Myeloma

Prognostic factors for hyperdiploid-myeloma: effects of chromosome 13 deletions and IgH translocations

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Abstract

Chromosomal hyperdiploidy is the defining genetic signature in 40–50% of myeloma (MM) patients. We characterize hyperdiploid-MM (H-MM) in terms of its clinical and prognostic features in a cohort of 220 H-MM patients entered into clinical trials. Hyperdiploid-myeloma is associated with male sex, kappa immunoglobulin subtype, symptomatic bone disease and better survival compared to nonhyperdiploid-MM (median overall survival 48 vs 35 months, log-rank P=0.023), despite similar response to treatment. Among 108 H-MM cases with FISH studies for common genetic abnormalities, survival is negatively affected by the existence of immunoglobulin heavy chain (IgH) translocations, especially those involving unknown partners, while the presence of chromosome 13 deletion by FISH did not significantly affect survival (median overall survival 50 vs 47 months, log-rank P=0.47). Hyperdiploid-myeloma is therefore a unique genetic subtype of MM associated with improved outcome with distinct clinical features. The existence of IgH translocations but not chromosome 13 deletion by FISH negatively impacts survival and may allow further risk stratification of this population of MM patients.

Introduction

The genetics of multiple myeloma (MM) have been gradually unraveled in the past decade, with the initial observation of frequent translocations into the immunoglobulin heavy chain switch region (IgH),1 to the identification and cloning of several oncogenes involved in these translocations,2, 3, 4, 5 to the more recent observation that most patients with these primary translocations have nonhyperdiploid chromosome number (nonhyperdiploid-MM, NH-MM) while the other group of patients are now recognized to have a lack of IgH translocations and have a hyperdiploid chromosome number characterized by trisomies of chromosomes 3, 5, 7, 9, 11, 15, 19 and 21 (hyperdiploid-MM, H-MM).6, 7, 8, 9 The recent confirmation of the hyperdiploid and nonhyperdiploid segregation in MGUS provides compelling evidence of early divergent pathways for these two broad genetic categories of MM.10 The validity of this genetic model has been substantiated by several studies showing distinct prognosis;7, 11, 12, 13, 14, 15, 16, 17, 18 and molecular signatures between the hyperdiploid and nonhyperdiploid genetic subtypes.19

Although much has been published on MM with primary translocations and NH-MM, there is as yet no published systematic evaluation of the clinical and biologic characteristic of H-MM, which constitute about 40–50% of all MM. Therefore, we conducted a retrospective analysis of 366 MM patients entered into Eastern Cooperative Oncology Group (ECOG) trials with the goal of characterizing H-MM in terms of its associated clinical, prognostic and biologic features. In particular, we were interested to know if the prognosis of patients with H-MM is modulated by the presence of other known important genetic prognostic factors such as chromosome 13 deletion (Δ13), 17p/p53 deletion or IgH translocations.

Methods

Patient characteristics

Patients enrolled in the ECOG E9486 clinical trial and its associated correlative laboratory study E9487 (N=561) were newly diagnosed MM and have been described in detail elsewhere.20 They were randomized to receive variations of melphalan-based conventional chemotherapy (VBMCP). The median survival of the whole group was 42 months (95% confidence intervals (CI) 39–46 months), which is comparable to most other chemotherapy trials. These patients have extensive follow-up information with median follow-up of survivors being 12 years that results in negligible censoring.

Routine demographic, clinical and laboratory results were obtained at time of trial entry. Plasma cell labeling index (PCLI) was performed using a slide-based method as previously described.21 Plasma cell morphology was also assessed as previously described.22 Patients were also assigned to the International Staging System (ISS) stages for MM23 for comparison. In addition, radiological evidence (lytic lesions), symptoms and serum markers of bone disease including osteocalcin (OC), carboxy-terminal propeptide of type I collagen (PICP), bone alkaline phosphatase (BAP), carboxy-terminal telopeptide of type I collagen (ICTP) and tartrate-resistant acid phosphatase (TRAP), measured as previously described,24 were compared. A total of 366 patients with ploidy status were included in our analysis of clinical, biologic and prognostic features of H-MM in comparison to the nonhyperdiploid variant (Table 1).

Table 1 Baseline clinical and laboratory descriptive features of hyperdiploid myeloma and nonhyperdiploid myeloma patients

A subset of 108 H-MM patients with all relevant FISH studies available were used to assess the prognostic importance of other common cytogenetic abnormalities in H-MM. Conventional karyotype analysis was not part of the study protocol and is thus not available for analysis.

