REGULAR ARTICLE
Network meta-analysis of randomized trials in multiple myeloma:
efficacy and safety in relapsed/refractory patients
Cirino Botta,1,* Domenico Ciliberto,1,* Marco Rossi,1 Nicoletta Staropoli,1 Maria Cucè,1 Teresa Galeano,1 Pierosandro Tagliaferri,1 and
Pierfrancesco Tassone1,2
1
Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore Venuta University Campus, Catanzaro, Italy; and 2Sbarro Institute for Cancer
Research and Molecular Medicine, Center for Biotechnology, College of Science and Technology, Temple University, Philadelphia, PA
Key Points
• Different therapeutic
agents are currently
available for the treatment of RRMM.
• By performing an
NMA, we identified
a lenalidomidedexamethasone 1 mAb
regimen as the most
active therapeutic option in this setting.
Despite major therapeutic advancements, multiple myeloma (MM) is still incurable and
relapsed/refractory multiple myeloma (RRMM) remains a challenge; the rational choice of the
most appropriate regimen in this setting is currently undefined. We performed a systematic
review and 2 standard pairwise meta-analyses to evaluate the efficacy of regimens that
have been directly compared with bortezomib or immunomodulatory imide drugs (IMiDs) in
head-to-head clinical trials and a network meta-analysis (NMA) to determine the relevance of
each regimen on the basis of all the available direct and indirect evidence. Sixteen trials were
included in the pairwise meta-analyses, and 18 trials were included in the NMA. Pairwise
meta-analyses showed that a 3-drug regimen (bortezomib- or IMiD-based) was superior to
a 2-drug regimen in progression-free-survival (PFS) and overall response rate (ORR). NMA
showed that an IMiD backbone associated with anti-MM monoclonal antibodies (mAbs)
(preferably) or proteasome inhibitors had the highest probability of being the most effective
regimen with the lowest toxicity. The combination of daratumumab, lenalidomide, and
dexamethasone ranked as the first regimen in terms of activity, efficacy, and tolerability
according to the average value between surface under the cumulative ranking curve of PFS,
overall survival, ORR, complete response rate, and safety. This is the first NMA comparing all
currently available regimens evaluated in published randomized trials for the treatment of
RRMM, but our results need to be interpreted taking into account differences in their patient
populations. Our analysis suggests that IMiDs plus new anti-MM mAb–containing regimens
are the most active therapeutic option in RRMM.
Introduction
Multiple myeloma (MM) is among the most common hematologic malignancies. Current milestones of
MM therapy include either a triple- or double-drug combination, based on proteasome inhibitors (PIs)
and/or immunomodulatory imide drugs (IMiDs) plus dexamethasone with or without chemotherapy.
Eligible patients further undergo autologous stem cell transplantation and consolidation therapy,1,2 and
patients not eligible for autologous stem cell transplantation enter follow-up.3,4 However, virtually all
patients still relapse and require further treatment. Thus, relapsed/refractory MM (RRMM) currently
represents the main focus of intensive clinical research. Indeed, a plethora of new agents, including
second-generation PIs, histone deacetylase (HDAC) inhibitors, and monoclonal antibodies (mAbs), have
shown consistent activity in prospective phase 2/3 clinical trials.5,6 On the basis of these premises, we
undertook a systematic review and 2 pairwise meta-analyses of phase 2/3 randomized trials in the
Submitted 16 December 2016; accepted 7 February 2017. DOI 10.1182/
bloodadvances.2016003905.
The full-text version of this article contains a data supplement.
© 2017 by The American Society of Hematology
*C.B. and D.C. contributed equally to this study.
28 FEBRUARY 2017 x VOLUME 1, NUMBER 7
455
RRMM setting to evaluate the impact and safety of new therapeutic
strategies compared with bortezomib with or without dexamethasone or IMiDs 1 dexamethasone regimens. Because each trial
usually compares only 2 regimens, it was challenging to evaluate the
relative efficacy of all the regimens we investigated. To overcome
these limitations, we further performed a network meta-analysis
(NMA), which is a recently introduced Bayesian statistical approach
that allows combining direct and indirect evidence to rank the
different treatments according to their efficacy and safety.
