Circulating plasma cells as a predictive biomarker in Multiple myeloma: an updated systematic review and meta-analysis

Figures & data

Figure 1. Flow diagram of study selection.

Table 1. Characteristics of studies included in the meta-analysis.

Study	Published Year (Study time)	Country	Sample size	Detection time	Median Age (range)	Median Follow-up (months)	Detection method	Cut-off	Staging System	High/Standard-risk CA rate	Initial Treatment	Data type	HR	NOS score
Witzig [9]	1996(1984–1993)	USA	254	Baseline	66 (20–93)	NR	IF	4%	DS	NR	Traditional chemotherapy	Multivariate	Reported	8
Gertz [30]	1997(1989–1994)	USA	33	B-ASCT	52 (32–64)	NR	IF	0.2 × 10^6 cells/L	NR	NR	Traditional chemotherapy	Others	Estimated	6
Nowakowski [10]	2005(1998–2003)	USA	302	Baseline	65 (29–94)	33.5	MFC	0.02%	ISS	NR	Traditional chemotherapy + Novel agents	Multivariate	Reported	9
Dingli [31]	2006(1999–2002)	USA	246	B-ASCT	57 (30–75)	34	MFC	0.002%	DS	NR	Traditional chemotherapy	Others	Reported	7
Korthals (a) [11]	2013(2000–2008)	Germany	21	B-ASCT	54 (31–66)	45	PCR	0.001%	DS/ISS	NR	Traditional chemotherapy	Others	Estimated	7
Korthals (b) [11]	2013(2000–2008)	Germany	32	A-ASCT	54 (31–66)	45	PCR	0.001%	DS/ISS	NR	Traditional chemotherapy	Others	Estimated	7
Gonsalves [32]	2014(2009–2011)	USA	145	R/R	63 (43 − 80)	21	MFC	0.067%	NR	NR	Novel agents	Multivariate	Reported	7
Vagnoni [13]	2015(2005–2013)	Italy	104	Baseline	72 (45–86)	36	MFC	0.082%	DS/ISS	23% / 63%	Traditional chemotherapy + Novel agents	Multivariate	Reported	9
An [8]	2015(2004–2012)	China	767	Baseline	59	41	CM	2%	DS/ISS	30% / NR	Novel agents	Others	Estimated	7
Chakraborty (a) [14]	2017(2007–2015)	USA	199	Baseline	62 (25–76)	58	MFC	0.01%	ISS/R-ISS	18% / NR	Novel agents	Multivariate	Reported	9
Chakraborty (b) [14]	2017(2007–2015)	USA	165	B-ASCT	62 (25–76)	58	MFC	0.01%	ISS/R-ISS	16% / NR	Novel agents	Multivariate	Reported	9
Bae [33]	2018(2013–2014)	South Korea	85	Baseline	68(45–87)	40	MFC	0.02%	ISS	25% / NR	Novel agents	Multivariate	Reported	9
Cowan [34]	2018(2012–2015)	USA	144	B-ASCT	66 (45–72)	22	MFC	0.01%	ISS	19% / NR	Novel agents	Multivariate	Reported	8
Abe [15]	2019(2012–2018)	Japan	167	Baseline	74 (66–81)	28	MFC	0.1%	DS/ISS /R-ISS	23% / NR	Novel agents	Multivariate	Reported	9
Gonsalves* [24]	2020(2009–2017)	USA	566	Baseline	66 (27 − 95)	NR	MFC	5 cells/μL	ISS/R-ISS	24% / NR	Novel agents	Multivariate	Reported	8
Cheng [17]	2021(2017–2020)	China	130	Baseline	59 (30–88)	11	MFC	0.038%	DS/ISS /R-ISS	48% / NR	Novel agents	Multivariate	Reported	8
Galieni [18]	2021(2005–2017)	Italy	168	Baseline	72 (43–90)	56	MFC	0.002%	DS/ISS /R-ISS	32% / 52%	Traditional chemotherapy + Novel agents	Multivariate	Reported	9
Vasco-Mogorrón [19]	2021(2010–2017)	Spain	242	Baseline	69	52	MFC	0.0035%	ISS	NR	Novel agents	Multivariate	Reported	9
Garcés [23]	2022(2013–2017)	Spain	374	Baseline	57 (31–65)	60	NGF	0.01%	ISS	22% / 57%	Novel agents	Multivariate	Reported	8
Bertamini [20]	2022(2014–2021)	Italy	401	Baseline	57	50	MFC	0.07%	ISS/R-ISS	29% / 57%	Novel agents	Multivariate	Reported	8
Jelinek (a) [21]	2023(2012–2019)	Czech	395	Baseline	72 (44–91)	37	NGF	2%	ISS	14% / 32%	Novel agents	Others	Reported	7
Jelinek (b) [21]	2023(2012–2019)	Czech	185	Baseline	60 (31–81)	49	NGF	2%	ISS	NR	Novel agents	Others	Reported	7
Xia [22]	2023(2015–2021)	China	301	Baseline	56 (30–84)	29	MFC	0.105%	DS/ISS/R-ISS/R2-ISS	NR	Traditional chemotherapy + Novel agents	Others	Estimated	7
Sathya [35]	2023(2018–2021)	India	66	Baseline	56	NR	MFC	0.197%	ISS	NR	Novel agents	Others	Reported	6
Yao [25]	2023(2018–2021)	China	145	Baseline	58(31–73)	22	MFC	0.165%	ISS/R-ISS	25% / 75%	Novel agents	Multivariate	Reported	8

