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NEOPLASIA
Identification of genes modulated in multiple myeloma using genetically
identical twin samples
Nikhil C. Munshi, Teru Hideshima, Daniel Carrasco, Masood Shammas, Daniel Auclair, Faith Davies, Nicholas Mitsiades,
Constantine Mitsiades, Ryung Suk Kim, Cheng Li, S. Vincent Rajkumar, Rafael Fonseca, Lief Bergsagel,
Dharminder Chauhan, and Kenneth C. Anderson
Genetic heterogeneity between individuals confounds the comparison of gene
profiling of multiple myeloma (MM) cells
versus normal plasma cells (PCs). To
overcome this barrier, we compared the
gene expression profile of CD138ⴙ MM
cells from a patient bone marrow (BM)
sample with CD138ⴙ PCs from a genetically identical twin BM sample using microarray profiling. Two hundred and ninetysix genes were up-regulated and 103 genes
were down-regulated at least 2-fold in MM
cells versus normal twin PCs. Highly expressed genes in MM cells included cell
survival pathway genes such as mcl-1,
dad-1, caspase 8, and FADD-like apoptosis
regulator (FLIP); oncogenes/transcriptional
factors such as Jun-D, Xbp-1, calmodulin,
Calnexin, and FGFR-3; stress response
and ubiquitin/proteasome pathway–related genes and various ribosomal genes
reflecting increased metabolic and translational activity. Genes that were downregulated in MM cells versus healthy twin
PCs included RAD51, killer cell immunoglobulin-like receptor protein, and apoptotic protease activating factor. Microarray
results were further confirmed by Western blot analyses, immunohistochemistry, fluorescent in situ hybridization
(FISH), and functional assays of telomerase activity and bone marrow angiogen-
esis. This molecular profiling provides
potential insights into mechanisms of malignant transformation in MM. For example, FGFR3, xbp-1, and both mcl-1 and
dad-1 may mediate transformation, differentiation, and survival, respectively, and
may have clinical implications. By identifying genes uniquely altered in MM cells
compared with normal PCs in an identical
genotypic background, the current study
provides the framework to identify novel
therapeutic targets. (Blood. 2004;103:
1799-1806)
© 2004 by The American Society of Hematology
Introduction
Multiple myeloma (MM) is presently an incurable malignancy that
affects more than 14 000 new patients each year in the United
States.1,2 Despite improvements in treatment, including high-dose
therapy, the disease remains incurable; new therapeutic targets are
therefore urgently needed. Cytogenetics, fluorescent in situ hybridization (FISH), and other molecular studies have identified alterations in cytokines, adhesion molecules, oncogenes, and tumor
suppressor genes in MM.3-11 Moreover, gene microarray-based
profiling now permits a comparison of global gene expression of
MM cells versus normal plasma cells (PCs).12-15 Although such
comparisons in a large number of patients may identify genes
modulated in MM, the genetically heterogeneous background of
patients with MM and controls confounds comparison since gene
expression is determined not only by environmental factors but
also by the genetic background.16-18 For example, studies in
monozygotic and dizygotic twins have demonstrated that the
expression of specific genes depends on heritable factors including
polymorphisms in transcription factors or promoter elements.17,18
Gene expression profiling of patients with MM and monoclonal
gammopathy of unknown significance (MGUS) has identified
From the Jerome Lipper Multiple Myeloma Center, Dana-Farber Cancer
Institute and the Boston VA Healthcare System, Harvard Medical School, the
Harvard School of Public Health, Boston, MA; the University of Leeds, United
Kingdom; the Mayo Clinic Medical Center, Rochester, MN; and the Weil
College of Cornell University, New York, NY.
Submitted February 10, 2003; accepted May 27, 2003. Prepublished online as
Blood First Edition Paper, September 11, 2003; DOI 10.1182/blood-2003-02-0402.
Supported by Multiple Myeloma Research Foundation Awards (T.H., N.M.,
D. Chauhan, N.C.M., K.C.A.); the VA Merit Review Grant, National Institutes of
Health (NIH) grants P50-100707 and PO1-78378; the Leukemia and
BLOOD, 1 MARCH 2004 䡠 VOLUME 103, NUMBER 5
mRNA expression patterns associated with MM and with stage of
disease.12-14 However, it is important to validate the observed
expression changes at the protein level and, when feasible, at the
level of cell function. In this study, we evaluated samples from a
patient with MM and her identical twin sister, providing a unique
opportunity to compare the expression profile of patient MM cells
with normal PCs in a genetically identical setting. Moreover, we
have further validated genes modulated in MM cells by immunoblotting for protein expression, FISH for cytogenetic correlations,
and functional studies. Our studies both allow for comparison of
MM cells with normal PCs and provide the basis for identification
of novel therapeutic targets.
