Gene Expression Profiling of B-Lymphocyte and Plasma Cell Populations from Waldenström’s Macroglobulinemia. Comparison with Expression Patterns of the Same Cell-Counterparts from Other B-Cell Neoplasms.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 503-503
Author(s):  
Norma C. Gutierrez ◽  
Enrique M. Ocio ◽  
Patricia Maiso ◽  
Encarna Ferminan ◽  
Manuel Delgado ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Patterns ◽  
Lymphocytic Leukemia ◽  
Differentially Expressed ◽  
Multiparameter Flow Cytometry ◽  
Cell Populations ◽  
Expression Levels

Abstract The tumoral clone of Waldenström’s macroglobulinemia (WM) shows a wide morphological heterogeneity which ranges from B lymphocytes (BL) to plasma cells (PC), including a lymphoplasmacytic population that defines the disease. The differences between these cell compartments and their cell-counterpart in other lymphoproliferative disorders have not yet been sufficiently explored. We compared the gene expression profiling (GEP) of BL and PC from patients diagnosed with WM, with clonal BL and PC from patients with chronic lymphocytic leukemia (CLL) and multiple myeloma (MM) respectively. Bone marrow samples from 10 WM and 10 MM patients and peripheral blood from 10 CLL patients were used for the analysis. The isolation of the different cell populations was carried out by multiparameter flow cytometry sorting with the following monoclonal antibodies combination: Kappa or Lambda-FITC, CD10-PE, CD38-PerCP-Cy5.5, CD19-PE-Cy7, CD34-APC and CD45-APC-Cy7. Total RNA (100–500 ng) was amplified and labeled using the “GeneChip Two-Cycle cDNA Synthesis Kit” and hybridized to “Human Genome U133A” microarray (Affymetrix). Processing of genechip data was carried out using the Robust Multi-chip Average (RMA) and the Affymetrix Microarray Suite v.5 (MAS5) gene expression algorithms. Two-way hierarchical cluster analysis showed that GEP was able to classify PC from WM and PC from MM in two different groups. In a similar way, BL from WM and CLL were grouped in different clusters. The Significance Analysis of Microarrays (SAM) algorithm identified gene expression changes in a total of 163 genes (103 up and 60 down-regulated) when MW PC were compared with MM PC. Some of these genes were related to regulation of PC development: PAX5 was overexpressed and IRF4 infraexpressed in WM PC. PC from WM displayed high expression levels of MYB and DEK oncogenes while MM PC showed an elevated expression level of v-MAF oncogene. Regarding gene functional categories, “immune response” and “signal transduction” were the biological process more active in WM PC. Comparison between BL from WM and BL from CLL revealed that 31 genes were differentially expressed: IL4 receptor, LEF1 (WNT/b-catenin pathway), fibromodulin (modulator of TGF-b activity) and FGR oncogene (tyrosine kinase protein) showed very low expression levels in WM BL compared to CLL BL. In contrast, the growth factor IL6 was over-expressed in WM BL. These results indicate that both PC and BL from WM are genetically different from the MM and CLL cell-counterpart. The differentially expressed genes have important functions in the B-cell differentiation and oncogenesis. Supported by Spanish Myeloma Network (G03/136) and “Ministerio de Ciencia y Tecnología” (SAF04/06587) and “Junta de Castilla y León” grants (SA032/04)

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 > 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.

Microarray Gene Expression Profiling of B-Cell Chronic Lymphocytic Leukemia Subgroups Defined by Genomic Aberrations and VH Mutation Status

2004 ◽  
Vol 22 (19) ◽  
pp. 3937-3949 ◽  
Author(s):  
Christian Haslinger ◽  
Norbert Schweifer ◽  
Stephan Stilgenbauer ◽  
Hartmut Döhner ◽  
Peter Lichter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chronic Lymphocytic Leukemia ◽  
B Cell ◽  
Differentially Expressed Genes ◽  
Expression Profiling ◽  
Lymphocytic Leukemia ◽  
Differentially Expressed ◽  
Mutational Status ◽  
Genomic Aberrations ◽  
Cell Chronic Lymphocytic Leukemia

