalities (CA) did not affect outcomes. FL-based GEP5-defined high-risk designation conferred poor OS and PFS. B2M>5.5mg/L and FL-derived GEP5 high-risk MM, pertaining to 29% and 11% of patients, survived the multivariate model for both OS and PFS. Next, in examining PC-GEP differences among RBM and FL sites, 199 gene probes were identified with a false discovery rate (FDR) of 1x10-6. Additionally, 55 of the 199 belong to four molecular networks of inter related genes associated with: lipid metabolism, cellular movement, growth and proliferation, and cell-to-cell interactions. Multivariate analysis identified the GEP5 high risk designation of focal lesion PCs to be significantly prognostic with a HR=3.73 (p=0.023).Table 1Cox regression analysis of variables linked to overall and progression-free survival in TT4.Overall SurvivalProgression-Free SurvivalVariablen/N (%)HR (95% CI)P-valueHR (95% CI)P-valueMultivariateB2M > 5.5 mg/L38/130 (29%)3.71 (1.49, 9.22)0.0053.84 (1.58, 9.31)0.003FL GEP5 High Risk14/130 (11%)3.68 (1.19, 11.41)0.0243.73 (1.20, 11.62)0.023HR- Hazard Ratio, 95% CI- 95% Confidence Interval, P-value from Wald Chi-Square Test in Cox RegressionNS2- Multivariate results not statistically significant at 0.05 level. All univariate p-values reported regardless of significance.Multivariate model uses stepwise selection with entry level 0.1 and variable remains if meets the 0.05 level.A multivariate p-value greater than 0.05 indicates variable forced into model with significant variables chosen using stepwise selection. Disclosures: No relevant conflicts of interest to declare.

Gene Expression Profiling of Bone Marrow Endothelial Cells in Patients with Multiple Myeloma

2009 ◽  
Vol 15 (17) ◽  
pp. 5369-5378 ◽  
Author(s):  
Roberto Ria ◽  
Katia Todoerti ◽  
Simona Berardi ◽  
Addolorata Maria Luce Coluccia ◽  
Annunziata De Luisi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
Endothelial Cells ◽  
Gene Expression Profiling ◽  
Expression Profiling

Phase I Exploratory Study of IV Formulation of Panobinostat in Combination with Bortezomib in Relapsed/Refractory Multiple Myeloma Patients: Effect On Serum PTH and Gene Expression Profiling (GEP) Studies

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 4073-4073 ◽  
Author(s):  
Maurizio Zangari ◽  
Tamara Berno ◽  
Giampaolo Talamo ◽  
Xin Zhan ◽  
Wen Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
Image Analysis ◽  
Phase I ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Exploratory Study ◽  
Post Treatment ◽  
Fixed Dose

Abstract Abstract 4073 Background: Inhibition of histone deacetylase (HDAC) provides a novel approach for cancer treatment. Bortezomib and panobinostat are active agents in multiple myeloma (MM) and have a synergistic antimyeloma effect when used in combination. Both compounds have also shown anabolic bone effect. Methods: We conducted a phase I exploratory study IV formulation of panobinostat in combination with bortezomib in relapsed/refractory MM patients who had failed at least one line of prior therapy. Patients were assigned to their dose levels in a classical 3+3 cohort design. Primary objective was to assess the toxicity of a fixed dose of bortezomib in combination with escalating doses of panobinostat to define the maximum tolerated dose (MTD). The secondary objective was to determine the osteoblastic response. Bortezomib was administered iv at the dose of 1 mg/m2 during cycles on days 1, 4, 8, and 11 of each 21 days cycle. Panobinostat was administered iv starting at cycle 2 on days 1 and 8 of the 21 day cycle, 5 mg/m2 n=1–3, 10 mg/m2 n=4–9, 15 mg/m2 n=10–12 (one screening failure). Patients with at least stable disease after the first 2 cycles (Phase I Portion of the Study) were allowed to continue to a maximum of 12 cycles. PTH was measured during the first two cycles, every 20 minutes starting one hour before infusion for a total of seven time points on days 1 and 4, and single pre dose on days 8 and 11 during cycles 1 and 2. Bone marrow (BM) aspiration and biopsies were collected at baseline, after the completion of second cycle and at the end of the study. H&E stained core biopsy slides were scanned using Scan Scope XT system. We developed classifier algorithms using Genie pattern recognition image analysis software to calculate Trabecular Volume (TV), Bone Volume (BV). RNA obtained from separate core biopsies at baseline and after second cycle was used to complete Gene Expression Profiling (GEP) studies. The paired t test was used to identify significantly gene changes. Results: Eleven patients with a median age of 58 years (range 44 to 70) were enrolled. 45% of patients received ≥ 3 previous lines of therapy. 45% was previously exposed to Bortezomib and 63% of patients failed prior autologous stem cell transplant. All eleven patients completed two cycles of therapy, 10 patients continued treatment, and three completed a total of 12 cycles. No MTD was reached with a 15 mg/m2IV Panobinostat dose. Using IMWG criteria after the second cycle of treatment we observed 1CR, 3 PR, 5 SD, 2 patients experienced PD. Best response during the entire treatment included 2 CR, 2 VGPR, 4 SD (≥ SD 72%). Common grade 3/4 AEs included: thrombocytopenia (36%), neutropenia (45%), leukopenia (54%), lymphopenia (18%), with no treatment-related mortality. The median baseline of Trabecular Volume/Hematopietic Area (TV/HA) was 27.6% (range 8.1–46%). Comparing the baseline to the end of study samples, an increment of TV/HA was noted in 6 of 9 available patients. The average TV/HA increment was 19.4% (range 9.9–38.3%). 72% of patients (n=8) showed changes in PTH level during treatment. Serial PTH samples significantly increased during treatment, from a mean of 39.4 pg/L (±22.3) at baseline, to a mean of 50.2 pg/L (±36.8) at day 4 cycle 2 (p=0.003). GEP from bone marrow baseline core biopsies were compared to post-treatment samples. A total of 3783 probe sets were found to be differentially expressed. 30% of genes were over-expressed and 70% were down regulated. Interestingly decorin, which function has been associated to antimyeloma effect of osteoblasts, and its down-stream target p21-activated kinase 4 (PAK4) were significantly increased in the post-treatment samples (p=0.04, p=0.03 respectively with 2.0 ratio) suggesting a bone anabolic and antimyeloma effect of these drugs in combination. Conclusions: The combination of IV Panobinostat and Bortezomib has predictable and manageable safety profile with significant antimyeloma activity as reflected by ≥ SD 72%, ≥ PR 36% including 18% CR. Changes in bone indices were observed by a computer assisted image analysis. Hormonal changes and genes studies also support an osteoblastic/anabolic effect of panobinostat in combination with bortezomib in relapsed/refractory MM patients. Disclosures: Zangari: Millennium, Onyx, Cephalon, Incyte: Research Funding, Speakers Bureau.

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

Blood ◽  
2010 ◽  
Vol 116 (14) ◽  
pp. 2543-2553 ◽  
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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