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NEOPLASIA
From the Donna D. and Donald M. Lambert Laboratory of
Myeloma Genetics, Myeloma Institute for Research and Therapy, or
Department of Pathology, University of Arkansas for Medical Sciences,
Little Rock, and the Southwest Oncology Group, Fred Hutchinson Cancer
Research Center, Seattle, WA.
Bone marrow plasma cells (PCs) from 74 patients with newly
diagnosed multiple myeloma (MM), 5 with monoclonal gammopathy of undetermined significance (MGUS), and 31 healthy volunteers (normal PCs) were purified by CD138+ selection. Gene expression of
purified PCs and 7 MM cell lines were profiled using high-density
oligonucleotide microarrays interrogating about 6800 genes. On
hierarchical clustering analysis, normal and MM PCs were differentiated
and 4 distinct subgroups of MM (MM1, MM2, MM3, and MM4) were
identified. The expression pattern of MM1 was similar to normal PCs and
MGUS, whereas MM4 was similar to MM cell lines. Clinical parameters
linked to poor prognosis, abnormal karyotype (P = .002)
and high serum Progress in understanding the biology and genetics
of and advancing therapy for multiple myeloma (MM) has been slow. MM
cells are endowed with a multiplicity of antiapoptotic signaling
mechanisms, which account for their resistance to current chemotherapy
and thus the ultimately fatal outcome for most patients.1
Although aneuploidy by interphase fluorescence in situ hybridization
(FISH)2 and DNA flow cytometry3 is observed in
more than 90% of cases, cytogenetic abnormalities in this typically
hypoproliferative tumor are informative in only about 30% of cases and
are typically complex, involving on average 7 different chromosomes.
Given this "genetic chaos," it has been difficult to establish
correlations between genetic abnormalities and clinical
outcomes.4,5 Only recently has chromosome 13 deletion been
identified as a distinct clinical entity with a grave
prognosis.6-8 However, even with the most comprehensive
analysis of laboratory parameters, such as
The advent of high-density oligonucleotide DNA microarray has made
possible a simultaneous analysis of messenger RNA (mRNA) expression
patterns of thousands of genes pertinent to various biologic
functions.15 Here we report that, in a comparison with normal plasma cells (PCs), MM PCs are distinctly different.
Furthermore, using hierarchical clustering, 4 distinct subgroups of MM
PCs were established that reveal significant correlations with clinical characteristics known to be associated with poor prognosis. This system
represents the framework for a new classification system and identifies
the genetic differences associated with these distinct subgroups.
Cell collection and total RNA purification
Preparation of labeled complementary RNA and hybridization to
high-density microarray
GeneChip data analysis To efficiently manage and mine high-density oligonucleotide DNA microarray data, a new data-handling tool was developed. GeneChip-derived expression data were stored on an MS SQL Server. This database was linked, via an MS Access interface called Clinical Gene-Organizer to multiple clinical parameter databases for patients with MM. This Data Mart concept allows gene expression profiles to be directly correlated with clinical parameters and clinical outcomes using standard statistical software. An MS JET version of Clinical Gene-Organizer can be downloaded from our Web page at http://lambertlab.uams.edu. All data used in our analysis were derived from Affymetrix 3.3 software. GeneChip 3.3 output files are given (1) as an average difference (AD) that represents the difference between the intensities of the sequence-specific perfect match probe set and the mismatch probe set, or (2) as an absolute call (AC) of present or absent as determined by the GeneChip 3.3 algorithm. AD calls were transformed by the natural log after substituting any sample with an AD of less than 60 with the value 60 (2.5 times the average Raw Q). Statistical analyses of the data were performed with software packages SPSS 10.0 (SPSS, Chicago, IL), S-Plus 2000 (Insightful, Seattle, WA), and Gene Cluster/Treeview.16Hierarchical clustering of average linkage clustering with the centered
correlation metric was used.16 The clustering was done on
the AD data of 5483 genes. Either Clinical parameters were tested across MM cluster groups. To test the
continuous variables, we used an ANOVA test; to test discrete
variables, a The natural logs of the AD data were used to find genes with a "spiked profile" of expression in MM. Genes were identified that had low to undetectable expression in the majority of patients and normal samples (no more than 4 present absolute calls [P-ACs]). A total of 2030 genes fit the criteria of this analysis. The median expression value of each of the genes across all patient samples was determined. For the ith gene, we called this value medgene (i). We called the ith gene a "spiked" gene if it had at least 4 patient expression values more than 2.5 + medgene (i). The constant 2.5 was based on the log of the AD data. These genes that were "spiked" were further divided into subsets according to whether or not the largest spike had an AD expression value more than 10 000. Reverse transcription-polymerase chain reaction Reverse transcription-polymerase chain reaction (RT-PCR) for the FGFR3 MMSET was performed on the same cDNAs used in the microarray analysis. Briefly, cDNA was mixed with the IGJH2 (5'-CAATGGTCACCGTCTCTTCA-3') primer and the MMSET primer (5'-CCTCAATTTCCTGAAATTGGTT-3'). PCR reactions consisted of 30 cycles with a 58°C annealing temperature and 1-minute extension time at 72°C using a Perkin-Elmer GeneAmp 2400 thermocycler (Wellesley, MA). PCR products were visualized by ethidium bromide staining after agarose gel electrophoresis.Immunohistochemistry Immunohistochemical staining was performed on a Ventana ES (Ventana Medical Systems, Tucson, AZ) using Zenker-fixed paraffin-embedded bone marrow sections, an avidin-biotin peroxidase complex technique (Ventana Medical Systems), and the antibody L26 (CD20, Ventana Medical Systems). Heat-induced epitope retrieval was performed by microwaving the sections for 28 minutes in a 1.0-mmol/L concentration of citrate buffer at pH 6.0.Interphase FISH For interphase detection of the t(11;14)(q13;q32) translocation fusion signal, we used a the LSI IGH/CCND1 dual-color, dual-fusion translocation probe (Vysis, Downers Grove, IL). The TRI-FISH procedure used to analyze the samples has been previously described.12 Briefly, at least 100 clonotypic PCs, identified by cytoplasmic immunoglobulin (cIg) staining were counted for the presence or absence of the translocation fusion signal in all samples except one, which yielded only 35 PCs. An MM sample was defined as having the translocation when more than 25% of the cells contained the fusion.Flow cytometry For flow cytometric analysis of CD marker expression, a panel of antibodies directly conjugated to fluorescein isothiocyanate (FITC) or phycoerythrin (PE) was used: FITC-labeled CD19, CD20, and CD22 (Becton Dickinson); CD38 and CD45 (BD Pharmingen, San Diego, CA); CD52 and CD138 (Serotec, Raleigh, NC), and PE-labeled CD21 (BD Pharmingen). Cells were harvested from culture, washed in phosphate-buffered saline (PBS) and stained at 4°C with CD antibodies or isotype-matched control antibodies. After staining, cells were fixed in 1% paraformaldehyde and analyzed using a FACSscan flow cytometer (Becton Dickinson).