Bone marrow samples

Under Mayo Clinic institutional review board approval and informed consent, bone marrow research samples were obtained, and cytospin slides were stored for future use at −70°C. Bone marrow aspirate samples were enriched for mononuclear cells using the Ficol method.

cIg-FISH

To precisely score the genetic markers, we combined interphase FISH with immune-fluorescent detection of the cytoplasmic-immunoglobulin light-chain as described previously (cIg-FISH).25 We used a previously reported set of probes to detect Δ13, 17p/p53 deletion, t(11;14)(q31;q32), t(4;14)(p16.3;q32); t(14;16)(q32;q23) and IgH translocation (VH/CH probes using a ‘break-apart’ strategy). Hybridization, validation and scoring procedures have been reported in detail in our previous studies.8, 12, 16, 17, 26, 27

DNA content analysis

The flow cytometry determination of total DNA content was analyzed by dual channel flow cytometry using propidium iodide to measure the DNA content and kappa and lambda light chain antisera to identify the clonal cells as previously described.28 The following DNA index criteria were used for ploidy categorization: 1.06–1.74 hyperdiploid, whereas less than 0.95 (hypodiploid), 0.95–1.05 (pseudodiploid) and more than 1.74 (tetraploid) all comprise the nonhyperdiploid group.

Statistical analysis

To characterize the patients in the study, we used descriptive statistics. For continuous variables the Wilcoxon rank sum test was used to test for differences between H-MM and NH-MM. Fisher exact test was used to test differences among levels of categorical variables between H-MM and NH-MM. The distributions for overall survival (OS) and progression-free survival (PFS) were estimated using the method of Kaplan and Meier. The log-rank test was used to test for differences in survival between groups. Median OS and PFS times were obtained from the estimated survival curves, and 95% CI for these estimated times were based on the sign test. Cox proportional hazards models were used to assess the association of several prognostic factors with OS and PFS. Variables were selected using a stepwise selection from a list of candidate variables. A P-value of less than 0.05 is considered significant.

Results

Clinical features of patients

Of the 366 patients with ploidy determined by flow cytometry, 220 (60%) were hyperdiploid and 146 (40%) were nonhyperdiploid. The patient characteristics were similar between the H-MM and NH-MM group except for significantly more male patients (P=0.016) and kappa light chain subtypes (P<0.001) among the hyperdiploid patients. There also appeared to be a bias towards IgG heavy chain subtype and the presence of an M-spike although these did not reach statistical significance. Laboratory investigations including established prognostic factors such as albumin,29 β2-microglobulin (B2M),30, 31 C-reactive protein (CRP)31 and plasmablastic morphology22 were also similar between the two groups whether analyzed as continuous (Table 1) or categorical variables (Table 2). Plasma cell labeling index appeared to be lower in H-MM patients although this is not statistically significant. The only significantly different laboratory parameter was the platelet count that was higher in H-MM (P=0.045). When categorized into ISS stages, the distribution of H-MM and NH-MM patients in the three stages appeared similar (Table 2).

Table 2 Comparison of categorical prognostic factors and ISS staging between hyperdiploid myeloma and nonhyperdiploid myeloma

In terms of bone disease, H-MM patients had significantly more evidence of bone disease compared to NH-MM patients. However, the levels of serum markers of bones disease were similar between the two groups (Table 3).

Table 3 Comparison of radiographic and serum markers of bone disease

Survival and response to treatment

The objective response rates were similar in the three different treatment arms in this trial for both H-MM and NH-MM patients and were summarized together. The objective response rate to treatment was not statistically different although there is a trend towards better response for H-MM patients (70 vs 63%, Fisher's exact P=0.17). However, PFS and OS were significantly better for H-MM patients. The median PFS of H-MM patients was 32 months (95% CI 28–35 months) as compared to 25 months (95% CI 20–30 months) for the NH-MM patients (log-rank P=0.023), while the median OS of H-MM patients was 48 months (95% CI 41–51 months) compared to 35 months (95% CI 30–40 months) for NH-MM patients (log-rank P=0.023) (Figure 1).

Figure 1
figure1

Kaplan–Meier curves for (a) overall survival (OS) and (b) progression-free survival (PFS) of Hyperdiploid-myeloma (H-MM) and nomhyperdiploid-MM (NH-MM) patients. Overall survival but not PFS is significantly better for H-MM patients.

