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Published in final edited form as: Br J Haematol. 2014 Aug 12;167(4):500–505. doi: 10.1111/bjh.13067

Quantification of Clonal Circulating Plasma cells in Relapsed Multiple Myeloma

Wilson I Gonsalves 1, William G Morice 2, S Vincent Rajkumar 1, Vinay Gupta 1, Michael M Timm 2, Angela Dispenzieri 1, Francis K Buadi 1, Martha Q Lacy 1, Preet P Singh 1, Prashant Kapoor 1, Morie A Gertz 1, Shaji K Kumar 1
PMCID: PMC4211997  NIHMSID: NIHMS616517  PMID: 25113422

Abstract

The presence of clonal circulating plasma cells (cPCs) remains a marker of high-risk disease in newly diagnosed multiple myeloma (MM) patients. However, its prognostic utility in MM patients with previously treated disease is unknown. We studied 647 consecutive patients with previously treated MM seen at the Mayo Clinic, Rochester who had their peripheral blood evaluated for cPCs by multi-parameter flow cytometry. Of these patients, 145 had actively relapsing disease while the remaining 502 had disease that was in a plateau and included 68 patients in complete remission (CR) and 434 patients with stable disease. Patients with actively relapsing disease were more likely to have clonal cPCs than those in a plateau (P < 0.001). None of the patients in CR had any clonal cPCs detected. Among patients whose disease was in a plateau, the presence of clonal cPCs predicted for a worse median survival (22 months vs. not reached; P=0.004). Among actively relapsing patients, the presence of ≥100 cPCs predicted for a worse survival after flow cytometry analysis (12 months vs. 33 months; P<0.001). Future studies are needed to determine the role of these findings in developing a risk-adapted treatment approach in MM patients with actively relapsing disease.

Keywords: circulating plasma cells, multiple myeloma, survival

INTRODUCTION

The incorporation of novel agents, such as immunomodulatory drugs and proteasome inhibitors(Kumar, et al 2008a), as well as high dose chemotherapy followed by autologous stem cell transplantation (ASCT) in eligible patients(Attal, et al 1996, Child, et al 2003) has improved the survival outcomes for MM patients over the last decade. Despite these therapeutic advances, MM remains incurable in the majority of patients as they eventually relapse after first line therapy.(Kumar, et al 2012) However, even upon relapse, MM remains biologically heterogeneous with significant variability among patients in terms of response to therapy and overall survival (OS).(Kumar, et al 2004) Although several prognostic factors have been identified in patients with newly diagnosed MM, their value in the context of actively relapsing disease is not equally defined. Prior studies have demonstrated that the presence of clonal circulating plasma cells (cPCs) can identify newly diagnosed MM patients with a shorter survival.(Nowakowski, et al 2005, Witzig, et al 1993) However, these studies mostly utilized a slide-based immunofluorescence assay to detect clonal cPCs, which is a complex and labour-intensive process requiring fluorescence microscopy, thus limiting its clinical availability. Furthermore, none of these studies were conducted in MM patients with previously treated disease, limiting its applicability to that population.

Flow cytometry is now a readily available tool that can detect clonal cPCs rapidly with good correlation with the immunofluorescence-based method (Witzig, et al 1996) as well as quantify the absolute number of clonal cPCs detected in a more sensitive and reproducible manner. Thus, we conducted this study to evaluate the clinical utility of quantifying clonal cPCs via flow cytometry in MM patients with previously treated disease.

PATIENTS AND METHODS

Since October 2009, the evaluation of peripheral blood samples for cPCs in MM patients was performed using flow cytometry rather than the slide-based immunofluorescence assay at our institution. We retrospectively evaluated all patients with previously treated MM seen at the Mayo Clinic, Rochester from October 2009 to November 2011 who had their peripheral blood samples evaluated for cPCs by flow cytometry. Approval for this study was obtained from the Mayo Clinic Institutional Review Board in accordance with the federal regulations and the principles of the Declaration of Helsinki.