Hierarchical clustering of PC gene expression demonstrates class distinction As a result of 656 000 measurements of gene expression in 118 PC samples, altered gene expression in the MM samples was identified. Two-dimensional hierarchical clustering differentiated cell types by gene expression when performed on 5483 genes when expression was present in at least one of the 118 samples (Figure 1A). The sample dendrogram derived 2 major branches (Figure 1A,D). One branch contained all 31 normal samples and a single MGUS case, whereas the second branch contained all 74 MM and 4 MGUS cases and the 8 cell lines. The MM-containing branch was further divided into 2 sub-branches, one containing the 4 MGUS and the other the 8 MM cell lines, which were all clustered next to one another, thus showing a high degree of similarity in gene expression among the cell lines. This suggested that MM could be differentiated from normal PCs and that at least 2 different classes of MM could be identified, one more similar to MGUS and the other similar to MM cell lines. To show reproducibility of the technique and analysis, we repeated the hierarchical clustering analysis with all 118 samples, including duplicate samples from 12 patients (PCs taken 24 hours or 48 hours after initial sample). All samples from the 12 patients studied longitudinally were found to cluster adjacent to one another. This indicated that gene expression in samples from the same patient were more similar to each other than they were to all other samples (data not shown).
The clustergram (Figure 1A) showed that genes of unrelated sequence but similar function clustered tightly together along the vertical axis. For example, a particular cluster of 22 genes, primarily those encoding immunoglobulin molecules and major histocompatibility genes, had relatively low expression in MM PCs and high expression in normal PCs (Figure 1B). This was anticipated, given that the PCs isolated from MM are clonal and hence only express single immunoglobulin light-chain and heavy-chain variable and constant region genes, whereas PCs from healthy donors are polyclonal and express many different genes of these 2 classes. Another cluster of 195 genes was highly enriched for numerous oncogenes/growth-related genes (eg, MYC, ABL1, PHB, and EXT2), cell cycle-related genes (eg, CDC37, CDK4, and CKS2), and translation machinery genes (EIF2, EIF3, HTF4A, and TFIIA) (Figure 1C). These genes were all highly expressed in MM, especially in MM cell lines, but had low expression levels in normal PCs. Hierarchical clustering of newly diagnosed MM identifies 4 distinct subgroups We performed 2-dimensional cluster analysis of the 74 MM cases alone. The sample dendrogram identified 2 major branches with 2 distinct subgroups within each branch (Figure 1E). We designated the 4 subgroups MM1, MM2, MM3, and MM4 containing 20, 21, 15, and 18 patients, respectively. The MM1 subgroup represented the patients whose PCs were most closely related to the MGUS PCs and whose MM4 were most like the MM cell lines (Figure 1D). These data suggested that the 4 gene expression subgroups were authentic and might represent 4 distinct clinical entities. We then examined differences in gene expression across the 4 subgroups using the 2 and WRS tests (Table
1). As expected the largest difference was between MM1 and MM4 (205 genes) and the smallest between MM1 and
MM2 (24 genes). We then looked at the top 30 genes turned on, or
up-regulated, in MM4 compared with MM1 (Table
2). These data demonstrated that 13 of
the 30 most significant genes (10 of the top 15 genes) were involved in
DNA replication/repair or cell cycle control. Thymidylate
synthase (TYMS), which was present in all 18 samples
comprising the MM4 subgroup, was only present in 3 of the 20 MM1
samples and represented the most significant gene in the
2 test. The DNA mismatch repair gene, mutS
(Escherichia coli) homolog 2 (MSH2) with a WRS
P value of 2.8 × 10 6 was the most
significant gene in the WRS test. Other notable genes in the list
included the CAAX farnesyltransferase (FNTA), the
transcription factors enhancer of zeste homolog 2 (EZH2) and MYC-associated zinc finger protein (MAZ),
eukaryotic translation initiation factors (EIF2S1 and
EIF2B1), as well as the mitochondrial translation initiation
factor 2 (MTIF2), the chaperone (CCT4), the
UDP-glucose pyrophosphorylase 2 (IUGP2), and the 26S
proteasome-associated pad1 homolog (POH1).
To assess the validity of the clusters with respect to clinical
features, correlations of various clinical parameters across the 4 subgroups were analyzed (Table 3). Of 17 clinical variables tested, the presence of an abnormal karyotype
(P = .0003) and serum
Altered expression of 120 genes differentiates malignant from normal PCs Our hierarchical cluster analysis showed that MM PCs could be differentiated from normal PCs. Genes distinguishing the MM from normal PCs were identified as significant by2 analysis and the
WRS test (P < .0001). A statistical analysis showed that
120 genes distinguished MM from normal PCs. Pearson correlation
analyses of the 120 differentially expressed genes were used to
identify whether the genes were up-regulated or down-regulated in MM.
