1. health and fitness
2. disease
3. cancer

KEEPING PACE WITH ADVANCES IN MYELOMA
How to use new biology to guide therapy in multiple
myeloma
Gareth J. Morgan1 and Martin F. Kaiser1
1Institute
of Cancer Research and Royal Marsden Hospital, London, United Kingdom
Recent advances in multiple myeloma (MM) therapy have led to significantly longer median survival rates and some
patients being cured. At the same time, our understanding of MM biology and the molecular mechanisms driving the
disease is constantly improving. Next-generation sequencing technologies now allow insights into the genetic
aberrations in MM at a genome-wide scale and across different developmental stages in the course of an individual
tumor. This improved knowledge about MM biology needs to be rapidly translated and transformed into diagnostic and
therapeutic applications to finally achieve cure in a larger proportion of patients. As a part of these translational efforts,
novel drugs that inhibit oncogenic proteins overexpressed in defined molecular subgroups of the disease, such as
FGFR3 and MMSET in t(4;14) MM, are currently being developed. The potential of targeted next-generation diagnostic
tests to rapidly identify clinically relevant molecular subgroups is being evaluated. The technical tools to detect and
define tumor subclones may potentially become clinically relevant because intraclonal tumor heterogeneity has
become apparent in many cancers. The emergence of different MM subclones under the selective pressure of
treatment is important in MM, especially in the context of maintenance therapy and treatment for asymptomatic stages
of the disease. Finally, novel diagnostic and therapeutic achievements have to be implemented into innovative clinical
trial strategies with smaller trials for molecularly defined high-risk patients and large trials with a long follow-up for the
patients most profiting from the current treatment protocols. These combined approaches will hopefully transform the
current one-for-all care into a more tailored, individual therapeutic strategy for MM patients.
Introduction
The last decade has seen significant advances in our understanding
of the biology of multiple myeloma (MM) and our approaches to its
treatment. As a result of these advances, the median survival of
patients has increased and we can now achieve a cure in at least
some patients. The immunomodulatory drugs and “proteasomeinhibitor” therapies now form the backbone of MM therapy;
however, these classes of drugs can clearly be developed further to
improve outcomes. Further, despite these advances, there remains a
need for therapies with novel modes of action. There is an
expanding set of candidate drugs with great potential that are
currently under investigation; however, we need to consider how
best to evaluate these agents. In traditional drug-development
approaches, novel agents are initially evaluated in cell line and
animal models and candidates with activity are then tested in
end-stage patients. This approach is not without its problems,
with a less than optimal correlation between activity in model
systems and activity in the patient. In addition, when drugs are
evaluated clinically, it is often in cases with relapsed refractory
MM. Although these are initially safety studies, a response and
outcome signal is used to make the decision to take the agent
forward. The molecular mechanisms present in this stage of MM
are different from those acting earlier in the disease process and
could result in our abandoning active agents that may be active in
the early phases of disease. Conversely, persisting with this
approach may lead to the development of agents that only work
in the relapse setting and have no merit for earlier stages of disease,
when the molecular mechanisms are different. Therefore, it makes
sense to pause and consider how we are going to continue with drug
development in MM.
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At the simplest level, there is a need for new in vitro model systems
that accurately reflect the response to treatment in the patient. In
addition, new clinical trial strategies for the evaluation of novel
agents are needed that take into account disease stage and molecular
subtype. For example, in the early stage of drug evaluation of a
targeted agent in unselected patients, drugs active in a specific
molecular subset could be missed. Since the introduction of the
immunomodulatory and proteasome-inhibitor drugs, there has been
an improvement in overall survival and this needs to be taken into
consideration in clinical study design. In both transplantationeligible and noneligible patients, the overall survival of the control
arms in phase 3 studies has improved dramatically. This suggests
that larger studies with longer follow-up times will be required in
the future to have adequate power to detect meaningful improvements in outcome, slowing down the evaluation process. This
chapter considers how we can exploit advances in our understanding of the biology of MM to facilitate the development of new
treatments and to direct their use in the clinic. The rapid acceptance
of this personalized medicine strategy and its widespread application in the clinical setting is a critical change that can be readily
implemented to maintain our current momentum to make MM a
curable disease.
Genetic basis of MM
In the classical view of the initiation and progression of MM, an
initiating hit is required to immortalize a myeloma-propagating cell
(MPC). Such a cell is then destined to acquire additional genetic hits
over time, mediated via translocation, loss of heterozygosity, gene
amplification, mutation, or epigenetic changes. The acquisition of
additional hits further deregulates the behavior of the MPC, leading
to the clinically recognized features of MM.1 The basic premise
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underlying these interactions is that multiple mutations in different
pathways deregulate the intrinsic biology of the plasma cell,
changing it in ways that generate the features of MM. Many of the
genes and pathways mediating this transformation process have
now been characterized.
