scholarly journals Gene expression profiling of B lymphocytes and plasma cells from Waldenström's macroglobulinemia: comparison with expression patterns of the same cell counterparts from chronic lymphocytic leukemia, multiple myeloma and normal individuals

Leukemia ◽  
2007 ◽  
Vol 21 (3) ◽  
pp. 541-549 ◽  
Author(s):  
N C Gutiérrez ◽  
E M Ocio ◽  
J de las Rivas ◽  
P Maiso ◽  
M Delgado ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Chronic Lymphocytic Leukemia ◽  
Gene Expression Profiling ◽  
B Lymphocytes ◽  
Expression Profiling ◽  
Plasma Cells ◽  
Expression Patterns ◽  
Lymphocytic Leukemia ◽  
Normal Individuals

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.

MyPRSR Molecular Subtypes of Multiple Myeloma Represent All High-Risk FISH Translocations Included in the mSMART 2.0 and R-ISS Guidelines

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3264-3264 ◽  
Author(s):  
Ryan K Van Laar ◽  
Ivan Borrelo ◽  
David Jabalayan ◽  
Ruben Niesvizky ◽  
Aga Zielinski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Research Funding ◽  
Plasma Cells ◽  
Molecular Subtypes ◽  
Risk Status ◽  
Significant Difference

Abstract Background: There is a global consensus that multiple myeloma patients with high-risk disease require additional monitoring and therapy compared to low/standard risk patients in order to maximize their chances of survival. Current diagnostic guidelines recommend FISH-based assessment of chromosomal aberrations to determine risk status (i.e. t(14;20), t(14;16), t(4;14) and/or Del17p), however, studies show FISH for MM may have a 20-30% QNS rate and is up to 15% discordant between laboratories, even when starting from isolated plasma cells. In this study we demonstrate that MyPRS gene expression profiling reproduces the key high risk translocations for MM risk stratification, in addition to having other significant advantages. Methods: Reproducibility studies show that MyPRS results are less than 1% discordant starting from isolated plasma cells and return successful results in up to 95% of cases. 270 MM patients from Johns Hopkins University (MD) and Weill Cornell Medicine (NY) had both FISH and MyPRS gene expression profiling performed between 2012 and 2016 using standard and previously published methodology, respectively. Results: Retrospective review of the matched FISH and MyPRS results showed: 25/28 (89%) patients wish FISH-identified t(4;14) were classified as MMSET (MS) subtype. 10/10 (100%) patients with t(14;16) or t(14;20) were classified as MAF-like (MF) subtype 62/67 (93%) patients with t(11;14) were assigned to the Cyclin D (1 or 2) subtype. Patients with FISH hyperdiploidy status were classified as the Hyperdiploid (HY) subtype or had multiple gains detected by the separate MyPRS Virtual Karyotype (VK) algorithm, included in MyPRS. TP53del was seen in patients with multiple molecular subtypes, predominantly Proliferation (PR) and MMSET (MS). Assessment of TP53 function by gene expression is a more clinically relevant prognostic marker than TP53del, as dysregulation of the tumor suppressor is affected by mutations as well as deletions. Analysis of the TP53 expression in the 39 patients with delTP53 showed a statistically significant difference, compared to patients without this deletion (P<0.0001). Conclusion: Gene expression profiling is a superior and more reliable method for determining an individual patients' prognostic risk status. The molecular subtypes of MM, as reported by Signal Genetics MyPRS assay, are driven by large-scale changes in gene expression caused by or closely associated with chromosomal changes, including translocations. Physicians who are managing myeloma patients and wishing to base their assessment of risk on R-ISS or mSMART Guidelines may obtain the required data points from either FISH or MyPRS, with the latter offering lower QNS rates, higher reproducibility, assessment of a larger number of cells and a substantially lower price point ($5,480 vs. $1,912; 2016 CMS data). A larger cohort study is now underway to further validate these observations. Figure GEP-based TP53 expression in patients with and without Del17p. P<0.0001 Figure. GEP-based TP53 expression in patients with and without Del17p. P<0.0001 Disclosures Van Laar: Signal Genetics, Inc.: Employment. Borrelo:Sidney Kimmel Cancer Institute: Employment. Jabalayan:Weill Cornell Medical Center: Employment. Niesvizky:Celgene: Consultancy, Research Funding, Speakers Bureau; Takeda: Consultancy, Research Funding, Speakers Bureau; Onyx: Consultancy, Research Funding, Speakers Bureau. Zielinski:Signal Genetics, Inc.: Employment. Leigh:Signal Genetics, Inc.: Employment. Brown:Signal Genetics, Inc.: Employment. Bender:Signal Genetics, Inc.: Employment.