When genes associated with immune function (eg, IGH,
IGL, HLA), representing the majority of
significantly down-regulated genes, were filtered out, 50 genes showed
significant down-regulation in MM (Table
4). The P values for the WRS
test ranged from 9.80 × 10
Correlation analysis showed that 70 genes were either turned on or
up-regulated in MM (Table
5). When considering
the
Gene expression "spikes" in subsets of MM A total of 156 genes not identified as differently expressed in the statistical analysis of MM versus normal PCs, yet highly overexpressed in subsets of MM, were also identified. A total of 25 genes with an AD spike more than 10 000 in at least one sample are shown (Table 6). With 27 spikes, the adhesion-associated gene FBLN2 was the most frequently spiked. The gene for the interferon induced protein 27, IFI27, with 25 spikes was the second most frequently spiked gene and contained the highest number of spikes over 10 000 (n = 14). The FGFR3 gene was spiked in 9 of the 74 cases (Figure 2A). It was the only gene for which all spikes were more than 10 000 AD. In fact, the lowest AD value was 18 961 and the highest 62 515, which represented the highest of all spikes. The finding of FGFR3 spikes suggested that these spikes were induced by the MM-specific, FGFR3-activating t(4;14)(p21;q32) translocation.22 To test this hypothesis, we performed RT-PCR for a t(4;14)(p21;q32) translocation-specific fusion transcript between the IGH locus and the gene MMSET (data not shown). The translocation-specific transcript was present in all 9 FGFR3 spike samples but was absent in 5 samples that did not express FGFR3. These data suggested that the spike was caused by the t(4;14)(p21;q32) translocation. The CCND1 gene was spiked with AD values of more than 10 000 in 13 cases. We performed TRI-FISH analysis for the t(11;14)(q13;q32) translocation (Table 7). All 11 evaluable samples were positive for the t(11;14)(q13;q32) translocation by TRI-FISH; 2 samples were not analyzable due to loss of cell integrity during storage. Thus, all FGFR3 and CCND1 spikes could be accounted for by the presence of either the t(4;14)(p21;q32) translocation or the t(11;14)(q13;q32) translocation, respectively.
We next determined the distribution of the FGFR3, CST6, IFI27, and CCND1 spikes within the gene expression-defined MM subgroups (Figure 2). The data showed that whereas FGFR3 and CST6 spikes were more likely to be found in MM1 or MM2 (P < .005), the spikes for IFI27 were associated with an MM3 and MM4 distribution (P < .005). CCND1 spikes were not associated with any specific subgroup (P > .1). It is noteworthy that both CST6 and CCND1 map to 11q13 and had no overlap in spikes. We are currently testing whether CST6 overexpression is due to a variant t(11;14)(q13;q32) translocation. The 5 spikes for MS4A2 (CD20) were found in either the MM1 (3 spikes) or MM2 (2 spikes) subgroups (data not shown). The gene MS4A2, which codes for the CD20 molecule, was
also found as a spiked gene in 4 cases (Figure
3A). To investigate whether spiked gene
expression correlated with protein expression, we performed
immunohistochemistry for CD20 on biopsies from 15 of the 74 MM samples
(Figure 3B). All 4 cases that had spiked MS4A2 gene
expression were also positive for CD20 protein expression, whereas 11 that had no MS4A2 gene expression were also negative for
CD20 by immunohistochemistry. To add additional validation to the gene
expression profiling, we performed a comparison of CD marker protein
and gene expression in the MM cell line CAG and the EBV-transformed
lymphoblastoid cell line ARH-77 (Figure 4). The expression of CD138 and CD38
protein and gene expression was high in CAG but absent in ARH-77 cells.
On the other hand, expression of CD19, CD20, CD21, CD22, CD45, and
CDw52 was found to be strong in ARH-77 and absent in CAG cells. The
nearly 100% coincidence of FGFR3 or CCND1 spiked
gene expression with the presence of the t(4;14)(p14;q32) or
t(11;14)(q13;q32) translocation, the strong correlation of CD20 and
MS42A gene expression in primary MM, and CD marker protein
and gene expression in B cells and PCs represent important validations
of the accuracy of our gene expression profiling.
In this report, we have shown that both normal and malignant PCs can be purified to homogeneity from bone marrow aspirates using anti-CD138-based immunomagnetic bead-positive selection. Using these cells we have provided the first comprehensive global gene expression profiling of newly diagnosed MM patients and contrasted these expression patterns with those of normal PCs. Hierarchical cluster analysis of MM and normal PCs, as well as the benign PC dyscrasia MGUS and the end-stage-like MM cell lines, revealed that normal PCs are unique and that primary MM is either like MGUS or MM cell lines. In addition, MM cell line gene expression was homogeneous as evidenced by the tight clustering in the hierarchical analysis. The similarity of MM cell line expression patterns to primary newly diagnosed forms of MM support the validity of using MM cell lines as models for MM, in particular for our gene expression-defined MM4 subgroup. On hierarchical clustering of MM alone, 4 MM subgroups were
distinguished. Differences indicate that gene expression signatures distinguish distinct clinical entities as (1) the MM1 subgroup contained samples that were more like MGUS (in our first cluster analysis), whereas the MM4 subgroup contained samples more like MM cell
lines; (2) the most significant gene expression patterns differentiating MM1 and MM4 were cell cycle control and DNA metabolism genes; and (3) the MM4 subgroup was more likely to have abnormal cytogenetics, elevated serum We speculate that the MM4 subgroup thus likely represents the most high-risk clinical entity. Thus, knowledge of the molecular genetics of this particular subgroup should provide insight into its biology and possibly provide a rationale for appropriate subtype-specific therapeutic interventions. On analysis, the most significant gene expression changes differentiating the MM1 and MM4 subgroups code for activities that clearly implicate MM4 as having a more proliferative and autonomous phenotype. The most significantly altered gene in the comparison, TYMS (thymidylate synthase), which functions in the pyrimidine biosynthetic pathway, has been linked to resistance to fluoropyrimidine chemotherapy and also poor prognosis in colorectal carcinomas.23 Other