J Clin Oncol 29: by American Society of Clinical Oncology INTRODUCTION

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1 VOLUME 29 NUMBER 14 MAY JOURNAL OF CLINICAL ONCOLOGY R E V I E W A R T I C L E Molecular Heterogeneity of Multiple Myeloma: Pathogenesis, Prognosis, and Therapeutic Implications Hervé Avet-Loiseau, Florence Magrangeas, Philippe Moreau, Michel Attal, Thierry Facon, Kenneth Anderson, Jean-Luc Harousseau, Nikhil Munshi, and Stéphane Minvielle From the University Hospital; Institut National de la Santé et de la Recherche Médicale U892; Centre René Gauducheau, Nantes; University Hospital, Toulouse; University Hospital, Lille, France; and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA. Submitted August 16, 21; accepted February 18, 211; published online ahead of print at on April 11, 211. Authors disclosures of potential conflicts of interest and author contributions are found at the end of this article. Corresponding author: Hervé Avet- Loiseau, MD, PhD, Laboratoire d Hématologie, Institut de Biologie, 9, quai Moncousu 4493 Nantes Cedex 1, France; havetloiseau@ chu-nantes.fr. 211 by American Society of Clinical Oncology X/11/ /$2. A B S T R A C T Multiple myeloma (MM) is characterized by a significant heterogeneity at the molecular level. The first level is the chromosomal one. Although cytogenetics is difficult to assess in MM, patients can be divided into two categories: hyperdiploidy and non-hyperdiploidy (about half in each group). Using molecular cytogenetic techniques, several subgroups of patients are identified, particularly on the basis of 14q32 translocations. This chromosomal heterogeneity is confirmed by genomic techniques (gene expression profiling or single nucleotide polymorphism/comparative genomic hybridization arrays). Unsupervised analyses of gene expression profiles identified several subgroups of patients, essentially on the basis of chromosomal abnormalities such as hyperdiploidy or 14q32 translocations. However, these analyses failed to separate MM into subentities, which could lead to specific therapeutic approaches, as is the case for non-hodgkin s lymphomas. Nevertheless, these chromosomal/genomic data can be used for prognostication of patients. Specific chromosomal changes, such as loss of the short arm of chromosome 17, or specific gene expression profiles clearly identify patients with short survival. No molecular change so far has been associated with long survival or even cure, probably because of the short follow-up observed in all studies. So far, it is unclear how to use this massive amount of data to treat patients. Because of the complex and heterogeneous picture of the molecular profiles, it is unexpected that targeted therapies might play a role in MM. The only recognized indication is to propose bortezomib-based approaches for the treatment of patients displaying the translocation t(4;14). J Clin Oncol 29: by American Society of Clinical Oncology DOI: 1.12/JCO INTRODUCTION Multiple myeloma (MM) is characterized by significant heterogeneity at multiple levels (ie, clinical presentation, biologic characteristics, response to treatment, and clinical outcome). Current data support the hypothesis that this heterogeneity is mainly related to molecular characteristics of the tumor clone. Although genetic changes are the hallmark of cancer cells, these changes are typically limited in many hematologic neoplasms (as in chronic myeloid leukemia and most acute leukemias). In contrast, solid tumors usually present a wide variety of chromosomal and genomic rearrangements. Myeloma probably lies between these two extremes. PATHOGENESIS The first level of molecular heterogeneity is the chromosomal level. Despite difficulties in generating metaphases from the tumor clone because of low proliferative index, informative karyotypes when obtained are usually complex, with a number of chromosomal abnormalities. 1-6 Some of them are recurrent, enabling the categorization of tumors into several subclasses. The most frequent subclass is defined by recurrent chromosome gains, known as hyperdiploidy. These gains are nonrandom and involve chromosomes 3, 5, 7, 9, 11, 15, 19, and The molecular basis of this nonrandom selection of odd chromosomes is still unknown. Structural chromosomal rearrangements are usually infrequent in this genetic category. The other subclass (ie, nonhyperdiploidy) is characterized by structural changes. Most of these cases include specific rearrangements involving the IGH gene (encoding imunoglobulin heavy chain), located at 14q These rearrangements are typical reciprocal translocations with various chromosomal partners leading to upregulation of the target genes. The most frequent 14q32 partners are 11q13 (targeting CCND1; 2% of patients), 11,12 4p16 (targeting both FGFR3 and MMSET; 15% of patients), and 16q23 17,18 and 2q11 19 (targeting MAF genes; 2% to 3% of patients each). These two classes probably define two different oncogenetic pathways, one based on chromosome gains, the other on upregulation of 211 by American Society of Clinical Oncology 1893

