Predicting early relapse in patients with multiple myeloma

Although emerging novel agents have improved patient survival outcomes significantly, early relapse (ER) remains an area of unmet clinical need in the multiple myeloma (MM) setting. There have been a number of attempts to better characterize patients who relapse: at the 25th European Hematology Association (EHA) Annual Congress, three posters outlined the latest advancements achieved by GIMEMA and the European Myeloma Network in Italy, in predicting ER and survival outcomes for patients with MM. The Multiple Myeloma Hub is pleased to provide a summary.

Simplified early relapse in multiple myeloma (S-ERMM) score¹

Gian Maria Zaccaria and colleagues aimed to design a predictive model and scoring system to define patients who will likely relapse within 18 months of diagnosis (ER18). The team hoped to correlate the simplified early relapse in multiple myeloma (S-ERMM) score with overall survival (OS) and progression-free survival until the second relapse (PFS2).

Below you will find a summary of how the authors created and validated this model, but you can calculate the S-ERMM score here: sermm.emnitaly.org/.

Study design

• Patient data from seven clinical trials (N = 2,190; NCT01093196, NCT01346787, NCT01857115, NCT01190787, NCT00551928, NCT01091831, NCT02203643) with 14 distinct clinical features (Table 1) were pooled
• Multivariate analyses and OS landmark analysis were undertaken to define low-, medium-, and high-risk ER
  • The S-ERMM model was constructed using this survival analysis
• The established model was validated in an independent study (FORTE trial, NCT02203643)

Results

• Of the 2,190 patient data sets, 1,218 were complete and eligible for the analysis. These patients subsequently comprised the training and validation cohorts (Table 1)

Table 1. Baseline characteristics of patients eligible for ER18 analysis vs reference population (late relapse or death without progression after 18 months)¹

B2M, beta 2 microglobulin; CA, chromosomal abnormality; ER18, early relapse (within 18 months of diagnosis); FLC, free light chain; IgA, immunoglobulin A; IQR, interquartile range; LDH, lactate dehydrogenase; PCbm, plasma-cells bone marrow; R-ISS, Revised International Staging System; ULN, upper limit of normal
Features	Training cohort (n = 844)			Validation cohort (n = 374)
	ER18 (n = 312)	Reference (n = 532)	p	ER18 (n = 61)	Reference (n = 313)	p
Median age, years (IQR)	68 (58–75)	65 (57–73)	0.03	56 (48—62)	58 (52–62)	0.17
Median albumin, g/dL (IQR)	3.7 (3.2–4.1)	3.9 (3.5–4.2)	< 0.01	3.7 (3.4–4.1)	3.9 (3.5–4.3)	0.05
LDH > ULN, %	14	7	< 0.01	28	12	0.02
Presence of CAs, %
Del(17p)	19	12	< 0.01	18	13	0.38
t(4;14)	20	10	< 0.01	28	13	0.04
t(11;14)	16	20	0.19	23	13	1
t(14;16)	5	3	0.17	6.6	5	0.77
R-ISS II/III, %	82	67	< 0.01	87	63	< 0.01
PCbm > 60%, %	36	25	< 0.01	44	36	0.26
FLC λ, %	39	34	0.1	30	40	0.17
Plasmacytoma, %	9	10	0.74	16	12	0.53

•  Comparing both subgroups, ER18 vs reference population, the researchers identified six clinical features that significantly increased the risk of ER18 (Table 2) and were incorporated into the S-ERMM model (Table 3)

Table 2. Clinical features significantly and positively correlated with an increased risk of ER18¹

FLC, free light chain; LDH, lactate dehydrogenase; PCbm, plasma-cells bone marrow; ULN, upper limit of normal
Variable	Coefficient, β proportionality	Score
LDH > ULN	2.4	5
Presence of t(4;14)	2.6	5
Presence of del(17p)	1.7	3
Abnormal albumin	1.5	3
PCbm > 60%	1.7	3
FLC λ	1.0	2

 

Table 3. S-ERMM risk groups¹

S-ERMM, simplified early relapse in multiple myeloma score
Risk	Score
S-ERMM Low	≤ 5
S-ERMM Intermediate	6–10
S-ERMM High	≥ 11

 As demonstrated in Table 4, median OS, PFS2, and 4-year probability rates were significantly lower in patients classified as

• • S-ERMM intermediate vs S-ERMM low
  • S-ERMM high vs S-ERMM low
  • S-ERMM high vs S-ERMM intermediate

Table 4. Patient OS and PFS2 rates with regard to S-ERMM characterization¹

CI, confidence interval; NR, not reached; OS, overall survival; PFS2, progression-free survival until second relapse
	OS			PFS2
S-ERMM characterization	Low (n = 574)	Intermediate (n = 214)	High (n = 56)	Low (n = 574)	Intermediate (n = 214)	High (n = 56)
Events	204	117	40	302	150	47
Median, months	NR	59.5	31.5	62.3	40.0	19.8
95% CI	83–NR	50.5–74.2	23–53.5	54.6–69.7	33.7–46.5	16.6–30.7
4-year probability	0.75	0.59	0.35	0.60	0.40	0.22
95% CI	0.72–0.79	0.52–0.66	0.24–0.51	0.55–0.64	0.33–0.47	0.13–0.36

Study conclusions

• Three distinct S-ERMM categories of patients were identified as being able to assess the risk of relapse within 18 months from diagnosis, and identify effectively those patients with a higher risk in order to plan therapy strategy and follow-up accordingly

Circulating plasma cells and sustained measurable residual disease negativity²

Poor patient survival outcomes have been associated with elevated levels of circulating plasma cells (CPCs). Luca Bertamini and colleagues aimed to determine the relationship between CPC levels at the time of MM diagnosis and sustained measurable residual disease (MRD) negativity (≥ 12 months; MRDsus12), and to identify clinical features that could couple with both variables in patients enrolled on the FORTE/UNITO-MM1 trial (NCT02203643).