Each patient’s peripheral blood sample had its mononuclear cells isolated by Ficoll gradient and stained with antibodies to CD19, CD38, CD45, CD138 and cytoplasmic kappa and lambda light chains. Six-colour multi-parameter flow cytometer was performed on BD FACSCantos II instruments (Becton Dickinson, Franklin Lakes, NJ, USA) with a target of collecting 150,000 cellular events, and data were analysed using the BD FACSDiva software (Beckton Dickinson). The cPCs were detected through combinatorial analysis of CD19, CD45, CD38 (bright) and CD138 expression. The clonal cPCs had an abnormal phenotype typically characterized by absence of CD19 expression and variable CD45 expression; clonality was confirmed in these cells by their cytoplasmic immunoglobulin light chain restriction (kappa: lambda expression ratio of either >4:1 (kappa restricted) or <1:2 (lambda restricted). The clonal cPCs detected were reported as the number of clonal events/150,000 collected total events. For those samples where less than 150,000 events were gated or examined, the number of final clonal events was adjusted to 150,000 events.

The primary end-point of the study was assessment of survival after peripheral blood flow cytometry analysis, which was measured from the day of flow cytometry to death from any cause, with censoring performed at the date of last contact. Host and disease variables at diagnosis and at the time of the flow cytometry evaluation that were examined for prognostic significance included: age, bone marrow plasma cell percentage, presence of fluorescent in-situ hybridization (FISH), International Staging System (ISS) stage, plasma cell labelling index (PCLI), serum M spike, urine M spike, haemoglobin, creatinine and lactate dehydrogenase (LDH). Patients who had a FISH analysis performed on their plasma cells were categorized as having high-risk disease if any of the following abnormalities: t(4;14), t(14;16), or t(14;20) were present at any time during their disease course, or a del 17p within 30 days of the diagnosis or any time before the diagnosis.(Kapoor, et al 2010) The status of patients MM disease at the time of flow cytometric analysis was characterized using the International Myeloma Working Group (IMWG) criteria for response.(Durie, et al 2006)

Statistical analysis was performed using the SAS biostatistical software JMP 9.0.1 (SAS Institute Inc., Cary, NC). Chi-square tests and Fisher exact tests were used to compare differences between nominal variables, and the Mann-Whitney U test or the Kruskal-Wallis test was used for continuous variables. Receiver operating characteristics (ROC) analysis was performed to determine the optimal cut point of cPCs that predicted for worse 2-year OS. Kaplan-Meier analysis was used to analyse and create the OS curves, and log rank test was used to compare these curves. Finally, a multivariate analysis was performed using the Cox proportions hazards model to assess the influence of various prognostic factors on OS.

RESULTS

From October 2009 to November 2011, 647 patients with previously treated MM had their peripheral blood evaluated by flow cytometry as part of their clinical evaluation. The median age of the patient population was 62 years and 55% were male. The majority of patients (96%) were treated with a novel anti-myeloma agent during their induction therapy and 39% had previously undergone an ASCT at the time of this analysis. The median estimated follow up from the flow cytometry evaluation for the entire cohort was 21 months (95% CI: 20 - 23) while the median time from diagnosis to flow cytometry analysis was 14 months (range: 2-363). The median OS from diagnosis for the entire cohort was 113 months (range: 96 – 143) with an estimated 1- year and 2-year OS of 94% and 89%, respectively. The estimated 1-year and 2-year survival after flow cytometry analysis for the entire cohort was 85% and 72%, respectively, with 167 (26%) patients having died at the time of this analysis.

Only eighty-one patients (13%) had clonal cPCs with a median of 368 cells (4 – 133,464) per 150,000 events analysed. The presence of clonal cPCs was associated with high-risk disease by FISH (P <0.001) as well as higher PCLI (P <0.001). When correlated with the disease status at flow cytometric analysis, 145 (22%) patients had actively relapsing disease while the remaining 502 (78%) had disease that was in a plateau, which and included 68 (11%) patients in complete remission (CR) and 434 patients with stable disease. The distribution of patients based on their disease characteristics and presence of cPCs is shown in Figure 1. Among the 81 patients with clonal cPCs, 62 patients (77%) belonged to the actively relapsing group while only 19 (23%) belonged to the patients in the plateau group (P < 0.001). Of the 19 patients in the plateau group with clonal cPCs, none of them were in a CR. In an analysis of only the 502 patients whose disease was in a plateau, the 19 with clonal cPCs had a median survival of 22 months whereas median survival was not reached in the remaining 483 patients (P=0.004).