2 Avet-Loiseau et al x various genes with oncogenic properties. 2,21 Apart from these primary events, other recurrent chromosomal changes have been identified. Monosomy 13 is observed in about half of patients, more frequently in the nonhyperdiploid pathway. 22 Finally, two secondary events are also recurrent: gains of the long arm of chromosome 1 (observedinonethirdofpatients) 23 andlossoftheshortarmofchromosome 17 (del(17p); observed in approximately 1% of patients). 24 These chromosomal abnormalities are discussed under Prognosis. This karyotypic oncogenetic classification has only been partially confirmed by molecular studies. Myeloma has been evaluated at the transcriptional level using gene expression profiling in several studies. The first study, in 22, clustered genes according to similarities with either monoclonal gammopathy of undetermined significance or human myeloma cell lines. 25 This proposed classification is no longer in use. In 23, another study showed that the most relevant profiles were related to immunoglobulin gene expression. 26 More recently, using nonsupervised analyses, three reports identified subgroups mostly driven by chromosomal aberrations. 27 The first report identified eight different subgroups, mainly on the basis of cyclin D gene expression and on the different 14q32 recurrent translocations. 2 This molecular classification was refined in 26, identifying seven subclasses of myelomas. 27 The first class is defined by the translocation t(4;14), identified by overexpression of the MMSET and/or FGFR3 genes. The second one is defined by upregulation of one of the MAF genes, related to the translocations t(14;16) or t(14; 2). Those with CCND1 or CCND3 upregulation (resulting from translocation t(11;14) or t(6;14)) are clustered in two different groups: CD1 and CD2. The CD2 group is characterized by CD2 expression. The fifth group is characterized by hyperdiploidy. The two last groups are characterized by low incidence of bone disease, according to low DKK1 expression, whereas the last group is characterized by the high expression of genes involved in proliferation. This molecular classification was partially confirmed in a recent study by the HOVON (Hemato-Oncologie voor Volwassenen Nederland) group. 28 The low bone disease group was not confirmed. In contrast, three other groups were identified: one group enriched by myeloid genes (which could be related to plasma cell sorting problems), one group characterized by overexpression of cancer testis antigen genes, and finally one group defined by overexpression of positive regulators of the nuclear factor kappa-b (NF B) pathway. The effect on the NF B pathway has also been identified using DNA-based techniques such as array comparative genomic hybridization. 29,3 In two separate studies, it has been shown that the NF B pathway can be activated either by deletions of NF B inhibitors or by activation of NF B activators. Other studies based on the analysis of copy number changes by high-density single nucleotide polymorphism (SNP) array have identified other levels of molecular heterogeneity (Fig 1). 31,32 These studies identified genomic heterogeneity within the hyperdiploid group driven by the presence of either chromosome 1q gain and/or chromosome 11 gain, chromosome 13 loss, or chromosome 5 gain, conferring significant outcome difference. Furthermore, these studies demonstrated that integration of copy number changes and gene expression values allowed the conversion of genomic heterogeneity into potential target cancer genes. Subclasses of hyperdiploid patients with multiple myeloma with clinical and biologic associations were also characterized by gene expression profiling. 7 Initial attempts at understanding the genesis of this genomic heterogeneity have involved uncontrolled recombination mechanisms, which may become potential targets in understanding the biology as well as developing effective therapeutic strategies. 