notable genes up-regulated in MM4 were the CAAX farnesyltransferase gene, FTNA. Farnesyltransferase prenylates RAS, a posttranslational modification required to allow RAS to attach to the plasma membrane. These data suggest that farnesyltransferase inhibitors may be effective in treating patients with high levels of FTNA expression. Two genes coding for components of the proteasome pathway, POH1 (26S proteasome-associated pad1 homolog) and UBL1 (ubiquitin-like protein 1) were also overexpressed in MM4. Overexpression of POH1 confers P-glycoprotein-independent, pleotropic drug resistance to mammalian cells.24,25 Given the uniform development of chemotherapy resistance in MM the combined overexpression of POH1 and MVP may have profound influences on this phenotype. In contrast to normal PCs, more than 75% of MM PCs express abundant mRNA for the multidrug resistance gene, lung resistance-related protein (MVP). These data are consistent with previous reports showing that expression of MVP in MM is a poor prognostic factor.26 Ubiquitin-like protein 1 (UBL1) also known as sentrin, is involved in many processes including associating with RAD51, RAD52, and p53 proteins in the double-strand repair pathway27-29; conjugating with RANGAP1, involved in nuclear protein import; and importantly for MM, protecting against both Fas/Apo-1 (TNFRSF6) or TNFR1-induced apoptosis.30 The deregulated expression of many genes whose products function in the proteasome pathway may be used in the pharmacogenomic analysis of efficacy of proteasome inhibitors like PS-341 (Millennium Pharmaceuticals, Cambridge, MA). Another significantly up-regulated gene in MM4 was the single-stranded DNA-dependent adenosine triphosphate (ATP)-dependent helicase (G22P1) also known as Ku70 autoantigen. The DNA helicase II complex, made up of p70 and p80, binds preferentially to forklike ends of double-stranded DNA in a cell cycle-dependent manner. Binding to DNA is thought to be mediated by p70 and dimerization with p80 forms the ATP-dependent DNA-unwinding enzyme (helicase II) and acts as the regulatory component of a DNA-dependent protein kinase (DNPK), which was also significantly up-regulated in MM4. The involvement of the helicase II complex in DNA double-strand break repair, V(D)J recombination, and notably chromosomal translocations has been proposed. Another gene up-regulated was the DNA fragmentation factor, 45 kd, alpha (DFFA). Caspase-3 cleaves the DFFA-encoded 45-kd subunit at 2 sites to generate an active factor that produces DNA fragmentation during apoptosis signaling. We speculate that, in light of the many blocks to apoptosis in MM, DFFA activation could result in DNA fragmentation, which in turn would activate the helicase II complex that then may facilitate chromosomal translocations. It is of note that abnormal karyotypes, and thus chromosomal translocations, are associated with the MM4 subgroup, which tended to overexpress these 2 genes. A direct comparison of gene expression patterns in MM and normal PCs identified novel genes with highly significant differences that could represent the fundamental changes associated with the malignant transformation of PCs. The progression of MM as a hypoproliferative tumor is thought to be linked to a defect in programmed cell death rather than rapid cell replication.31 Two genes, prohibitin (PHB) and quiescin Q6 (QSCN6), overexpressed in MM are involved in growth arrest. The overexpression of these genes may be responsible for the typically low proliferation indices seen in MM. It is hence conceivable that therapeutic down-regulation of these genes possibly resulting in enhanced proliferation could render MM cells more susceptible to cell cycle-active chemotherapeutic agents. The gene coding for CD27, TNFRSF7, the second most significantly underexpressed gene in MM, is a member of the TNFR superfamily that provides costimulatory signals for T- and B-cell proliferation and B-cell immunoglobulin production and apoptosis.18 Anti-CD27 significantly inhibits the induction of Blimp-1 and J-chain transcripts, which are turned on in cells committed to PC differentiation,19 suggesting that ligation of CD27 on B cells may prevent terminal differentiation. CD27 ligand (CD70) prevents apoptosis mediated by interleukin 10 (IL-10) and directs differentiation of CD27+ memory B cells toward PCs in cooperation with IL-10.17 Thus, it is possible that the down-regulation of CD27 gene expression in MM may block an apoptotic program. The overexpression of CD47 on MM may be related to escape of MM cells from immune surveillance. Studies have shown that cells lacking CD47 are rapidly cleared from the bloodstream by splenic red pulp macrophages and CD47 on normal red blood cells prevents this elimination.32 The gene product of DNA methyltransferase 1, DNMT1, overexpressed in MM, is responsible for cytosine methylation in mammals and has an important role in epigenetic gene silencing. In fact, aberrant hypermethylation of tumor suppressor genes plays an important role in the development of many tumors (for a review, see Baylin33). De novo methylation of p16/INK4a is a frequent finding in primary MM.34,35 Also, recent studies have shown that up-regulated expression of DNMTs may contribute to the pathogenesis of leukemia by inducing aberrant regional hypermethylation.36 DNA methylation represses genes partly by recruitment of the methyl-CpG-binding protein MeCP2, which in turn recruits a histone deacetylase activity. Fuks et al37 have shown that the process of DNA methylation, mediated by Dnmt1, may depend on or generate an altered chromatin state via histone deacetylase activity. It is potentially significant that MM cases also demonstrate significant overexpression of the gene for metastasis-associated 1 (MTA1). MTA1 was originally identified as being highly expressed in metastatic cells.38 MTA1 has more recently been discovered to be one subunit of the nucleosome remodeling and histone deacetylation (NURD) complex, which contains not only ATP-dependent nucleosome disruption activity, but also histone deacetylase activity.39 Thus, overexpression of DNMT1 and MTA1 may have dramatic effects on repressing gene expression in MM. Oncogenes activated in MM included ABL and MYC. Although it is not clear whether ABL tyrosine kinase activity is present in MM, it is important to note that overexpression of abl and c-myc results in the accelerated development of mouse plasmacytomas.40 