IMWG consensus on risk stratification in multiple myeloma

Abstract

Multiple myeloma is characterized by underlying clinical and biological heterogeneity, which translates to variable response to treatment and outcome. With the recent increase in treatment armamentarium and the projected further increase in approved therapeutic agents in the coming years, the issue of having some mechanism to dissect this heterogeneity and rationally apply treatment is coming to the fore. A number of robustly validated prognostic markers have been identified and the use of these markers in stratifying patients into different risk groups has been proposed. In this consensus statement, the International Myeloma Working Group propose well-defined and easily applicable risk categories based on current available information and suggests the use of this set of prognostic factors as gold standards in all clinical trials and form the basis of subsequent development of more complex prognostic system or better prognostic factors. At the same time, these risk categories serve as a framework to rationalize the use of therapies.

Introduction

Multiple Myeloma (MM) is a malignancy characterized by the infiltration of clonal plasma cells in the bone marrow that secrete a monoclonal protein in the majority of patients. Several new therapies have been approved for the treatment of myeloma in the last decade, with a resultant improvement in outcome. However, considerable heterogeneity exists in the survival outcomes among patients diagnosed with MM. Although a large number of prognostic markers have been described in MM, none of them completely explain the heterogeneity seen in this disease and attempts have been made to develop systems using several of these prognostic factors to better risk stratify patients with MM. To complicate matters further, some of the new treatment appears to overcome the high-risk defined by one or more of these prognostic factors. With the increased treatment options, the ability of some treatment to overcome certain risk factors and the availability of markers to define risk categories, the question of risk stratification in the management of myeloma is becoming an important issue. Development of a uniform risk stratification system will also allow better comparison of patient groups across different trials, given the heterogeneity seen in the outcomes. During the International Myeloma Working Group (IMWG) Meeting in London in 2011 June, a group was convened to discuss issues around risk stratification in MM. In particular, the following were discussed in the context of evolving treatment paradigms and prognostic factors:

  1. 1)

    What defines high-risk and low-risk patients?

  2. 2)

    What should we use to define the risk groups?

  3. 3)

    Can risk-adapted therapy be recommended at this time?

  4. 4)

    What are the best trial designs for risk stratification?

In the following sections, we shall discuss current evidence and arguments for risk stratification in myeloma based on the above questions and discussions. We will then present our consensus recommendations at the end. Some of these recommendations will be applicable clinically, whereas others will be for research and design of clinical trials as we continue to optimize management of myeloma patients.

Current treatment landscape

In recent years, the treatment landscape of myeloma has evolved considerably. Three effective novel agents have been approved for myeloma treatment and incorporated into the treatment armamentarium in the last decades. This includes thalidomide and lenalidomide, so-called immunomodulatory drugs (Imids), and bortezomib, the first in class proteasome inhibitor.1 More recently, Carfilzomib, the second-generation proteasome inhibitor, as well as Pomalidomide, a third-generation Imids has also been approve by the US Food and Drug Administration for the treatment of relapse myeloma. The incorporation of these drugs in combination with steroid and/or alkylating agents at different phases of myeloma treatment (induction phase, consolidation phase and maintenance phase) has significantly improved response rates and overall survival.2, 3

Predictive versus prognostic markers

With increase therapeutic choices, and improved outcomes, the issue of risk stratification is becoming important as we could potentially tailor treatment for different groups of patients.

In this regard, the concept of predictive versus prognostic markers should be distinguished. Prognostic markers provide information about the outcome, whereas predictive markers provide information specifically about different drugs or regimen and the likelihood of good response and the outcome with them. A marker can be either prognostic or predictive or both. For example, 17p13 deletion is a poor prognostic marker but not predictive of response or outcome to any specific drugs. TRAF3 deletion or mutation may predict response to bortezomib but is not itself a prognostic factor.4 Cereblon expression may predict resistance to immunomodulatory drugs but is itself not a prognostic factor.5 In myeloma, we have a number of markers associated with prognosis but few predictive markers.

The predictive markers are useful for individualizing treatment, whereas the prognostic markers are useful in risk stratification. Although we do not yet have robust predictive markers for selecting treatment, treatment doses should be individualized according to host factors such as age and renal function. The European Myeloma Network has recently proposed useful dose reduction guidelines according to patient’s constitution.6

What are the prognostic markers?

A large number of prognostic biomarkers has been identified over the years. These markers may reflect host factors and hence fitness to receive therapy, tumor-related factors which reflect tumor biology, tumor stage and disease burden and tumor response to treatment. It is important to note that most of these prognostic factors are derived from patient cohorts treated with alkylator-based and/or transplant-based treatment in the era before novel agents. Nevertheless, many of them are still relevant in the era of novel agents and are used to assess how treatment with the novel agents can modify the patient’s risk. Several markers have been more widely validated and utilized in prognostication.

Host factors

The most important host factor is age. There is an incremental shortening of survival in every increasing 10-year age band in a large cohort from Europe, United States and Japan treated with both conventional and novel agents.7 Furthermore, in a recent Francophone Myeloma Intergroup (IFM) analysis to identify factors associated with long survival, young age was one such independent factor.8

Tumor characteristics

Tumor-related factors include proliferation as measured by the plasma cell labeling index,9 presence of circulating plasma cells10 and plasmablastic morphology.11 However, these are either not widely adopted or not easily reproducible. The most important tumor factors are genetic aberrations and gene expression profiles. A number of genetic aberrations have been shown to be associated with poor survival consistently across studies. These includes t(4;14) and 17p13 deletion.12, 13, 14 The data for t(14;16) are mixed with the Mayo Clinic and MRC (Medical Research Council) group showing that it is associated with poorer outcome, whereas this was not the case in the IFM studies.15, 16, 17 Another factor where data are conflicting is chromosome 1q21 amplification (1q21+). Some reports have shown 1q21+ to be an independent prognostic factor,17, 18 whereas others have not.19, 20 Although its role as a poor prognostic factor is controversial, the lack of 1q21+ may be useful in identifying patients with very good prognosis.8 More recently, the deletion of 1p has also been shown to be independent prognostic factors associated with shorter survival.21, 22, 23 The prognostic importance of 1p deletion and the locus of relevance (1p21 or 1p32; although the latter is common to all studies) need to be further confirmed. t(4;14) and t(14;16) can only be detected by fluorescent in situ hybridization (FISH) as these translocations are not detectable by conventional cytogenetics. 17p13 deletion and 1q21 gain can be detected by both FISH and conventional cytogenetics and is associated with poor outcome when detected by either method although most of the studies used FISH as the detection method.