At the cytogenetic level, the MM genome is recognized as being
complex.2-4 The study of chromosomal translocations generated by
aberrant class-switch recombination shows that several oncogenes,
including cyclin D1 (CCND1), CCND3, fibroblast growth factor
receptor 3 (FGFR3), the MM SET domain (MMSET; also known as
WHSC1), MAF, and MAFB, are placed under the control of the
strong enhancers of the heavy chain Ig (IGH) loci, leading to their
deregulation.5,6 Deregulation of the G1/S transition is a key early
molecular abnormality in MM and the consistent deregulation of a
D-group cyclin was first noted as a consequence of studying the
t(11;14) and t(6;14) translocations, which deregulate cyclin D1 and
cyclin D3, respectively.1 Nontranslocation-based overexpression of
a D-group cyclin can also occur, and in the t(14;16) is modulated via
MAF, which up-regulates CCND2 by binding directly to its
promoter. Patients with the t(4;14), which translocates FGFR3 and
MMSET to the IGH enhancers, also overexpress cyclin D2, but in
this case the underlying mechanism is uncertain.6 Other IGH
translocations are seen in MM and, in contrast to the class-switch
recombination– driven events, tend to occur later in the disease
process. The gene typically deregulated by such events is MYC, the
deregulation of which may lead to a more aggressive disease phase.
Translocations outside of the Ig gene loci can also occur and
constitute a significant mechanism leading to gene deregulation that
has not been explored fully.1 However, it is known that such
translocations can range from single to multiple events per patient,
but no recurrent events deregulating a single crucial gene have yet
been identified.
The frequency and recurrent nature of interstitial loss of copy
number and loss of heterozygosity suggests that the minimally
deleted regions contain tumor-suppressor (TS) genes that are driver
events.3,7,8 Most TS genes require inactivation of both alleles and
have either been identified by the study of homozygous deletions or
through the integration of mutational analysis with copy number
status.9 Examples of relevant TS genes include FAM46C, DIS3,
CYLD, baculoviral IAP repeat containing protein 2 (BIRC2; also
known as cIAP1), BIRC3, and TNF receptor associated factor 3
(TRAF3).2,3,7,8,10 Deregulation of the G1/S transition by overexpression of a D-group cyclin is a key early molecular abnormality in
MM. Conversely, also important are loss of a negative cell-cycle
regulator, down-regulation of CDKN2C by loss of chromosome
1p32, and inactivation of CDKN2A by methylation.3,4,11 Inactivation
of RB1 also affects this checkpoint and may occur as a result of loss
of chromosome 13, which is present in 58% of cases of MM;
however, homozygous loss and mutational inactivation of this gene
is infrequent.2 Other important regions of loss of heterozygosity
include 11q, the site of the BIRC2 and BIRC3 genes; 16q, the site of
CYLD; and 14q32, the site of TRAF3.7,8,10 All of these genes are
involved in the NF-␬B pathway, indicating that up-regulation of
NF-␬B signaling is important in MM.
The other major set of recurrent genetic abnormalities seen in MM
is hyperdiploidy associated with the gain of the odd-numbered
chromosomes, including 3, 5, 7, 9, 11, 15, 19, and 21. Interstitial
copy number gain associated with increased gene expression or with
activating mutations in oncogenes represents another set of “driver”
genes that can lead to MM progression. A classic example of this is
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the amplification of 1q, which potentially harbors more than one
relevant oncogene; for example, CDC28 protein kinase 1B (CKS1B),
acidic leucine rich nuclear phosphoprotein 32 family member E
(ANP32E), BCL9, and PDZK1.3 Interstitial copy number gains
consistent with the activation of the NF-␬B pathway are also seen,
including amplification of NIK (MAP3K14), TACI (TNFRSF13B),
and LTBR.8
There are approximately 35 nonsynonymous mutations per case in
MM,2,12 which is intermediate between the numbers present in the
genetically simpler acute leukemias (8)13 and those present in the
more complex epithelial tumors, such as lung cancer (540).14 There
are few recurrently mutated genes in MM and, for the most part,
these affect known oncogenes. However, a few novel genes have
been identified (FAM46C in 13% of cases, DIS3 in 11% of cases)
and as the numbers of samples analyzed increases, the incidence of
recurrent genes will undoubtedly increase as well. This observation
is consistent with a hypothesis in which deregulation of pathways
is pathogenically important, rather than the deregulation of a
specific gene.