Note: * Same author but different study.

NR, Not Reported; R/R, Relapsed or Refractory; B-ASCT, Before Autologous Stem Cell Transplantation; A-ASCT, After Autologous Stem Cell Transplantation; IF, Immunofluorescence; MFC, Multi-parameter Flow Cytometry; PCR, Polymerase Chain Reaction; CM, Conventional Morphology; NGF, Next-generation Flow Cytometry; CA, Chromosomal Abnormalities.

Figure 2. Meta-analysis of the association between elevated CPCs and OS in MM.

Table 2. Subgroup analysis of prognostic role of CPCs for OS in patients with MM.

					Heterogeneity
Subgroup analysis	No. of cohorts	Effects model	Pooled HR (95%CI)	p	I^2	p
OS All	24	Random	2.19 (1.81, 2.66)	<0.001	64.3%	<0.001
Region
America	9	Random	1.85 (1.48, 2.31)	<0.001	43.9%	0.075
Europe	8	Random	2.64 (1.88, 3.70)	<0.001	50.8%	0.048
Asia	8	Random	2.30 (1.49, 3.54)	<0.001	67.4%	0.005
Sample size
≥200	10	Random	1.97 (1.52, 2.57)	<0.001	79.6%	<0.001
<200	14	Random	2.49 (1.95, 3.20)	<0.001	14.7%	0.292
Cut-off value
≥1%	4	Random	1.72 (1.27, 2.33)	<0.001	52.6%	0.097
0.01% − 1%	9	Random	2.34 (1.76, 3.12)	<0.001	39.1%	0.107
≤0.01%	9	Random	2.43 (1.61, 3.67)	<0.001	73.7%	<0.001
Other CPCs definition	2	Random	1.89 (1.37, 2.61)	<0.001	0.0%	0.532
Detection time
Baseline	17	Random	2.22 (1.77, 2.78)	<0.001	69.2%	<0.001
Post-treatment	7	Random	2.16 (1.42, 3.28)	<0.001	51.3%	0.055
Initial treatment
Traditional chemotherapy	5	Random	1.72 (1.35, 2.19)	<0.001	0.0%	0.746
Traditional chemotherapy + Novel agents	3	Random	2.23 (1.25, 3.99)	0.007	76.2%	0.015
Novel agents	16	Random	2.33(1.79, 3.04)	<0.001	71.5%	<0.001
Data type
Multivariate	14	Random	2.45 (1.90, 3.17)	<0.001	64.6%	<0.001
Others	10	Random	1.84 (1.42, 2.38)	<0.001	48.4%	0.042

Note: Novel agents refer to proteasome inhibitors and immunomodulatory.

Figure 3. Meta-analysis of the association between elevated CPCs and PFS in MM.

Table 3. Subgroup analysis of prognostic role of CPCs for PFS in patients with MM.