However, even within groups with these genetic prognostic factors, there may be further heterogeneity. For example, the IFM group showed that among the patients with t(4;14), those with a hemoglobin greater than 10 g/l and beta-2 microglobulin less than 4 mg/l had significantly longer survival compared to those without.24 At the same time, a recent analysis showed that patients with high-risk genetic changes have significantly different survival depending on the presence or absence of trisomies.25 These results suggest that with the current state of knowledge about myeloma genetics, genetics alone may be suboptimal as prognostic factors. Combining information about genetic abnormalities with other parameters may further improve their prognostic value.

A number of prognostic gene expression signatures have been reported. Some of these are derived directly as a measure of poor survival, whereas others were derived to reflect underlying biology and subsequently found to be prognostic (Table 1). Several of these have been validated in a number of independent data sets as independent prognostic factors. The prognostic utility of some of these gene expression profiling (GEP) signatures are currently being prospectively evaluated in the EMN02 trials and IFM/USA trials.

Table 1 GEP-based prognostic signatures

The technology of gene expression microarray is now mature and robust, with good inter-laboratory agreement.26 However, there are still three major hindrances to its clinical application: (1) there is not yet one standard recognized GEP-signature; and (2) the perception of a lack of reproducibility as so many different signatures exists. The lack of overlap in the genes constituting the different prognostic signatures is a function of the different ways in which these signatures are generated. Different signatures may signify different aspects of myeloma biology. In this context, a combination of signatures may be better than individual signatures. To this end, the International Myeloma Working Group is conducting a study to unify the GEP prognostic signatures using prognostic modeling. Lastly, the lack of ability of most myeloma physicians to analyze and interpret GEP data leads to the perception that it is still a research tool and not clinically applicable. To move GEP toward the clinic, it is critical that we create user-friendly platforms. Dedicated chips can be created (for example, Mammaprint (Agendia Inc, Irvine, CA, USA) for breast cancer and MyPRS (Signal Genetics AR, New York, NY, USA) for MM) or automated analytical algorithm that generates reports on results and interpretation can be incorporated to laboratory workflow. The feasibility of one such attempt has been demonstrated.27 The parallel resolution of these issues should make clinical application of GEP in myeloma a reality in next few years.

Tumor burden/stage

The Durie-Salmon staging system was the first commonly used staging system largely reflecting tumor burden.28 It has been superseded by the International staging system (ISS)29 which is based on two simple and routine laboratory tests, serum albumin and beta-2 microglobulin, that is widely available. The ISS therefore combines factors that relates to the host (albumin) and the disease activity (beta-2 microglobulin). The ISS is robustly derived and validated and applicable across geographical areas. During the derivation of the ISS, traditional prognostic factors such as blood counts, renal function, hypercalcaemia, M-protein levels, percentage of bone marrow involvement and immunoglobulin isotypes were not significant in the multivariate analysis. However, genetic abnormalities were not included in the derivation of the ISS. It was subsequently shown by the IFM group that high-risk cytogenetics have prognostic impact independent of the ISS,20 suggesting that the integration of both ISS and genetics produces a more robust model. Recently, it was shown that the extent of disease detected by magnetic resonance imaging and positron emission tomography-computed tomography correlated with tumor burden and has prognostic utility.30 These results require further confirmation before clinical implementation.

Combined genetics-ISS model

Indeed a recent analysis by IMWG incorporating data from the international community demonstrated that a combined model could segregate patients into three risk groups. High-risk patients with either ISS II or III and the presence of either t(4;14) and/or 17p13 deletion detected by FISH have a median survival of about 2 years, whereas low-risk patients with ISS I or II and absence of these high-risk genetics have 5- and 10-year overall survival rates of 70% and 51%, respectively.31 Similar results were seen in an independent German study32 and an independent MRC study17 (Table 2). The best outcomes were seen in the German study, most likely because it included only transplant patients and it was a more recent study, making it more likely that a higher percentage of patients had access to novel therapies.

Table 2 Comparison of studies using combined prognostic models of ISS and FISH

Tumor responsiveness to treatment

There is substantial data showing the association of depth of response and outcome. Most, but not all, studies showed that achieving complete remission (CR) is an important surrogate for improved OS.33 There are studies that suggest that CR is not needed for prolonged survival of patients with low-risk disease based on gene expression profile.34 In addition, the Arkansas group has shown that it is not the achievement of CR but the ability to sustain the CR that is important for overall survival.35, 36 Further, the IFM group have shown in their trials that very good partial response is associated with good outcome,37, 38 although it is important to note that the consideration of all patients who achieve at least very good partial response will include a substantial proportion of patients who actually achieve CR. In two large series of patients treated in Spanish33 and Italian trials,39 respectively, the achievement of CR has a significant impact on survival when compared with very good partial response only. Furthermore, there are a number of studies showing that the achievement of immunophenotypic remission, where abnormal plasma cells are no longer detectable by flow cytometry, is associated with longer survival than CR.40, 41 There are also data suggesting that achieving a molecular CR with no detectable clones by PCR is better than CR.42, 43 On the other hand, the detection of disease by magnetic resonance imaging44 or positron emission tomography-computed tomography45 after treatment is associated with high-risk of relapse. There are therefore ample evidence suggesting that for a given treatment strategy, a deeper response is associated with better outcome. However, this does not suggest that strategies producing greater depth of response are inherently better. Which treatment strategy is better will need to be assessed in the setting of randomized studies.

What is the best measure of response is at present still not clear. There are potential limitations with each technology and some technologies are still evolving. It is conceivable that a combination of these methods would be needed to define the deepest response.

Counter intuitively, patients with identifiable high-risk genetic findings have response rates that are comparable to those with favorable genetics. What distinguishes these high-risk patients is their inability to stay in response. Not achieving partial response or very good partial response among patients with apparently favorable genetics is another surrogate for adverse biology, and should be considered post hoc as a risk factor. Early relapse, however, is a far worse prognostic factor.46

Why should we risk stratify?

Patient counseling

One of the main reasons for assigning risk to each patient with a disease is to inform the patient of his prognosis. This is and still remains a very important reason for risk categorization and provides a framework for patient counseling, providing the answer to one of the most commonly ask question of ‘How long do I have to live?’, after someone is told of their diagnosis of cancer. This is no different for myeloma patients.

Minimize toxicity and maximize outcome

Risk-adapted therapy is not new in hematologic malignancy and is routinely used in the management of acute leukemias and Hodgkin’s lymphoma. In these diseases, low-risk patients may get away with less intensive treatment and still be cured, whereas high-risk patients will require more intensive treatment to achieve long-term remission. Risk stratification, in principle, allows the reduction in treatment toxicity and optimizing outcome. It may also allow the better optimization of therapeutic resources to prevent over-treatment in subgroups of patients. The risk of this approach is that some patients may be under-treated.