B-Cell Scaffold Protein with Ankyrin Repeats (BANK1) Is Differentially Expressed in ZAP-70 Negative Compared to ZAP-70 Positive Chronic Lymphocytic Leukemia Cells.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 2938-2938
Author(s):  
Frank Dicker ◽  
Susanne Schnittger ◽  
Claudia Schoch ◽  
Alexander Kohlmann ◽  
Wei-Min Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
Chronic Lymphocytic Leukemia ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Differentially Expressed Genes ◽  
Expression Profiling ◽  
Lymphocytic Leukemia ◽  
Differentially Expressed ◽  
Cell Scaffold

Abstract The lack of somatic mutations of the immunoglobulin variable heavy chain (IgVH) gene has been established as poor prognostic marker for chronic lymphocytic leukemia (CLL) patients at early stage disease. Expression of the non receptor tyrosine kinase zeta chain associated protein (ZAP-70) was proposed as a surrogate marker for an unmutated IgVH, however, up to 30% discordant samples have been reported depending on the respective study. B cell receptor (BCR) mediated signaling is enhanced by ZAP-70 expression in CLL cells in vitro and ZAP-70 expression also tends to decrease the time from diagnosis to treatment irrespective of the IgVH status. Therefore, we wanted to identify differentially expressed genes between the ZAP-70 positive and negative CLLs by gene expression profiling of peripheral blood mononuclear cells (PBMCs) using Affymetrix microarrays (HG-U133 Plus 2.0). ZAP-70 expression was analyzed by quantitative real time PCR of CD19 purified (purity &gt; 99%) PBMCs (n=62) using a LightCycler instrument. Expression of ZAP-70 mRNA was normalized against the housekeeping gene ABL and a relative quantitation against Jurkat T cells as a calibrator was performed. Results are expressed as normalized ratio and a cut-off of 0.5 normalized ratio gave the best correlation to the IgVH status with 77% concordant samples between ZAP-70 expression and the IgVH status. The discordant samples consisted of 5 unmutated IgVHs in the ZAP-70 negative group and 9 mutated in the ZAP-70 positive group. In a second step PBMCs of the same samples were analyzed by gene expression profiling and differentially expressed genes were identified by t-test. Among the two best genes that could be used in a classification algorithm (SVM) to distinguish between the 2 subsets with 92% accuracy were ZAP-70 and B cell scaffold protein with ankyrin repeats (BANK1). The expression of BANK1 was increased 3–4-fold in the ZAP-70 negative compared to the ZAP-70 positive CLL subset (P = 0,001). In the literature, BANK1 has been identified in human BCR expressing B cells and seems to be B cell restricted. In B cells the scaffolding protein BANK1 enhances BCR-mediated Ca2+-signaling, a signaling pathway that is also enhanced by ZAP-70 expression in CLL B cells. Based on these data we show that increased BANK1 expression correlates with a ZAP-70 negative status in CLL B cells. The functional consequences of BANK1 expression in the ZAP-70 negative subset of CLL B cells, which are usually associated with a more favorable prognosis, still need to be established further.

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

Blood ◽  
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 (&gt; 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

Proteomic Profiling of Multiple Myeloma: Correlation of Protein and Gene Expression Data.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1705-1705
Author(s):  
Ricky D Edmondson ◽  
Sheeno P Thyparambil ◽  
Veronica MacLeod ◽  
Bart Barlogie ◽  
John D. Shaughnessy
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Plasma Cells ◽  
Expression Profiles ◽  
Single Agent ◽  
Clinical Samples ◽  
Proteomics Data