Figure 1.

Figure 1

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Shows the schematic distribution of the 647 previously treated patients based on their disease status and presence of circulating plasma cells (cPCs).

When the analysis was restricted to the 145 actively relapsing patients alone, using a ROC analysis, the optimum cutoff predicting for the highest risk of death within an year of testing was approximtely 100 clonal cPCs; yielding an area under the curve of 0.682 with a sensitivity of 62% and specificity of 73%. Based on this, we defined ≥100 events as a cutoff for defining the prognostic role of cPCs in actively relapsing patients. Patient and disease characteristics of these two groups defined by the presence or absence of ≥100 cPCs are described in Table I. The median survival after flow cytometry analysis for those with ≥100 cPCs was 12 months compared to 33 months for those with < 100 cPCs (p<0.0001; Figure 2). The 1-year and 2-year survival after flow cytometry analysis for patients with ≥100 cPCs was 48% and 23% compared to 80% and 64% for those patients with < 100 cPCs.

Table I.

Clinical characteristics of the previously treated MM patients with actively relapsing disease based on the presence of 100 or more cPCs


Variables		 Less than 100 cPCs
(N = 92)		 100 or more cPCs
(N=53)		 P - value
Age (years) 65 (42 – 83) 63 (43 - 80) 0.904
PCLI 0.7 (0 – 4.2) 2.0 (0 – 16) < 0.001
Bone marrow PC% 20 (5 - 90) 80 (5 – 100) < 0.001
LDH (u/l) 175 (75 - 605) 203 (79 – 2048) 0.012
Beta-2-microglobulin (mg/l) 3.0 (1.5 – 12.7) 6.7 (2.0 – 27.3) < 0.001
Creatinine (μmol/l) 88.4 (35.4 – 247.5) 97.2 (53.0 – 592.3) 0.070
Albumin (g/l) 34 (26 - 42) 35 (16 – 43) 0.333
FISH (No of patients available)

 High risk FISH

  t(4,14)

  t(14,16)

  t(14,20)

  Deletion 17p 		Available in 66 patients

  17 (26%)

  8 (12%)

  1 (2%)

  0 (0%)

  11 (17%) 		Available in 36 patients

  14 (39%)

  1 (3%,)

  5 (14%)

  1 (3%)

  11 (31%)

0.184
Prior therapies received (n)

 Prior novel agents received

 -Thalidomide

 -Lenalidomide

 -Bortezomib

 Prior ASCT 		3 (1-11)

89 (97%)

31 (34%)

73 (79%)

58 (63%)

59 (64%)		 4 (1-10)

51 (96%)

25 (47%)

42 (79%)

38 (72%)

31 (59%)		 0.329

0.150







0.594
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MM, multiple myeloma; PC%, plasma cell percentage; cPCs, circulating plasma cells; PCLI, plasma cell labelling index; LDH, lactate dehydrogenase; FISH, fluorescent in-situ hybridization; ASCT, autologous stem cell transplantation.

Figure 2.

Figure 2

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Shows the Kaplan-Meier Curve for survival from the time of peripheral blood flow cytometry analysis in all previously treated patients with actively relapsing disease based on the presence of circulating plasma cells (cPCs) based on the presence of 100 or more cPCs.