Prevalence of other major genetic abnormalities in hyperdiploid-myeloma

The prevalence of genetic abnormalities such as IgH translocations and Δ13 in this cohort of H-MM patients has previously been published.8 In that study, we showed the association between primary IgH translocations and nonhyperdiploidy. In the present study, we have expanded on those previous observations. We found that secondary IgH translocations (i.e. those involving nonrecurrent chromosome partners) were more common in H-MM. Of all patients with secondary IgH translocations in this series (n=37), 26 cases belonged to the H-MM category (70%). Of all the IgH translocations observed in H-MM patients, most were secondary (26 out of 42, 62%), which was in sharp distinction to the NH-MM variant (11 of 75 IgH translocations were secondary, 15%) (P<0.001). The prevalence of 17p deletion involving the p53 locus was similar in both H-MM and NH-MM patients (10 vs 12%, P=0.818).

Impact of immunoglobulin heavy chain translocation and chromosome 13 and 17p deletions on survival of hyperdiploid-myeloma patients

When the effect of these genetic abnormalities on the survival of H-MM patients was analyzed, the presence of IgH translocations, especially those with t(4;14) or t(14;16) and those with unknown partners, adversely affected survival (Table 4, Figure 2a). Among these IgH translocations, only IgH translocation with unknown partners was significantly associated with poor survival in H-MM (Table 5).

Table 4 Overall survival and progression-free survival among hyperdiploid myeloma patients with additional genetic abnormalities
Figure 2
figure2

Kaplan–Meier curves for overall survival (OS) of hyperdiploid-MM (H-MM) patients with and without (a) Immunoglobulin heavy chain (IgH) translocations, (b) Δ13 and (c) chromosome 17p deletion. Overall survival of H-MM patients with IgH translocations, especially t(4;14), t(14;16) and those with unknown partners was significantly inferior to those without whereas survival was not significantly different between patients with and without chromosome 13 and 17p deletions.

Table 5 Cox proportional hazard regression model for overall survival within hyperdiploid myeloma when only immunoglobulin heavy chain translocations are considered

Hyperdiploid-myeloma patients with or without Δ13 had similar PFS (median 31 months vs 30 months, P=0.55) and OS (median 47 months vs 50 months, P=0.47). Hyperdiploid-myeloma patients with chromosome 17p deletion had a shorter survival than those without (median PFS 20 months vs 31 months, P=0.16; median OS 20 months vs 50 months, P=0.12) although this did not reach statistical significance (Table 4, Figure 2b and c).

Prognostic factors for hyperdiploid-myeloma patients

A Cox proportional hazard analysis including variables such age, albumin, B2M, CRP, bone abnormality, PCLI, plasmablastic morphology, treatment arms, chromosome 13 and 17p deletion and IgH translocations was performed in a subset of 102 hyperdiploid patients with all the relevant information available. β2-Microglobulin3.5 mg/dl and PCLI1% emerged as independent factors predictive of poorer OS, whereas CRP>0.8 mg/dl, B2M3.5 mg/dl and PCLI1% were independent factors predictive of disease progression (Table 6).

Table 6 Cox proportional hazard regression model for overall survival and progression-free survival within hyperdiploid patients (n=102)a

Discussion

This is the largest series of H-MM patients published to date. Owing to the homogeneity of treatment in a trial setting, long clinical follow-up (median follow-up of 12 years) and comprehensive clinical and genetic investigations of this cohort of patient, this is a useful resource of characterizing and studying potential prognostic factors in H-MM.

The percentage of H-MM patients in our cohort may appear high but is close to the 55% reported in the original French study using conventional karyotyping.6 Our study cohort consists of unselected patients entered into a clinical trial, and the difference in percentage of H-MM probably reflects variability often encountered in different study cohorts. The method used to define ploidy could potentially account for the higher percentage of H-MM patients report. However, flow cytometry is an established method for defining ploidy in MM. Its main shortcoming is in identifying hypodiploid state but not hyperdiploidy. Furthermore, the cutoffs we used were established and validated in previous studies.17, 28

We did not consider the clonal diversity and karyotypic complexity of H-MM in this analysis, as karyotyping results were not available for these patients. However, due to the well-documented difficulties with karyotypic analysis in MM, only a minority would be informative karyotype even if they were available. Furthermore, the extent of clonal diversity in MM is considerable and its biologic and clinical significance currently unknown, making analysis and interpretation difficult if taken into account. As the size of our cohort of H-MM patients is considerable, we believe the spectrum of karyotypic complexities demonstrated in previous studies will also be found in our patients.