Purpose Genomic aberrations and mutational status of the immunoglobulin variable heavy chain (VH) gene have been shown to be among the most important predictors for outcome in patients with B-cell chronic lymphocytic leukemia (B-CLL). In this study, we report on differential gene expression patterns that are characteristic for genetically defined B-CLL subtypes. Materials and Methods One hundred genetically well-characterized B-CLL samples, together with 11 healthy control samples, were analyzed using oligonucleotide arrays, which test for the expression of some 12,000 human genes. Results Aiming at microarray-based subclassification, class predictors were constructed using sets of differentially expressed genes, which yielded in zero or low misclassification rates. Furthermore, a significant number of the differentially expressed genes clustered in chromosomal regions affected by the respective genomic losses/gains. Deletions affecting chromosome bands 11q22-q23 and 17p13 led to a reduced expression of the corresponding genes, such as ATM and p53, while trisomy 12 resulted in the upregulation of genes mapping to chromosome arm 12q. Using an unsupervised analysis algorithm, expression profiling allowed partitioning into predominantly VH-mutated versus unmutated patient groups; however, association of the expression profile with the VH mutational status could only be detected in male patients. Conclusion The finding that the most significantly differentially expressed genes are located in the corresponding aberrant chromosomal regions indicates that a gene dosage effect may exert a pathogenic role in B-CLL. The significant difference in the partitioning of male and female B-CLL samples suggests that the genomic signature for the VH mutational status might be sex-related.

Characterization of Early Human B Cell Development by Gene Expression Profiling.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 1352-1352
Author(s):  
Marit E. Hystad ◽  
Trond H. Bo ◽  
Edith Rian ◽  
June H. Myklebust ◽  
Einar Sivertsen ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Cell Development ◽  
B Cell Development ◽  
Cell Populations ◽  
Early Human ◽  
Human B Cell

Abstract B cells develop from hematopoietic stem cells (HSC) in the bone marrow (BM) through a number of distinct stages before they migrate to the periphery as naïve mature B lymphocytes. These developmental stages can be identified by expression of cell surface antigens and Ig gene rearrangement status. The aim of this study was to characterize the earliest steps of normal human B cell development by gene expression profiling. Immunomagnetic selection and subsequent fluorescence-activated cell sorting (FACS) were used to isolate five populations from adult human BM: CD34+CD38− (HSC), CD34+CD10+CD19− early lymphoid progenitor cells (ELP), CD34+CD10+CD19+IgM− progenitor B cells (pro-B), CD34−CD10+CD19+IgM− precursor B cells (pre-B) and CD34−CD10+CD19+IgM+ immature B cells (IM). Total RNA was extracted from the purified cell populations, amplified and hybridized to Lymphochip cDNA microarrays. Six independent experiments from different donors were performed for each cell population. Expression of the genes encoding the selection markers confirmed the validity of the approach. Interestingly, genes necessary for the V(D)J-recombination such as RAG-1, RAG-2, TdT and ADA showed higher gene expression in the ELP population than in the HSC. In contrast, the transcription factors E2A, EBF, and Pax-5, which are all essential for early B-cell development, were first turned on in pro-B cells, in accordance with the B-cell lineage commitment. The ELP did not express B, T or NK lineage markers, except for a higher expression level of CD2 in the ELP population than in the four other cell populations. Taken together, the expression pattern of CD2 and the V(D)J-recombination genes in the ELP population, indicate that these cells have developed a lymphocyte potential, but are not fully committed to B-lineage cells. Hierarchical cluster analysis of the 758 differentially expressed genes (differences in relative expression by a factor of two or more and with maximum10% FDR) revealed a pattern that clearly separated the five consecutive cell populations. Furthermore, we created expression signatures based on information from Gene Ontology (GO) http://source.stanford.edu/cgi-bin/source/sourceSearch. One of the clearest distinctions between the gene expressions of the five developmental populations involved genes associated with proliferation, and showed that the HSC and IM populations are relatively indolent while the pro-B and pre-B populations comprised high expression levels of nearly all the proliferation associated genes. Finally, we examined in further detail the transitions between HSC, ELP and pro B cells. We found 25 genes to be differently expressed in the ELP population in comparison to the HSC and pro-B populations, including IGJ, BCL2 and BLNK. To identify combinations of markers that could better discriminate the ELP population, we also performed a gene pair class separation test. This resulted in 68 gene pairs with score above 10 that were denoted very good discriminators. For several of the markers the differences in gene expression were verified at the protein level by five colour FACS analysis. Taken together, these results provide new insight into the molecular processes that take place in the early human B cell differentiation, and in particular provide new information regarding expression of genes in the ELP population.