33 To summarize the broad and comprehensive work performed in the molecular description of myeloma, heterogeneity is present, which has to date prevented the identification of specific definitive entities to dissect myeloma into different diseases. However, it is important to note that the emerging classifications are essentially based on chromosomal changes. This situation is highly reminiscent of that regarding non-hodgkin s lymphomas. In the latter, classification is based on chromosomal changes specific to each entity, such as t(11;14) for mantle-cell lymphoma, t(8;14) for Burkitt s lymphoma, and t(14;18) for follicular lymphoma. Additional work is needed to confirm such a classification as well as its potential clinical value in myeloma. PROGNOSIS Log ratio -2 2 Fig 1. Molecular classification of myeloma using single nucleotide polymorphism array proposed by the Intergroupe Francophone du Myélome. Apart from the hyperdiploid group, several copy number changes identify other subgroups. Data adapted. 31 If molecular studies have so far not enabled the definite subclassification of myeloma into specific entities, they have certainly contributed to the identification of several prognostic factors that significantly influence patient outcome. Usually, prognostic genetic abnormalities are analyzed through interphase fluorescence in situ hybridization (FISH). It is important to point out that FISH must be performed on immunologically recognized or sorted plasma cells and not on total by American Society of Clinical Oncology JOURNAL OF CLINICAL ONCOLOGY

3 Molecular Heterogeneity of Multiple Myeloma Overall Survival (probability) Low risk High risk HR, 6.77; 95% CI, 3.92 to P < Time (months) No. at risk Low risk High risk Fig 2. Overall survival according to the 15-gene model proposed by the Intergroupe Francophone du Myélome. Data adapted. 48 HR, hazard ratio. bone marrow cells. The first identified genetic abnormality with specific prognostic value was the loss of chromosome Several studies have shown that this loss (observed in almost half of patients) negatively affects both event-free and overall survival. However, other reports have suggested that its prognostic value is actually related to other genetic changes, especially t(4;14) and del(17p), often observed in these patients. 38 Among the specific 14q32 translocations discussed here, t(4;14) is definitely the most important one from a clinical point of view. A number of studies have confirmed that patients who display this translocation (15% of patients) have a specifically poor prognosis Interestingly, these patients may require a specific therapeutic approach. More conflicting data exist regarding t(14;16); some studies have shown a negative prognostic impact, 43,44 whereas another did not show any impact. 44a The most important chromosomal change for prognosis is del(17p). Present in 8% to 1% of patients, this deletion is associated with remarkably short survival, irrespective of the therapy used. 24,38,45 The molecular target of this deletion could be TP53, but no clear biologic evidence supports this hypothesis, and mutations are observed only in the subset of patients with del(17p). 46 Finally, several reports have shown that gains of chromosome 1q (observed in one third of patients) also confer poor prognosis. 23,47 This abnormality is typically a secondary event, not specific to myeloma, acquired during evolution of the disease. In recent reports, prognostic molecular changes have been analyzed by high-throughput microarray profiling techniques, using either gene expression profiling or copy number alteration via SNP array. These techniques are potentially more powerful because they analyze the whole genome. However, they require a sufficient number of purified plasma cells and specific bioinformatic facilities. Two large studies using gene expression profiling have been reported (Fig 2). These two reports describe two different sets of genes that identify poor-risk patient populations. One of the two models the UAMS (University of Arkansas for Medical Sciences) 7-gene model has 3% of the informative genes mapped to chromosome In the other model the IFM (Intergroupe Francophone du Myélome) 15- gene model high-risk patients were enriched in genes controlling proliferation, whereas low-risk patients were enriched in hyperdiploid karyotypes. 49 It is interesting to note that the two models do not have a single common gene, reflecting either differences in platforms used for the microarray analyses and/or differences in the treatment used to define the patient population and/or redundancy in the genes and pathways that control growth, proliferation, and survival. However, in an attempt to validate the techniques, the IFM 15-gene set was shown to be powerful in the UAMS population but with a lower significance. 