Clinical characteristics of hyperdiploid-myeloma patients

The H-MM patients were comprehensively characterized using a wide range of clinical parameters. Hyperdiploid-myeloma appears to be associated with male sex, kappa light chain subtypes, and more bone disease. The higher prevalence of bone disease in H-MM patients is consistent with a previous analysis based on MRI detection of bone disease and using the translocation and cyclin D (TC) molecular classification of MM where the D1 group (corresponding to H-MM) have a high prevalence of bone lesion.32 In that study, patients with t(11;14)(q13;q32) also have a higher incidence of bone disease. However, other studies have failed to make similar correlations between other more aggressive genetic subtypes such as t(4;14)(p16;q32) and bone disease.12, 33, 34 Overall it appears that the more favorable categories (t(11;14) and H-MM) have a higher prevalence of bone disease. This may be due to a slower rate of progression from MGUS to MM allowing more time for bone lesions to develop. Alternatively, the duration of contact of plasma cells with the bone marrow microenvironment may be important in the development of bone lesions.

Hyperdiploid-myeloma patients have better survival than nonhyperdiploid-myeloma

We confirmed the better PFS and OS of H-MM that has previously been reported in smaller cohorts.7, 11, 35 Although survival is different between the H-MM and NH-MM, their response to therapy is not different. This suggests that response of H-MM patients to chemotherapy is more durable. Furthermore, as patients in our series are treated with chemotherapy, the difference in OS may also suggest that H-MM are more salvageable with secondary treatment especially stem cell transplantation. A recent study in patients treated by autologous stem cell transplantation has shown that H-MM patients has significantly better outcome.15 However, it is presently unclear whether the different ploidy categories of MM have different outcome when treated with recent novel therapeutics such as thalidomide and its derivatives, and bortezomib.

It also appears that the difference in survival is not mediated by known clinical prognostic factors. Levels of prognostic markers such albumin, CRP, B2M, and PCLI are not different between H-MM and NH-MM. When we classify patients according to the ISS stages, the distribution of the two variants across the stages is also similar. This would suggest that the important factors mediating improved survival are intrinsic to the genetics of the two variants. Of note, genetic factors were not incorporated into the ISS staging.

Presence of IgH translocations and not chromosome 13 and 17p deletions negatively impact survival of H-MM patients

Previous studies have shown the specific genetic abnormalities such as t(4;14),12, 13, 18, 34, 36 t(14;16),12 Δ1312, 14, 15, 17 and 17p/p53 deletion37, 38 are associated with poorer prognosis regardless of treatment modalities. On the other hand, t(11;14) has been associated with better prognosis in patients treated with high dose chemotherapy and stem cell support in some series13, 16 but not others.39 These abnormalities are also present in H-MM albeit at lower percentages. In this study, we showed that the survival of H-MM patients is negatively affected by the coexistence of IgH translocations, in particular those with unknown partners, but not by chromosome 13. Our results agreed with a French study, which showed similar observations using conventional karyotype analysis in a smaller cohort of patients,11 but contrasted with that of Fassas et al.15 who showed that Δ13 retained independent prognostic significance within ploidy categories. This discrepancy could be due to the fact that the two studies differ in the techniques used to detect Δ13. The karyotypic detection of Δ13 by Fassas et al.15 could confer additional prognostic significance as it may also indicate greater proliferation of the underlying clone.40, 41 Our FISH-based study allow us to overcome some of the limitations of conventional cytogenetics. In addition, we were able to investigate the modulating effect of t(4;14) which is cytogenetically cryptic.

The adverse impact on prognosis of the IgH translocations with unknown partners in H-MM suggests that these genetic changes probably occur as late secondary events due to ongoing genomic instability in more advance tumors.42 Regarding Δ13, our findings add credence to the argument that the previously demonstrated poor prognostic impact of Δ13 could be due to its strong association with poor prognostic genetic abnormalities like t(4;14) and t(14;16) and not Δ13 per se.42

Chromosome 17p/p53 deletion has not been previously examined in relation to ploidy. We found that the prevalence of 17p/p53 deletion was similar between H-MM and NH-MM patients, consistent with its role as a secondary progression event in MM. Patients with 17p/p53 deletion demonstrates a strong trend towards poorer survival although this did not reach statistical significance. The impact of 17p/p53 deletion on the survival of hyperdiploid patients will need to be clarified in larger studies in the future.