Materials and methods
Sample preparation
A 42-year-old patient was diagnosed with indolent MM in 1996 with mild
anemia (hematocrit 0.31 [31%]), serum immunoglobulin G (IgG) level of
31 g/L (3.1 g/dL), low ␤-2 microglobulin at 2.32 mg/L, 11% bone marrow
Lymphoma Society Scholar in Translational Research Award (N.C.M.); NIH
grants RO1-50947, P50-100707, and PO1-78378; the Doris Duke
Distinguished Clinical Research Scientist Award; and the Cure for Myeloma
Fund (K.C.A.).
Reprints: Nikhil C. Munshi, Dana-Farber Cancer Institute, 44 Binney St, M557,
Boston MA 02115; e-mail: [email protected].
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.
© 2004 by The American Society of Hematology
1799
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BLOOD, 1 MARCH 2004 䡠 VOLUME 103, NUMBER 5
MUNSHI et al
(BM) plasmacytosis, and no lytic bone lesions. Her serum IgG level in 2002
remains at 40 g/L (4.0 g/dL) with bone marrow plasmacytosis of 15%, and
she has to date received only bisphosphonate therapy. Bone marrow
aspirations were performed in the patient and her identical twin after
obtaining IRB-approved (Dana-Farber Cancer Institute) informed consent.
Mononuclear cells were separated by Ficoll-hypaque gradient centrifugation; at least 95% CD138⫹ patient MM cells or twin plasma cells were
purified with CD138 immunomagenetic beads using the Auto MACS
system (Miltenyi Biotech, Auburn, CA).12 CD138⫺ fractions were also
processed for further analysis. RNA was obtained by the RNAeasy method,
as previously described.19
Generation of SMART cDNAs from CD138-enriched
MM cells from BM
A cDNA amplification step was added following reverse transcription, since
purification of plasma cells resulted in quantities of RNA insufficient to
place directly on the gene chip. Using the switch mechanism at RNA
template (SMART) approach (Clontech, Palo Alto, CA), we obtained
sufficient quantities of cDNA amplified in a nonbiased fashion. Specifically,
all commonly used cDNA synthesis methods rely on the ability of reverse
transcriptase (RT) to transcribe mRNA into single-stranded DNA in the
“first-strand” synthesis reaction; however, since RT cannot always transcribe the entire mRNA sequence, the 5⬘ ends of genes tend to be
underrepresented in cDNA populations. We therefore used the modifiedSMART method, which exploits a template-switching effect at the 5⬘ end
and ensures the generation of full-length cDNA. The template-switching,
primer-dependent second-strand cDNA synthesis occurs at high temperature, thereby overcoming potential secondary and tertiary structure problems in the template and the potential 3⬘ bias of common methods. Briefly, 1
␮g total RNA was combined with an oligo dT-T7 RT primer and template
switch oligonucleotide prior to first strand synthesis (20 nmol Dithiothreitol, 10 nmol dNTP with PowerScript reverse transcriptase; BD Biosciences,
San Jose, CA) at 42°C for 1 hour. The resulting first strand SMART cDNA
was then combined in a reaction containing 0.4 mM dNTPs, 1.5 mM
MgCl2, 1x polymerase chain reaction (PCR) buffer, 0.1 ␮M T7 PCR
primer, and 0.1 ␮M SMART PCR primer, according to the manufacturer’s
instructions. Following a hot start (TaKaRa LA Taq, 1 minute at 95°C),
thermal cycling conditions for the appropriate number of cycles were 95°C
for 5 seconds, 65°C for 5 seconds, and 68°C for 6 minutes (DNA Engine
thermal cycler; MJ Research, Waltham, MA). As PCR inevitably favors
short sequences over long ones, it was necessary to assess the optimum
number of PCR cycles (15, 18, 21, 24, or 27) for each sample so that the
reaction could be terminated prior to overcycling of the shorter sequences.
The optimum cycle number was determined to be one cycle less than when
the products became visible on a 1.2% agarose EtBr gel. Following thermal
cycling, the PCR products were cleaned up using the QiaQuick PCR
Purification Kit (Qiagen, Valencia, CA), per the manufacturer’s instructions. Control experiments (quantitative RT-PCR and gene expression
analysis) using cell lines to assess reproducibility of the modified SMART
method demonstrated linear amplification.
Biotinylated probe preparation and hybridization on microarray
Double-stranded cDNA was prepared from 5 ␮g total RNA using the Life
Technologies (Bethesda, MD) Superscript choice system and an oligo(dT)24anchored T7 primer. Biotinylated RNA was synthesized using the BioArray
RNA transcript labeling kit (Enzo, Farmingdale, NY) with biotin-11–
cytidine-5⬘-triphosphate II (CTP) and biotin-16–UTP (uridine-5⬘-triphosphate) for 5 hours at 37°C. Either the entire cDNA reaction or 0.5 ␮g
M-SMART cDNA was used for the reaction. In vitro transcription products
were purified using RNeasy columns (Qiagen). Biotinylated RNA was then
treated for 35 minutes at 94°C in a buffer composed of 200 mM Tris acetate,
pH 8.1, 500 mM potassium acetate, and 150 mM magnesium acetate.