Methods
Search strategy
An electronic search for relevant publications was performed by using PubMed,
Embase, Ovid, the Cochrane collaboration database, and proceedings from
the major international meetings in hematology and oncology. Only prospective
studies were allowed in this analysis.7 The following search headings were
used: “multiple myeloma”, “relapse”, “refractory”, “randomized clinical trial”,
“management”, “bortezomib”, “lenalidomide”, “thalidomide”, and “therapy” in
different combinations (eg, “relapsed/refractory multiple myeloma”). All titles
were screened and abstracts were reviewed. The related articles function,
article references, and Google Scholar were also screened for other applicable
publications and were used for searching related studies, abstracts, and
citations. Only articles in English were considered. The last date of the search
was June 24, 2016. A systematic review was performed according to the
guidelines and recommendations from the preferred reporting items for
systematic reviews and meta‐analyses (PRISMA) checklist.8,9
Inclusion criteria
To be included in the analysis, the studies had to meet several criteria. They
had to involve only patients with a diagnosis of RRMM (including patients in
second-line treatment), be randomized controlled trials (RCTs) with or
without blinding, be abstracts or unpublished data (to be included only if they
had sufficient information on study design, characteristics of participants,
interventions, and outcomes), have patients who received an unconventional
or new regimen in the experimental arm, and have patients who received a
standard regimen for RRMM in the control arm.
Exclusion criteria
Studies were excluded from the analysis if they were not comparative or not
prospective, if outcomes of interest were not reported, if the methodology
was not clearly reported, and if only patients after the second line of treatment were included.
Data extraction and quality assessment
Two reviewers (C.B. and N.S.) independently reviewed the literature according
to the above predefined strategy and criteria. Each reviewer extracted the
following data: title and reference details (first author, year), study population
characteristics (number of patients in study, number treated by each
approach, number of treatment lines, previous exposure to bortezomib or
IMiDs), type of interventions, and outcome data. For each trial, we evaluated
hazard ratios (HRs) of progression-free survival (PFS) and overall survival
(OS); odds ratio (OR) of overall response rate (ORR), very good partial
response rate (VGPR), and complete response (CR); and risk ratio (RR) for
safety (evaluation of common toxicities). If the HR of survival curves was not
reported,10,11 it was derived from the graph by using the method of Tierney
et al12 (see supplemental Data).
All data were recorded independently in separate databases and were
compared at the end of the reviewing process to limit selection bias. The
database was also reviewed by 2 additional investigators (D.C. and M.C.).
Duplicates were removed and any disparities were clarified.
Selected studies were assessed for quality according to the Cochrane
Handbook for Systematic Reviews of Interventions, as described elsewhere,13 by assigning 1 point for each of the following 5 requirements:
456
BOTTA et al
method of randomization, allocation concealment, blindness, withdrawal or
dropout, and adequacy of follow-up. Studies were ranked as A if they had
4 to 5 points, B if they had 2 to 3 points, or C if they had 0 to 1 point of a total
of 5 points.14 The presence of publication bias was investigated by using
Egger’s test15 or Begg’s test16 and by visual inspection of funnel plots.
Pairwise meta-analyses
Traditional pairwise meta-analyses were carried out as described elsewhere.17-19
The following comparisons were used: experimental therapy vs conventional bortezomib-based therapy or experimental therapy vs conventional
IMiD-based therapy (additional details are provided in the supplemental
Data). Survival data were extracted as HRs of OS and PFS with relative
confidence intervals (95% CIs); response rates and toxicity rate were
calculated by using the method for dichotomous data (OR and RR
assessment with 95% CIs). Cochrane’s Q test and I2 statistics were used
to assess heterogeneity between studies, and both fixed-effects (FE) and
random-effects (RE) models were used for the analysis. Pooled data
analysis was performed according to the DerSimonian and Laird test.
P , .05 was considered statistically significant. Statistical analysis was
conducted by using STATA software (Version 14.1, STATA, College Station,
TX) and R (meta package).