Examples of deregulated pathways include the frequent deregulation of the NF-␬B pathway, and strategies targeting this pathway
upstream of mutated genes may fail if the presence of activating
mutations are not taken into account. The ERK pathway is
frequently deregulated (NRAS in 24% of cases, KRAS in 27% of
cases, and BRAF in 4% of cases), which suggests the need for a
novel treatment strategy targeting this pathway. Deregulation of the
PI3K pathway is also important in MM, but in contrast to the RAS
pathway, the PI3K pathway is not frequently mutated.2 However,
phosphorylated AKT, which is indicative of PI3K activity, is
detected in 50% of cases. In addition, DEP domain containing
mTOR-interacting protein (DEPTOR), a positive regulator of the
pathway, is frequently up-regulated, especially in cases with MAF
translocations.15 However, at a molecular level, the cause of
increased DEPTOR is currently unknown. The frequency of these
events makes MM a good model system in which to evaluate
targeted inhibitors of the RAS and PI3K pathways.
Although there has been substantial work on the genetics of MM,
little is known about the epigenetic changes leading to disease
progression and their impact on treatment resistance. DNA can be
modified by methylation of cytosine residues in CpG dinucleotides
and, in addition, chromatin structure may be modified via histone
modifications such as methylation, acetylation, phosphorylation,
and ubiquitination. Both DNA and histone modifications can play a
part in modulating gene expression.16 The most important epigenetic change relevant to the pathogenesis of MM is global hypomethylation and gene-specific hypermethylation during the transformation of monoclonal gammopathy of undetermined significance
(MGUS) to MM.4 The most pronounced DNA methylation change
is seen in the 15% of patients with the t(4;14) translocation, who
have increased gene-specific hypermethylation compared with other
cytogenetic subgroups. This subgroup overexpresses MMSET, which
encodes a histone methyltransferase and transcriptional repressor.17,18 MMSET mediates histone 3 lysine 36 (H3K36) dimethylation, and its deregulation leads to global changes in histone
modifications that promote cell survival, cell-cycle progression, and
DNA repair.19,20 Other chromatin modifiers are also deregulated in
MM, including UTX, a histone demethylase, MLL, KDM6B, and
HOXA9, and the full relevance of these modifications needs further
validation.2,21
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To date, no consistent mutations of DNA-repair genes, apart from
possibly ATM, have been identified in MM; however, deletions of
17p occur in 8% of patients at presentation and this frequency
increases in the later stages of the disease. The key gene at this site is
thought to be TP53, mutations of which are associated with
increased genomic instability and impaired clinical outcomes.22,23
Deregulation of miRNAs affecting this pathway have also been
described, in particular miR-192 and miR-32. Several studies have
produced data suggesting a role for miRNA in both normal plasma
cell development and the pathogenesis of MM. In particular, the
expression of the miRNA cluster miR-17-92, located at chromosome 13, has been shown to change during the progression of
MGUS to MM.24,25 miRNA changes are also known to deregulate
several pathways relevant to the pathogenesis of MM, including
cell-cycle progression, p53, and MYC.26 The complexity of the
genetic deregulation of MM is further enhanced by the recent
identification of recurrent mutations in DIS3, FAM46C, and SF3B1
suggesting a potential role for RNA processing.2
It is clear that inherited genetic variation can predispose to the
development of MGUS based on a 2-fold increased risk in the
families of index cases with MM.27 Molecular epidemiological
approaches have been used to gain insights into the earliest genetic
factors leading to the development of MM and an increased risk of
developing MM is associated with 3 genetic loci located at 2p, 3p,
and 7p, identifying the gene pairs DNMT3A/DTNB, ULK4/TRAK1
and DNAH11/CDCA7L, respectively.28 It seems likely that more
loci will be identified and that some of these will be subtype
specific. In addition, these inherited variants can have an impact on
both clinical outcome and the side effect profiles of specific drugs.
Genetics-based risk stratification
It is critical that we should try to utilize these new genetic data for
the benefit of patients with MM. Because MM is not a single disease
entity, it is not a giant step of logic that treatment should be targeted
to the molecular subtype of disease rather than using a “one size fits
all” approach. These targeted treatment approaches can be based on
predicting subtypes of disease, predicting prognostic groups, or
predicting the presence of a molecular lesion that can be targeted
with a specific therapy.
Prognostication and molecular subtype prediction
One important way to use molecular data in the clinical setting is to
stratify patients for clinical risk status and to use this information to
select an appropriate treatment. The presence of a specific cytogenetic lesion cannot simply be interpreted in isolation; several factors
need to be taken into account. The clinical and cellular background
in which a genetic lesion occurs mediates its prognostic impact. For
example, MAF translocations at presentation in MM are associated
with a poor prognosis, but when these translocations are present in
MGUS, they are not associated with adverse outcomes.29,30 These
observations suggest that MAF deregulation alone is not responsible
for adverse outcomes and that it probably collaborates with other
genetic events (Table 1). This concept underlies the development of
the International Staging System (ISS) combined with FISH, which
is a significant improvement on the ISS, the previous best prognostic classification in MM.29 Another important observation relevant
to predicting clinical outcomes using genetic data is that at disease
presentation, genomic events such as the t(4;14) translocation, MAF
translocations, gain(1q), and del(17p) can all occur simultaneously.23,29 When each of these abnormalities occurs on its own, the
adverse impact on prognosis is less than when multiple lesions
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occur in the same patient, suggesting that if we are to use these
events as prognostic factors, we need to describe and count the
presence of all pertinent lesions.29,31 Metaphase cytogenetics only
gives information in a subset of cases; in contrast, interphase FISH
is universally applicable. Single FISH probes for a range of
prognostically important genetic events, including gain(1q), del(17p),
and the adverse translocation groups t(4;14), t(14;20), and t(14;16),
can be used to define prognosis. However, some patients who are
positive for these apparently poor prognostic markers have very
good survival.32,33 This issue of poor specificity for the relevant
clinical outcome complicates the use of stratified treatment because
of the potential risk of either over- or undertreatment with toxic
chemotherapy regimens.