Among these 145 patients with actively relapsing disease, ≥100 cPCs was associated with a higher ISS stage (P < 0.001), β2-microglobulin (P < 0.001), LDH (P = 0.012), PCLI (P < 0.001) and bone marrow PC% (P < 0.001) compared with those with < 100 cPCs. In a univariate model, elevated creatinine, β2-microglobulin, LDH (i.e. > 222; ULN), number of lines of prior therapy and the presence of ≥100 cPCs predicted for worse survival after flow cytometric analysis; however, only elevated LDH (P < 0.001), number of lines of prior therapy (P = 0.005) and ≥100 cPCs (P = 0.015) retained significance in the multivariate model (Table II). Though PCLI serves as a marker of high-risk biology, it was not included in the multivariate model given that more than a third of the patients did not have it performed, as the methodology for plasma cell proliferation had transitioned during this time period to a flow cytometry-based method.

Table II.

Univariate and Multivariate analysis of factors predicting worse overall

Variable Overall survival
Univariate Multivariate
HR (95% CI) P - value HR (95% CI) P - value
≥100 clonal cPCs
detected 		3.32 (2.05 - 5.41) < 0.0001 2.67 (1.37 - 5.17) 0.0041
Number of prior lines
of therapy 		1.15 (1.05 - 1.26) 0.0027 4.09 (1.56 - 10.14) 0.0048
Serum creatinine 1.56 (1.23 - 1.88) 0.0008 1.29 (0.89 - 1.87) 0.1723
β2-microglobulin 1.12 (1.07 – 1.16) < 0.0001 1.05 (0.97 - 1.12) 0.2165
Elevated LDH (> 222
u/l) 		1.02 (1.01 – 1.03) < 0.0001 2.93 (1.67 - 5.08) 0.0003
High bone marrow
PC% 		1.00 (0.99 - 1.02) 0.0785 - -
High risk status by
FISH 		1.64 (0.88 - 2.95) 0.1138 - -
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survival

Bolded P-values and HRs represent statistically significant variables (i.e. P < 0.05)

cPCs, circulating plasma cells; LDH, lactate dehydrogenase; PC%, plasma cell percentage; FISH, fluorescent in-situ hybridization; HR, Hazard ratio; 95% CI, 95% confidence interval.

DISCUSSION

The presence of clonal cPCs is not only prognostic in MM patients(Gonsalves, et al 2014, Nowakowski, et al 2005, Witzig, et al 1993) but also predictive for early relapse after ASCT(Dingli, et al 2006) and correlates with response to therapy.(Rawstron, et al 1997) Most previous studies have utilized a slide-based immunofluorescence assay to detect cPCs and included primarily newly diagnosed MM patients. To date, no study has evaluated the prognostic implications of quantifying clonal cPCs via flow cytometry in MM patients with relapsed disease. Thus, using a rapid 6-colour flow cytometric approach, we evaluated the prognostic value of this more sensitive method of quantifying clonal cPCs in previously treated MM patients.

In our cohort of previously treated MM patients with actively relapsing patients, quantifying the number of clonal cPCs detected by flow cytometry appeared to have independent prognostic significance; i.e. the presence of ≥100 cPCs per 150,000 gated mononuclear events analysed provided the best possible cut-off for predicting poor outcomes based on 1-year survival from the time of analysis and yielded a median subsequent survival of only 12 months (Figure 2). Patients with actively relapsing disease have progressively shorter durations of response to consecutive treatment regimens, reflecting the development of drug resistance and aggressive disease biology.(Richardson, et al 2007) In this study, the disease characteristics of patients with actively relapsing disease (Table I) support the development of an aggressive biology, as reflected by their higher PCLI, number of cPCs and high-risk FISH in comparison to patients whose disease is in a plateau. Our study also highlights the significance of detecting clonal cPCs in those previously treated patients whose disease is in a plateau (CR or stable disease), i.e., patients in CR were unlikely to have clonal cPCs in comparison to those with stable disease not in CR. Furthermore, among patients whose disease was in a plateau, those with clonal cPCs had a worse median OS compared to patients who did not have any clonal cPCs.