NMA
We performed an NMA by using both Bayesian and frequentist approaches
to compare the different therapeutic regimens simultaneously. The Bayesian
NMA was performed with STATA software by using the mvmeta package,
and the frequentist NMA was performed with R software by using
the netmeta package.20 NMA synthesizes data from a network of trials that
involve multiple interventions and therefore has the potential to rank the
treatments according to the outcome. This method integrates direct and
indirect comparisons. Within the framework of NMA, we ranked the
evaluated regimens based on survival outcomes (OS and PFS), treatment
efficacy (ORR and CR), and safety (the most frequent grade 3-4 adverse
event in each trial). For each outcome, we performed a Bayesian NMA with
an (RE) model by using a Markov chain Monte Carlo simulation technique
with up to 30 000 iterations. Loop inconsistency and heterogeneity were
assessed by evaluating the logarithm of the ratio of 2 odds ratios (RoR)
from direct and indirect evidence in the loop with the ifplot command in
STATA.21,22 RoR values close to 0 indicate that both direct and indirect
evidence are in agreement. Heterogeneity of the loop was then assessed
through the restricted maximum likelihood method.21,22
Relative effects of treatments are reported as HRs for survival outcomes
(PFS and OS) and OR or RR for binary outcomes (ORR, CR, and toxicity)
along with corresponding 95% credible intervals, the Bayesian equivalent
of 95% CIs. Ranking probabilities and surface under the cumulative
ranking curve (SUCRA), which provides a numerical summary of the rank
distribution of each treatment schedule on the different end points, were
used to provide hierarchy probabilities.21-23 The larger the SUCRA value
(ie, closer to 1), the better the rank of the intervention. The frequentist
equivalent of the SUCRA, the P-score, was further used to confirm the
results of the NMA.20
Results
Study selection
Figure 1 shows a PRISMA chart of RCT selection and search
strategy. In the time frame covered by this systematic review, a total
of 1679 studies, including full articles or meeting abstracts, were
retrieved. Nineteen trials with a total of 8997 patients were finally
selected and included in the analysis (Table 1).10,11,24-42 Two
trials41,42 did not report data or provide a graph showing OS and
were therefore excluded from all OS analyses. At least 1 data
comparison in terms of survival, ORR, or toxicity was reported in all
selected RCTs, which were therefore deemed eligible for the
28 FEBRUARY 2017 x VOLUME 1, NUMBER 7
Figure 1. PRISMA flowchart for study selection and review.
MA, meta-analysis; PRISMA, preferred reporting items for
systematic reviews and meta‐analyses (checklist); SEER,
Articles meeting initial search
criteria (n=1679)
Surveillance, Epidemiology, and End Results (program).
Excluded articles
Review (n=487)
Potentially relevant studies
(n=1192)
Excluded articles
Trial design (n=1134)
Studies reviewed in detail
(n=58)
Excluded articles
No appropriate
comparator/SEER
study/overlapping with other
studies (n=39)
Included in Bortezomib
pairwise-MA (n=10)
Included in IMiDs pairwiseMA (n=6)
analysis. A summary of the 19 trials included in the final analyses
showed that 17 trials were eligible for OS analysis; 19 were eligible
for PFS, ORR, and safety analyses; 16 were included in the
pairwise meta-analyses; and 18 were included in the NMA.
Quality assessment
As depicted in Table 2, 15 trials were scored as A (low risk of bias),
and 4 trials were scored as B (intermediate risk of bias). None of the
trials evaluated was scored as C (high risk of bias).
28 FEBRUARY 2017 x VOLUME 1, NUMBER 7
Included in NMA (n=18)
Pairwise meta-analyses
Our analyses included 16 trials for pairwise meta-analyses (involving 7500 patients). In particular, there were 10 trials (4371
patients)10,11,33-37,39-41 in which an experimental treatment was compared with a bortezomib-based conventional treatment and 6 trials
(3129 patients)11,24-27,42 in which an experimental treatment
was compared with an IMiD-based conventional treatment. Funnel
plot visual inspection (supplemental Figure 1), Egger’s test (t 5 0.57;
P 5 .58), and Begg’s test (z 5 0.80; P 5 .42) showed no evidence
META-ANALYSIS OF RELAPSED/REFRACTORY MYELOMA TRIALS
457
Table 1. Characteristics of the studies included in the meta-analyses
Reference
Trial name
Year Phase
No. of
patients
Drug combinations
in arm 1
Drug combinations
in arm 2
Patients with 1 previous
treatment (%)
Patients with 2 or more
previous treatments (%)
58
40
VANTAGE 088
2013
3
637
VOR/BOR
BOR
42
39
DOXIL-MMY-3001
2007
3
646
PLD/BOR
BOR
34
66
10
NCT00401843
2015
2
281
SIL/BOR
BOR
52
48
41
MMY 3021
2011
3
222
subBOR
BOR
63
37
37
AMBER
2012
2
102
BOR 1 BEV
BOR
48
52
36
ENDEAVOR
2015
3
929
CAR/DEX
BOR/DEX
50
50
35
PANORAMA 1
2014
3
768
PAN/BOR/DEX
BOR/DEX
51
49
34
CASTOR
2016
3
498
DAR/BOR/DEX
BOR/DEX
47
53
33
NCT01478048
2016
2
152
ELO/BOR/DEX
BOR/DEX
66
34
11
NCT00602511
2012
3
131
THA/DEX
BOR/DEX
NA
NA
32
MM-010
2007
3
351
LEN/DEX
DEX
32
68
31
MM-009
2007
3
353
LEN/DEX
DEX
38
62
29
APEX
2009
3
669
BOR
DEX
38
62
28
OPTIMUM
2012
3
499
THA
DEX
57
43
27
ASPIRE
2014
3
792
CAR/LEN/DEX
LEN/DEX
43
57
26
ELOQUENT-2
2015
3
646
ELO/LEN/DEX
LEN/DEX
48
52
42
TOURMALINE-MM1
2015
3
722
IXA/LEN/DEX
LEN/DEX
61
39
25
POLLUX
2016
3
569
DAR/LEN/DEX
LEN/DEX
52
48
24
MMVAR/IFM 2005-04 2012
3
269
BOR/THA/DEX
THA/DEX
53
47
BEV, bevacizumab; BOR, bortezomib; CAR, carfilzomib; DAR, daratumumab; DEX, dexamethasone; ELO, elotuzumab; IXA, ixazomib; LEN, lenalidomide; NA, not applicable; PAN, panobinostat;
PLD, pegylated liposomal doxorubicin; SIL, siltuximab; subBOR, subcutaneous bortezomib; THA, thalidomide; VOR, vorinostat.