Prognostic factors in hyperdiploid-myeloma patients

Conventional prognostic factor such as CRP, B2M, and PCLI emerged as independent prognostic factors in H-MM on Cox proportional hazard analysis. Although IgH translocations significantly affected survival in univariate analysis, their significance was not retained when other variables was considered in the Cox proportional hazard model. This is most likely due to the low number of H-MM patients with these additional genetic abnormalities.

In conclusion, our study provides further evidence that H-MM represents a distinct clinical entity associated with male sex, kappa light chain subtype, bone disease and better prognosis. We showed that among other common genetic abnormalities, the presence of IgH translocations but not Δ13 confer a worse prognosis in these patients. Biologically, the adverse impact of IgH translocations may be mediated through upregulation of proliferation genes in the case of translocations with known oncogenic partners. Alternatively, the presence of IgH translocations may be a reflection of the advance stage of genomic instability of the underlying tumor. Understanding the clinical impact of the interactions between recurrent genetic abnormalities and ploidy will allow better risk stratification and prioritization of investigations in these patients.

References

  1. 1

    Bergsagel PL, Chesi M, Nardini E, Brents LA, Kirby SL, Kuehl WM . Promiscuous translocations into immunoglobulin heavy chain switch regions in multiple myeloma. Proc Natl Acad Sci USA 1996; 93: 13931–13936.

  2. 2

    Chesi M, Bergsagel PL, Brents LA, Smith CM, Gerhard DS, Kuehl WM . Dysregulation of cyclin D1 by translocation into an IgH gamma switch region in two multiple myeloma cell lines. Blood 1996; 88: 674–681.

  3. 3

    Chesi M, Nardini E, Brents LA, Schrock E, Ried T, Kuehl WM et al. Frequent translocation t(4;14)(p16.3;q32.3) in multiple myeloma is associated with increased expression and activating mutations of fibroblast growth factor receptor 3. Nat Genet 1997; 16: 260–264.

  4. 4

    Chesi M, Nardini E, Lim RS, Smith KD, Kuehl WM, Bergsagel PL . The t(4;14) translocation in myeloma dysregulates both FGFR3 and a novel gene, MMSET, resulting in IgH/MMSET hybrid transcripts. Blood 1998; 92: 3025–3034.

  5. 5

    Chesi M, Bergsagel PL, Shonukan OO, Martelli ML, Brents LA, Chen T et al. Frequent dysregulation of the c-maf proto-oncogene at 16q23 by translocation to an Ig locus in multiple myeloma. Blood 1998; 91: 4457–4463.

  6. 6

    Smadja NV, Fruchart CC, Isnard F, Louvet C, Dutel JL, Cheron N et al. Chromosomal analysis in multiple myeloma: cytogenetic evidence of two different diseases. Leukemia 1998; 12: 960–969.

  7. 7

    Debes-Marun CS, Dewald GW, Bryant S, Picken E, Santana-Davila R, Gonzalez-Paz N et al. Chromosome abnormalities clustering and its implications for pathogenesis and prognosis in myeloma. Leukemia 2003; 17: 427–436.

  8. 8

    Fonseca R, Debes-Marun CS, Picken EB, Dewald GW, Bryant SC, Winkler JM et al. The recurrent IgH translocations are highly associated with nonhyperdiploid variant multiple myeloma. Blood 2003; 102: 2562–2567.

  9. 9

    Fonseca R, Oken MM, Greipp PR . The t(4;14)(p16.3;q32) is strongly associated with chromosome 13 abnormalities in both multiple myeloma and monoclonal gammopathy of undetermined significance. Blood 2001; 98: 1271–1272.

  10. 10

    Chng WJ, Van Wier SA, Ahmann GJ, Winkler JM, Jalal SM, Bergsagel PL et al. A validated FISH trisomy index demonstrates the hyperdiploid and nonhyperdiploid dichotomy in MGUS. Blood 2005; 106: 2156–2161.

  11. 11

    Smadja NV, Bastard C, Brigaudeau C, Leroux D, Fruchart CC . Hypodiploidy is a major prognostic factor in multiple myeloma. Blood 2001; 98: 2229–2238.