Alternate approaches to the definition of high-risk cases have been
developed based on gene-expression profiling (GEP). Using these
GEP approaches, several signatures have been derived that can be
used to risk stratify patients9,34; however, these tests are not specific
for a given clinical outcome, and high-risk groups defined by this
approach also have a range of outcomes. In addition, these types of
tests can be difficult to interpret in the laboratory and require
consistent quality control, making their widespread uptake in
clinical laboratories difficult at this time. GEP can, however,
identify most groups of the currently used molecular subgroups of
MM.6,34-36 However, at this stage to obtain all of the necessary
information for definition of prognostic groups, GEP needs to be
combined with FISH analysis. An example of this is the detection of
del(17p), one of the most important prognostic factors in MM that
cannot be assayed by GEP so its detection remains dependent upon
the use of a FISH probe. In the future, however, TP53 mutational
analysis may replace the reliance on FISH data. An alternative to
GEP is the design of specific real-time quantitative PCR reactions
for the key expressed genes underlying the translocation and cyclin
D classification and combining these with a limited set of FISH
probes that fill in missing data, such as del(17p) and gain(1q), which
currently cannot be obtained by these approaches.
Predictive markers and targeted treatment
As an approach to treatment, risk-stratified approaches are suboptimal in MM, and in the near future, targeted treatment based on the
presence of a specific molecular lesion predictive for response to
that treatment is the likely way forward so that we can achieve
personalized cancer care for MM patients. The best illustration of
this approach is the treatment of the t(4;14) subtype of MM, which
has been associated with poor prognosis.29 At least some recent
clinical trial data support frontline treatment with proteasome
inhibitors for this subtype37 and, in view of the characteristic
oncogene profile, it is the optimum group in which to address the
potential role of FGFR3 and MMSET inhibitors. This is a good
example of how, by characterizing the biology of an initiating lesion
thought to be present in 100% of cells, we can push forward the
development of novel targeted treatments. The realization that
MMSET is a member of a family of oncogenes with H3K36me2
transferase activity raised the possibility of targeting this activity as
a therapy for MM. Therefore, the crystal structure of the MMSET
protein is currently being resolved and this information is being
used in specific structure-function– based drug design approaches to
specifically inhibit the activity of this enzyme. Other translocations
such as t(14;16) or t(14;20), leading to MAF or MAFB overexpression, are interesting molecular targets as well. A list of potentially
therapeutically relevant molecular subgroups of MM is shown in
Table 2. The potential for a targeted treatment approach in MM has
significantly increased recently with the availability of data derived
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3p ULK4/TRAK1
7p
DNAH11/CDCA7L
2p DTNB/DNMT3A
SNPs
Inherited variation
Hyperdiploidy (57%)
⫺trisomies of chromosomes 3,
5, 7, 9, 11, 15, 19, 21
⫺t(14;16) MAF (3%)
⫺t(14;20) MAFB (1.5%)
⫺t(4;14) FGFR3/MMSET
(11%)
⫺t(6;14) CCND3 (⬍1%)
⫺t(11;14) CCND1 (14%)
IGH translocations (43%)
Primary genetic events
CDKN2C, FAF1,
FAM46C
12p
17q
1q (40%)
Non-IgH translocations
⫺t(8;14)
MYC
Secondary translocations
Genes
CDKN2C, RB1 (3%), CCND1 (3%), CDKN2A
NRAS (21%), KRAS (28%), BRAF (5%), MYC (1%)
NRAS (21%), KRAS (28%), BRAF (5%), MYC (1%)
PI3k/AKT
TRAF3 (3%), CYLD (3%), IkB
DKK1, FRZB, DNAH5 (8%)
XBP1 (3%), PRDM1 (BLIMP) (6%), IRF4 (5%)
TP53 (6%), MRE11A (1%), PARP1
DIS3 (13%), FAM46C (10%), LRRK2 (5%)
KDM6A (UTX) (10%), MLL (1%), MMSET (8%), HOXA9,KDM6B
LTBR
NIK
CKS1B, ANP32E
Gain
Chromosomal copy number
Secondary genetic events
13 (45%)
RB1, DIS3
11q (7%)
BIRC2/BIRC3
14q (38%)
TRAF3
16q (35%)
CYLD, WWOX
17p (8%)
TP53
20 (12%)
22 (18%)
X (28%)
Mutational events
Molecular hallmarks of MM
Immortalization
G1/S abnormality
Proliferation
Resistance to apoptosis
NF-␬B pathway
Abnormal localization and bone disease
Abnormal PC Differentiation
Abnormal DNA repair
RNA editing