The current MM risk stratification system applies primarily to newly diagnosed patients in order to identify aggressive disease by placing emphasis on the biology of the plasma cells through molecular cytogenetics or FISH, PCLI and gene expression profiling.(Mikhael, et al 2013) This system provides a personalized approach to initial therapy in MM patients by directing the intensity of therapy based on aggressiveness of the disease.(Mikhael, et al 2013) There is no risk-adapted approach or algorithm in MM patients with relapsing disease. Although several prognostic factors have been identified for newly diagnosed MM, their value in the context of relapsed disease is less well-defined. Nonetheless, actively relapsing patients with poor outcomes include those with already present or newly acquired high-risk cytogenetic abnormalities, high β2 microglobulin and/or low serum albumin (i.e. ISS stage 3), abnormal LDH, light chain or IgA isotype, renal dysfunction and extramedullary disease.(Kumar, et al 2012) Short duration of response or remission, rapid progression and more number of prior lines of therapy used are also predictors of poor outcomes.(Kumar, et al 2008b, Kumar, et al 2004, Migkou, et al 2011) While incorporating these aforementioned factors in our study of 145 actively relapsing patients, detecting ≥100 cPCs by flow cytometry remained a significant predictor for shorter survival after flow cytometric analysis. This has the ability to provide added prognostic information upon disease relapse to both clinicians and patients while raising the possibility of instituting earlier and more aggressive combination therapy.

The presence of cPCs in MM patients with relapsed disease reflects their independence from the bone marrow microenvironment, which is required for proliferation(Teoh and Anderson 1997), thus signifying a more self-sustaining and aggressive disease. However, the specific molecular mechanisms leading to the development of cPCs are not clear. A recent study (Mishima, et al 2013), using whole exome sequencing of cPCs and their corresponding bone marrow clonal plasma cells, suggests sub-clonal outgrowth of cPCs from one of the bone marrow plasma cell clones with acquisition of additional mutations over time outside of the bone marrow microenvironment. This has been further implicated by Egan et al (2012), who described the genomic transformation of newly diagnosed MM to a more aggressive variant of MM, secondary plasma cell leukaemia (sPCL), in a single patient who was followed through the course of the disease. This study identified that there is inherent tumour heterogeneity at MM diagnosis , which is followed by a fluctuating dominance of tumour clones over time that acquire various mutations that contribute to the development of refractory disease with many cPCs, i.e. sPCL.(Egan, et al 2012) Furthermore, seven single nucleotide variants (SNVs) were found to be unique to sPCL that may contribute to its leukaemic transformation from myeloma. Five of these SNVs were present in genes somatically mutated in similar genomic study cohorts (Chapman, et al 2011, Forbes, et al 2011) and include: RB1, ZKSCAN3, TNN, TUBB8 and ZNF521.(Egan, et al 2012) Future longitudinal genomic and immunophenotypic studies of cPCs as well as their corresponding bone marrow plasma cells in a larger group of patients could shed further light on their pathogenesis and role in disease progression.

There are several limitations to our study, the first being its retrospective nature. Secondly, the cut-off of ≥100 clonal cPCs is based on our single institution data and needs to be validated across other institutions. The lack of gene expression profiling (GEP) information in our patients limits our ability to assess whether the poor outcomes seen in patients with ≥100 clonal cPCs are related to high-risk biology predicted by GEP rather than cytogenetics by FISH. Furthermore, Pavia et al (2013) demonstrated that the absolute number of cPCs fluctuate along a circadian rhythm in a pattern opposite to stromal cell-derived factor 1 plasma levels and corresponding surface expression of CXC chemokine receptor 4 on cPCs. Thus, the absolute number of cPCs quantified could vary throughout the day. Furthermore, prospective validation of our data demonstrating the prognostic value of detecting ≥100 clonal cPCs is needed to assess its ability to be adopted as a standard test among other practicing clinicians. Nevertheless, this study suggests that the quantitative estimation of clonal cPCs in patients with actively relapsing disease is a powerful predictor of early mortality and may represent a cost-effective single measure of disease aggressiveness. This could help clinicians determine which of their actively relapsing patients could have an even more aggressive clinical course, requiring the institution of salvage therapy earlier, and, if so, whether clinical trials or more aggressive combination therapy would be warranted.

Key point.