Gene Expression Profiling of Highly Purified Malignant and Non-Malignant Cells: Characterization of the Tumor-Microenvironment Cellular Synapse in De Novo Follicular Lymphoma (FL).

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 355-355 ◽  
Author(s):  
Karin Tarte ◽  
Céline Pangault ◽  
John de Vos ◽  
Philippe Ruminy ◽  
Fabienne Sauvee ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
B Cells ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
De Novo ◽  
Differentially Expressed ◽  
Malignant Cells ◽  
Activated T Cell

Abstract Genetic and functional studies have demonstrated that FL cells retain the major features of normal germinal center (GC)-derived B cells, in particular the dependency on an active crosstalk with their specialized microenvironment. In agreement, microarray analyses have recently revealed that FL patient outcome is primarily predicted by molecular characteristics of tumor-infiltrating immune cells instead of tumor cells. However, our knowledge of the crucial interactions between malignant and non-malignant cells in FL remains limited by the use of whole biopsy specimen to perform gene expression profiling (GEP). We thus conducted GEP on both CD19pos B cells and CD19negCD22neg non-B cells purified from lymph nodes of 17 patients with de novo FL & 4 normal donors (CD20pos >94.5%, median=98.2%) and 9 de novo FL patients & 5 normal donors (CD20pos<6.7%, median=0.5%), respectively. Biotinylated cRNA were amplified according to the small sample labelling protocol and hybridized onto HGU133 Plus 2.0 arrays (Affymetrix). Raw data were normalized using GC-RMA methodology (ArrayAssist, Stratagene) and finally, based on a CV>80, 10870 probesets were selected for further analyses. Unsupervised hierarchical clustering (Eisen’s software) allowed the correct classification of the 35 samples into the 4 groups: FL B-cell, Normal B-cells, FL non-B cells, and Normal non-B cells. Supervised analyzes were done using asymptotic non-parametric Mann-Whitney U-test (fold change ≥2, P<0.01) and confirmed by permutation analysis (500 permutations, false discovery rate <5%) using SAM software. We first established the list of the 841 probesets that were differentially expressed between FL and normal B-cells containing, 355 probesets overexpressed in malignant B cells including genes involved in GC B-cell biology (BCL6, MTA3, ID2, CD80, SDC4) and oncogenes as well (BCL2, AURK2) and conversely, 486 probesets downregulated in malignant B cells involving several interferon-stimulated genes for example. We then looked for the FL-specific microenvironment signature and pointed out the 1206 probesets that were differentially expressed between FL and normal non-B cells. Interestingly, all these genes were upregulated in the lymphoma context. Among them, we identified a striking follicular helper T-cell (TFH) signature (CXCR5, ICOS, CXCL13, CD200, PDCD1, SH2D1A) and an activated T-cell signature (IFNG, FASLG, GZMA, ZAP70, CD247). Notably, the TFH and activated T-cell signatures were not merely a surrogate for the number of T cells since many standard T-cell genes (i.e. CD2, CD4, CD7, LEF1, CD8A) were not induced in the FL microenvironment. Finally, in order to draw an overview of the FL-specific synapse between B and non-B cell compartments, we isolated a group of 2323 probesets that were differentially expressed between both compartments in FL and not in normal context. Using Ingenuity Pathway Analysis software we then identified among them FL-specific functional networks, including an IL-4- & an IL-15-centered pathway. Altogether, these data shed new light on our understanding of FL biology and could be a source of new therapeutics targeting the interplay between B cells and their microenvironment.