					Heterogeneity
Subgroup analysis	No. of cohorts	Effects model	Pooled HR (95%CI)	p	I^2	p
PFS All	17	Random	2.45 (1.93, 3.12)	<0.001	79.3%	<0.001
Region
America	3	Random	1.82 (1.24, 2.68)	0.002	62.6%	0.069
Europe	7	Random	3.14 (2.28, 4.33)	<0.001	67.6%	0.005
Asia	7	Random	2.17 (1.52, 3.10)	<0.001	74.3%	<0.001
Sample size
≥200	6	Random	2.60 (1.63, 4.13)	<0.001	91.8%	<0.001
<200	11	Random	2.31 (1.85, 2.88)	<0.001	38.5%	0.092
Cut-off value
≥1%	3	Random	2.92 (1.05, 8.13)	0.041	93%	<0.001
0.01% − 1%	8	Random	2.44 (2.05, 2.91)	<0.001	0.0%	0.893
≤0.01%	6	Random	2.37 (1.55, 3.63)	<0.001	84.1%	<0.001
Detection time
Baseline	15	Random	2.54 (1.95, 3.30)	<0.001	80.0%	<0.001
Post-treatment	2	Random	1.98 (1.00, 3.92)	0.051	80.8%	0.022
Initial treatment
Traditional chemotherapy + Novel agents	3	Random	2.70 (2.07, 3.51)	<0.001	0.0%	0.892
Novel agents	14	Random	2.40 (1.82, 3.18)	<0.001	82.4%	<0.001
Data type
Multivariate	11	Random	2.37 (1.83, 3.06)	<0.001	70.0%	<0.001
Others	6	Random	2.67 (1.61, 4.45)	<0.001	86.9%	<0.001

Note: Novel agents refer to proteasome inhibitors and immunomodulatory.

Table 4. Association between CPCs and clinicopathological features in patients with MM.

					Heterogeneity
Factors	No. of cohorts	Effects model	OR (95%CI)	p	I^2	p
ISS stage (III vs I-II)	14	Fixed	2.89 (2.41, 3.46)	<0.001	44.6%	0.037
R-ISS stage (III vs I-II)	8	Random	3.65 (2.43, 5.50)	<0.001	50.2%	0.050
Chromosomal abnormalities (high risk vs standard risk)	5	Fixed	2.22 (1.60, 3.08)	<0.001	23.1%	0.267

Note: High-risk was defined as the presence of t(4,14), t(14,16), and/or del(17p); standard risk was defined as the absence of these genetic abnormalities.

Figure 4. Sensitivity analysis of the enrolled studies. (A) Sensitivity analysis using the leave-one-out test for OS; (B) Sensitivity analysis using the leave-one-out test for PFS.

Figure 5. Publication bias of the studies. (A) Begg’s funnel plot for OS; (B) Begg’s funnel plot for PFS.

Witzig TE, Gertz MA, Lust JA, et al. Peripheral blood monoclonal plasma cells as a predictor of survival in patients with multiple myeloma. Blood. 1996;88(5):1780–1787. doi: 10.1182/blood.V88.5.1780.1780.

 PubMed Web of Science ®Google Scholar

Gertz MA, Witzig TE, Pineda AA, et al. Monoclonal plasma cells in the blood stem cell harvest from patients with multiple myeloma are associated with shortened relapse-free survival after transplantation. Bone Marrow Transplant. 1997;19(4):337–342. doi: 10.1038/sj.bmt.1700670.

 PubMed Web of Science ®Google Scholar

Nowakowski GS, Witzig TE, Dingli D, et al. Circulating plasma cells detected by flow cytometry as a predictor of survival in 302 patients with newly diagnosed multiple myeloma. Blood. 2005;106(7):2276–2279. doi: 10.1182/blood-2005-05-1858.

 PubMed Web of Science ®Google Scholar

Dingli D, Nowakowski GS, Dispenzieri A, et al. Flow cytometric detection of circulating myeloma cells before transplantation in patients with multiple myeloma: a simple risk stratification system. Blood. 2006;107(8):3384–3388. doi: 10.1182/blood-2005-08-3398.

 PubMed Web of Science ®Google Scholar

Korthals M, Sehnke N, Kronenwett R, et al. Molecular monitoring of minimal residual disease in the peripheral blood of patients with multiple myeloma. Biol Blood Marrow Transplant. 2013;19(7):1109–1115. doi: 10.1016/j.bbmt.2013.04.025.

 PubMed Web of Science ®Google Scholar

Gonsalves WI, Morice WG, Rajkumar V, et al. Quantification of clonal circulating plasma cells in relapsed multiple myeloma. Br J Haematol. 2014;167(4):500–505. doi: 10.1111/bjh.13067.

 PubMed Web of Science ®Google Scholar

Vagnoni D, Travaglini F, Pezzoni V, et al. Circulating plasma cells in newly diagnosed symptomatic multiple myeloma as a possible prognostic marker for patients with standard-risk cytogenetics. Br J Haematol. 2015;170(4):523–531. doi: 10.1111/bjh.13484.

 PubMed Web of Science ®Google Scholar

An G, Qin X, Acharya C, et al. Multiple myeloma patients with low proportion of circulating plasma cells had similar survival with primary plasma cell leukemia patients. Ann Hematol. 2015;94(2):257–264. doi: 10.1007/s00277-014-2211-0.