Epigenetic abnormalities
Abnormal immune surveillance
Abnormal energy metabolism and ADME events
Epigenetic events
Global hypomethylation from MGUS to MM
Gene-specific hypermethylation from MM to plasma cell leukemia
6q (33%)
8p (25%)
1p (30%)
Deletion
Table 1. Genetic events underlying the initiation and progression of MM to plasma cell leukemia
Table 2. MM molecular subgroups potentially suitable for future targeted trials
Molecular feature
t(4;14)
Overexpression of MMSET
and FGFR3
t(14;16), t(14;20)
Overexpression of MAF
or MAFB
ISS/FISH high risk
Combination of t(4;14) or
t(14;16)/t(14;20), del(17p)
and/or gain(1q)
BRAF V600E mutation
Unfavorable GEP
Absence of unfavorable
features
Current
detection method
Future detection method
Potential targeted treatment
FISH
Targeted NGS, RQ-PCR, GEP
Proteasome inhibitors (?), MMSET inhibitors,
FGFR3 inhibitors, MEK inhibitors
FISH
Targeted NGS, RQ-PCR, GEP
MEK inhibitors
FISH, SSCP
Targeted NGS,
RQ-PCR ⫹ FISH/SSCP,
GEP ⫹ FISH/SSCP
Treatment intensification, novel drugs
Various (SSCP, Sanger
sequencing)
GEP
Targeted NGS
BRAF inhibitors
Validated GEP signature,
GEP-derived RQ-PCR (?)
Targeted NGS, RQ-PCR ⫹ FISH,
GEP ⫹ FISH
Novel inhibitors targeting overexpressed
genes, eg, AURKA inhibitors
Combinations of established agents, innovative
maintenance strategies
FISH
RQ-PCR indicates real-time quantitative PCR.
from the application of genome-wide sequencing strategies. These
strategies have led to the identification of recurrent mutations, such
as those in the RAS/MAPK pathway, which can be specifically
targeted. However, the best current example of a mutation that can
be targeted is BRAF, which mutated in 4% of cases of MM.2
However, for the effective use of such a strategy, there will need to
be an accompanying molecular diagnostic strategy able to identify
the presence of the V600E mutation. Such variants may only be
present in a subclone, which suggests potential difficulties with
targeted treatment of these lesions.
Molecular diagnostics: current and future strategies
If we are to pursue a targeted approach, it is important to consider
how this can be achieved in the clinical setting. Traditional
metaphase cytogenetic analysis is not universally applicable, is
labor intensive, and is expensive, making it a poor diagnostic test. In
contrast, interphase FISH can give results in nearly 100% of cases.
Despite this, it does not have desirable characteristics as a diagnostic test because it is also labor intensive, costly, and requires the
application of a wide range of probes to describe sufficient lesions to
give reasonable prognostic information. Going forward, changes in
technology will allow us to overcome many of these current issues.
There are already single nucleotide polymorphism– based mapping
arrays that can detect all of the copy number abnormalities in MM,
but these would need to be combined with an alternate testing
strategy able to detect translocations. It is possible to detect such
translocations at reasonable cost using DNA capture techniques for
the Ig regions and the application of next -generation sequencing
(NGS) technology. Relevant mutations can currently be detected
using PCR-based diagnostics or single-stranded conformational
polymorphism–type approaches, but the flexibility of NGS can be
exploited to detect these variants using specific capture and primer
combinations and relevant mutations. We are designing NGS
strategies that will detect all of the relevant mutations, copy number
abnormalities, and translocations typical of MM, which, when
combined with high throughput and batch testing, can deliver all of
this information at a fraction of the current costs. It seems likely that
this MM-specific strategy is a better way forward than relying on
whole-genome or exome sequencing strategies, for which there
remain serious issues with cost, throughput, and data handling. The
same arguments about the complexity of the data generated from a
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test apply to expression-based analyses with GEP, in which there
are issues with reproducibility. Simpler, signature-specific real-time
quantitative PCR tests can be applied more generally and give
similar clinical information. It can be argued that what is needed is a
uniform MM-specific strategy that can be applied in clinical trials
and in routine practice.