Affymetrix HG-U95av2 arrays (Affymetrix, Santa Clara, CA) were hybridized with biotinylated in vitro transcription products (10 ␮g/chip) for 16
hours at 45°C. Fluidic station 400 (Affymetrix) was used for washing and
staining the arrays. Due to the size-reduced hybridization features of the
huGene arrays (24 ⫻ 24 ␮M), a 3-step protocol was used to enhance
detection of the hybridized biotinylated RNA: incubation with a streptavidinphycoerythrin conjugate, labeling with antistreptavidin goat biotinylated
antibody (Ab) (Vector Laboratories, Burlingame, CA), and staining with the
streptavidin-phycoerythrin conjugate. The DNA chips were then scanned
using a gene chip scanner (Affymetrix). The excitation source was an argon
ion laser, and emission was detected by a photomultiplier tube through a
570-nm long pass filter. Digitized image data were processed using the
GeneChip software (version 4.0; Affymetrix).
Data analysis
Affymetrix CEL files were normalized using the dChip software20,21 and
then the expression values were computed. The “Compare samples”
function was used to obtain the differentially expressed genes between the
MM cells and normal twin PCs.
Antibodies and Western blot analyses
Cell lysates were prepared as previously described.22 Briefly, equal amounts
of proteins (250-300 ␮g) were resolved by 10% SDS-PAGE, transferred
onto nitrocellulose membranes, blocked by incubation in 5% dry milk in
PBST (0.05% Tween-20 in PBS), and probed with anti-hsp70, anti–mcl-1,
and anti-CDC34 Abs. Blots were then developed by enhanced chemiluminesence (ECL; Amersham, Arlington Heights, IL).
Cytogenetics and fluorescent in situ hybridization (FISH)
Karyotyping and FISH analysis were performed using standard approaches.23 FISH for abnormalities of chromosomes 4, 13, and 14 was
performed using commercially available probes and standard protocols
(Oncor, Gaithersburg, MD).
Bone marrow CD34 immunostaining and angiogenesis
The extent of BM angiogenesis was assessed using standard immunohistochemical methods to identify BM microvessels.24 Briefly, CD34 immunostaining was performed using a labeled streptavidin-biotin peroxidase
method24 on a Ventana ES automated immunohistochemistry stainer
(Ventana Medical Systems, Tucson, AZ). Deparaffinized tissues were
pretreated with EDTA (ethylenediaminetetraacetic acid; pH 8.0) in a
steamer for 30 minutes, followed by a cool down for 5 minutes. Anti-CD34
monoclonal antibody (mAb) (Becton Dickinson, San Diego, CA; diluted
1:10) was then incubated with tissue sections for 32 minutes. The
aminoethyl carbazole detection kit (Ventana Medical Systems) was used for
antigen visualization; sections were counterstained with hematoxylin and
then covered with Kaiseri glycerol jelly (Mayo Medical Laboratories,
Rochester, MN). Paraffin sections of well-vascularized tonsil were run as a
positive control, and a section stained with nonimmune rabbit immunoglobulin was used as a negative control.
Angiogenesis grading and microvessel density (MVD) estimation
All estimations were done in a blinded manner as in previous studies.24 For
simple grading, slides were scanned at ⫻ 100, ⫻ 200, and ⫻ 400 magnification; based on the extent of microvessel staining, each slide was assigned
an angiogenesis grade of low, intermediate, or high, as previously described. For MVD estimation each slide was first scanned at ⫻ 100
magnification to determine 3 “hot spots” defined as areas with the
maximum number of microvessels. Microvessels were counted in each of
the 3 hot spots at ⫻ 400 magnification; large vessels and vessels in the
periosteum or bone were excluded. Areas of staining with no discrete breaks
were counted as a single vessel, and the presence of a lumen was not
required. MVD was estimated by determining the average number of
vessels per ⫻ 400 high power field in each of the 3 hot spots examined.
Immunohistochemistry
Cytospin samples of purified MM cells from the patient and PCs from the
healthy twin were prepared in medium containing 10% fetal calf serum
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BLOOD, 1 MARCH 2004 䡠 VOLUME 103, NUMBER 5
GENES MODULATED IN MULTIPLE MYELOMA
Table 1. Selected up-regulated and down-regulated genes in patient
MM cells versus normal twin PCs
Gene
GenBank
accession no.
Table 1. Selected up-regulated and down-regulated genes in patient
MM cells versus normal twin PCs (continued)
Gene
Fold change
Cell cycle/proliferation/adhesion
1801
GenBank
accession no.