Thus, it may be more than a coincidence that MM cells significantly overexpress MYC and ABL. Chromosomal translocations involving the MYC oncogene and IGH and IGL genes, resulting in dysregulated MYC expression, are hallmarks of Burkitt lymphoma41 and experimentally induced mouse plasmacytomas42; however, MYC/IGH-associated translocations are rare in MM.43,44 Although high MYC expression was a common feature in our panel of MM, it was quite variable, ranging from little or no expression to highly elevated expression. It is also of note that the MAZ gene whose product is known to bind to and activate MYC expression was significantly up-regulated in the MM4 subgroup. Given the important role of MYC in B-cell neoplasia, we speculate that overexpression of MYC, and possibly ABL, in MM may have biologic and possibly prognostic significance. EXT1 and EXT2, which are tumor suppressor genes involved in hereditary multiple exostoses,45 heterodimerize and are critical in the synthesis and display of cell surface heparan sulfate glycosaminoglycans (GAGs).46,47 EXT1 is expressed in both MM and normal PCs. EXT2L was overexpressed in MM, suggesting that a functional glycosyltransferase could be created in MM. It is of note that syndecan-1 (CD138/SDC1), a transmembrane heparan sulfate proteoglycan, is abundantly expressed on MM cells and, when shed into the serum, is a negative prognostic factor.48 Thus, abnormal GAG-modified SDC1 may be important in MM biology. The link of SDC1 to MM biology is furthered by the recent association of SDC1 in the signaling cascade induced by the WNT proto-oncogene products. Alexander et al49 showed that syndecan-1 (SDC1) is required for Wnt-1-induced mammary tumorigenesis. We observed significant down-regulation of WNT10B in primary MM cases. It is also of note that the WNT5A gene and the FRZB gene, which codes for a decoy WNT receptor,50,51 were also marginally up-regulated in newly diagnosed MM (J.S., unpublished data, May 2001). Given that the WNTs represent a novel class of B-cell regulators,52,53 deregulation of the expression of these growth factors (WNT5A, WNT10B) and their receptors (eg, FRZB) and gene products that modulate receptor signaling (eg, SDC1), may be important in the genesis of MM. In addition to identifying genes that were statistically different between the group of normal PCs and MM PCs, we also identified genes, like FGFR3 and CCND1, that demonstrate highly elevated "spiked" expression in subsets of MMs. Patients with elevated expression of these genes can have significant distribution differences among the 4 gene expression cluster subgroups. For example, FGFR3 spikes are found in MM1 and MM2, whereas spikes of IFI27 are more likely to be found in MM3 and MM4. Highly elevated expression of the interferon-induced gene, IFI27, may be indicative of a viral infection, either systemic or specifically within the PCs from these patients, as correlation analysis has shown that IFI27 spikes are significantly linked (Pearson correlation coefficient values of .77 to .60) to elevated expression of 14 interferon-induced genes, including MX1, MX2, OAS1, OAS2, IFIT1, IFIT4, PLSCR1, and STAT1 (J.S., unpublished data, May 2001). More recent analysis of a large population of MM patients (n = 280), indicated that nearly 25% of all patients had spikes of the IFI27 gene, thus including a large percentage of patients (J.S., unpublished data, May 2001). Studies are now ongoing that are investigating (1) whether or not the patients showing the IFI27 spike who cluster in the MM4 subgroup are more likely to have a poor clinical course and (2) to identify the suspected viral infection causing the up-regulation of this class of genes. Thus, spiked gene expression may also be used in the development of clinically relevant prognostic groups. Finally, the 100% coincidence of spiked FGFR3 or CCND1 gene expression with the presence of the t(4;14)(p14;q32) or t(11;14)(q13;q32) translocations as well as the strong correlations between protein expression and gene expression represent important validations of the accuracy of gene expression profiling and suggests that gene expression profiling may eventually supplant the labor intensive and expensive clinical laboratory procedures, such as cell surface marker immunophenotyping and molecular and cellular cytogenetics. Because cancer is thought to arise from permanent alterations in gene expression, our comparison of global gene expression patterns in normal and malignant PCs provides a snapshot of the genetic abnormalities that create the malignant MM phenotype. Many of the genes known to be involved in myeloma genesis, for example, CCND1, FGFR3, MYC, HGF, and MVP, were identified by high-density oligonucleotide DNA microarray comparison of normal and malignant PCs. Importantly, an abundance of heretofore unrecognized classes of genes have been discovered that may be intimately involved in the malignant transformation of PCs and should provide a new framework for studying MM molecular and cellular biology. Similar to investigations in leukemia54 and lymphoma,55 gene expression profiling is anticipated to result in the identification of distinct and prognostically relevant clinical subgroups of MM. Recognition of new therapeutic targets, for example, farnesyltransferase and proteasome components, may lead to a rational design of tumor-specific therapies.
We would like to thank members of the Lambert Laboratory, Jena Derrick, Ailian Li, Kelly McCastlain, Ruston Smith, Elizabeth Williamson, Yan Xiao, and Hongwei Xu for technical assistance without which this project would not have been possible. We thank the MIRT staff, especially Clyde Bailey and Randell Terry for data management; Joth Jacobson and Trey Spencer for statistical support; and P. L. Bergsagel for advice on RT-PCR experimental design; and Paula Card-Higginson for technical writing and editorial assistance.
Submitted June 6, 2001; accepted October 23, 2001.
Supported by private funding from Donna D. and Donald M. Lambert and grant no. CA55819 from the National Cancer Institute, Bethesda, MD.
The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked "advertisement" in accordance with 18 U.S.C. section 1734.
Reprints: John D. Shaughnessy Jr, Donna D. and Donald M. Lambert Laboratory of Myeloma Genetics, University of Arkansas for Medical Sciences, 4301 W Markham St, Slot 776, Little Rock, AR 72205; e-mail: shaughnessyjohn{at}uams.edu.