Study design

• Patients enrolled on the FORTE trial (N = 474) were assessed for
  • pre-maintenance MRD
  • 6-month periodical MRD
  • MRDsus12
  • CPC at diagnosis (% and absolute count determined using flow cytometry)

• Eligible patients comprised two study cohorts:
  • Cohort 1: All patients evaluated for MRDsus12 (n = 390)
  • Cohort 2: Of these, patients presenting CPC at baseline who were evaluated for MRDsus12 (n = 328; median follow-up, 34.4 months)

Results

• CPC cut-offs for cohort 2 were defined as
  • low CPC: ≤ 0.07%
  • high CPC: > 0.07%
• A number of clinical features differed significantly between high and low CPC readouts (Table 5)
• Favorable MRDsus12 rates were associated with low CPC, and differed between treatment regimens (Figure 1)

Table 5. Clinical features associated with CPC levels at the time of MM diagnosis in patients from cohort 2²

CPC, circulating plasma cells; FISH, fluorescence in situ hybridization; ISS, International Staging System; LDH, lactate dehydrogenase; PCbm, plasma-cells bone marrow; R-ISS, Revised ISS; ULN, upper limit of normal
Feature, %	Low CPC (n = 220)	High CPC (n = 108)	p
ISS
I	62	30	< 0.001
II	27	41
III	10	30
FISH
Standard risk	74	51	< 0.001
High risk	26	49
del(17p)	11	24	0.006
del(13)	44	67	< 0.001
t(14;16)	2	12	0.001
amp(1q)	42	59	0.006
LDH > ULN	9	24	< 0.001
R-ISS
I	40	12	< 0.001
II	56	71
III	4	17
PCbm (> 60%)	26	50	< 0.001

 

Figure 1. MRDsus12 rates²

A MRDsus12 rates including all eligible patients (cohort 2), and B MRDsus12 rates considering patients with MRD negativity only; analyzed according to high vs low CPC at time of MM diagnosis²

ASCT, autologous stem cell transplantation; CPC, circulating plasma cells; KCd, carfilzomib + cyclophosphamide + dexamethasone; KRd, carfilzomib + lenalidomide + dexamethasone; KRd12, 12 cycles of KRd; MRD, measurable residual disease, MRDsus12, sustained MRD negativity for ≥ 12 months

Study conclusions

• Several clinical features are associated with high CPC levels which, in turn, are considered to reduce the probability of achieving an MRDsus12
• Patients with high CPC who were treated with KRd-ASCT demonstrated superior MRDsus12 rates vs those treated with KRd12 or KCd-ASCT (Figure 1)
• Data from this study provide valuable information on independent factors contributing to ER, and additional tools to identify those patients at a higher risk, but longer-term analyses are required to consolidate these results

Validation of ER predictors—S-ERMM and MRD negativity³

A team led by Francesca Gay aimed to establish and strengthen the S-ERMM score as well as identify additional features that are associated with MRD negativity achievement and may predict ER18.

Study design

• Patients enrolled on the FORTE trial (N = 474) were included in multivariate analyses to determine baseline features associated with ER18 and MRD status

Results

• In patients receiving novel agents for the treatment of NDMM, predictors of MRD negativity and risk of ER18 were identified by multivariate analyses (Table 6)

Table 6. Clinical factors, other than therapy, that significantly impact MRD negativity rates and patient risk of ER18³

CPC, circulating plasma cells; ER18, early relapse (within 18 months of diagnosis); ISS, International Staging System; MRD, measurable residual disease; PCbm, plasma cells in bone marrow; S-ERMM, simplified early relapse in multiple myeloma
	Odds ratio (95% CI)	p
Decreased MRD negativity
ISS III vs ISS II	0.50 (0.29–0.85)	0.01
PCbm > 60% vs ≤ 60%	0.65 (0.44–0.97)	0.03
del(17p) present vs not present	0.40 (0.22–0.72)	< 0.001
Increased risk of ER18
S-ERMM
Intermediate vs low	1.93 (0.99–3.74)	0.049
High vs low	4.45 (1.68–11.72)	< 0.001
CPC, > 0.13 vs ≤ 0.13	2.68 (1.38–5.20)	< 0.001
Decreased risk of ER18
MRD negative vs MRD positive	0.26 (0.14–0.48)	< 0.001

Study conclusions

• This analysis validated S-ERMM as an accurate predictor of ER18
• CPC level at baseline was identified as an independent factor to predict ER18 and could be included in the S-ERMM model
• Achieving MRD negativity significantly reduces the risk of early relapse
• International Staging System (ISS)-III, PCbm ≥ 60%, and presence of del(17p) could help identify at diagnosis the patients that have a lower probability of achieving a deep response with therapy, and enable adaptation of a therapeutic strategy accordingly

Conclusion

Data from these studies provide a clearer understanding of the factors contributing towards ER in patients with MM. The combination of the S-ERMM system with other ER contributors may pave the way towards risk-adaptive treatment regimens.