Fold change
Cytokine and cytokine receptors
IFITM1
J04164
32.4
MD-2
AB018549
86.9
ILF1
U58198
28.8
RING1
AL031228
70.4
TNFR4
Z29574
25.1
JUN
J04111
66.6
EDF1
AJ005259
14.4
PAK3
AF068864
54.3
CXCR4
L06797
13.8
FOSA
V01512
38.1
ISG20
U88964
12.5
NFKBIA
M69043
37.1
LIFR
X61615
9.0
JUND
X56681
32.1
FST
M19481
9.0
KPNA4
AB002533
29.4
ANGPT1
D13628
5.8
RAN
AF054183
28.8
EPOR
X97671
5.5
PECAM1
L34657
19.0
ACVRL1
Z22533
5.0
CALM2
M19311
15.2
CSF2
M13207
3.8
CANX
L10284
10.2
CSF1
M37435
3.7
S100A4
W72186
9.4
TGFB3B
M60556
FGFR3
M64347
9.0
EPOR
AF053356
⫺2.5
FZR1
AA932443
8.0
CD68
S57235
7.2
SSR4
Z69043
87.5
PIM2
U77735
6.9
DBI
AI557240
86.2
CALR
M84739
6.3
NACA
AF054187
71.4
hTERT
AF015950
5.7
ZNF91
L11672
67.6
AXL
M76125
5.5
EEF1A1
J04617
29.9
TRIM32
U18543
5.2
XBP1
Z93930
26.8
MSI1
AB012851
5.0
KRT8
X74929
17.9
CD63
X62654
4.7
EIF4G2
U73824
5.8
101f6
AF040704
4.5
EVIN1
AB002405
USF2
Y07661
4.3
CNOT2
AI123426
⫺5.0
VAV2
S76992
4.0
ARHD
AW003733
3.7
HSP70
X87949
106.1
FLT3LG
U03858
3.7
GP96
X15187
80.0
CNNM2
AI827730
3.6
G3PD
U34995
60.1
DDX18
X98743
3.5
PFDN5
D89667
29.2
KATNB1
AF052432
3.3
FKBP2
AA158243
17.8
HCLS1
X16663
3.2
HSP10
AI912041
15.2
RELB
M83221
2.9
LDHB
X13794
13.5
WIT-1
M60614
2.8
HAX1
U68566
11.3
SNF2-BETA
U29175
⫺3.0
HSF1
M64673
⫺2.9
VAMP5
AF054825
⫺3.2
DKC1
U59151
⫺5.1
SKII
Z19588
⫺6.0
NPR2
AF040707
⫺7.0
RAD51
D14134
3.0
Differentiation/translation/ER
3.2
Miscellaneous
List of genes selected based on their potential functional categories. Fold change
shown represents the ratio of gene expression in patient MM cells versus healthy twin
PCs. The gene expression values were obtained using Dchip analyzer.
⫺25.4
Apoptosis/survival/drug resistance
DAD1
D15057
57.9
FLIP
AF005775
29.9
MCL1
L08246
25.3
XRCC5
M30938
8.9
CFTR/MRP3
U83659
7.7
GSTM4
M96233
6.8
ANXA2
D00017
3.6
KIR2DL4
X97229
⫺5.6
UBB
X04803
153.8
UBC
M26880
97.1
PSMA2
D00760
74.5
PSMB1
D00761
72.2
SMT3 L
AL031133
38.7
PSME2
D45248
20.0
UQCR
T58471
18.6
UBE2L3
S81003
7.2
USP22
AB028986
3.9
USP9X
X98296
2.9
UCHL1
X04741
⫺3.3
Proteasome and ubiquitins
(FCS) using Shandon Cytocentrifuge (Astmoor, United Kingdom).
Cytospins were fixed at room temperature for 2 minutes in a mixture of
acetone and methanol (1:1), air-dried, and stained. For double immunofluorescence, cytospins were incubated at room temperature for 1 hour
with rabbit anti–h-FGFR3 and mouse anti–h-kappa–fluorescein isothiocyanate (FITC) Abs. FGFR3 was visualized with a donkey polyclonal
antirabbit immunoglobulin Ab conjugated with Texas red (TR; Amersham). Slides were mounted using VectaShield mounting medium with
DAPI (Vector).
Telomerase assay
The telomerase assay was performed using a TRAPeze Telomerase
Detection Kit (Oncor). For a more sensitive and semiquantitative assay, 35
ng MM cell extract was used in the telomerase assay. PCR amplification
was performed with 30 cycles at 94°C for 30 seconds, at 58°C for 30
seconds, and at 72°C for 60 seconds. The PCR products were analyzed by
electrophoresis on 12% polyacrylamide nondenaturating gels and stained
with SYBR Green I (Molecular Probes, Eugene, OR). Telomerase activity
was assessed by determining the ratio of the entire telomerase ladder to that
of the internal control, using NIH image analysis software.
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BLOOD, 1 MARCH 2004 䡠 VOLUME 103, NUMBER 5
MUNSHI et al
Results
Figure 1. t(4;14) translocation and FGFR3 overexpression in patient MM cells.
(Ai) Myeloma cells were evaluated for abnormalities of chromosome 4 and 14; (Aii)
chromosome 13 abnormalities were evaluated with 2 probes (D13S319 and Rb) in the 13q14
region, using fluorescent in situ hybridization (original magnification, ⫻ 400). cIg indicates
cytoplasmic immunoglobulin. (B) Normalized expression values from gene chip Hu95av2 for
FGFR3 gene transcripts in CD138⫹ and CD138⫺ cells from BM from the patient with MM and
the healthy twin. MM cells and healthy twin PCs were immunostained with anti-FGFR3 and
anti-kappa light chain Abs (inset; original magnification, ⫻ 100). (C) Normalized expression
values from gene chip Hu95av2 for oncogenes v-fos, c-myc, and v-myc gene transcripts in
CD138⫹ and CD138⫺ cells from BM from the patient with MM and the healthy twin.
