The molecular characterization and clinical management of multiple myeloma in the
Y Zhou1,2, B Barlogie1,2and JD Shaughnessy Jr1,2,3
1Donna D and Donald M Lambert Laboratory for Myeloma Genetics, University of Arkansas for Medical Sciences, Little Rock, AR,
USA;2Myeloma Institute for Research and Therapy, University of Arkansas for Medical Sciences, Little Rock, AR, USA and
3Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
Cancer-causing mutations disrupt coordinated, precise pro-
grams of gene expression that govern cell growth and
(GEP) is a powerful tool to globally analyze these changes to
study cancer biology and clinical behavior. Despite overwhelm-
ing genomic chaos in multiple myeloma (MM), expression
patterns within tumor samples are remarkably stable and
reproducible. Unique expression patterns associated with
recurrent chromosomal translocations and ploidy changes
defined molecular classes with differing clinical features and
outcomes. Combined molecular techniques also dissected two
distinct, reproducible forms of hyperdiploid disease and have
molecularly defined MM with high risk for poor clinical
outcome. GEP is now used to risk-stratify patients with newly
diagnosed MM. Groups with high-risk features are evident in all
GEP-defined MM classes, and GEP studies of serial samples
showed that risk increases over time, with relapsed disease
showing dramatic GEP shifts toward a signature of poor
outcomes. This suggests a common mechanism of disease
evolution and potentially reflects preferential expansion of
therapy-resistant cells. Correlating GEP-defined disease class
and risk with outcomes of therapeutic regimens reveals class–
specific benefits for individual agents, as well as mechanistic
insights into drug sensitivity and resistance. Here, we review
modern genomics contributions to understanding MM patho-
genesis, prognosis, and therapy.
Leukemia advance online publication, 6 August 2009;
Keywords: gene-expression profiling; array comparative
hybridization; multiple myeloma; classification; prognosis
Multiple myeloma: the disease
Multiple myeloma (MM) is a plasma-cell dyscrasia that homes to
and expands in the bone marrow, in which it causes a
constellation of disease manifestations that includes osteolytic
lesions because of osteoblast inactivation and osteoclast
activation, anemia, and immunosuppression because of loss of
normal hematopoietic stem cell function and end-organ damage
because of excessive monoclonal immunoglobulin secretion;1
increased bone-marrow angiogenesis is also frequently ob-
served.2MM presents with a common histological diagnosis, but
it displays enormous genomic complexity as well as marked
variations in clinical characteristics and patient survival. To
advance treatments, clinical outcome data must be interpreted
within the framework of genetic entities, which has proved
useful in leukemia and lymphoma,3–11and over the past 10
years has contributed to advances in treatment and survival of
patients with MM.
For many years, investigations into the molecular lesions
driving initiation and progression of MM languished, in part
because of the enormously complex karyotypes typically seen in
this malignancy. In fact, MM has cytogenetic features more
similar to tumors of epithelial origin than to hematological
malignancies. Whereas most leukemias and lymphomas present
with single chromosomal translocations, karyotypes of myeloma
cells from newly diagnosed disease have an average of seven
different structural and/or numeric chromosomal abnormalities.
This genomic chaos, along with the rarity of the disease, made it
difficult to perform the comprehensive correlative studies
necessary to identify and better understand the abnormalities
involved in initiation and/or progression of the disease and to
distinguish nonspecific bystander effects of chromosome in-
stability. Indeed, the first link between a recurrent chromosome
abnormality and prognosis was observed only 10 years ago,
when deletions of chromosome 13 were associated with
aggressive clinical course.12
The advent of new technologies, such as interphase fluores-
cence in situ hybridization (FISH), spectral karyotyping,
comparative genomic hybridization, single nucleotide poly-
morphism genotyping, and gene-expression profiling (GEP), has
provided the necessary tools to study MM in unprecedented
detail. Combining these approaches with maturing technolo-
gies, such as high-throughput proteomics, microRNA profiling,
and whole-genome sequencing, broadens the spectrum of
molecular variables that can be tested, but also poses immense
bioinformatics challenges to integrate the massive complexity of
these high-dimensional datasets to improve management of
MM. This review focuses on the use of GEP of primary disease to
classify the disease, define risk, and elucidate underlying
mechanisms that are beginning to change clinical decision
making and inform drug design.