The challenges of treatment resistance and
targeted therapy
Targeted treatment resistance is a real phenomenon, and several
lessons have been learned about the development of treatment
resistance in other disease settings that are relevant to the application of this approach in MM. Additional molecular diagnostic
strategies that identify resistance mechanisms can help in the
application of targeted treatment. Treatment resistance can occur
when the target is absent or mutated, and this mandates the
development of alternate diagnostic approaches able to detect the
presence of the mutated target. An alternate mechanism of resistance is that the target for therapy, such as the chronic myeloid
leukemia stem cell, can be quiescent and refractory to treatment; the
relevance of this mechanism to MM is unknown.
The other broad mechanism that underlies treatment resistance is
intraclonal heterogeneity and clonal evolution. Although we and
others have shown significant complexity in the genetic basis of
MM, the technologies used in these studies reflect the predominant
clonal population and fail to take into account the presence of
subclonal heterogeneity.2,3 Recent clinical and biological data,
however, are consistent with such heterogeneity being a common
characteristic of MM. It is likely that, from a Darwinian selection
perspective, based on this intraclonal heterogeneity, clonal evolution underlies disease progression and relapse. Based on this
knowledge, it is becoming increasingly clear that after disease
initiation, the molecular events necessary for MM development are
not attained in a linear fashion, but rather via branching, nonlinear
pathways typical of those used by Darwin to explain the evolution
of the species.38 In this respect, if the MPC evolves according to
Darwinian principles, there is the potential for differential responses
within the tumor clone and for the selection and expansion of
resistant subclones. These observations pose a serious hazard for
targeted treatment and also suggest that optimum therapeutic
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Figure 1. Clonal evolution in myeloma. Illustration of the multistep evolution pathway to plasma cell leukemia. Each step in this process seems to be
the result of an acquired growth advantage in a subclone that comes to dominate the clinical picture associated with the transformed plasma cell clone.
At each stage the genetic complexity of the disease will increase as each of the prior stages of disease should also be detectable if a test with sufficient
sensitivity is available. In plasma cell leukemia because of rapid growth and clonal expansion the prior phases may be present at apparently lower levels.
strategies should be targeted at initiating events that develop early in
the disease process and not at late events present in a subclone.
There are clear examples of how intraclonal heterogeneity of
acquired mutations may be relevant to the therapy of MM, and it is
interesting that even for a dominantly acting oncogene such as
NRAS or KRAS, it is possible to identify variation in the size of the
clone carrying the mutation. Therefore, if mutational events are to
be targeted, several key pieces of information must be available,
such as the presence of a mutation, its driver status, whether the
gene is expressed, the activating or inactivating nature of the
variant, and the size of the clone carrying it. There is already some
evidence for the clinical relevance of this concept with the analysis
of gene mapping and massively parallel sequencing data from a
patient at multiple time points through their disease, suggesting that
treatment can select subclones that may expand and lead to
relapse.39 These data suggest that the sequence in which treatments
with different mechanisms of action are used may affect clonal
selection and thus overall clinical outcomes.
Such considerations are relevant to treatment decisions in both the
induction and consolidation settings, but are particularly relevant to
the use of maintenance therapy, which is showing promise as a
treatment strategy in MM. There is a suggestion that thalidomide
maintenance may select for resistant clones when used in high-risk
disease subsets, and explanations for this can be posed in the context
of the model system presented.40 It is clear that induction treatment
markedly reduces disease bulk, acting as a classic evolutionary
restriction point and resetting intraclonal dynamics. In this context,
postinduction therapy could be used to modulate the expansion of
more indolent clones favoring long-term survival. In contrast in
high-risk biological subsets, such treatment could favor the development of more aggressive clones, potentially reducing postrelapse
survival. It is also interesting to postulate, in the context of disease
progression, what may happen with the introduction of early
treatment in smoldering MM. If there is a dominant clone with
indolent behavior that governs access of a more aggressive but
minor clone to the MM niche, there may be the potential to enhance
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disease progression if this indolent clone is eradicated by early
treatment. Although exploratory, this Darwinian model of disease
progression provides an interesting conceptual framework in which
to consider the impact of standard and novel agents used for the
treatment of MM and future targeted treatment strategies.
Conclusions
The new insights into MM biology will continue to affect our
approach to treating MM. To capitalize on this information, we have
to embrace the technologies and approaches that will allow us to
apply this knowledge in the clinical setting. As we continue to make
progress with favorable-risk MM defined by these technologies, it
will be increasingly difficult to carry out small trials over short
median follow-up times and expect to gain meaningful clinical
information. Therefore, approaches to evaluating drugs in this type
of disease will need to change. In contrast, in high-risk disease, we
are making less progress and smaller, focused studies may allow us
to rapidly evaluate and find new agents for this subgroup. Molecular
subgroup–specific trials using targeted agents represent a very
important way forward in which we can evaluate the impact of
switching off MM relevant signaling pathways using targeted
agents. Despite the complexity of this approach, targeting specific
molecular lesions represents a very clear way forward for patients
with MM, but it demands the development of MM-specific diagnostic platforms and their widespread dissemination through the
clinical system in the immediate future.