To define those genes differentially expressed in MM cells versus
normal PCs in a genetically identical setting, we purified CD138⫹
MM cells from patient BM and its normal cellular counterpart from
identical twin BM. We also compared the gene expression profile in
the CD138⫺ fraction, which predominantly contain stromal, hematopoietic, and lymphoid cell populations, as a control. Total cellular
RNA was prepared and subjected to cDNA microarray analysis
using probe sets corresponding to more than 12 600 human genes,
followed by data analysis using the DNA chip analyzer. We have
previously confirmed that PCR amplification provides a nonbiased
amplification of the expressed genes.25 Control experiments (quantitative RT-PCR and gene expression analysis) performed using cell
lines, with and without amplification, to assess reproducibility of
the modified SMART method demonstrated both linear amplification and lack of bias. Following normalization of data in CEL files
using the dChip software,20,21 the expression values were computed. The
“compare samples” function was used to determine a specific profile of
MM cells compared with normal twin PCs (CD138⫹).
Two hundred and ninety-six genes were up-regulated and 103
genes were down-regulated at least 2-fold in MM cells versus
healthy twin PCs. These included genes with important roles in cell
proliferation, malignant transformation, antiapoptosis, and differentiation, as well as metabolic and biochemical activity (Table 1).
Genes significantly increased in MM cells versus healthy twin PCs
included oncogenes/transcriptional factors (FGFR-3, Jun-D, v-fos,
Xbp-1, calmoduliun, Calnexin); genes involved in cell survival
pathways (mcl-1, dad-1, caspase 8, and FADD-like apoptosis
regulator, katanin p80); stress response genes (hsp90 and hsp70);
cell-cycle–related genes (CDC2-related kinase, CDK6, CDK7,
Fzhr-1, p57/kip2); and ubiquitin/proteasome pathway–related genes
(CDC34, UbC, UbB, ubiquitin specific protease, proteasome
subunit alpha, ubiquitin-activating E1-like enzyme). Ribosomal
genes, reflecting increased metabolic and translational activity,
were also increased in MM cells versus healthy twin PCs. Genes
significantly decreased in MM cells compared with healthy twin
PCs included RAD51, killer cell immunoglobulin-like receptor
protein, and apoptotic protease activating factor.
Microarray results have been further confirmed by Western blot
analyses and immunohistochemistry for protein expression. We
have focused on those highly expressed genes which are known to
be involved in B-cell growth and survival. The increased expression of FGFR3 in patient MM cells was further evaluated by FISH
analysis for cytogenetic correlations. As seen in Figure 1A, a
Figure 2. Increased antiapoptotic gene expression in MM
cells versus healthy twin PCs. (A) Normalized expression
values from gene chip Hu95av2 for antiapoptotic genes
dad-1, FLIP, and mcl-1 gene transcripts in CD138⫹ and
CD138⫺ cells from BM from the patient with MM and the
healthy twin. (B) Cell lysates were prepared from MM cells
and normal twin PCs, resolved by 10% SDS-PAGE, transferred onto nitrocellulose membrane, and probed with anti–
mcl-1 and anti-tubulin Abs. Blots were developed by enhanced chemiluminesence, and relative protein expression in
patient MM cells compared with normal twin PCs was
calculated using densitometry.
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BLOOD, 1 MARCH 2004 䡠 VOLUME 103, NUMBER 5
GENES MODULATED IN MULTIPLE MYELOMA
1803
Figure 3. Telomerase expression in MM cells versus healthy
twin PCs. (A) Normalized expression values from gene chip
Hu95av2 for telomerase and other telomere-related genes in
CD138⫹ and CD138⫺ cells from BM from the patient with MM and
the healthy twin. (B) Telomerase activity was measured using the
TRAPeze telomerase detection kit. Cell lysates from patient MM
cells and normal twin PCs following PCR amplification were
electrophoresed on 12% nondenaturing polyacrylamide gel and
stained with SYBR green I. Telomerase activity is determined by
the ratio of the entire telomerase ladder to that of the internal
control using densitometric analysis.
t(4;14) translocation was detected in 96% of the kappa-restricted
cells present in patient MM samples from 1997, 1999, and 2001.
Interestingly, MM cells also showed deletion of 13q14, detected
using Rb and D13S319 probes. Immunohistochemistry confirms
high levels of FGFR3 protein expression in patient MM cells versus
healthy twin PCs (Figure 1B). As seen in Figure 1C, v-fos and c-myc
were also highly expressed in MM cells compared with healthy twin
PCs; there was no significant difference in v-myc expression.
