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.
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:
What defines high-risk and low-risk patients?
What should we use to define the risk groups?
Can risk-adapted therapy be recommended at this time?
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.
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-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.
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.
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.
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?
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.
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.
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.
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
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).
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.
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
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.
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.
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.
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The authors declare no conflict of interest.
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, CS. 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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