 PubMed Web of Science ®Google Scholar

Chakraborty R, Muchtar E, Kumar SK, et al. Serial measurements of circulating plasma cells before and after induction therapy have an independent prognostic impact in patients with multiple myeloma undergoing upfront autologous transplantation. Haematologica. 2017;102(8):1439–1445. doi: 10.3324/haematol.2017.166629.

 PubMed Web of Science ®Google Scholar

Bae MH, Park CJ, Kim BH, et al. Increased circulating plasma cells detected by flow cytometry predicts poor prognosis in patients with plasma cell myeloma. Cytometry B Clin Cytom. 2018;94(3):493–499. doi: 10.1002/cyto.b.21606.

 PubMed Web of Science ®Google Scholar

Cowan AJ, Stevenson PA, Libby EN, et al. Circulating plasma cells at the time of collection of autologous PBSC for transplant in multiple myeloma patients is a negative prognostic factor even in the age of Post-Transplant maintenance therapy. Biol Blood Marrow Transplant. 2018;24(7):1386–1391. doi: 10.1016/j.bbmt.2018.02.017.

 PubMed Web of Science ®Google Scholar

Abe Y, Narita K, Kobayashi H, et al. Pretreatment (18)F-FDG PET/CT combined with quantification of clonal circulating plasma cells as a potential risk model in patients with newly diagnosed multiple myeloma. Eur J Nucl Med Mol Imaging. 2019;46(6):1325–1333. doi: 10.1007/s00259-019-4275-5.

 PubMed Web of Science ®Google Scholar

Gonsalves WI, Jevremovic D, Nandakumar B, et al. Enhancing the R-ISS classification of newly diagnosed multiple myeloma by quantifying circulating clonal plasma cells. Am J Hematol. 2020;95(3):310–315. doi: 10.1002/ajh.25709.

 PubMed Web of Science ®Google Scholar

Cheng Q, Cai L, Zhang Y, et al. Circulating plasma cells as a biomarker to predict newly diagnosed multiple myeloma prognosis: developing nomogram prognostic models. Front Oncol. 2021;11:639528. doi: 10.3389/fonc.2021.639528.

 PubMedGoogle Scholar

Galieni P, Travaglini F, Vagnoni D, et al. The detection of circulating plasma cells may improve the revised international staging system (R-ISS) risk stratification of patients with newly diagnosed multiple myeloma. Br J Haematol. 2021;193(3):542–550. doi: 10.1111/bjh.17118.

 PubMed Web of Science ®Google Scholar

Vasco-Mogorrón MA, Campillo JA, Periago A, et al. Blood-based risk stratification for pre-malignant and symptomatic plasma cell neoplasms to improve patient management. Am J Cancer Res. 2021;11:2736–2753.

 PubMed Web of Science ®Google Scholar

Garcés JJ, Cedena MT, Puig N, et al. Circulating tumor cells for the staging of patients with newly diagnosed transplant-eligible multiple myeloma. J Clin Oncol. 2022;40(27):3151–3161. doi: 10.1200/jco.21.01365.

 PubMed Web of Science ®Google Scholar

Bertamini L, Oliva S, Rota-Scalabrini D, et al. High levels of circulating tumor plasma cells as a key hallmark of aggressive disease in transplant-eligible patients with newly diagnosed multiple myeloma. J Clin Oncol. 2022;40(27):3120–3131. doi: 10.1200/jco.21.01393.

 PubMed Web of Science ®Google Scholar

Jelinek T, Bezdekova R, Zihala D, et al. More than 2% of circulating tumor plasma cells defines plasma cell Leukemia-Like multiple myeloma. J Clin Oncol. 2023;41(7):1383–1392. doi: 10.1200/jco.22.01226.

 PubMed Web of Science ®Google Scholar

Xia Y, Shen N, Zhang R, et al. High-risk multiple myeloma predicted by circulating plasma cells and its genetic characteristics. Front Oncol. 2023;13:1083053. doi: 10.3389/fonc.2023.1083053.

 PubMed Web of Science ®Google Scholar

Sathya P, Kayal S, Srinivas BH, et al. Quantification of circulating clonal plasma cells by multiparametric flow cytometry as a prognostic marker in patients with newly diagnosed multiple myeloma. Int J Lab Hematol. 2023;45(6):917–926. doi: 10.1111/ijlh.14156.

 PubMed Web of Science ®Google Scholar

Yao W, Yang H, You H, et al. The prognostic significance of circulating plasma cells in newly diagnosed multiple myeloma patients. Front Oncol. 2023;13:1266868. doi: 10.3389/fonc.2023.1266868.

 PubMed Web of Science ®Google Scholar

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.