In myeloma, there is a strong argument that this concept does not apply as we still view the disease as incurable. In this situation, all patients should receive the most optimal treatment tested in phase III clinical trials and currently available to achieve the best outcome. Certainly in recent published trials using novel agents, the benefits are seen across the risk categories, often benefiting the non-high-risk patients more.47, 48, 49 Therefore, by treating low-risk patients with less, we may potentially undertreat these patients. However, the recent description of factors—absence of t(4;14), del(17p), and 1q gain and b2-microglobulin less than 5.5 mg/l—that could identify the patients who have greater than 50% chance of surviving more than 10 years may start to challenge this concept.8 It is therefore important that future cinical trials should take the different risk groups into consideration when asking the therapeutic questions.

Research

Perhaps more importantly, risk stratification sets the framework to identify continuing area of clinical needs and to allow the continuous optimization of treatment. For the very high-risk patients, such as those with 17p13 deletion, who have generally poor outcome with current treatment strategies, alternative strategies should be considered. For the low-risk patients that have a more than 50% chance of surviving more than 10 years perhaps treatment strategies without stem cell transplantation and even maintenance can be tested (Table 3). Therefore, risk stratification provides the framework for testing these therapeutic strategies in clinical trials and also to identify new and more effective treatment for high-risk patients.

Table 3 Risk stratification and possible therapeutic questions within each risk categories

What defines high-risk and low-risk patients?

The panel agrees that a reasonable benchmark to define high-risk patients will be an overall survival of 2 years or less despite the use of novel agents. Conversely, low-risk patients will be those that survive more than 10 years.

What should we use to define the risk groups?

On the basis of existing laboratory tests, there are already good and robust markers for risk stratification. These include serum albumin and beta-2 microglobulin for ISS staging, and FISH for t(4;14), deletion 17p13 and 1q21 gain. Using these markers that can be applied to more than 90% of all myeloma patients, a high-risk group of patients can be defined by ISS stage II/III and the presence of either t(4;14) or 17p13.31 At the same time, a low-risk group can be defined by age less than 55 years, ISS stage I or II and normal results for the three FISH markers.8 These can be applied today and it is felt that this risk stratification should form the current standard to which future prognostic markers should be compared and be the platform upon which new prognostic markers can be integrated. This schema also fits in with the mayo stratification of myeloma and risk-adapted therapy (mSMART) risk categories proposed by the Mayo Clinic50 and provides further refinement through the addition of a low-risk group of patients. In the mSMART schema, t(4;14) is considered to have intermediate risk as its risk can be modulated by bortezomib treatment.

Prognostic markers may evolve with new understanding of disease, new treatments and new technologies. However, new prognostic makers should be assessed against a baseline standard. Currently, different studies may utilize different standards for risk stratification and the prognostic impact of new markers is not always compared with the same standards. There is a strong argument that the assessment of a standard set of prognostic markers should be mandated for all studies and that any new prognostic markers should always be compared with this standard. This will make the interpretation of data easier and more consistent, and facilitate more rapid adoption of new prognostic markers. We therefore propose the use of the IWMG combined ISS-genetic prognostic system as the new standard to define high-risk disease (Table 3).

Can risk-adapted therapy be recommended at this time?

It was felt that at the present time, although we have the markers to stratify patients into different risk groups, we are still not in a position to recommend different treatments for patients in the different risk groups. The only possible exception is that bortezomib-based treatment for induction and maintenance should be recommended for patients with t(4;14), as results from different trials have consistently showed that bortezomib-based treatment improved outcome of these patients.51 Some studies suggest that the poor risk of t(4;14) is completed negated,47, 48 whereas others suggest that the risk is attenuated but not completely abrogated.49, 52 Two reports utilizing bortezomib combinations before and after autologous stem cell transplantation appear to be able to overcome the poor prognosis conferred by 17p13 deletion.53, 54 In the University of Arkansas for Medical Sciences (UAMS) study, this benefit with bortezomib was seen in the GEP defined high-risk but not low-risk patients.53 However, the number of patients with 17p13 deletions in these studies is relatively small and current strategies await further confirmation. On the other hand, it is clear that thalidomide maintenance is detrimental to patients with 17p13 deletions.55 In fact, there is no clear evidence till now that maintenance therapy, with any agents, benefits patients with very high-risk disease such as patients with 17p13 deletions.

Although it is true that there are little data from randomized studies to suggest the benefit of specific treatments for patients in different risk groups, the risk stratification schema is nevertheless a useful framework for rational selection of treatment taking into consideration the cost of drugs, toxicities and efficacies. For high-risk patients, the threshold for using more expensive and potent treatment with potentially greater toxicity could be lower, whereas for low-risk patients, this threshold will be much higher with a preference for less toxic and less costly regimen albeit with slight compromise on efficacy. These thresholds varies according to the inclinations of the treating physicians, and are determined to a large extent based on the dichotomy in treatment philosophies that currently exist in myeloma; one of cure (adopting a more aggressive approach) versus control (adopting a less aggressive approach with focus on quality of life).56

What are the best trial designs for risk stratification?

It was unanimously agreed that information on risk assignment should be prospectively collected in all trials in a standard manner so that results can be compared across trials. It is also agreed that samples should be prospectively collected from large clinical trials so that GEP can be performed and GEP prognostic signatures applied. This will facilitate the creation of repositories of good quality prospectively collected material from patients who entered into clinical trials that can be retrospective analyzed to either validate or generate new prognostic markers/signatures.

In terms of trial design, there can be two general approaches. One is where risk stratified treatment is not applied and not the focus of study, but markers used for risk stratification are prospectively collected so that post hoc analysis looking at the impact of treatment in relation to the different risk factors can be analyzed. This is particularly important for trials involving new drugs and new combinations and will allow the assessment of how the impact of risk factors can be modified by new treatments.

The other approach is to apply risk stratification in the trial design in order to specifically answer questions on treatment within each risk group. This may be appropriate for testing different approaches to treat patients in different risk groups. For example, as the high-risk patients have poor outcome with current treatment strategies, novel treatment approaches may be tested such as the incorporation of allogeneic transplantation following reduced intensity conditioning or the incorporation of immune-based therapy for consolidation compared with current approach. On the other hand, with the identification of low-risk patients who can survive for more than 10 years, it may be appropriate to ask the questions of whether maintenance therapy is required for these patients.

Future perspective

The biology of myeloma is increasingly being unraveled and will only escalate as the results of ongoing whole-genome sequencing efforts in myeloma starts to emerge.57, 58 The multitude of molecular abnormalities and their different combinations in different patients underlie the tremendous heterogeneity in myeloma. It would be elegant if the current risk groups can be further refined by the underlying biological heterogeneity as many of these molecular pathways may be targeted for treatment. Although this remains an important goal of ongoing research, it will remain a challenge for some years. Coupled with the clear demonstration of clonal heterogeneity in individual patients,59 it is unlikely that biology-based individualized treatment can be delivered to large number of myeloma patients in the near future. Until that becomes clinically feasible on a large scale, our current proposed risk categorization provides a practical approach to defining clinically relevant heterogeneity in patients, which can be used as a framework to study issues regarding treatment strategies. It also acts as a foundation that can be built upon and refined as biological heterogeneity become better define through current genomic initiatives.