of publication bias for bortezomib comparison. For IMiD comparison
(supplemental Figure 1), only funnel plot visual inspection was
performed because of the low number of studies (6) included in the
analysis, again suggesting no evidence for publication bias. All
these tests should be considered to have low power because of the
small number of studies involved in our analysis.
Table 2. Quality assessment of studies included in the meta-analysis
Reference
Trial name
Year
Method of randomization
Allocation concealment
Blinded
Withdrawal and dropout
Baseline
Quality level*
2013
Centralized
Central office
Yes
Detailed criteria
Identical baseline
A
40
VANTAGE 008
39
DOXIL-MMY-3001
2007
Centralized
Central office
No
Detailed criteria
Identical baseline
A
10
NCT00401843
2015
Centralized
Central office
Yes
Detailed criteria
Identical baseline
A
41
MMY 3021
2011
Centralized
Central office
No
Detailed criteria
Identical baseline
A
37
AMBER
2012
Centralized
Central office
Yes
Detailed criteria
Identical baseline
A
36
ENDEAVOR
2015
Centralized
Central office
No
Detailed criteria
Identical baseline
A
35
PANORAMA 1
2014
Centralized
Central office
Yes
Detailed criteria
Identical baseline
A
34
CASTOR
2016
Centralized
Central office
No
Detailed criteria
Identical baseline
A
33
NCT01478048
2016
Centralized
No details
No
Detailed criteria
Identical baseline
B
11
NCT00602511
2012
Centralized
Central office
No
Detailed criteria
Identical baseline
A
32
MM-010
2007
Centralized
Central office
Yes
Detailed criteria
Identical baseline
A
31
MM-009
2007
Centralized
Central office
Yes
Detailed criteria
Identical baseline
A
29
APEX
2009
No details
No details
No
Detailed criteria
Identical baseline
B
28
OPTIMUM
2012
No details
No details
No
Detailed criteria
Identical baseline
B
27
ASPIRE
2014
Centralized
Central office
No
Detailed criteria
Identical baseline
A
26
ELOQUENT-2
2015
Centralized
Central office
No
Detailed criteria
Identical baseline
A
42
TOURMALINE-MM1
2015
Centralized
Central office
Yes
Detailed criteria
Identical baseline
A
25
POLLUX
2016
Centralized
Central office
No
Detailed criteria
Identical baseline
A
24
MMVAR/IFM 2005-04
2012
No details
No details
No
Detailed criteria
Identical baseline
B
*A, quality score of 4 to 5 of a total score of 5; B, quality score of 2 to 3 of a total score of 5.