Patients With DLBCL Commonly Have B-Cell Lymphopenia and Circulating Clonal B-Cell Populations With a Phenotype Concordant With The Underlying Tumour

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 3013-3013
Author(s):  
Ruth M de Tute ◽  
Sharon Barrans ◽  
Andy C. Rawstron ◽  
Peter W.M. Johnson ◽  
Andrew J Davies ◽  
...  
Keyword(s):  
Gene Expression ◽  
Flow Cytometry ◽  
B Cells ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Peripheral Blood ◽  
Expression Profiling ◽  
Common Finding ◽  
Cell Populations ◽  
The Impact

Abstract Clonal B-cell populations with either a CLL or a non-CLL phenotype are a common finding in normal individuals but uncertainty remains about how this relates to the development of clinically significant disease. The aim of this study was to investigate the frequency of peripheral blood clonal B-cell populations and B-cell subset abnormalities in newly presenting DLBCL patients and to determine whether the incidence of these abnormalities differed between the GCB and ABC subtypes, which are regarded as having distinct pathogenesis. The study was carried out using peripheral blood samples collected from patients entered in the UK-REMoDL-B trial. This trial is testing the hypothesis that the ABC subtype of DLBCL responds preferentially to R-CHOP- Bortezomib. Gene expression profiling is performed on the diagnostic tissue biopsy (FFPE) using the Illumina WG-DASL assay prior to randomisation classified as GCB, ABC or unclassified (UN). The availability of GEP data allows meaningful comparison with the phenotype of clonal populations detected by flow cytometry. Peripheral blood taken prior to first treatment was analysed using multi-colour flow cytometry. Following red cell lysis with ammonium chloride, samples were incubated with a panel of antibodies comprising of a CD19 and CD20 backbone, with Kappa, Lambda, CD5, CD45, CD49d, LAIR-1, CXCR5, CD31, CD95, CD38 and CD10, supplemented in some cases by CD81, CD79b, and CD43. A minimum of 500,000 events were acquired on a FacsCanto II flow cytometer (Becton Dickinson). B-cells were enumerated and any monoclonal populations identified were classified as CLL, germinal centre (GC), non-GC or not otherwise specified (NOS) where the phenotype was indeterminate. 358 samples were eligible for inclusion from patients with an average age of 62.2years (range 22.9-86.1). Abnormalities were detected in 52% of cases (B-lymphopenia ((<0.06 x 109/l) 33%, B-lymphocytosis (>1 x 109/l) 2.8%, CLL clone 3.6%, GC clone 9.8%, non-GC clone 9.8%, clonal population NOS 2.2%). Gene expression profiling results were available for 278 individuals; 51% GCB, 32% ABC and 17% unclassified. The relationship between peripheral blood B-cell findings and the GEP determined phenotype of the tumour is shown in the table:TableB-lymphopeniaCLL CloneMonoclonal GC typeMonoclonalNon-GC typeMonoclonal NOSNormalB-cellGCB n=14241/142 (29%)5/142 (3.5%)21/142 (15%)8/142 (5.6%)2/142 (1%)72/142 (51%)ABC n=8927/89 (30%)2/89 (2%)2/89 (2%)12/89 (13.5%)2/89 (2%)49/89 (55%)Unclassified n=4726/47 (55%)0/50 (0%)2/47 (4%)6/47 (12%)6/47 (5%)14/47 (30%) In patients where clonal populations were detected in the peripheral blood there was striking concordance between the phenotype of the clone and the GEP of the underlying tumour. Presence of a GC-population by flow was highly predictive of GCB GEP (84% GC–type populations detected were in GCB cases). The number of discordant cases and the number of CLL clones detected approximate to the numbers that would be expected in a normal population of a similar age. It is, therefore, likely that in most cases circulating tumour cells or a closely related precursor clone are being detected. The similarity between the results of the ABC and unclassified GEP groups suggest that these are biologically related. An unexpected finding in this study was the high incidence of B-lymphopenia at a level that might be expected to be associated with increased risk of infection. This may reflect suppression of normal B-cells by the neoplastic clone or be a marker of underlying immune dysfunction that may predispose to the development of the tumour. Immuosuppression has a role in the pathogenesis of DLBCL in the elderly and this study suggests that this may also be a factor in the wider patient population. These results may have implications for prognostic assessment and may offer opportunities for early diagnosis and possibly response assessment in some patients. The impact on outcome will be assessed in the course of the trial. Disclosures: Jack: Roche /Genentech: Research Funding.

scholarly journals Gene network approach reveals co-expression patterns in nasal and bronchial epithelium

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kai Imkamp ◽  
Victor Bernal ◽  
Marco Grzegorzcyk ◽  
Peter Horvatovich ◽  
Cornelis J. Vermeulen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Gene Expression Profiling ◽  
Graphical Models ◽  
Expression Profiling ◽  
Expression Patterns ◽  
Bronchial Epithelium ◽  
Differentially Expressed ◽  
Tobacco Exposure ◽  
Genome Wide