Summary of IMWG consensus recommendations

Recommendations for clinical practice

  1. 1)

    Combination of ISS and FISH should be used for risk stratification. This includes the following makers: Serum Beta-2 microglobulin, serum albumin, t(4;14), 17p13 and 1q21 by FISH. Using this combination, high-risk patients will survive less than 2 years despite novel agents, and low-risk patients can survive for more than 10 years (Table 3).

  2. 2)

    This risk stratification system should be adopted into clinical practice and used as the standard for comparison in all future studies looking at prognostic markers and also in clinical trials.

  3. 3)

    There is no evidence so far to suggest altering treatment based on risk groups with the exception that prolonged proteasome inhibitor-based treatment should be given to patients with t(4;14) and possibly 17p13 deletion.54

Recommendations for clinical trials

  1. 1)

    Clinical trials testing different treatment strategies for specific risk group should be considered especially for high-risk myeloma patients who have short survival with current treatment.

  2. 2)

    Even if risk stratification is not incorporated into the trial design, factors needed for risk stratification, including samples for GEP, should be prospectively collected so that post hoc analysis of impact of treatment on the outcome of risk groups can be assessed. In the context of GEP, this would be important for the validation of GEP models that is required for eventual implementation.

  3. 3)

    In the design of these trials, consideration should be given to adequate sample size to adequately assess the impact of risk factors and to provide as much molecular testing as possible (including GEP when feasible) to gather the best correlative data.

References

  1. 1

    Rajkumar SV . Treatment of multiple myeloma. Nat Rev Clin Oncol 2011; 8: 479–491.

  2. 2

    Kumar SK, Rajkumar SV, Dispenzieri A, Lacy MQ, Hayman SR, Buadi FK et al. Improved survival in multiple myeloma and the impact of novel therapies. Blood 2008; 111: 2516–2520.

  3. 3

    Stewart AK, Richardson PG, San-Miguel JF . How I treat multiple myeloma in younger patients. Blood 2009; 114: 5436–5443.

  4. 4

    Keats JJ, Fonseca R, Chesi M, Schop R, Baker A, Chng WJ et al. Promiscuous mutations activate the noncanonical NF-kappaB pathway in multiple myeloma. Cancer Cell 2007; 12: 131–144.

  5. 5

    Zhu YX, Braggio E, Shi CX, Bruins LA, Schmidt JE, Van Wier S et al. Cereblon expression is required for the antimyeloma activity of lenalidomide and pomalidomide. Blood 2011; 118: 4771–4779.

  6. 6

    Palumbo A, Bringhen S, Ludwig H, Dimopoulos MA, Blade J, Mateos MV et al. Personalized therapy in multiple myeloma according to patient age and vulnerability: a report of the European Myeloma Network (EMN). Blood 2011; 118: 4519–4529.

  7. 7

    Ludwig H, Bolejack V, Crowley J, Blade J, Miguel JS, Kyle RA et al. Survival and years of life lost in different age cohorts of patients with multiple myeloma. J Clin Oncol 2010; 28: 1599–1605.

  8. 8

    Avet-Loiseau H, Attal M, Campion L, Caillot D, Hulin C, Marit G et al. Long-term analysis of the IFM 99 trials for myeloma: cytogenetic abnormalities [t(4;14), del(17p), 1q gains] play a major role in defining long-term survival. J Clin Oncol 2012; 30: 1949–1952.

  9. 9

    Greipp PR, Lust JA, O'Fallon WM, Katzmann JA, Witzig TE, Kyle RA . Plasma cell labeling index and beta 2-microglobulin predict survival independent of thymidine kinase and C-reactive protein in multiple myeloma. Blood 1993; 81: 3382–3387.

  10. 10

    Nowakowski GS, Witzig TE, Dingli D, Tracz MJ, Gertz MA, Lacy MQ et al. Circulating plasma cells detected by flow cytometry as a predictor of survival in 302 patients with newly diagnosed multiple myeloma. Blood 2005; 106: 2276–2279.

  11. 11

    Greipp PR, Leong T, Bennett JM, Gaillard JP, Klein B, Stewart JA et al. Plasmablastic morphology—an independent prognostic factor with clinical and laboratory correlates: Eastern Cooperative Oncology Group (ECOG) myeloma trial E9486 report by the ECOG Myeloma Laboratory Group. Blood 1998; 91: 2501–2507.

  12. 12

    Munshi NC, Anderson KC, Bergsagel PL, Shaughnessy J, Palumbo A, Durie B et al. Consensus recommendations for risk stratification in multiple myeloma: report of the International Myeloma Workshop Consensus Panel 2. Blood 2011; 117: 4696–4700.

  13. 13

    Fonseca R, Bergsagel PL, Drach J, Shaughnessy J, Gutierrez N, Stewart AK et al. International Myeloma Working Group molecular classification of multiple myeloma: spotlight review. Leukemia 2009; 23: 2210–2221.

  14. 14

    Avet-Loiseau H . Role of genetics in prognostication in myeloma. Best Pract Res Clin Haematol 2007; 20: 625–635.

  15. 15

    Fonseca R, Blood E, Rue M, Harrington D, Oken MM, Kyle RA et al. Clinical and biologic implications of recurrent genomic aberrations in myeloma. Blood 2003; 101: 4569–4575.

  16. 16

    Avet-Loiseau H, Malard F, Campion L, Magrangeas F, Sebban C, Lioure B et al. Translocation t(14;16) and multiple myeloma: is it really an independent prognostic factor? Blood 2011; 117: 2009–2011.

  17. 17

    Boyd KD, Ross FM, Chiecchio L, Dagrada GP, Konn ZJ, Tapper WJ et al. 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: 349–355.

  18. 18

    Hanamura I, Stewart JP, Huang Y, Zhan F, Santra M, Sawyer JR et al. Frequent gain of chromosome band 1q21 in plasma-cell dyscrasias detected by fluorescence in situ hybridization: incidence increases from MGUS to relapsed myeloma and is related to prognosis and disease progression following tandem stem-cell transplantation. Blood 2006; 108: 1724–1732.

  19. 19

    Fonseca R, Van Wier SA, Chng WJ, Ketterling R, Lacy MQ, Dispenzieri A et al. Prognostic value of chromosome 1q21 gain by fluorescent in situ hybridization and increase CKS1B expression in myeloma. Leukemia 2006; 20: 2034–2040.