Abstract Although melphalan-based autologous stem cell transplantation has improved prognosis for patients diagnosed with Multiple Myeloma, survival varies from a few months to more than 15 years with an individual’s risk not accurately predicted with standard prognostic variables. Correlating genome-wide mRNA expression profiles in purified myeloma cells with outcome, we recently showed that that the differential expression of 70 genes could identify patients at high risk for early disease related death [1]. The utility of a high throughput proteomics platform in the analysis of clinical samples has great potential but as of yet none have been firmly established. Herein, we describe the use of such a platform and its utility in stratifying patients with Multiple Myeloma in terms of high and low risk disease. Preliminary analysis indicates that the proteomics data can separate the patients into risk groups, although the proteins responsible for the assignment are not identical to the 70 genes identified in the gene expression profiling experiments. In addition to the proteomic analysis of plasma cells enriched using anti-CD138 immunomagnetic beads from mononuclear cell fractions of bone marrow aspirates from newly diagnosed myeloma patients; we have performed (in triplicate) LCMS profiling on plasma cells from 30 patients isolated prior to and 48 hours after a single test-dose application of bortezomib at 1.0mg/m2. An aliquot of 100,000 plasma cells was enzymatically digested with trypsin and a fraction (~5,000 cells) analyzed using our proteomics platform (an Eksigent nanoHPLC coupled to a ThermoElectron LTQ-Orbitrap with data analyzed using the Elucidator software package from Rosetta Biosoftware). The correlation of the proteomic profiles to gene expression profiles and clinical parameters will be presented. The analysis of proteins that were observed to change (p&lt;0.01) in abundance after the single agent dose of the proteasome inhibitor bortezomib yielded an unanticipated finding; the abundance of 30 proteins associated with the proteasome were observed to increase in a subset of patients. The majority of the patients with the increased levels of proteasome related proteins are predicted by GEP to have high risk disease. The proteomic data will be discussed in terms of its utility in the identification of activated pathways as well as in the development of a prognostic indicator as was achieved using gene expression profiling.

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.

The Role Of PRDM1 and Its Interacting Proteins In The Pathogenesis Of Chronic Lymphocytic Leukemia

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 2865-2865
Author(s):  
Ashima Shukla ◽  
Nagendra K Chaturvedi ◽  
Shantaram S Joshi ◽  
Philip Bierman ◽  
Adam Cornish ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chronic Lymphocytic Leukemia ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Poor Prognosis ◽  
Cellular Proliferation ◽  
Good Prognosis ◽  
Lymphocytic Leukemia ◽  
Negative Regulator ◽  
Time To Treatment

Abstract Chronic Lymphocytic Leukemia (CLL) represents the most common adult leukemia in western countries, affecting approximately 10,000 individuals every year in United States of America. In order to develop effective therapeutic strategies there is a need to understand the precise molecular events associated with CLL development and progression. In an attempt to understand the process we performed transcriptome analysis of CLL cells from peripheral blood (PB) of 7 good prognosis and 8 poor prognosis patients. Additionally to validate the results further, we have utilized gene expression profiling data of CLL cells from 77 good or poor prognosis patients. Using transcriptome and gene expression profiling, we identified PR Domain Zinc Finger Protein1 (PRDM1) as one of the candidate tumor suppressor genes in CLL. PRDM1/BLIMP1 is a transcriptional repressor which is crucial for terminal differentiation of mature B cells into plasma cells. PRDM1 has been shown to promote differentiation by repressing genes essential for B cell receptor signaling and cellular proliferation. Our results demonstrated that PRDM1 expression was significantly (p < 0.05) decreased in CLL cells from poor prognosis compared to good prognosis CLL cases (Figure 1). In addition, to determine the clinical significance, the expression levels of PRDM1 were found to correlate with time to treatment in CLL patients (Figure 2). Lower expression of PRDM1 was associated (p=0.001) with shorter time to treatment in CLL (n = 40). Furthermore, using IPA analyses we identified PRDM1 interacting partners. Among those, there was increased expression of BCL2, PAX5, EHMT2, SPIB and decreased expression of BCL6, IRF4, BCR, CASP3, pFOS, TP53 and HDAC2 in CLL cells from poor prognosis patients in comparison with good prognosis. Also we have observed the differential expression of TLE1, a co-repressor molecule associated with PRDM1 and member of the Groucho family of proteins.Figure1Transcript levels of PRDM1 in Good Prognosis and Poor Prognosis.Figure1. Transcript levels of PRDM1 in Good Prognosis and Poor Prognosis.Figure2Clinical correlation of PRDM1 expression with time to treatment.Figure2. Clinical correlation of PRDM1 expression with time to treatment. Together these results are suggestive of a role for PRDM1 as a negative regulator of CLL aggressiveness and progression. These studies warranted additional investigation to elucidate the mechanism of PRDM1 mediated CLL progression and to identify a therapeutic target. Disclosures: No relevant conflicts of interest to declare.

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