Although expression of bcl-2 and bcl-xl, as well as survivin and
inhibitors of apoptosis proteins (IAPs), was not significantly
different between patient MM cells and the healthy twin PCs (data
not shown), there was a significant up-regulation of dad-1 (56fold), FLIP (31-fold), and mcl-1 (25-fold) in MM cells compared with
healthy twin PCs (Figure 2). Increased expression of mcl-1 protein
(5.7-fold) in MM cells was confirmed using Western blot analysis.
A high level of telomerase expression was observed in MM
cells versus normal twin PCs, although no significant differences
were observed in TRF-1, TRF-2, and Tankyrase transcript levels
(Figure 3A). Interestingly, telomerase levels were also elevated in the
patient versus twin CD138⫺ BM fractions. Telomerase activity was
measured by telomeric repeat amplification protein (TRAP) assay in
lysates from MM cells and healthy twin PCs. As can be seen in Figure
3B, there was 2.3-fold higher telomerase activity in patient MM cells
compared with healthy twin PCs.
Since we observed increased levels of expression of angiogenesis-related interleukin 8 (IL-8; 5-fold) and angiopoietin-1 (5.8fold) transcripts in MM cells versus healthy twin PCs (Figure 4A),
we next evaluated microvessel density (MVD) in MM patient BM.
As seen in Figure 4B, BM angiogenesis was intermediate and
increased to 7 microvessels per high power field.
Patient MM cells also showed increased expression of stressrelated metabolic and biochemical pathway–related genes. As seen
in Figure 5A, heat shock protein (hsp) 90 and hsp 70 were
up-regulated (⬎ 10-fold) in MM cells versus normal twin PCs; an
8-fold increased expression of hsp 70 in patient MM cells versus
healthy twin PCs was confirmed using Western blotting. There was
no difference in hsp 40 expression. Ubiquitin-proteasome–related
genes were also highly expressed in patient MM cells versus
healthy twin PCs (Figure 5B); 3.4-fold increased protein level of
CDC34 in patient MM cells versus normal twin PCs was confirmed
by Western blotting. Expression of more than 52 ribosomal proteins
was also increased (⬎ 20-fold) in patient MM cells versus normal
twin PCs (data not shown), consistent with increased paraprotein
synthesis. We also observed a 15-fold increase in xbp-1
transcripts in patient MM cells versus normal twin PCs;
however, no significant difference in expression of Pax-5 and
PRDII-BF1 was observed.
We have shown that cytokines mediate growth (IL-6, insulinlike growth factor 1 [IGF-1]), survival (IL-6, IGF-1), drug resistance (IL-6, IGF-1), and migration (vascular endothelial growth
factor [VEGF]) of MM cells. Although IL-6 and VEGF were not
altered in patient MM cells versus normal twin PCs (data not
shown), IGF-1 gene expression was elevated (⬎ 2-fold) in patient
MM cells versus normal twin PCs (Figure 6A); however, IGF1R
and IGFBP4 were expressed in a similar fashion. There was no
significant difference in cytokine or cytokine receptor profile in
CD138⫺ fractions from the BM from the patient with MM versus
BM from the healthy twin.
Discussion
One of the unique aspects of this study is a comparison of patient
MM cells with normal cellular counterparts (PCs) from an identical
genetic background. The observed differences in gene expression
therefore represent the phenotype of the MM cell and its biologic
behavior. The cellular environment, composed of both cytokines
and cellular factors, can influence gene expression in patients with
MM, and studies to date have compared patient MM cells with
Figure 4. Elevated angiogenesis-related gene expression and increased BM angiogenesis in MM.
(A) Normalized expression values from gene chip
Hu95av2 for angiogenesis-related IL-8 and angiopoetin-1
gene transcripts in CD138⫹ and CD138⫺ cells from BM
from the patient with MM and the healthy twin.
(B) Deparaffinized MM patient BM biopsy specimens
from 2002 were immunostained with anti-CD34 Abs using
a labeled streptavidin-biotin peroxidase; microvessels
per high power field (⫻ 100) and the grade of angiogenesis were evaluated.
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1804
MUNSHI et al
BLOOD, 1 MARCH 2004 䡠 VOLUME 103, NUMBER 5
Figure 5. Elevated stress response and proteasome-related gene expression in MM cells versus healthy twin PCs. Normalized gene expression values in CD138⫹ and
CD138⫺ cells from BM from the patient with MM and the healthy twin were detected by gene chip Hu95av2 microarray profiling for heat shock proteins hsp 90, hsp 70, and hsp
40 transcripts (A); and proteasome-related genes CDC34, proteasome alpha and beta subunits (B). Cell lysates were prepared from MM cells and normal twin PCs, resolved
by 10% SDS-PAGE, transferred onto nitrocellulose membranes, and then probed with (A) anti–hsp 70 and anti-actin Abs; or (B) anti-CDC34 and anti-actin Abs. Blots were
developed by enhanced chemiluminescence, and relative protein expression in patient MM cells compared with normal twin PCs was calculated using densitometry.
normal PCs from individuals with nonidentical genetic backgrounds.12-15 Therefore, many of the differences observed may be
attributable to this genetic heterogeneity. For example, a study
evaluating IL-10 expression in blood cells following lipopolysaccharide (LPS) stimulation in 246 monozygotic and dizygotic twins
confirmed the significant role of genetic determinants in influencing gene expression.17 This differential gene expression is attributed to promoter polymorphism and differential modulation of
transcriptional factors. Since it has been demonstrated that the
human immunoglobulin V(H) gene repertoire is genetically controlled in monozygotic twins,18 the current study represents a
unique opportunity to profile MM cells versus normal PCs.