The quantitation of clonal circulating plasma cells provides prognostic value in patients with relapsed multiple myeloma

ACKNOWLEDGEMENTS

This work is supported in part by Mayo Clinic Hematological Malignancies Program and in part by grants CA107476, CA62242, CA100707, CA168762 and CA 83724 from the National Cancer Institute, Rockville, MD, USA. It is also supported in part by the Jabbs Foundation, Birmingham, United Kingdom and the Henry J. Predolin Foundation, USA as well as the CTSA Grant UL1 TR000135 from the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (NIH).

Footnotes

Conflict-of-interest disclosure:

S.K.K, W.I.G, M.A.G., M.Q.L., S.V.R., A.D., W.G.M., M.M.T., P.P.S., V.G., F.K.B. and P.K.: These authors declare no competing financial interests.

REFERENCES

  1. Attal M, Harousseau JL, Stoppa AM, Sotto JJ, Fuzibet JG, Rossi JF, Casassus P, Maisonneuve H, Facon T, Ifrah N, Payen C, Bataille R. A prospective, randomized trial of autologous bone marrow transplantation and chemotherapy in multiple myeloma. Intergroupe Francais du Myelome. The New England journal of medicine. 1996;335:91–97. doi: 10.1056/NEJM199607113350204. [DOI] [PubMed] [Google Scholar]
  2. Chapman MA, Lawrence MS, Keats JJ, Cibulskis K, Sougnez C, Schinzel AC, Harview CL, Brunet JP, Ahmann GJ, Adli M, Anderson KC, Ardlie KG, Auclair D, Baker A, Bergsagel PL, Bernstein BE, Drier Y, Fonseca R, Gabriel SB, Hofmeister CC, Jagannath S, Jakubowiak AJ, Krishnan A, Levy J, Liefeld T, Lonial S, Mahan S, Mfuko B, Monti S, Perkins LM, Onofrio R, Pugh TJ, Rajkumar SV, Ramos AH, Siegel DS, Sivachenko A, Stewart AK, Trudel S, Vij R, Voet D, Winckler W, Zimmerman T, Carpten J, Trent J, Hahn WC, Garraway LA, Meyerson M, Lander ES, Getz G, Golub TR. Initial genome sequencing and analysis of multiple myeloma. Nature. 2011;471:467–472. doi: 10.1038/nature09837. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Child JA, Morgan GJ, Davies FE, Owen RG, Bell SE, Hawkins K, Brown J, Drayson MT, Selby PJ. High-dose chemotherapy with hematopoietic stem-cell rescue for multiple myeloma. The New England journal of medicine. 2003;348:1875–1883. doi: 10.1056/NEJMoa022340. [DOI] [PubMed] [Google Scholar]
  4. Dingli D, Nowakowski GS, Dispenzieri A, Lacy MQ, Hayman SR, Rajkumar SV, Greipp PR, Litzow MR, Gastineau DA, Witzig TE, Gertz MA. Flow cytometric detection of circulating myeloma cells before transplantation in patients with multiple myeloma: a simple risk stratification system. Blood. 2006;107:3384–3388. doi: 10.1182/blood-2005-08-3398. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Durie BG, Durie BG, Harousseau JL, Miguel JS, Bladé J, Barlogie B, Anderson K, Gertz M, Dimopoulos M, Westin J, Sonneveld P, Ludwig H, Gahrton G, Beksac M, Crowley J, Belch A, Boccadaro M, Cavo M, Turesson I, Joshua D, Vesole D, Kyle R, Alexanian R, Tricot G, Attal M, Merlini G, Powles R, Richardson P, Shimizu K, Tosi P, Morgan G, Rajkumar SV. International uniform response criteria for multiple myeloma. Leukemia. 2006;20:1467–1473. doi: 10.1038/sj.leu.2404284. [DOI] [PubMed] [Google Scholar]