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L. Agnelli, S. Bicciato, M. Mattioli, S. Fabris, D. Intini, D. Verdelli, L. Baldini, F. Morabito, V. Callea, L. Lombardi, et al. Molecular Classification of Multiple Myeloma: A Distinct Transcriptional Profile Characterizes Patients Expressing CCND1 and Negative for 14q32 Translocations J. Clin. Oncol., October 10, 2005; 23(29): 7296 - 7306. [Abstract] [Full Text] [PDF]
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W. J. Chng, S. A. Van Wier, G. J. Ahmann, J. M. Winkler, S. M. Jalal, P. L. Bergsagel, M. Chesi, M. C. Trendle, M. M. Oken, E. Blood, et al. A validated FISH trisomy index demonstrates the hyperdiploid and nonhyperdiploid dichotomy in MGUS Blood, September 15, 2005; 106(6): 2156 - 2161. [Abstract] [Full Text] [PDF]
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A. K. Stewart and R. Fonseca Prognostic and Therapeutic Significance of Myeloma Genetics and Gene Expression Profiling J. Clin. Oncol., September 10, 2005; 23(26): 6339 - 6344. [Abstract] [Full Text] [PDF]
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E. P. M. Tjin, R. J. Bende, P. W. B. Derksen, A.-P. van Huijstee, H. Kataoka, M. Spaargaren, and S. T. Pals Follicular Dendritic Cells Catalyze Hepatocyte Growth Factor (HGF) Activation in the Germinal Center Microenvironment by Secreting the Serine Protease HGF Activator J. Immunol., September 1, 2005; 175(5): 2807 - 2813. [Abstract] [Full Text] [PDF]
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Y.-W. Qiang, K. Walsh, L. Yao, N. Kedei, P. M. Blumberg, J. S. Rubin, J. Shaughnessy Jr, and S. Rudikoff Wnts induce migration and invasion of myeloma plasma cells Blood, September 1, 2005; 106(5): 1786 - 1793. [Abstract] [Full Text] [PDF]
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S. Girlanda, C. Fortis, D. Belloni, E. Ferrero, P. Ticozzi, C. Sciorati, M. Tresoldi, A. Vicari, T. Spies, V. Groh, et al. MICA Expressed by Multiple Myeloma and Monoclonal Gammopathy of Undetermined Significance Plasma Cells Costimulates Pamidronate-Activated {gamma}{delta} Lymphocytes Cancer Res., August 15, 2005; 65(16): 7502 - 7508. [Abstract] [Full Text] [PDF]
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J. Moreaux, F. W. Cremer, T. Reme, M. Raab, K. Mahtouk, P. Kaukel, V. Pantesco, J. De Vos, E. Jourdan, A. Jauch, et al. The level of TACI gene expression in myeloma cells is associated with a signature of microenvironment dependence versus a plasmablastic signature Blood, August 1, 2005; 106(3): 1021 - 1030. [Abstract] [Full Text] [PDF]
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P. L. Bergsagel, W. M. Kuehl, F. Zhan, J. Sawyer, B. Barlogie, and J. Shaughnessy Jr Cyclin D dysregulation: an early and unifying pathogenic event in multiple myeloma Blood, July 1, 2005; 106(1): 296 - 303. [Abstract] [Full Text] [PDF]
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D. R. Hodge, B. Peng, J. C. Cherry, E. M. Hurt, S. D. Fox, J. A. Kelley, D. J. Munroe, and W. L. Farrar Interleukin 6 Supports the Maintenance of p53 Tumor Suppressor Gene Promoter Methylation Cancer Res., June 1, 2005; 65(11): 4673 - 4682. [Abstract] [Full Text] [PDF]
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F. van Rhee, S. M. Szmania, F. Zhan, S. K. Gupta, M. Pomtree, P. Lin, R. B. Batchu, A. Moreno, G. Spagnoli, J. Shaughnessy, et al. NY-ESO-1 is highly expressed in poor-prognosis multiple myeloma and induces spontaneous humoral and cellular immune responses Blood, May 15, 2005; 105(10): 3939 - 3944. [Abstract] [Full Text] [PDF]
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L. Guedez, A. Martinez, S. Zhao, A. Vivero, S. Pittaluga, M. Stetler-Stevenson, M. Raffeld, and W. G. Stetler-Stevenson Tissue inhibitor of metalloproteinase 1 (TIMP-1) promotes plasmablastic differentiation of a Burkitt lymphoma cell line: implications in the pathogenesis of plasmacytic/plasmablastic tumors Blood, February 15, 2005; 105(4): 1660 - 1668. [Abstract] [Full Text] [PDF]
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R. S. Abraham, K. V. Ballman, A. Dispenzieri, D. E. Grill, M. K. Manske, T. L. Price-Troska, N. G. Paz, M. A. Gertz, and R. Fonseca Functional gene expression analysis of clonal plasma cells identifies a unique molecular profile for light chain amyloidosis Blood, January 15, 2005; 105(2): 794 - 803. [Abstract] [Full Text] [PDF]
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H. Hov, R. U. Holt, T. B. Ro, U.-M. Fagerli, H. Hjorth-Hansen, V. Baykov, J. G. Christensen, A. Waage, A. Sundan, and M. Borset A Selective c-Met Inhibitor Blocks an Autocrine Hepatocyte Growth Factor Growth Loop in ANBL-6 Cells and Prevents Migration and Adhesion of Myeloma Cells Clin. Cancer Res., October 1, 2004; 10(19): 6686 - 6694. [Abstract] [Full Text] [PDF]
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A. M. Dring, F. E. Davies, J. A. L. Fenton, P. L. Roddam, K. Scott, D. Gonzalez, S. Rollinson, A. C. Rawstron, K. S. Rees-Unwin, C. Li, et al. A Global Expression-based Analysis of the Consequences of the t(4;14) Translocation in Myeloma Clin. Cancer Res., September 1, 2004; 10(17): 5692 - 5701. [Abstract] [Full Text] [PDF]
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C. A. Maxwell, E. Rasmussen, F. Zhan, J. J. Keats, S. Adamia, E. Strachan, M. Crainie, R. Walker, A. R. Belch, L. M. Pilarski, et al. RHAMM expression and isoform balance predict aggressive disease and poor survival in multiple myeloma Blood, August 15, 2004; 104(4): 1151 - 1158. [Abstract] [Full Text] [PDF]