  12. 12

    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.

  13. 13

    Moreau P, Facon T, Leleu X, Morineau N, Huyghe P, Harousseau JL et al. Recurrent 14q32 translocations determine the prognosis of multiple myeloma, especially in patients receiving intensive chemotherapy. Blood 2002; 100: 1579–1583.

  14. 14

    Shaughnessy J, Jacobson J, Sawyer J, McCoy J, Fassas A, Zhan F et al. Continuous absence of metaphase-defined cytogenetic abnormalities, especially of chromosome 13 and hypodiploidy, ensures long-term survival in multiple myeloma treated with Total Therapy I: interpretation in the context of global gene expression. Blood 2003; 101: 3849–3856.

  15. 15

    Fassas AB, Spencer T, Sawyer J, Zangari M, Lee CK, Anaissie E et al. Both hypodiploidy and deletion of chromosome 13 independently confer poor prognosis in multiple myeloma. Br J Haematol 2002; 118: 1041–1047.

  16. 16

    Fonseca R, Blood EA, Oken MM, Kyle RA, Dewald GW, Bailey RJ et al. Myeloma and the t(11;14)(q13;q32); evidence for a biologically defined unique subset of patients. Blood 2002; 99: 3735–3741.

  17. 17

    Fonseca R, Harrington D, Oken MM, Dewald GW, Bailey RJ, Van Wier SA et al. Biological and prognostic significance of interphase fluorescence in situ hybridization detection of chromosome 13 abnormalities (delta13) in multiple myeloma: an eastern cooperative oncology group study. Cancer Res 2002; 62: 715–720.

  18. 18

    Chang H, Sloan S, Li D, Zhuang L, Yi QL, Chen CI et al. The t(4;14) is associated with poor prognosis in myeloma patients undergoing autologous stem cell transplant. Br J Haematol 2004; 125: 64–68.

  19. 19

    Bergsagel PL, Kuehl WM, Zhan F, Sawyer J, Barlogie B, Shaughnessy Jr J . Cyclin D dysregulation: an early and unifying pathogenic event in multiple myeloma. Blood 2005; 106: 296–303.

  20. 20

    Oken MM, Leong T, Lenhard Jr RE, Greipp PR, Kay NE, Van Ness B et al. The addition of interferon or high dose cyclophosphamide to standard chemotherapy in the treatment of patients with multiple myeloma: phase III Eastern Cooperative Oncology Group Clinical Trial EST 9486. Cancer 1999; 86: 957–968.

  21. 21

    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.

  22. 22

    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.

  23. 23

    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.

  24. 24

    Fonseca R, Trendle MC, Leong T, Kyle RA, Oken MM, Kay NE et al. Prognostic value of serum markers of bone metabolism in untreated multiple myeloma patients. Br J Haematol 2000; 109: 24–29.

  25. 25

    Ahmann GJ, Jalal SM, Juneau AL, Christensen ER, Hanson CA, Dewald GW et al. A novel three-color, clone-specific fluorescence in situ hybridization procedure for monoclonal gammopathies. Cancer Genet Cytogenet 1998; 101: 7–11.

  26. 26

    Fonseca R, Bailey RJ, Ahmann GJ, Rajkumar SV, Hoyer JD, Lust JA et al. Genomic abnormalities in monoclonal gammopathy of undetermined significance. Blood 2002; 100: 1417–1424.

  27. 27

    Fonseca R, Oken MM, Harrington D, Bailey RJ, Van Wier SA, Henderson KJ et al. Deletions of chromosome 13 in multiple myeloma identified by interphase FISH usually denote large deletions of the q arm or monosomy. Leukemia 2001; 15: 981–986.

  28. 28

    Greipp PR, Trendle MC, Leong T, Oken MM, Kay NE, Van Ness B et al. Is flow cytometric DNA content hypodiploidy prognostic in multiple myeloma? Leuk Lymphoma 1999; 35: 83–89.

  29. 29

    Jacobson JL, Hussein MA, Barlogie B, Durie BG, Crowley JJ . A new staging system for multiple myeloma patients based on the Southwest Oncology Group (SWOG) experience. Br J Haematol 2003; 122: 441–450.

  30. 30

    Durie BG, Stock-Novack D, Salmon SE, Finley P, Beckord J, Crowley J et al. Prognostic value of pretreatment serum beta 2 microglobulin in myeloma: a Southwest Oncology Group Study. Blood 1990; 75: 823–830.