  20. 20

    Avet-Loiseau H, Attal M, Moreau P, Charbonnel C, Garban F, Hulin C et al. Genetic abnormalities and survival in multiple myeloma: the experience of the Intergroupe Francophone du Myelome. Blood 2007; 109: 3489–3495.

  21. 21

    Hebraud B, Leleu X, Lauwers-Cances V, Roussel M, Caillot D, Marit G et al. Deletion of the 1p32 region is a major independent prognostic factor in young patients with myeloma: the IFM experience on 1195 patients. Leukemia 2013; e-pub ahead of print 29 July 2013; doi: 10.1038/leu.2013.225.

  22. 22

    Chng WJ, Gertz MA, Chung TH, Van Wier S, Keats JJ, Baker A et al. Correlation between array-comparative genomic hybridization-defined genomic gains and losses and survival: identification of 1p31-32 deletion as a prognostic factor in myeloma. Leukemia 2010; 24: 833–842.

  23. 23

    Boyd KD, Ross FM, Walker BA, Wardell CP, Tapper WJ, Chiecchio L et al. Mapping of chromosome 1p deletions in myeloma identifies FAM46C at 1p12 and CDKN2C at 1p32.3 as being genes in regions associated with adverse survival. Clin Cancer Res 2011; 17: 7776–7784.

  24. 24

    Moreau P, Attal M, Garban F, Hulin C, Facon T, Marit G et al. Heterogeneity of t(4;14) in multiple myeloma. Long-term follow-up of 100 cases treated with tandem transplantation in IFM99 trials. Leukemia 2007; 21: 2020–2024.

  25. 25

    Kumar S, Fonseca R, Ketterling RP, Dispenzieri A, Lacy MQ, Gertz MA et al. Trisomies in multiple myeloma: impact on survival in patients with high-risk cytogenetics. Blood 2012; 119: 2100–2105.

  26. 26

    Shi L, Campbell G, Jones WD, Campagne F, Wen Z, Walker SJ et al. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat Biotechnol 2010; 28: 827–838.

  27. 27

    Meissner T, Seckinger A, Reme T, Hielscher T, Mohler T, Neben K et al. Gene expression profiling in multiple myeloma—reporting of entities, risk, and targets in clinical routine. Clin Cancer Res 2011; 17: 7240–7247.

  28. 28

    Durie BG, Salmon SE . A clinical staging system for multiple myeloma. Correlation of measured myeloma cell mass with presenting clinical features, response to treatment, and survival. Cancer 1975; 36: 842–854.

  29. 29

    Greipp PR, San Miguel J, Durie BG, Crowley JJ, Barlogie B, Blade J et al. International staging system for multiple myeloma. J Clin Oncol 2005; 23: 3412–3420.

  30. 30

    Waheed S, Mitchell A, Usmani S, Epstein J, Yaccoby S, Nair B et al. Standard and novel imaging methods for multiple myeloma: correlates with prognostic laboratory variables including gene expression profiling data. Haematologica 2013; 98: 71–78.

  31. 31

    Avet-Loiseau H, Durie BG, Cavo M, Attal M, Gutierrez N, Haessler J et al. Combining fluorescent in situ hybridization data with ISS staging improves risk assessment in myeloma: an International Myeloma Working Group collaborative project. Leukemia 2013; 27: 711–717.

  32. 32

    Neben K, Jauch A, Bertsch U, Heiss C, Hielscher T, Seckinger A et al. Combining information regarding chromosomal aberrations t(4;14) and del(17p13) with the International Staging System classification allows stratification of myeloma patients undergoing autologous stem cell transplantation. Haematologica 2010; 95: 1150–1157.

  33. 33

    Lahuerta JJ, Mateos MV, Martinez-Lopez J, Rosinol L, Sureda A, de la Rubia J et al. Influence of pre- and post-transplantation responses on outcome of patients with multiple myeloma: sequential improvement of response and achievement of complete response are associated with longer survival. J Clin Oncol 2008; 26: 5775–5782.

  34. 34

    Haessler J, Shaughnessy JD Jr., Zhan F, Crowley J, Epstein J, van Rhee F et al. Benefit of complete response in multiple myeloma limited to high-risk subgroup identified by gene expression profiling. Clin Cancer Res 2007; 13: 7073–7079.

  35. 35

    Hoering A, Crowley J, Shaughnessy JD Jr, Hollmig K, Alsayed Y, Szymonifka J et al. Complete remission in multiple myeloma examined as time-dependent variable in terms of both onset and duration in total therapy protocols. Blood 2009; 114: 1299–1305.

  36. 36

    Barlogie B, Anaissie E, Haessler J, van Rhee F, Pineda-Roman M, Hollmig K et al. Complete remission sustained 3 years from treatment initiation is a powerful surrogate for extended survival in multiple myeloma. Cancer 2008; 113: 355–359.

  37. 37

    Harousseau JL, Avet-Loiseau H, Attal M, Charbonnel C, Garban F, Hulin C et al. Achievement of at least very good partial response is a simple and robust prognostic factor in patients with multiple myeloma treated with high-dose therapy: long-term analysis of the IFM 99-02 and 99-04 Trials. J Clin Oncol 2009; 27: 5720–5726.

  38. 38

    Moreau P, Attal M, Pegourie B, Planche L, Hulin C, Facon T et al. Achievement of VGPR to induction therapy is an important prognostic factor for longer PFS in the IFM 2005-01 trial. Blood 2011; 117: 3041–3044.

  39. 39

    Gay F, Larocca A, Wijermans P, Cavallo F, Rossi D, Schaafsma R et al. Complete response correlates with long-term progression-free and overall survival in elderly myeloma treated with novel agents: analysis of 1175 patients. Blood 2011; 117: 3025–3031.

  40. 40

    Paiva B, Vidriales MB, Cervero J, Mateo G, Perez JJ, Montalban MA et al. Multiparameter flow cytometric remission is the most relevant prognostic factor for multiple myeloma patients who undergo autologous stem cell transplantation. Blood 2008; 112: 4017–4023.

  41. 41

    Paiva B, Gutierrez NC, Rosinol L, Vidriales MB, Montalban MA, Martinez-Lopez J et al. High-risk cytogenetics and persistent minimal residual disease by multiparameter flow cytometry predict unsustained complete response after autologous stem cell transplantation in multiple myeloma. Blood 2012; 119: 687–691.

  42. 42

    Ladetto M, Pagliano G, Ferrero S, Cavallo F, Drandi D, Santo L et al. Major tumor shrinking and persistent molecular remissions after consolidation with bortezomib, thalidomide, and dexamethasone in patients with autografted myeloma. J Clin Oncol 2010; 28: 2077–2084.

  43. 43

    Kroger N, Badbaran A, Zabelina T, Ayuk F, Wolschke C, Alchalby H et al. Impact of high-risk cytogenetics and achievement of molecular remission on long-term freedom from disease after autologous-allogeneic tandem transplantation in patients with multiple myeloma. Biol Blood Marrow Transplant 2013; 19: 398–404.