Abstract Nasal gene expression profiling is a new approach to investigate the airway epithelium as a biomarker to study the activity and treatment responses of obstructive pulmonary diseases. We investigated to what extent gene expression profiling of nasal brushings is similar to that of bronchial brushings. We performed genome wide gene expression profiling on matched nasal and bronchial epithelial brushes from 77 respiratory healthy individuals. To investigate differences and similarities among regulatory modules, network analysis was performed on correlated, differentially expressed and smoking-related genes using Gaussian Graphical Models. Between nasal and bronchial brushes, 619 genes were correlated and 1692 genes were differentially expressed (false discovery rate <0.05, |Fold-change|>2). Network analysis of correlated genes showed pro-inflammatory pathways to be similar between the two locations. Focusing on smoking-related genes, cytochrome-P450 pathway related genes were found to be similar, supporting the concept of a detoxifying response to tobacco exposure throughout the airways. In contrast, cilia-related pathways were decreased in nasal compared to bronchial brushes when focusing on differentially expressed genes. Collectively, while there are substantial differences in gene expression between nasal and bronchial brushes, we also found similarities, especially in the response to the external factors such as smoking.

Gene Expression Profiling of Bulk CD138+ Plasma Cells in Preneoplastic Gammopathy/Asymptomatic Myeloma Identifies Distinct Subsets of Patients.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1218-1218 ◽  
Author(s):  
Madhav V. Dhodapkar ◽  
Fenghuang Zhan ◽  
Erik Rasmussen ◽  
Bart Burington ◽  
Brian Durie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Tumor Cells ◽  
Expression Profiling ◽  
Plasma Cells ◽  
Expression Patterns ◽  
Hierarchical Cluster ◽  
Magnetic Bead ◽  
Study Data ◽  
Differentially Expressed

Abstract Gene expression profiling of plasma cells (GEP-PC) has provided major insights into myeloma pathobiology. However the data about GEP-PC in preneoplastic gammopathy (MGUS) or asymptomatic myeloma (AM) are limited, and gene expression patterns that might predict outcome in these patients have not been defined. We analyzed GEP (using U133Plus Affymetrix microarrays), of plasma cells isolated by immuno-magnetic bead selection with CD138 microbeads, from the bone marrow of patients with MGUS (n=16) and asymptomatic myeloma (AM; n=18) enrolled in a prospective South West Oncology Group (SWOG) observational study. Data from normal plasma cells (PCs) and from 105 myeloma PCs were included as controls. Myeloma PCs were randomly selected to include at least 15 patients from each of the 7 subgroups previously identified based on GEP of myeloma tumor cells (Zhan and Shaughnessy, ASH 2004). After the suppression of immunoglobulin (Ig) genes, there were 1297 genes that significantly differed in expression between MGUS-PCs and MM-PCs, and 1099 genes that differed between MGUS-PCs and normal PCs with a 1% false discovery rate. Hierarchical cluster analysis of all samples was performed using 1000 plasma cell signature genes that were most differentially expressed between normal and myeloma PCs. These data demonstrated that both MGUS and AM samples were distributed between normal and MM samples. A prediction analysis of microarrays (PAM) model (PNAS99:6567, 2002) utilizing 134 genes was then developed to determine if the signature from these genes in MGUS/AM was more similar to normal or to myeloma plasma cells. In this analysis, 11/16 (69%) of the MGUS samples were more similar to normal PC, compared to 6/18 (33%) of the AM samples (p=0.04). At present, there are no reliable phenotypic markers to distinguish between normal and malignant PCs within the bulk CD138+ population. Gene expression spikes for cyclin D1 and MAF/MAF-B were seen in both MGUS and AM cohorts, including in some patients with normal PC signature. These data provide the largest comparison to date, of GEP of PCs in preneoplastic versus malignant gammopathies and suggest that GEP may be a useful tool to prospectively identify subsets of patients within the MGUS/AM population with dominant normal PC or MM PC signatures, and potentially differing prognosis. Further analysis of differentially expressed genes between MGUS/MM PCs identified in this dataset may allow insights into the genomic changes in tumor cells underlying the malignant progression of myeloma.

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