  31. 31

    Bataille R, Boccadoro M, Klein B, Durie B, Pileri A . C-reactive protein and beta-2 microglobulin produce a simple and powerful myeloma staging system. Blood 1992; 80: 733–737.

  32. 32

    Robbiani DF, Chesi M, Bergsagel PL . Bone lesions in molecular subtypes of multiple myeloma. N Engl J Med 2004; 351: 197–198.

  33. 33

    Chang H, Stewart AK, Qi XY, Li ZH, Yi QL, Trudel S . Immunohistochemistry accurately predicts FGFR3 aberrant expression and t(4;14) in multiple myeloma. Blood 2005; 106: 353–355.

  34. 34

    Keats JJ, Reiman T, Maxwell CA, Taylor BJ, Larratt LM, Mant MJ et al. In multiple myeloma, t(4;14)(p16;q32) is an adverse prognostic factor irrespective of FGFR3 expression. Blood 2003; 101: 1520–1529.

  35. 35

    Garcia-Sanz R, Orfao A, Gonzalez M, Moro MJ, Hernandez JM, Ortega F et al. Prognostic implications of DNA aneuploidy in 156 untreated multiple myeloma patients. Castelano-Leones (Spain) Cooperative Group for the Study of Monoclonal Gammopathies. Br J Haematol 1995; 90: 106–112.

  36. 36

    Winkler JM, Greipp P, Fonseca R . t(4;14)(p16.3;q32) is strongly associated with a shorter survival in myeloma patients. Br J Haematol 2003; 120: 170–171.

  37. 37

    Chang H, Qi C, Yi QL, Reece D, Stewart AK . p53 gene deletion detected by fluorescence in situ hybridization is an adverse prognostic factor for patients with multiple myeloma following autologous stem cell transplantation. Blood 2005; 105: 358–360.

  38. 38

    Drach J, Ackermann J, Fritz E, Kromer E, Schuster R, Gisslinger H et al. Presence of a p53 gene deletion in patients with multiple myeloma predicts for short survival after conventional-dose chemotherapy. Blood 1998; 92: 802–809.

  39. 39

    Gertz MA, Lacy MQ, Dispenzieri A, Greipp PR, Litzow MR, Henderson KJ et al. Clinical implications of t(11;14)(q13;q32), t(4;14)(p16.3;q32), and -17p13 in myeloma patients treated with high-dose therapy. Blood 2005; 106: 2837–2840.

  40. 40

    Rajkumar SV, Fonseca R, Dewald GW, Therneau TM, Lacy MQ, Kyle RA et al. Cytogenetic abnormalities correlate with the plasma cell labeling index and extent of bone marrow involvement in myeloma. Cancer Genet Cytogenet 1999; 113: 73–77.

  41. 41

    Dewald GW, Kyle RA, Hicks GA, Greipp PR . The clinical significance of cytogenetic studies in 100 patients with multiple myeloma, plasma cell leukemia, or amyloidosis. Blood 1985; 66: 380–390.

  42. 42

    Fonseca R, Barlogie B, Bataille R, Bastard C, Bergsagel PL, Chesi M et al. Genetics and cytogenetics of multiple myeloma: a workshop report. Cancer Res 2004; 64: 1546–1558.

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Acknowledgements

RF is a Clinical Investigator of the Damon Runyon Cancer Research Fund. This work was supported by the Donaldson Charitable Trust, the International Waldenström Macroglobulinemia Foundation, and Grants R01 CA83724-01, SPORE P50 CA100707-01and P01 CA62242 from the National Cancer Institute, and the Fund to Cure Myeloma. PRG is supported by the ECOG Grant CA21115-25C from the National Cancer Institute. WJC is funded by an International Fellowship from the Agency for Science, Technology and Research (A*STAR), Singapore.

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Chng, W., Santana-Dávila, R., Van Wier, S. et al. Prognostic factors for hyperdiploid-myeloma: effects of chromosome 13 deletions and IgH translocations. Leukemia 20, 807–813 (2006) doi:10.1038/sj.leu.2404172

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Keywords

  • hyperdiploid
  • myeloma
  • prognosis
  • IgH translocations
  • chromosome 13 deletion

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    • , Patricia T. Greipp
    • , Morie A. Gertz
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    • , Martha Q. Lacy
    • , David Dingli
    • , Angela Dispenzieri
    • , Amie L. Fonder
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