  44. 44

    Hillengass J, Ayyaz S, Kilk K, Weber MA, Hielscher T, Shah R et al. Changes in magnetic resonance imaging before and after autologous stem cell transplantation correlate with response and survival in multiple myeloma. Haematologica 2012; 97: 1757–1760.

  45. 45

    Zamagni E, Patriarca F, Nanni C, Zannetti B, Englaro E, Pezzi A et al. Prognostic relevance of 18-F FDG PET/CT in newly diagnosed multiple myeloma patients treated with up-front autologous transplantation. Blood 2011; 118: 5989–5995.

  46. 46

    Kumar S, Mahmood ST, Lacy MQ, Dispenzieri A, Hayman SR, Buadi FK et al. Impact of early relapse after auto-SCT for multiple myeloma. Bone Marrow Transplant 2008; 42: 413–420.

  47. 47

    San Miguel JF, Schlag R, Khuageva NK, Dimopoulos MA, Shpilberg O, Kropff M et al. Bortezomib plus melphalan and prednisone for initial treatment of multiple myeloma. N Engl J Med 2008; 359: 906–917.

  48. 48

    Cavo M, Tacchetti P, Patriarca F, Petrucci MT, Pantani L, Galli M et al. Bortezomib with thalidomide plus dexamethasone compared with thalidomide plus dexamethasone as induction therapy before, and consolidation therapy after, double autologous stem-cell transplantation in newly diagnosed multiple myeloma: a randomised phase 3 study. Lancet 2010; 376: 2075–2085.

  49. 49

    Sonneveld P, Schmidt-Wolf IG, van der Holt B, El Jarari L, Bertsch U, Salwender H et al. Bortezomib induction and maintenance treatment in patients with newly diagnosed multiple myeloma: results of the randomized phase III HOVON-65/ GMMG-HD4 Trial. J Clin Oncol 2013; 30: 2946–2955.

  50. 50

    Kumar SK, Mikhael JR, Buadi FK, Dingli D, Dispenzieri A, Fonseca R et al. Management of newly diagnosed symptomatic multiple myeloma: updated Mayo Stratification of Myeloma and Risk-Adapted Therapy (mSMART) consensus guidelines. Mayo Clin Proc 2009; 84: 1095–1110.

  51. 51

    Cavo M, Sonneveld P, Moreau P, Blade J, Goldschmidt H, San Miguel J et al. Impact of Bortezomib Incorporated Into Autotransplantation on outcomes of myeloma patients with high-risk cytogenetics: an integrated analysis of 1894 patients enrolled in four European Phase 3 Studies. Blood 2012; 120, Abstract 749.

  52. 52

    Avet-Loiseau H, Leleu X, Roussel M, Moreau P, Guerin-Charbonnel C, Caillot D 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: 4630–4634.

  53. 53

    Shaughnessy JD, Zhou Y, Haessler J, van Rhee F, Anaissie E, Nair B et al. TP53 deletion is not an adverse feature in multiple myeloma treated with total therapy 3. Br J Haematol 2009; 147: 347–351.

  54. 54

    Neben K, Lokhorst HM, Jauch A, Bertsch U, Hielscher T, van der Holt B et al. Administration of bortezomib before and after autologous stem cell transplantation improves outcome in multiple myeloma patients with deletion 17p. Blood 2012; 119: 940–948.

  55. 55

    Morgan GJ, Gregory WM, Davies FE, Bell SE, Szubert AJ, Brown JM et al. The role of maintenance thalidomide therapy in multiple myeloma: MRC Myeloma IX results and meta-analysis. Blood 2012; 119: 7–15.

  56. 56

    Rajkumar SV, Gahrton G, Bergsagel PL . Approach to the treatment of multiple myeloma: a clash of philosophies. Blood 2011; 118: 3205–3211.

  57. 57

    Chapman MA, Lawrence MS, Keats JJ, Cibulskis K, Sougnez C, Schinzel AC et al. Initial genome sequencing and analysis of multiple myeloma. Nature 2011; 471: 467–472.

  58. 58

    Egan JB, Shi CX, Tembe W, Christoforides A, Kurdoglu A, Sinari S 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: 1060–1066.

  59. 59

    Keats JJ, Chesi M, Egan JB, Garbitt VM, Palmer SE, Braggio E et al. Clonal competition with alternating dominance in multiple myeloma. Blood 2012; 120: 1067–1076.

  60. 60

    Shaughnessy JD Jr, Zhan F, Burington BE, Huang Y, Colla S, Hanamura I et al. A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1. Blood 2006; 109: 2276–2284.

  61. 61

    Decaux O, Lode L, Magrangeas F, Charbonnel C, Gouraud W, Jezequel P et al. Prediction of survival in multiple myeloma based on gene expression profiles reveals cell cycle and chromosomal instability signatures in high-risk patients and hyperdiploid signatures in low-risk patients: a study of the Intergroupe Francophone du Myelome. J Clin Oncol 2008; 26: 4798–4805.

  62. 62

    Chng WJ, Braggio E, Mulligan G, Bryant B, Remstein E, Valdez R et al. The centrosome index is a powerful prognostic marker in myeloma and identifies a cohort of patients that might benefit from aurora kinase inhibition. Blood 2008; 111: 1603–1609.

  63. 63

    Dickens NJ, Walker BA, Leone PE, Johnson DC, Brito JL, Zeisig A et al. Homozygous deletion mapping in myeloma samples identifies genes and an expression signature relevant to pathogenesis and outcome. Clin Cancer Res 2010; 16: 1856–1864.

  64. 64

    Moreaux J, Klein B, Bataille R, Descamps G, Maiga S, Hose D et al. A high-risk signature for patients with multiple myeloma established from the molecular classification of human myeloma cell lines. Haematologica 2011; 96: 574–582.

  65. 65

    Hose D, Reme T, Hielscher T, Moreaux J, Messner T, Seckinger A et al. Proliferation is a central independent prognostic factor and target for personalized and risk-adapted treatment in multiple myeloma. Haematologica 2011; 96: 87–95.

  66. 66

    Kuiper R, Broyl A, de Knegt Y, van Vliet MH, van Beers EH, van der Holt B et al. A gene expression signature for high-risk multiple myeloma. Leukemia 2012; 26: 2406–2413.

  67. 67

    Chung TH, Mulligan G, Fonseca R, Chng WJ . A novel measure of chromosome instability can account for prognostic difference in multiple myeloma. PLoS One 2013; 8: e66361.