Acknowledgments
M.F.K. is funded by the Deutsche Forschungsgemeinschaft (DFG
KA 3338/1-1).
Disclosures
Conflict-of-interest disclosure: G.J.M. has consulted for Novartis,
Celgene, and J&J. M.F.K. declares no competing financial interests.
Off-label drug use: None disclosed.
347
Correspondence
Gareth J. Morgan, Institute of Cancer Research, Brooks Lawley
Building, Sutton, London SW7 3RP, United Kingdom; Phone:
020-8722-4130; e-mail: gareth.morgan@icr.ac.uk.
References
1. Palumbo A, Anderson K. Multiple myeloma. N Engl J Med.
2011;364(11):1046-1060.
2. Chapman MA, Lawrence MS, Keats JJ, et al. Initial genome
sequencing and analysis of multiple myeloma. Nature. 2011;
471(7339):467-472.
3. Walker BA, Leone PE, Chiecchio L, et al. A compendium of
myeloma-associated chromosomal copy number abnormalities
and their prognostic value. Blood. 2010;116(15):e56-e65.
4. Walker BA, Wardell CP, Chiecchio L, et al. Aberrant global
methylation patterns affect the molecular pathogenesis and
prognosis of multiple myeloma. Blood. 2011;117(2):553-562.
5. Gonza´lez D, van der Burg M, García-Sanz R, et al. Immunoglobulin gene rearrangements and the pathogenesis of multiple
myeloma. Blood. 2007;110(9):3112-3121.
6. Bergsagel PL, Kuehl WM, Zhan F, Sawyer J, Barlogie B,
Shaughnessy J Jr. Cyclin D dysregulation: an early and
unifying pathogenic event in multiple myeloma. Blood. 2005;
106(1):296-303.
7. Annunziata CM, Davis RE, Demchenko Y, et al. Frequent
engagement of the classical and alternative NF-kappaB pathways by diverse genetic abnormalities in multiple myeloma.
Cancer Cell. 2007;12(2):115-130.
8. Keats JJ, Fonseca R, Chesi M, et al. Promiscuous mutations
activate the noncanonical NF-kappaB pathway in multiple
myeloma. Cancer Cell. 2007;12(2):131-144.
9. Dickens NJ, Walker BA, Leone PE, et al. Homozygous deletion
mapping in myeloma samples identifies genes and an expression signature relevant to pathogenesis and outcome. Clin
Cancer Res. 2010;16(6):1856-1864.
10. Jenner MW, Leone PE, Walker BA, et al. Gene mapping and
expression analysis of 16q loss of heterozygosity identifies
WWOX and CYLD as being important in determining clinical
outcome in multiple myeloma. Blood. 2007;110(9):3291-3300.
11. Dib A, Barlogie B, Shaughnessy JD Jr, Kuehl WM. Methylation and expression of the p16INK4A tumor suppressor gene
in multiple myeloma. Blood. 2007;109(3):1337-1338.
12. Walker BA, Wardell CP, Melchor L, et al. Intraclonal heterogeneity and distinct molecular mechanisms characterize the
development of t(4;14) and t(11;14) myeloma. Blood. 2012;
120(5):1077-1086.
13. Ley TJ, Mardis ER, Ding L, et al. DNA sequencing of a
cytogenetically normal acute myeloid leukaemia genome. Nature. 2008;456(7218):66-72.
14. Lee W, Jiang Z, Liu J, et al. The mutation spectrum revealed by
paired genome sequences from a lung cancer patient. Nature.
2010;465(7297):473-477.
15. Boyd KD, Walker BA, Wardell CP, et al. High expression
levels of the mammalian target of rapamycin inhibitor DEPTOR
are predictive of response to thalidomide in myeloma. Leuk
Lymphoma. 2010;51(11):2126-2129.
16. Cedar H, Bergman Y. Linking DNA methylation and histone
modification: patterns and paradigms. Nat Rev Genet. 2009;
10(5):295-304.
17. Kuo AJ, Cheung P, Chen K, et al. NSD2 links dimethylation of
histone H3 at lysine 36 to oncogenic programming. Mol Cell.
2011;44(4):609-620.
348
18. Martinez-Garcia E, Popovic R, Min DJ, et al. The MMSET
histone methyl transferase switches global histone methylation
and alters gene expression in t(4;14) multiple myeloma cells.
Blood. 2011;117(1):211-220.
19. Brito JL, Walker B, Jenner M, et al. MMSET deregulation
affects cell cycle progression and adhesion regulons in
t(4;14) myeloma plasma cells. Haematologica. 2009;94(1):
78-86.
20. Pei H, Zhang L, Luo K, et al. MMSET regulates histone H4K20
methylation and 53BP1 accumulation at DNA damage sites.
Nature. 2011;470(7332):124-128.