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T. Hideshima, P. L. Bergsagel, W. M. Kuehl, and K. C. Anderson Advances in biology of multiple myeloma: clinical applications Blood, August 1, 2004; 104(3): 607 - 618. [Abstract] [Full Text] [PDF]
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S. Trudel, S. Ely, Y. Farooqi, M. Affer, D. F. Robbiani, M. Chesi, and P. L. Bergsagel Inhibition of fibroblast growth factor receptor 3 induces differentiation and apoptosis in t(4;14) myeloma Blood, May 1, 2004; 103(9): 3521 - 3528. [Abstract] [Full Text] [PDF]
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P. W. B. Derksen, E. Tjin, H. P. Meijer, M. D. Klok, H. D. Mac Gillavry, M. H. J. van Oers, H. M. Lokhorst, A. C. Bloem, H. Clevers, R. Nusse, et al. Illegitimate WNT signaling promotes proliferation of multiple myeloma cells PNAS, April 20, 2004; 101(16): 6122 - 6127. [Abstract] [Full Text] [PDF]
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N. C. Munshi, T. Hideshima, D. Carrasco, M. Shammas, D. Auclair, F. Davies, N. Mitsiades, C. Mitsiades, R. S. Kim, C. Li, et al. Identification of genes modulated in multiple myeloma using genetically identical twin samples Blood, March 1, 2004; 103(5): 1799 - 1806. [Abstract] [Full Text] [PDF]
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R. Fonseca, B. Barlogie, R. Bataille, C. Bastard, P. L. Bergsagel, M. Chesi, F. E. Davies, J. Drach, P. R. Greipp, I. R. Kirsch, et al. Genetics and Cytogenetics of Multiple Myeloma: A Workshop Report Cancer Res., February 15, 2004; 64(4): 1546 - 1558. [Abstract] [Full Text] [PDF]
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B. Barlogie, J. Shaughnessy, G. Tricot, J. Jacobson, M. Zangari, E. Anaissie, R. Walker, and J. Crowley Treatment of multiple myeloma Blood, January 1, 2004; 103(1): 20 - 32. [Abstract] [Full Text] [PDF]
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E. S. Wang, K. Wu, A. C. Chin, S. Chen-Kiang, K. Pongracz, S. Gryaznov, and M. A. S. Moore Telomerase inhibition with an oligonucleotide telomerase template antagonist: in vitro and in vivo studies in multiple myeloma and lymphoma Blood, January 1, 2004; 103(1): 258 - 266. [Abstract] [Full Text] [PDF]
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E. Tian, F. Zhan, R. Walker, E. Rasmussen, Y. Ma, B. Barlogie, and J. D. Shaughnessy Jr. The Role of the Wnt-Signaling Antagonist DKK1 in the Development of Osteolytic Lesions in Multiple Myeloma N. Engl. J. Med., December 25, 2003; 349(26): 2483 - 2494. [Abstract] [Full Text] [PDF]
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T. Kelly, H.-Q. Miao, Y. Yang, E. Navarro, P. Kussie, Y. Huang, V. MacLeod, J. Casciano, L. Joseph, F. Zhan, et al. High Heparanase Activity in Multiple Myeloma Is Associated with Elevated Microvessel Density Cancer Res., December 15, 2003; 63(24): 8749 - 8756. [Abstract] [Full Text] [PDF]
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P. M. Voorhees, E. C. Dees, B. O'Neil, and R. Z. Orlowski The Proteasome as a Target for Cancer Therapy Clin. Cancer Res., December 15, 2003; 9(17): 6316 - 6325. [Abstract] [Full Text] [PDF]
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F. E. Davies, A. M. Dring, C. Li, A. C. Rawstron, M. A. Shammas, S. M. O'Connor, J. A.L. Fenton, T. Hideshima, D. Chauhan, I. T. Tai, et al. Insights into the multistep transformation of MGUS to myeloma using microarray expression analysis Blood, December 15, 2003; 102(13): 4504 - 4511. [Abstract] [Full Text] [PDF]
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J. McCafferty-Grad, N. J. Bahlis, N. Krett, T. M. Aguilar, I. Reis, K. P. Lee, and L. H. Boise Arsenic trioxide uses caspase-dependent and caspase-independent death pathways in myeloma cells Mol. Cancer Ther., November 1, 2003; 2(11): 1155 - 1164. [Abstract] [Full Text] [PDF]
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N. Ochiai, R. Uchida, S.-i. Fuchida, A. Okano, M. Okamoto, E. Ashihara, T. Inaba, N. Fujita, H. Matsubara, and C. Shimazaki Effect of farnesyl transferase inhibitor R115777 on the growth of fresh and cloned myeloma cells in vitro Blood, November 1, 2003; 102(9): 3349 - 3353. [Abstract] [Full Text] [PDF]
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P. A. Croonquist, M. A. Linden, F. Zhao, and B. G. Van Ness Gene profiling of a myeloma cell line reveals similarities and unique signatures among IL-6 response, N-ras-activating mutations, and coculture with bone marrow stromal cells Blood, October 1, 2003; 102(7): 2581 - 2592. [Abstract] [Full Text] [PDF]
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O. N. Onwuazor, X.-Y. Wen, D.-Y. Wang, L. Zhuang, E. Masih-Khan, J. Claudio, B. Barlogie, J. D. Shaughnessy Jr, and A. K. Stewart Mutation, SNP, and isoform analysis of fibroblast growth factor receptor 3 (FGFR3) in 150 newly diagnosed multiple myeloma patients Blood, July 15, 2003; 102(2): 772 - 773. [Full Text] [PDF]
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K. Tarte, F. Zhan, J. De Vos, B. Klein, and J. Shaughnessy Jr Gene expression profiling of plasma cells and plasmablasts: toward a better understanding of the late stages of B-cell differentiation Blood, July 15, 2003; 102(2): 592 - 600. [Abstract] [Full Text] [PDF]
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M. V. Dhodapkar, M. D. Geller, D. H. Chang, K. Shimizu, S.-I. Fujii, K. M. Dhodapkar, and J. Krasovsky A Reversible Defect in Natural Killer T Cell Function Characterizes the Progression of Premalignant to Malignant Multiple Myeloma J. Exp. Med., June 16, 2003; 197(12): 1667 - 1676. [Abstract] [Full Text] [PDF]