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Appendix

Appendix

International Myeloma Working Group

1. Niels Abildgaard, Syddansk Universitet, Odense, Denmark

2. Rafat Abonour, Indiana University School of Medicine, Indianapolis, Indiana, USA

3. Ray Alexanian, MD Anderson, Houston, Texas, USA

4. Melissa Alsina, H Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA

5. Kenneth C Anderson, DFCI, Boston, Massachusetts, USA

6. Michel Attal, Purpan Hospital, Toulouse, France

7. Hervé Avet-Loiseau, Institute de Biologie, Nantes, France

8. Ashraf Badros, University of Maryland, Baltimore, Maryland, USA

9. Dalsu Baris, National Cancer Institute, Bethesda, Maryland, USA

10. Bart Barlogie, M.I.R.T. UAMS Little Rock, Arkanas, USA

11. Régis Bataille, Institute de Biologie, Nantes, France

12. Meral Beksaç, Ankara University, Ankara, Turkey

13. Andrew Belch, Cross Cancer Institute, Alberta, Canada

14. Dina Ben-Yehuda, Hadassah University Hospital, Hadassah, Israel

15. Bill Bensinger, Fred Hutchinson Cancer Center, Seattle, Washington, USA

16. P Leif Bergsagel, Mayo Clinic Scottsdale, Scottsdale, Arizona, USA

17. Jenny Bird, Bristol Haematology and Oncology Center, Bristol, UK

18. Joan Bladé, Hospital Clinica, Barcelona, Spain

19. Mario Boccadoro, University of Torino, Torino, Italy

20. Jo Caers, Centre Hospitalier Universitaire de Liège, Liège, Belgium

21. Michele Cavo, Universita di Bologna, Bologna, Italy

22. Asher Chanan-Khan, Mayo Clinic, Jacksonville, Florida, USA

23. Wen Ming Chen, MM Research Center of Beijing, Beijing, China

24. Marta Chesi, Mayo Clinic Scottsdale, Scottsdale, Arizona, USA

25. Tony Child, Leeds General Hospital, Leeds, United Kingdom

26. James Chim, Department of Medicine, Queen Mary Hospital, Hong Kong

27. Wee-Joo Chng, National University Health System, Singapore

28. Ray Comenzo, Tufts Medical School, Boston, Massachusetts, USA

29. John Crowley, Cancer Research and Biostatistics, Seattle, Washington, USA

30. William Dalton, H Lee Moffitt, Tampa, Florida, USA

31. Faith Davies, Royal Marsden Hospital, London, England

32. Javier de la Rubia, Hospital Universitario La Fe, Valencia, Spain

33. Cármino de Souza, Univeridade de Campinas, Caminas, Brazil

34. Michel Delforge, University Hospital Gasthuisberg, Leuven, Belgium

35. Meletios Dimopoulos, University of Athens School of Medicine, Athens, Greece

36. Angela Dispenzieri, Mayo Clinic, Rochester, Minnesota, USA

37. Johannes Drach, University of Vienna, Vienna, Austria

38. Matthew Drake, Mayo Clinic Rochester, Rochester, Minnesota, USA

39. Brian G.M. Durie, Cedars-Sinai Samuel Oschin Cancer Center, Los Angeles, California, USA

40. Hermann Einsele, Universitätsklinik Würzburg, Würzburg, Germany

41. Theirry Facon, Centre Hospitalier Regional Universitaire de Lille, Lille, France

42. Dorotea Fantl, Socieded Argentinade Hematolgia, Buenos Aires, Argentina

43. Jean-Paul Fermand, Hopitaux de Paris, Paris, France

44. Carlos Fernández de Larrea, Hospital Clínic de Barcelona, Barcelona, Spain

45. Rafael Fonseca, Mayo Clinic Arizona, Scottsdale, Arizona, USA

46. Gösta Gahrton, Karolinska Institute for Medicine, Huddinge, Sweden

47. Ramón García-Sanz, University Hospital of Salamanca, Salamanca, Spain

48. Christina Gasparetto, Duke University Medical Center, Durham, North Carolina, USA

49. Morie Gertz, Mayo Clinic, Rochester, Minnesota, USA

50. Irene Ghobrial, Dana-Farber Cancer Institute, Boston, MA, USA

51. John Gibson, Royal Prince Alfred Hospital, Sydney, Australia

52. Peter Gimsing, University of Copenhagen, Copenhagen, Denmark

53. Sergio Giralt, Memorial Sloan-Kettering Cancer Center, New York, NY, USA

54. Hartmut Goldschmidt, University Hospital Heidelberg, Heidelberg, Germany

55. Philip Greipp, Mayo Clinic, Rochester, Minnesota, USA

56. Roman Hajek, School of Medicine, University of Ostrava and University Hospital Ostrava, Ostrava, Czech Republic

57. Izhar Hardan, Tel Aviv University, Tel Aviv, Israel

58. Parameswaran Hari, Medical College of Wisconsin, Milwaukee, Wisconsin, USA

59. Hiroyuki Hata, Kumamoto University Hospital, Kumamoto, Japan

60. Yutaka Hattori, Keio University School of Medicine, Tokyo, Japan

61. Tom Heffner, Emory University, Atlanta, Georgia, USA

62. Joy Ho, Royal Prince Alfred Hospital, Sydney, Australia

63. Antje Hoering, Cancer Research and Biostatistics, Seattle, WA, USA

64. Jian Hou, Shanghai Chang Zheng Hospital, Shanghai, China

65. Vania Hungria, Clinica San Germano, Sao Paolo, Brazil

66. Shinsuke Ida, Nagoya City University Medical School, Nagoya, Japan

67. Peter Jacobs, Constantiaberg Medi-Clinic, Plumstead, South Africa

68. Sundar Jagannath, Mt. Sinai Cancer Institute, New York, New York, USA

69. Hans Johnsen, Aalborg Hospital Science and Innovation Center, Aalborg, Denmark

70. Douglas Joshua, Royal Prince Alfred Hospital, Sydney, Australia

71. Artur Jurczyszyn, Department of Hematology University Hospital, Cracow, Poland

72. Jonathan Kaufman, Emory Clinic, Atlanta, Georgia, USA

73. Michio Kawano, Yamaguchi University, Ube, Japan

74. Eva Kovacs, Cancer Immunology Research-Life, Birsfelden, Switzerland

75. Amrita Krishnan, City of Hope, Duarte, California, USA

76. Sigurdur Kristinsson, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden

77. Nicolaus Kröger, University Hospital Hamburg, Hamburg, Germany

78. Shaji Kumar, Department of Hematology, Mayo Clinic, Minnesota, USA 24

79. Robert A. Kyle, Department of Laboratory Med. and Pathology, Mayo Clinic, Minnesota, USA

80. Chara Kyriacou, Northwick Park Hospital, London, United Kingdom

81. Martha Lacy, Mayo Clinic Rochester, Rochester, Minnesota, USA

82. Juan José Lahuerta, Grupo Español di Mieloma, Hospital Universitario 12 de Octubre, Madrid, Spain