  6. Egan JB, Shi CX, Tembe W, Christoforides A, Kurdoglu A, Sinari S, Middha S, Asmann Y, Schmidt J, Braggio E, Keats JJ, Fonseca R, Bergsagel PL, Craig DW, Carpten JD, Stewart AK. 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. doi: 10.1182/blood-2012-01-405977. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Forbes SA, Bindal N, Bamford S, Cole C, Kok CY, Beare D, Jia M, Shepherd R, Leung K, Menzies A, Teague JW, Campbell PJ, Stratton MR, Futreal PA. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic acids research. 2011;39:D945–950. doi: 10.1093/nar/gkq929. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Gonsalves WI, Rajkumar SV, Gupta V, Morice WG, Timm MM, Singh PP, Dispenzieri A, Buadi FK, Lacy MQ, Kapoor P, Gertz MA, Kumar SK. Quantification of clonal circulating plasma cells in newly diagnosed multiple myeloma: implications for redefining high-risk myeloma. Leukemia. 20142014 doi: 10.1038/leu.2014.98. Prepublished on 2014/03/13 as DOI 10.1038/leu.2014.98. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Kapoor P, Fonseca R, Rajkumar SV, Sinha S, Gertz MA, Stewart AK, Bergsagel PL, Lacy MQ, Dingli DD, Ketterling RP, Buadi F, Kyle RA, Witzig TE, Greipp PR, Dispenzieri A, Kumar S. Evidence for cytogenetic and fluorescence in situ hybridization risk stratification of newly diagnosed multiple myeloma in the era of novel therapie. Mayo Clinic proceedings. Mayo Clinic. 2010;85:532–537. doi: 10.4065/mcp.2009.0677. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Kumar SK, Therneau TM, Gertz MA, Lacy MQ, Dispenzieri A, Rajkumar SV, Fonseca R, Witzig TE, Lust JA, Larson DR, Kyle RA, Greipp PR. Clinical course of patients with relapsed multiple myeloma. Mayo Clinic proceedings. Mayo Clinic. 2004;79:867–874. doi: 10.4065/79.7.867. [DOI] [PubMed] [Google Scholar]
  11. Kumar SK, Rajkumar SV, Dispenzieri A, Lacy MQ, Hayman SR, Buadi FK, Zeldenrust SR, Dingli D, Russell SJ, Lust JA, Greipp PR, Kyle RA, Gertz MA. Improved survival in multiple myeloma and the impact of novel therapies. Blood. 2008a;111:2516–2520. doi: 10.1182/blood-2007-10-116129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Kumar S, Mahmood ST, Lacy MQ, Dispenzieri A, Hayman SR, Buadi FK, Dingli D, Rajkumar SV, Litzow MR, Gertz MA. Impact of early relapse after auto-SCT for multiple myeloma. Bone marrow transplantation. 2008b;42:413–420. doi: 10.1038/bmt.2008.180. [DOI] [PubMed] [Google Scholar]
  13. Kumar SK, Lee JH, Lahuerta JJ, Morgan G, Richardson PG, Crowley J, Haessler J, Feather J, Hoering A, Moreau P, LeLeu X, Hulin C, Klein SK, Sonneveld P, Siegel D, Bladé J, Goldschmidt H, Jagannath S, Miguel JS, Orlowski R, Palumbo A, Sezer O, Rajkumar SV, Durie BG. Risk of progression and survival in multiple myeloma relapsing after therapy with IMiDs and bortezomib: a multicenter international myeloma working group study. Leukemia. 2012;26:149–157. doi: 10.1038/leu.2011.196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Migkou M, Kastritis E, Roussou M, Gkotzamanidou M, Gavriatopoulou M, Nikitas N, Mparmparoussi D, Matsouka C, Gika D, Terpos E, Dimopoulos MA. Short progression-free survival predicts for poor overall survival in older patients with multiple myeloma treated upfront with novel agent-based therapy. European journal of haematology. 2011;87:323–329. doi: 10.1111/j.1600-0609.2011.01659.x. [DOI] [PubMed] [Google Scholar]