Studying the complexities of the transcriptome
It is likely that each of the six hallmarks of cancer, outlined in
the Hanahan–Weinberg model,13ultimately causes or is related
to reproducible changes in the expression of subsets of genes
within clonal tumor cells and that these patterns are unique and
specific to each malignancy. This hypothesis was difficult to test,
project14,15and the development of high-throughput tools
capable of analyzing the activities of all genes simultaneously.16
It is now believed that the human genome consists of
Received 28 February 2009; revised 7 May 2009; accepted 14 May
Correspondence: Dr JD Shaughnessy Jr, Donna D and Donald M
Lambert Laboratory of Myeloma Genetics, Myeloma Institute for
Research and Therapy, University of Arkansas for Medical Sciences,
4301 West Markham, Slot 776, Little Rock, AR 72205, USA.
Leukemia (2009), 1–16
& 2009 Macmillan Publishers Limited All rights reserved 0887-6924/09 $32.00
approximately 25000 mRNA-encoding genes, and this com-
plexity is increased by post-transcriptional modifications, such
as alternative splicing.
In the mid-1990s, Brown and coworkers developed a system
that used DNA microarrays to monitor the expression levels of
thousands of genes in parallel,16–18which paved the way for
tools that revolutionized molecular biology. The system worked
similar to reverse northern blots: cloned DNA fragments
immobilized on a solid matrix were used simultaneously to
probe mRNA pools from a control source and from the tumor or
other tissue of interest, each labeled with a different fluorescent
dye (e.g. Cy5 and Cy3). Building on this concept, more
advanced high-density oligonucleotide microarrays capable of
unprecedented levels of sensitivity and throughput was deve-
loped using photolithography and solid-phase chemistry. Now
in the industry standard, these whole-genome high-density
oligonucleotide microarrays contain hundreds of thousands of
oligonucleotide probes, packed at extremely high densities.19
The probes are designed to maximize sensitivity, specificity, and
reproducibility, which allows consistent discrimination between
specific and background signals and between closely related
target sequences.20Using microarrays for GEP generates large
amounts of complex data, demanding equally complex ana-
lyses. Indeed, GEP analysis has evolved into a field of its own
and in many ways represents a central node in translational
research; a comprehensive review of the principles and tools
used to analyze microarray data was recently published.21Here,
we focus on the specific use of microarray profiling in MM, a
research that has exploded over the past 10 years.
Microarray technology was first used to study cancer in
1996,22and De Vos et al.23were the first to use GEP to study
MM in 2001. In these early experiments, human myeloma cell
lines and plasma-cell leukemia samples were analyzed on
small-scale, filter-based cDNA arrays to identify genes involved
in intercellular signaling. In spite of its small scale, this study
revealed that key signaling molecules within the Wnt pathway
were altered in MM. Subsequently, Stewart et al. used a
combination of high-throughput DNA sequencing and micro-
arrays on cells pooled from several cases of plasma-cell
leukemia to establish a comprehensive list of genes expressed
Microarray profiling of MM
On account of the heterogeneous nature of MM growth within
the bone marrow, with variable percentages of tumor in a given
site as low as 5%, molecular profiling of unfractionated bone-
marrow aspirates complicates interpretation of results. To
overcome this limitation, researchers have used various means
of cell enrichment of plasma cells from bone-marrow aspirates.
Plasma cells typically make up o1% of the cells in healthy
human bone marrow, so isolation of sufficient numbers of
plasma cells from healthy human marrow made large-scale GEP
experiments an impractical endeavor for most laboratories. To
isolate sufficient numbers of cells for GEP, two different but
complementary specialized methodologies were developed.
Zhan et al.25used automated immunomagnetic bead sorting
of plasma cells from large-volume bone-marrow aspirates using
a monoclonal antibody, BB4, raised against syndecan-1/CD138;
this technique routinely has isolated highly homogeneous
populations of healthy plasma cells from both bone marrow
and tonsil.26To create a source of polyclonal plasma cells from
healthy donors, Tarte et al.27developed a method for in vitro
differentiation of peripheral blood B cells. Global GEP of
polyclonal plasma cells and healthy bone-marrow plasma cells
derived from immunomagnetic sorting has revealed strong
similarities, but also distinct and reproducible differences
between the two populations and myeloma cells,27,28suggest-
ing that polyclonal plasma cells may not fully recapitulate the
molecular biology of a bone-marrow plasma cell.