A second important aspect of this study is confirmation of the
observed gene expression changes at the protein and/or functional
level. Although it is difficult to recapitulate all the sequential
molecular changes that may have taken place to result in MM cell
growth, we can hypothesize sequential genetic events. The initial
oncogenic event may be oncogene activation providing an initial
proliferative advantage.3 FGFR3 is an oncogenic receptor tyrosine
kinase, which is activated due to Ig switch region translocation
t(4;14)(p16.3;q32) in 15% of patients with MM.26-29 Signalling via
FGFR3 and its activating mutations triggers mitogen-activated
protein (MAP) kinase cascade, resulting in growth of MM cells.30
In our patient, detection of the t(4;14) translocation using immunohistochemistry confirmed aberrant FGFR3 gene expression. Interestingly, FISH analysis also showed an abnormality involving the
13q14 region (⌬13), consistent with the reported association of
t(4;14) translocation and ⌬13 in MM and MGUS.31 Although the
majority of t(4;14) translocations are associated with ⌬13, many
patients with ⌬13 lack t(4;14), suggesting that ⌬13 may be an
earlier event in the disease pathogenesis. Although both FGFR3
up-regulation and ⌬13 have been independently associated with
adverse outcome,32,33 the prognostic significance of these coexistent abnormalities is undefined, and our patient’s disease course has
remained indolent for more than 6 years despite these genetic
abnormalities. Preliminary array comparative genomic hybridization (CGH) profiles between MM cells and normal twin PCs did
not reveal many detectable chromosomal alterations (D. Carrasco,
unpublished observation, November 2002). This observed lack of
significant genetic rearrangements and progression of the disease is
consistent with observed down-regulation of RAD51 expression in
MM cells compared with normal twin PCs. Elevated RAD51 is
associated with high recombination activity.
One important characteristic of MM cells is decreased apoptosis, both spontaneous and drug induced, and our data demonstrate
up-regulation of 3 antiapoptotic genes in patient MM cells versus
normal twin PCs. Myeloid cell factor 1 (mcl-1) is an antiapoptotic
member of the Bcl-2 family, an important survival factor in MM
cells which is regulated by IL-6.34,35 Blockade using antisense
oligonucleotides has shown that mcl-1, but not bcl-2 or bcl-xl, is an
essential antiapoptotic gene in MM.36 In our patient with MM,
up-regulation of mcl-1 protein expression was also confirmed, consistent with its role in promoting growth and survival of MM cells.
We found an approximately 15-fold up-regulation of X-box
binding protein 1 (XBP-1) in patient MM cells compared with
normal twin PCs. XBP-1 is an IL-6 target gene implicated in
IL-6–mediated growth of MM.37 It is also one of 3 major
transcription factors (Pax-5, PRDII-BF1, and XBP-1) involved in
Figure 6. Elevated cytokine gene expression in MM cells
versus healthy twin PCs. Normalized expression values from
gene chip HG-U95av2 microarray profiling for IGF-1 and its
receptor gene transcripts in CD138⫹ and CD138⫺ cells from BM
from the patient with MM and the healthy twin.
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BLOOD, 1 MARCH 2004 䡠 VOLUME 103, NUMBER 5
GENES MODULATED IN MULTIPLE MYELOMA
the differentiation of normal B cells into plasmablastic cells.38 We
observed increased XBP-1 gene expression in patient MM cells, but no
change in expression of other B-cell–specific transcription factors,
suggesting XBP-1 as an important potential target for therapy.
Elevated telomerase expression and activity was observed in
patient MM cells versus healthy twin PCs.39 Telomerase extends
telomeric DNA, thereby providing sustained replicative capacity to
MM cells. Activation of telomerase is therefore observed in most
patient MM cells, compared with PCs from individuals with
MGUS and healthy donors.39 We have shown that IGF-1 and IL-6
induce telomerase activity via NF␬B activation and Akt kinase
phosphorylation in MM cells.40,41 The results in this patient with
MM provide further rationale for the development of novel
therapeutic strategies to inhibit telomerase.