83. Ola Landgren, National Cancer Institute, Bethesda, Maryland, USA

84. Jacob Laubach, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

85. Garderet Laurent, Hôpital Saint Antoine, Paris, France

86. Fernando Leal da Costa, Instituto Portugues De Oncologia, Lisbon, Portugal

87. Jae Hoon Lee, Gachon University Gil Hospital, Incheon, Korea

88. Merav Leiba, Sheba Medical Center, Tel Hashomer, Israel

89. Xavier LeLeu, Hospital Huriez, CHRU Lille, France

90. Suzanne Lentzsch, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

91. Henk Lokhorst, University Medical CenterUtrecht, Utrecht, The Netherlands

92. Sagar Lonial, Emory University Medical School, Atlanta, Georgia, USA

93. Heinz Ludwig, Wilhelminenspital Der Stat Wien, Vienna, Austria

94. Anuj Mahindra, Dana-Farber Cancer Institute, Massachusetts General Hospital, Boston, MA, USA

95. Angelo Maiolino, Rua fonte da Saudade, Rio de Janeiro, Brazil

96. María Mateos, University of Salamanca, Salamanca, Spain

97. Amitabha Mazumder, NYU Comprehensive Cancer Center, New York, USA

98. Philip McCarthy, Roswell Park Cancer Center, Buffalo, New York, USA

99. Jayesh Mehta, Northwestern University, Chicago, Illinois, USA

100. Ulf-Henrik Mellqvist, Sahlgrenska University Hospital, Gothenburg, Sweden

101. GiamPaolo Merlini, University of Pavia, Pavia, Italy

102. Joseph Mikhael, Mayo Clinic Arizona, Scottsdale, Arizona, USA

103. Philippe Moreau, University Hospital, Nantes, France

104. Gareth Morgan, Royal Marsden Hospital, London, England

105. Nikhil Munshi, Diane Farber Cancer Institute, Boston, Massachusetts, USA

106. Hareth Nahi, Karolinska University Hospital, Stockholm, Sweden

107. Ruben Niesvizky, Weill Cornell Medical College, New York, USA

108. Amara Nouel, Hospital Rutz y Paez, Bolivar, Venezuela

109. Yana Novis, Hospital Sírio Libanês, Bela Vista, Brazil

110. Enrique Ocio, Salamanca, Spain

111. Robert Orlowski, MD Anderson Cancer Center, Houston, Texas, USA

112. Antonio Palumbo, Cathedra Ematologia, Torino, Italy

113. Santiago Pavlovsky, Fundaleu, Buenos Aires, Argentina

114. Linda Pilarski, University of Alberta, Alberta, Canada

115. Raymond Powles, Leukemia & Myeloma, Wimbledon, England

116. Noopur Raje, Massachusetts General Hospital, Boston, Massachusetts, USA 25

117. S. Vincent Rajkumar, Mayo Clinic, Rochester, Minnesota, USA

118. Donna Reece, Princess Margaret Hospital, Toronto, Canada

119. Tony Reiman, Saint John Regional Hospital, Saint John, New Brunswick, Canada

120. Paul G. Richardson, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

121. Angelina Rodríguez Morales, Bonco Metro Politano de Sangre, Caracas, Venezuela

122. Kenneth R. Romeril, Wellington Hospital, Wellington, New Zealand

123. David Roodman, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania USA

124. Laura Rosiñol, Hospital Clinic, Barcelona, Spain

125. Stephen Russell, Mayo Clinic, Rochester, Minnesota, USA

126. Jesús San Miguel, University of Salamanca, Salamanca, Spain

127. Rik Schots, Universitair Ziekenhuis Brussel, Brussels, Belgium

128. Sabina Sevcikova, Masaryk University, Brno, Czech Republic

129. Orhan Sezer, Universität Hamburg, Hamburg, Germany

130. Jatin J. Shah, MD Anderson Cancer Institute, Houston, Texas, USA

131. John Shaughnessy, M.I.R.T. UAMS, Little Rock, Arkansas, USA

132. Kazuyuki Shimizu, Nagoya City Midori General Hospital, Nagoya, Japan

133. Chaim Shustik, McGill University, Montreal, Canada

134. David Siegel, Hackensack, Cancer Center, Hackensack, New Jersey, USA

135. Seema Singhal, Northwestern University, Chicago, Illinois, USA

136. Pieter Sonneveld, Erasmus MC, Rotterdam, The Netherlands

137. Andrew Spencer, The Alfred Hospital, Melbourne, Australia

138. Edward Stadtmauer, University of Pennsylvania, Philadelphia, Pennsylvania, USA

139. Keith Stewart, Mayo Clinic Arizona, Scottsdale, Arizona, USA

140. Evangelos Terpos, University of Athens School of Medicine, Athens, Greece

141. Patrizia Tosi, Italian Cooperative Group, Istituto di Ematologia Seragnoli, Bologna, Italy

142. Guido Tricot, Huntsman Cancer Institute, Salt Lake City, Utah, USA

143. Ingemar Turesson, SKANE University Hospital, Malmo, Sweden

144. Saad Usmani, M.I.R.T UAMS, Little Rock, Arkansas, USA

145. Ben Van Camp, Vrije Universiteit Brussels, Brussels, Belgium

146. Brian Van Ness, University of Minnesota, Minneapolis, Minnesota, USA

147. Ivan Van Riet, Brussels Vrija University, Brussels, Belgium

148. Isabelle Vande Broek, Vrije Universiteit Brussels, Brussels, Belgium

149. Karin Vanderkerken, Vrije University Brussels VUB, Brussels, Belgium

150. Robert Vescio, Cedars-Sinai Cancer Center, Los Angeles, California, USA

151. David Vesole, Hackensack Cancer Center, Hackensack, New Jersey, USA

152. Peter Voorhees, University of North Carolina, Chapel Hill, North Carolina, USA

153. Anders Waage, University Hospital, Trondheim, Norway NSMG

154. Michael Wang, MD Anderson, Houston, Texas, USA 26

155. Donna Weber, MD Anderson, Houston, Texas, USA

156. Jan Westin, Sahlgrenska University Hospital, Gothenburg, Sweden

157. Keith Wheatley, University of Birmingham, Birmingham, United Kingdom

158. Elena Zamagni, University of Bologna, Bologna, Italy

159. Jeffrey Zonder, Karmanos Cancer Institute, Detroit, Michigan, USA

160. Sonja Zweegman, VU University Medical Center, Amsterdam, The Netherlands

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Chng, W., Dispenzieri, A., Chim, C. et al. IMWG consensus on risk stratification in multiple myeloma. Leukemia 28, 269–277 (2014). https://doi.org/10.1038/leu.2013.247

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Keywords

  • prognosis
  • treatment
  • biomakers

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