Early studies made several contributions to understanding the
molecular basis of MM by comparing gene-expression profiles
of CD138-enriched plasma cells from the bone marrow of
healthy donors and patients with monoclonal gammopathy of
unknown significance (MGUS), newly diagnosed MM, and end-
stage MM.25These studies uncovered potential clues to the
molecular pathogenesis of MMFdisease-specific changes in
gene expression. Myeloma plasma cells can be clearly
distinguished from those of healthy donors based on expression
of approximately 120 of 6800 genes analyzed. Unsupervised
clustering of these early global gene-expression data showed
that MM could be divided into four distinct molecular
subgroups, MM1–MM4, with MM1 being more similar to
MGUS and MM4 being related to myeloma cell lines. The
MM4 group also had a higher incidence of cytogenetic
abnormalities (CAs) and high serum levels of b-2-microglobulin,
clinical features historically linked to poor prognosis. Consistent
with these data, genes distinguishing MM4 from the other
groups were related to cell proliferation. More advanced
microarray technologies and larger sample sizes have now
further divided MM into seven disease classes (discussed
These results provided the first evidence that MM is likely
numerous molecular entities that presumably use different
molecular mechanisms to get to a tumor with a common
histology, which has enormous clinical implications. First, the
high resolution of molecular classifications allows retrospective
evaluation of class-specific efficacy of current therapeutic
regimens, which is exceedingly important when designing
clinical trials. For example, a new drug might not show a
significant effect on a given end point when considering MM as
a whole, but the results might be dramatically different if the end
point is examined in the context of a particular molecular
classification of MM, which might include only 5% of the
overall population. Second, identifying the genes whose
expression is driving these classes can inform the use of existing
agents that might not have been considered and can direct
development of new class-specific drugs.
To provide insights into the molecular characterization of
plasma-cell dyscrasias and to investigate the contributions of
specific genetic lesions to the biological and clinical heterogeneity
of MM, Mattioli et al. compared the GEP of plasma cells isolated
from 7 cases of MGUS, 39 of MM, and 6 of plasma-cell leukemia.
MM was heterogeneous at the transcriptional level, whereas
MGUS was distinguished from plasma-cell leukemias and the
majority of MM cases by differential expression of genes involved
in DNA metabolism and proliferation. The clustering of MM cases
was mainly driven by the presence of one of five recurrent
translocations involving the immunoglobulin heavy-chain (IGH)
locus.29For example, overexpression of CCND2 and genes
involved in cell-adhesion pathways was observed in cases with
t(14;16) and t(14;20), whereas upregulated genes showed
apoptosis-related functions in cases with t(4;14). The peculiar
finding in cases with t(11;14) was downregulation of the a-subunit
of the interleukin-6 receptor (IL6R). Finally, cancer-testis antigens
were specifically expressed in a subgroup of patients character-
ized by aggressive clinical evolution of MM.30
To further decipher the differences between malignant and
normal plasma cells, Jourdan et al.31recently focused on
Genomics in myeloma
Y Zhou et al
58 genes linked with extrinsic and intrinsic apoptotic pathways,
caspases, and inhibitor of apoptosis proteins and found B-cell
differentiation was associated with change in the expression of
pro-apoptotic and anti-apoptotic genes with TRAIL being
upregulated, whereas FAS, APAF1, and BNIP3 were down-
regulated in MM cells compared with normal bone-marrow
GEP reveals interactions between MM cells and
The bone-marrow microenvironment (BMME) has a critical
function in MM cell proliferation, apoptosis, migration, and drug
resistance.32MM cells in turn disrupt normal bone-marrow
homeostasis leading to bone destruction and impaired hemato-
poiesis. Clarifying these interactions between the BMME and
MM cells is a prerequisite necessary to completely understand
MM initiation and progression. GEP has been used to reveal the
molecular basis of these interactions.
Corre et al.33studied bone-marrow mesenchymal stem cells,
the only long-lived cells of the BMME, by GEP in patients with
MM, patients with MGUS, and healthy subjects. Among the 145
distinct genes differentially expressed in MM and normal bone-
marrow mesenchymal stem cells, 46% were classified in the
‘tumor microenvironment’ category according to gene ontology
mesenchymal stem cells included known myeloma growth
factors such as IL-6, amphiregulin, and IL-1b, angiogenic
factors, and proteins involved in bone disease such as DKK1.
Pe ´rez-Andre ´s et al.34showed different expression profiles of
molecules involved in the interaction with the immunological
BMME across MGUS, plasma-cell leukemia, and MM.
MM cells have an affinity for bone, in which they cause
osteolytic lesions.35Comparing the GEP of MM cells from the
MM patients with and without focal bone lesions, Tian et al.36
identified DKK1, a Wnt signaling antagonist, as a key gene that
was secreted by MM cells and regulates homeostatsis of bone
lysis and formation.37
Growing evidence indicates a function for increased angio-
genesis in MM progression, and markers of angiogenesis have
prognostic potential.2,38,39Vacca et al.40used GEP to show an
overall induction of VEGF, FGF-2, HGF-SF, IGF-1, and IGF-
binding protein 3 genes in the endothelial cells derived from
MM and MGUS patients compared with human umbilical vein
endothelial cell. Hedvat et al.41compared GEP of MM cells,
plasma-cell leukaemia plasma cells, and extramedullary plas-
macytoma cells (EPC) and identified several angiogenesis-
related genes upregulated in extramedullary plasmacytoma
cells, which could confer malignant plasma cells with the
ability to grow outside the normal bone-marrow environment.