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K.-D. Wu, L. M. Orme, J. Shaughnessy Jr, J. Jacobson, B. Barlogie, and M. A. S. Moore Telomerase and telomere length in multiple myeloma: correlations with disease heterogeneity, cytogenetic status, and overall survival Blood, June 15, 2003; 101(12): 4982 - 4989. [Abstract] [Full Text] [PDF]
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F. Magrangeas, V. Nasser, H. Avet-Loiseau, B. Loriod, O. Decaux, S. Granjeaud, F. Bertucci, D. Birnbaum, C. Nguyen, J.-L. Harousseau, et al. Gene expression profiling of multiple myeloma reveals molecular portraits in relation to the pathogenesis of the disease Blood, June 15, 2003; 101(12): 4998 - 5006. [Abstract] [Full Text] [PDF]
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J. Shaughnessy, J. Jacobson, J. Sawyer, J. McCoy, A. Fassas, F. Zhan, K. Bumm, J. Epstein, E. Anaissie, S. Jagannath, et al. Continuous absence of metaphase-defined cytogenetic abnormalities, especially of chromosome 13 and hypodiploidy, ensures long-term survival in multiple myeloma treated with Total Therapy I: interpretation in the context of global gene expression Blood, May 15, 2003; 101(10): 3849 - 3856. [Abstract] [Full Text] [PDF]
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G. H. Underhill, D. George, E. G. Bremer, and G. S. Kansas Gene expression profiling reveals a highly specialized genetic program of plasma cells Blood, May 15, 2003; 101(10): 4013 - 4021. [Abstract] [Full Text] [PDF]
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D. Chauhan, G. Li, D. Auclair, T. Hideshima, P. Richardson, K. Podar, N. Mitsiades, C. Mitsiades, C. Li, R. S. Kim, et al. Identification of genes regulated by 2-methoxyestradiol (2ME2) in multiple myeloma cells using oligonucleotide arrays Blood, May 1, 2003; 101(9): 3606 - 3614. [Abstract] [Full Text] [PDF]
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M. Santra, F. Zhan, E. Tian, B. Barlogie, and J. Shaughnessy Jr A subset of multiple myeloma harboring the t(4;14)(p16;q32) translocation lacks FGFR3 expression but maintains an IGH/MMSET fusion transcript Blood, March 15, 2003; 101(6): 2374 - 2376. [Abstract] [Full Text] [PDF]
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P. L. Bergsagel Prognostic Factors in Multiple Myeloma: It's in the Genes Clin. Cancer Res., February 1, 2003; 9(2): 533 - 534. [Full Text] [PDF]
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C. Milazzo, V. L. Reichardt, M. R. Muller, F. Grunebach, and P. Brossart Induction of myeloma-specific cytotoxic T cells using dendritic cells transfected with tumor-derived RNA Blood, February 1, 2003; 101(3): 977 - 982. [Abstract] [Full Text] [PDF]
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F. Zhan, E. Tian, K. Bumm, R. Smith, B. Barlogie, and J. Shaughnessy Jr Gene expression profiling of human plasma cell differentiation and classification of multiple myeloma based on similarities to distinct stages of late-stage B-cell development Blood, February 1, 2003; 101(3): 1128 - 1140. [Abstract] [Full Text] [PDF]
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S. Barille-Nion, B. Barlogie, R. Bataille, P. L. Bergsagel, J. Epstein, R. G. Fenton, J. Jacobson, W. M. Kuehl, J. Shaughnessy, and G. Tricot Advances in Biology and Therapy of Multiple Myeloma Hematology, January 1, 2003; 2003(1): 248 - 278. [Abstract] [Full Text] [PDF]
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M. S. Bendre, D. Gaddy-Kurten, T. Mon-Foote, N. S. Akel, R. A. Skinner, R. W. Nicholas, and L. J. Suva Expression of Interleukin 8 and not Parathyroid Hormone-related Protein by Human Breast Cancer Cells Correlates with Bone Metastasis in Vivo Cancer Res., October 1, 2002; 62(19): 5571 - 5579. [Abstract] [Full Text] [PDF]
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J. O. Claudio, E. Masih-Khan, H. Tang, J. Goncalves, M. Voralia, Z. H. Li, V. Nadeem, E. Cukerman, O. Francisco-Pabalan, C. C. Liew, et al. A molecular compendium of genes expressed in multiple myeloma Blood, August 28, 2002; 100(6): 2175 - 2186. [Abstract] [Full Text] [PDF]
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K. Tarte, J. De Vos, T. Thykjaer, F. Zhan, G. Fiol, V. Costes, T. Reme, E. Legouffe, J.-F. Rossi, J. Shaughnessy Jr, et al. Generation of polyclonal plasmablasts from peripheral blood B cells: a normal counterpart of malignant plasmablasts Blood, July 30, 2002; 100(4): 1113 - 1122. [Abstract] [Full Text] [PDF]
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M. Akiyama, T. Hideshima, T. Hayashi, Y.-T. Tai, C. S. Mitsiades, N. Mitsiades, D. Chauhan, P. Richardson, N. C. Munshi, and K. C. Anderson Cytokines Modulate Telomerase Activity in a Human Multiple Myeloma Cell Line Cancer Res., July 1, 2002; 62(13): 3876 - 3882. [Abstract] [Full Text] [PDF]
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K. C. Anderson, J. D. Shaughnessy Jr., B. Barlogie, J.-L. Harousseau, and G. D. Roodman Multiple Myeloma Hematology, January 1, 2002; 2002(1): 214 - 240. [Abstract] [Full Text]
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Top
Abstract
Introduction
2-microglobulin levels
(P = .0005), were most prevalent in MM4. Also, genes
involved in DNA metabolism and cell cycle control were overexpressed in a comparison of MM1 and MM4. In addition, using 
important variables that historically
have been linked to poor prognosis.