21. van Haaften G, Dalgliesh GL, Davies H, et al. Somatic
mutations of the histone H3K27 demethylase gene UTX in
human cancer. Nat Genet. 2009;41(5):521-523.
22. Avet-Loiseau H, Leleu X, Roussel M, et al. Bortezomib plus
dexamethasone induction improves outcome of patients with
t(4;14) myeloma but not outcome of patients with del(17p).
J Clin Oncol. 2010;28(30):4630-4634.
23. Boyd KD, Ross FM, Tapper WJ, et al; NCRI Haematology
Oncology Studies Group. The clinical impact and molecular
biology of del(17p) in multiple myeloma treated with conventional or thalidomide-based therapy. Genes Chromosomes
Cancer. 2011;50(10):765-774.
24. Pichiorri F, Suh SS, Ladetto M, et al. MicroRNAs regulate
critical genes associated with multiple myeloma pathogenesis.
Proc Natl Acad Sci U S A. 2008;105(35):12885-12890.
25. Pichiorri F, Suh SS, Rocci A, et al. Downregulation of
p53-inducible microRNAs 192, 194, and 215 impairs the
p53/MDM2 autoregulatory loop in multiple myeloma development. Cancer Cell. 2010;18(4):367-381.
26. Chang TC, Yu D, Lee YS, et al. Widespread microRNA
repression by Myc contributes to tumorigenesis. Nat Genet.
2008;40(1):43-50.
27. Altieri A, Chen B, Bermejo JL, Castro F, Hemminki K.
Familial risks and temporal incidence trends of multiple
myeloma. Eur J Cancer. 2006;42(11):1661-1670.
28. Broderick P, Chubb D, Johnson DC, et al. Common variation at
3p22.1 and 7p15.3 influences multiple myeloma risk. Nat
Genet. 2011;44(1):58-61.
29. Boyd KD, Ross FM, Chiecchio L, et al; NCRI Haematology
Oncology Studies Group. A novel prognostic model in myeloma based on co-segregating adverse FISH lesions and the
ISS: analysis of patients treated in the MRC Myeloma IX trial.
Leukemia. 2012;26(2):349-355.
30. Ross FM, Chiecchio L, Dagrada G, et al; UK Myeloma Forum.
The t(14;20) is a poor prognostic factor in myeloma but is
associated with long-term stable disease in monoclonal gammopathies of undetermined significance. Haematologica. 2010;
95(7):1221-1225.
31. Munshi NC, Anderson KC, Bergsagel PL, et al; International
Myeloma Workshop Consensus Panel 2. Consensus recommendations for risk stratification in multiple myeloma: report of the
International Myeloma Workshop Consensus Panel 2. Blood.
2011;117(18):4696-4700.
32. Avet-Loiseau H, Li C, Magrangeas F, et al. Prognostic significance of copy-number alterations in multiple myeloma. J Clin
Oncol. 2009;27(27):4585-4590.
33. Morgan GJ, Davies FE, Gregory WM, et al; NCRI Haematological Oncology Study Group. Cyclophosphamide, thalidomide,
and dexamethasone (CTD) as initial therapy for patients with
multiple myeloma unsuitable for autologous transplantation.
Blood. 2011;118(5):1231-1238.
American Society of Hematology
34. Broyl A, Hose D, Lokhorst H, et al. Gene expression profiling
for molecular classification of multiple myeloma in newly
diagnosed patients. Blood. 2010;116(14):2543-2553.
35. Kassambara A, Hose D, Moreaux J, et al. Genes with a spike
expression are clustered in chromosome (sub)bands and
spike (sub)bands have a powerful prognostic value in
patients with multiple myeloma. Haematologica. 2012;97(4):
622-630.
36. Shaughnessy JD Jr, Zhan F, Burington BE, et al. A validated
gene expression model of high-risk multiple myeloma is
defined by deregulated expression of genes mapping to chromosome 1. Blood. 2007;109(6):2276-2284.
37. Neben K, Lokhorst HM, Jauch A, et al. Administration of
bortezomib before and after autologous stem-cell transplanta-
Hematology 2012
tion improves outcome in multiple myeloma patients with
deletion 17p. Blood. 2012;119(4):940-948.
38. Anderson K, Lutz C, van Delft FW, et al. Genetic variegation of
clonal architecture and propagating cells in leukaemia. Nature.
2011;469(7330):356-361.
39. Egan JB, Shi CX, Tembe W, et al. Whole genome sequencing
of multiple myeloma from diagnosis to plasma cell leukemia
reveals genomic initiating events, evolution and clonal tides.
Blood. 2012;120(5):1060-1066.
40. Morgan GJ, Gregory WM, Davies FE, et al; National Cancer
Research Institute Haematological Oncology Clinical Studies
Group. The role of maintenance thalidomide therapy in multiple myeloma: MRC Myeloma IX results and meta-analysis.
Blood. 2012;119(1):7-15.
349