Comparing GEP of genetically identical twin samples, Munshi
et al.42observed increased levels of expression of angiogenesis-
related interleukin-8 (5-fold) and angiopoietin-1 (5.8-fold)
transcripts in MM cells versus healthy twin PCs. Hose et al.43
assessed the expression of 402 angiogenesis-associated genes by
GEP in 466 samples, including CD138-purified myeloma cells.
They found that although MM cells did not show a significantly
higher median number of expressed pro- or anti-angiogenic
genes, 97% of MM cell samples aberrantly express at least one
of the angiogenic factors: HGF, IL-15, ANG, APRIL, CTGF, or
Heparanase (HPSE) degrades Syndecan-1 and, therefore,
enhances tumor cell metastasis,44and may have an important
function in regulating the growth and progression of MM.45
Using GEP, Mahtouk et al.46found that HPSE was expressed
mainly in the bone-marrow environment from polymorpho-
nuclear cells, T cells, monocytes, and osteoclasts. Bret et al.47
investigated 100 genes involved in synthesis of heparan sulfate
(HS) and chondroitin sulfate (CS) chains, which covalently bind
to syndecan-1 and are the bioactive components of syndecan-1
in MM cells. Among 100 genes involved in synthesis of HS and
CS, 16 were significantly different between normal and
malignant plasma cells. Nine of these genes, EXT2, CHSY3,
CSGALNACT1, HS3ST2, HS2ST1, CHST11, CSGALNACT2,
HPSE and SULF2, encode proteins involved in glycosaminogly-
can-chain synthesis or modifications. GEP data also revealed
that syndecan-1 (SDC1) is essential for MM cell growth activity
of EGF-family ligands in MM.48
BAFF and APRIL promote survival and growth of MM
cells.49,50BMME was the main source of BAFF and APRIL in
MM patients.51TACI is the receptor of BAFF/APRIL and its
expression level varies in MM patients and is a good indicator of
a BAFF-binding receptor. Using GEP, Moreaux et al.51found
that MM cells with high TACI displayed a mature plasma-cell
gene signature, indicating dependence on the bone-marrow
environment. In contrast, MM cells with low TACI exhibited a
gene signature of plasmablasts, suggesting an attenuated
dependence on the bone-marrow environment.
To investigate the molecular consequences of myeloma cell
interactions with osteoclasts, Ge et al.52exploited an ex vivo
culture system and GEP to identify genes whose expression
was consistently altered in osteoclasts by coculture with
myeloma cells. They identified fibroblast activation protein
(FAP), a serin protease, as one of 28 genes significantly
overexpressed in cocultured osteoclasts and suggested FAP as
a critical microenvironmental factor and a potential therapeutic
target for MM.
GEP reveals universal event in MM is cyclin D
Genomic profiling in a large cohort of primary disease revealed
that dysregulated expression of cyclin D might be a universal
event in myelomagenesis. Relative to plasma cells from the
bone marrow of healthy donors, myeloma plasma cells exhibit
increased and/or dysregulated expression of either CCND1,
CCND2, or CCND3.53IGH-mediated translocations can directly
activate CCND1 (11q13)54or CCND3 (6p21);55MAF (16q23)- or
MAFB (20q11)-activating translocations lead to their transactiva-
tion of adhesion molecules and CCND2, which is elevated in
t(4;14)-positive tumors.56Biallelic dysregulation of CCND1
occurs in nearly 40% of tumors, most of which are hyperdi-
ploid.53Elevated levels of CCND2 and the absence of IGH
translocation spikes characterize a novel form of MM discovered
through GEP of primary disease (termed ‘low bone,’ discussed
below);57interestingly, elevated expression of CCND2 is not an
adverse prognostic factor in this setting.58
Validated molecular classification of MM
Using a supervised classification approach that uses prior
knowledge of the disease, Bergsagel and Kuehl29developed a
classification schema based on GEP spikes of the five recurrent
translocations, specific trisomies, and expression of cyclin D
genes. Reducing the complexity of the microarray from over
50000 probes to o30 genes, eight translocation/cyclin D (TC)
groups were identified. These were termed the 11q13/TC1,
D2/TC7, and none/TC8 classes.53The authors proposed that
Genomics in myeloma
Y Zhou et al
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