An important observation in our patient with MM is elevated
levels of ubiquitin proteasome pathway (UPP) genes. Proteasomes
are multicatalytic complexes located both in the cytoplasm and in
the nucleus which catalyze the degradation of intracellular proteins
regulating cell growth, survival, and cell cycle.42 They also play a
significant role in antigen presentation by major histocompatibility
(MHC) class I molecules and elimination of abnormal proteins
with mutation or posttranslational damage.43,44 High levels of
UPP-related genes observed in our patient with MM and reported
by others12,15,45 provide an additional basis for proteasome-directed
therapy. In this regard, PS-341 is a boronic acid analog proteasome
inhibitor which has already shown remarkable activity even in
patients with refractory relapsed MM.46
An important class of proteins up-regulated in our patient with
MM is the stress response hsp’s. Hsp 90 and hsp 70 are molecular
chaperones with significant roles in protein assembly, folding,
structural integrity, and degradation.47 Hsp 90 is known to interact
specifically with certain protein kinases and receptors mediating
cell growth and survival including p53, Bcr-Abl, Raf-1, Akt, and
ErbB2; in contrast, hsp 70 and hsp 40 are less specific and interact
with a wide range of polypeptides.48 Up-regulation of hsp’s is
observed in various cancers including MM14,15; moreover, the
levels of hsp’s are further elevated in MM cells following
proteasome inhibition.45 Therefore, inhibition of the chaperoning
function of hsp 90 using geldanamycin, a specific inhibitor of its
ATP binding site, induces apoptosis of both drug-sensitive and
drug-resistant MM cells in vitro, and represents an additional novel
therapeutic strategy in MM.49
We have compared, using the set of genes identified here, the
expression profile of MM cells from the patient and normal PCs
from the identical twin with prior gene expression studies in MM.
Patient MM cells cluster with MM cells, whereas normal PCs
cluster with normal PCs, confirming that this set of genes
discriminates patients with MM versus healthy donors. The magnitude of the change was not consistently or significantly different
from patients with active disease. The spectrum of change rather
1805
Figure 7. Molecular pathways in MM pathogenesis and potential therapeutic
targets.
than their magnitude may therefore determine the progression of
disease from indolent myeloma to symptomatic myeloma and
plasma cell leukemia.
Although this patient with MM has had an indolent disease
course to date, identification of this gene profile provides insight
into various molecular pathways of disease pathogenesis and
suggests novel therapeutic strategies (Figure 7). First, efficacy
of proteasome inhibitors is suggested due to the high activity of
UPP genes. Second, an important antiapoptotic mechanism
operative in this patient’s MM cells is up-regulation of FLIP,
which inhibits caspase 8. Thalidomide and its novel analog
Revimid act via the caspase 8 pathway,50 and may therefore be
less effective; however mcl-1 may be targeted using an antisense
approach. Third, increased hsp 90 levels in the patient’s MM
cells suggest potential utility of targeted therapy with hsp 90
inhibitor geldanamycin. Fourth, increased expression of IGF-1
in patient MM cells may be targeted by the monoclonal IGF-1R
Ab currently under development for phase 1 testing.51,52 Finally,
elevated FGFR3 in this patient’s MM cells suggests potential
utility of targeting FGFR3 kinase using inhibitor PD173074.53
Additional identified genes with a potential role in myeloma
pathogenesis, which therefore represent potential targets of
novel therapeutics, are shown in Table 2.
Gene expression profiling therefore offers the potential to
further define disease pathogenesis and to identify potential
molecular therapeutic targets in an individual patient with MM.
This identification and characterization of important genetic events
and their sequelae involved in MM cell growth and survival,
coupled with our understanding of mechanisms of drug sensitivity
and resistance, may predict whether individual patients will
respond to a given therapy and identify future targets for novel
therapeutic approaches.
Table 2. Selected genes with potential importance in myeloma biology
Gene
Other names
Chromosomal
location
Function
Involvement/role in other cancers
MD-2
Lymphocyte antigen 96
8q13.3
Immune response, NF␬B activation
Immune modulation
RING1
Ring finger protein 1
6p21.3
Transcriptional repressor, polycomb protein complex
Mantle cell lymphoma, acute leukemia
PAK-3
P21 (CDKN1A)–activated kinase-3
Xq22.3-q23
Serine threonine kinase activated by CDC42, Rac
Breast
RAN
RAS-related nuclear protein
6p21
Small GTP-binding protein, translocation of RNA
Teratocarcinoma
and protein through nuclear complex
RAD51
Rec A homologue
15q15.1
Homologous recombination
Breast, lung, pancrease, CLL
DAD1
Defender against death 1
14q11-q12
Antiapoptotic
Hepatocellular carcinoma
TNFR17
BCMA
16p13.1
B-cell maturation
B-cell malignancies
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1806
BLOOD, 1 MARCH 2004 䡠 VOLUME 103, NUMBER 5
MUNSHI et al
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From www.bloodjournal.org by guest on August 3, 2017. For personal use only.
2004 103: 1799-1806
doi:10.1182/blood-2003-02-0402 originally published online
September 11, 2003
Identification of genes modulated in multiple myeloma using genetically
identical twin samples
Nikhil C. Munshi, Teru Hideshima, Daniel Carrasco, Masood Shammas, Daniel Auclair, Faith Davies,
Nicholas Mitsiades, Constantine Mitsiades, Ryung Suk Kim, Cheng Li, S. Vincent Rajkumar, Rafael
Fonseca, Lief Bergsagel, Dharminder Chauhan and Kenneth C. Anderson
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