  15. Mikhael JR, Dingli D, Roy V, Reeder CB, Buadi FK, Hayman SR, Dispenzieri A, Fonseca R, Sher T, Kyle RA, Lin Y, Russell SJ, Kumar S, Bergsagel PL, Zeldenrust SR, Leung N, Drake MT, Kapoor P, Ansell SM, Witzig TE, Lust JA, Dalton RJ, Gertz MA, Stewart AK, Rajkumar SV, Chanan-Khan A, Lacy MQ. Management of newly diagnosed symptomatic multiple myeloma: updated Mayo Stratification of Myeloma and Risk-Adapted Therapy (mSMART) consensus guidelines 2013. Mayo Clinic proceedings. Mayo Clinic. 2013;88:360–376. doi: 10.1016/j.mayocp.2013.01.019. [DOI] [PubMed] [Google Scholar]
  16. Mishima Y, Lohr J, Tai Y, Flores L, Aljawai Y, Laubacj JP, Richardson PC, Roccaro AM, Anderson KC, Munshi NC, Ghobrial IM. Molecular Analysis Of Circulating Tumor Cells Identifies Mutations That Are Distinct From Those Present In The Bone Marrow Of Patients With Multiple Myeloma. Blood (ASH Annual Meeting Abstracts) 2013;122:533. [Google Scholar]
  17. Nowakowski GS, Witzig TE, Dingli D, Tracz MJ, Gertz MA, Lacy MQ, Lust JA, Dispenzieri A, Greipp PR, Kyle RA, Rajkumar SV. 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. doi: 10.1182/blood-2005-05-1858. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Paiva B, Paino T, Sayagues JM, Garayoa M, San-Segundo L, Martín M, Mota I, Sanchez ML, Bárcena P, Aires-Mejia I, Corchete L, Jimenez C, Garcia-Sanz R, Gutierrez NC, Ocio EM, Mateos MV, Vidriales MB, Orfao A, San Miguel JF. Detailed characterization of multiple myeloma circulating tumor cells shows unique phenotypic, cytogenetic, functional, and circadian distribution profile. Blood. 2013;122:3591–3598. doi: 10.1182/blood-2013-06-510453. [DOI] [PubMed] [Google Scholar]
  19. Rawstron AC, Owen RG, Davies FE, Johnson RJ, Jones RA, Richards SJ, Evans PA, Child JA, Smith GM, Jack AS, Morgan GJ. Circulating plasma cells in multiple myeloma: characterization and correlation with disease stage. British journal of haematology. 1997;97:46–55. doi: 10.1046/j.1365-2141.1997.72653.x. [DOI] [PubMed] [Google Scholar]
  20. Richardson P, Mitsiades C, Schlossman R, Ghobrial I, Hideshima T, Chauhan D, Munshi N, Anderson K. The treatment of relapsed and refractory multiple myeloma. Hematology / the Education Program of the American Society of Hematology. American Society of Hematology. Education Program. 2007:317–323. doi: 10.1182/asheducation-2007.1.317. [DOI] [PubMed] [Google Scholar]
  21. Teoh G, Anderson KC. Interaction of tumor and host cells with adhesion and extracellular matrix molecules in the development of multiple myeloma. Hematology/oncology clinics of North America. 1997;11:27–42. doi: 10.1016/s0889-8588(05)70413-5. [DOI] [PubMed] [Google Scholar]
  22. Witzig TE, Dhodapkar MV, Kyle RA, Greipp PR. Quantitation of circulating peripheral blood plasma cells and their relationship to disease activity in patients with multiple myeloma. Cancer. 1993;72:108–113. doi: 10.1002/1097-0142(19930701)72:1<108::aid-cncr2820720121>3.0.co;2-t. [DOI] [PubMed] [Google Scholar]
  23. Witzig TE, Kimlinger TK, Ahmann GJ, Katzmann JA, Greipp PR. Detection of myeloma cells in the peripheral blood by flow cytometry. Cytometry. 1996;26:113–120. doi: 10.1002/(SICI)1097-0320(19960615)26:2<113::AID-CYTO3>3.0.CO;2-H. [DOI] [PubMed] [Google Scholar]

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