49 Interestingly, both sets identified patients with short survival, but neither identified very good risk patients, probably because of a quite short follow-up. An international effort is needed to fully validate a platform and uniform set of genes predicting outcome irrespective of the treatment used to make gene expression profiling routine in clinical practice. Another study used SNP array to detect copy number changes associated with specific outcome. 31 In this large series, chromosome 1q gains and deletions of the short arm of chromosome 12 were associated with poor prognosis, whereas the gain of chromosome 5q (reflects hyperdiploidy) Overall Survival (probability) P < Time (months) No. at risk Amp(5q) no del(12p) no amp(1q) Others Amp(5q) amp(1q) del(12p) or no amp (5q) and [amp(1q) and/or del(12p)] Amp(5q) no del(12p) no amp(1q) Others Amp(5q) amp(1q) del(12p) or no amp (5q) and [amp(1q) and/or del(12p)] Fig 3. Prognostic model using single nucleotide polymorphism array proposed by the Intergroupe Francophone du Myélome. The model is based on three copy number changes in chromosomes 1q, 5q, and 12p. Data adapted by American Society of Clinical Oncology 1895

4 Avet-Loiseau et al was associated with longer survival (Fig 3). This prognostic model has to be confirmed in a larger independent series. THERAPEUTIC IMPLICATIONS Whether these molecular abnormalities can drive therapeutic choices is an unresolved question. So far, treatment options have been especially driven by age and/or comorbid conditions as well as by the clinical features of each patient s disease. In patients younger than 65 years of age, the standard of care is usually short induction followed by high-dose melphalan with stem-cell rescue. For older patients or for those with significant comorbidities, long-term treatment with different combinations is usually chosen. However, a first attempt to use genetic data in treatment selection has been proposed for patients displaying t(4;14). A number of studies have shown that patients with t(4;14) may benefit from use of bortezomib, either as induction therapy or long-term treatment In some of these studies, long-term use of bortezomib totally overcame the poor prognosis associated with t(4;14). 5,51 For other high-risk parameters, such as del(17p) or gene expression defined high-risk disease, no specific treatment has so far demonstratedadefiniteandreproduciblebeneficialeffect, althoughcombinations of novel therapies do seem to have some promise in this group. 53 Another important question would be to define a standard of careforverygood riskpatients. However, thesepatientsarenotyetclearly recognized, and long-term analyses are needed to try to best identify these patients and then possibly propose less toxic approaches. SUMMARY In summary, reported studies to date suggest that myeloma is characterized by a broad molecular heterogeneity, both between patients and in each patient over time. The next steps will reside in developing a combination of several molecular approaches, including copy number change analyses, gene expression profiling, massive parallel sequencing, mirna analyses, and epigenetic change surveys in large, uniformly treated patient cohorts. This is turn will produce a clearer landscape of the molecular changes underlying the disease and their impact on myeloma classification, prognosis, and ultimately therapeutic approaches. AUTHORS DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST Although all authors completed the disclosure declaration, the following author(s) indicated a financial or other interest that is relevant to the subject matter under consideration in this article. Certain relationships marked with a U are those for which no compensation was received; those relationships marked with a C were compensated. For a detailed description of the disclosure categories, or for more information about ASCO s conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors. Employment or Leadership Position: None Consultant or Advisory Role: None Stock Ownership: None Honoraria: Jean-Luc Harousseau, Janssen, Celgene Research Funding: None Expert Testimony: None Other Remuneration: None AUTHOR CONTRIBUTIONS Manuscript writing: All authors Final approval of manuscript: All authors REFERENCES 1. Dewald GW, Kyle RA, Hicks GA, et al: The clinical significance of cytogenetic studies in 1 patients with multiple myeloma, plasma cell leukemia, or amyloidosis. Blood 66:38-39, Sawyer JR, Waldron JA, Jagannath S, et al: Cytogenetic finding in 2 patients with multiple myeloma. 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