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BOTTA et al
28 FEBRUARY 2017 x VOLUME 1, NUMBER 7
A
C
OS
PFS
Study
W(fixed)
BORT+HDAC
Dimopoulos 2013
San-Miguel 2014
Fixed effect model
Random effects model
2
2
16.2%
17.3%
33.5%
––
W(random)
Hazard Ratio
HR
12.4%
12.5%
––
24.9%
95%–CI
2
2
6.1%
15.6%
21.7%
––
0.77 [0.64; 0.94]
0.63 [0.52; 0.76]
0.70 [0.61; 0.80]
0.70 [0.57; 0.85]
0.39 [0.28; 0.54]
0.72 [0.59; 0.88]
0.61 [0.51; 0.72]
0.54 [0.29; 0.98]
BORT+mAbs
Palumbo 2016
Jakubowiak 2016
Fixed effect model
Random effects model
2
2
2
10.2%
7.4%
4.8%
2.1%
24.5%
––
9.1%
12.3%
––
21.4%
16.3%
16.3%
––
2
11.0%
9.8%
8.1%
5.0%
––
33.9%
0.59 [0.46; 0.76]
0.87 [0.65; 1.16]
0.82 [0.57; 1.18]
0.74 [0.43; 1.28]
0.72 [0.62; 0.85]
0.73 [0.60; 0.90]
4.0%
4.0%
––
2
100%
––
95%–CI
11.6%
19.9%
––
31.5%
0.86 [0.62; 1.18]
0.87 [0.69; 1.10]
0.87 [0.72; 1.05]
0.87 [0.72; 1.05]
5.3%
3.2%
––
8.6%
0.77 [0.47; 1.26]
0.61 [0.32; 1.16]
0.71 [0.48; 1.04]
0.71 [0.48; 1.04]
30.5%
9.7%
0.0%
1.7%
––
41.8%
0.96 [0.80; 1.14]
1.37 [0.96; 1.96]
12.4%
––
12.4%
0.79 [0.58; 1.08]
0.79 [0.58; 1.08]
0.79 [0.58; 1.08]
5.7%
––
5.7%
0.85 [0.53; 1.37]
0.85 [0.53; 1.37]
0.85 [0.53; 1.37]
––
100%
0.91 [0.82; 1.01]
0.90 [0.80; 1.01]
[0.73; 1.10]
2
4.5%
2.7%
7.2%
––
2
BORT+OTHER
Orlowski 2007/2016
Orlowski 2015
Moreau 2011
White 2012
Fixed effect model
Random effects model
2
36.0%
8.6%
0.0%
1.4%
45.9%
––
0.63 [0.26; 1.55]
1.01 [0.86; 1.18]
1.04 [0.76; 1.43]
2
0.53 [0.44; 0.64]
0.53 [0.44; 0.64]
0.53 [0.44; 0.64]
CARF
Dimopoulos 2015
Fixed effect model
Random effects model
7.4%
––
7.4%
0.97 [0.65; 1.44]
0.97 [0.65; 1.44]
0.97 [0.65; 1.44]
IMiDs
Hjorth 2012
Fixed effect model
Random effects model
––
100%
0.66 [0.61; 0.71]
0.67 [0.58; 0.78]
[0.42; 1.07]
Fixed effect model
Random effects model
Prediction interval
12.4%
––
12.4%
11.3%
11.3%
––
Heterogeneity: not applicable for a single study
Heterogeneity: not applicable for a single study
Fixed effect model
Random effects model
Prediction interval
HR
Heterogeneity: I = 53.1%, T = 0.04, P = .1188
Heterogeneity: not applicable for a single study
IMiDs
Hjorth 2012
Fixed effect model
Random effects model
Hazard Ratio
Heterogeneity: I = 0%, T = 0, P = .5719
Heterogeneity: I = 34.5%, T = 0.0152, P = .2053
CARF
Dimopoulos 2015
Fixed effect model
Random effects model
10.6%
20.1%
30.7%
––
W(random)
Heterogeneity: I = 0%, T = 0, P = .9454
Heterogeneity: I = 90.2%, T = 0.1695, P = .0014
BORT+OTHER
Orlowski 2007/2016
Orlowski 2015
Moreau 2011
White 2012
Fixed effect model
Random effects model
W(fixed)
BORT+HDAC
Dimopoulos 2013
San-Miguel 2014
Fixed effect model
Random effects model
Heterogeneity: I = 54.3%, T = 0.0115, P = .1390
BORT+mAbs
Palumbo 2016
Jakubowiak 2016
Fixed effect model
Random effects model
Study
4.9%
4.9%
––
Heterogeneity: not applicable for a single study
2
Heterogeneity: I = 68.2%, T = 0.036, P = .0009
2
100%
––
2
Heterogeneity: I = 11.8%, T = 0.0038, P = .3366
0.1
1
Favor ET
10
0.1
Favor ST
1
Favor ET
B
10
Favor ST
D
PFS
Study
W(fixed)
BORT
Hjorth 2012
Fixed effect model
Random effects model
6.5%
6.5%
––
OS
Hazard Ratio
W(random)
HR
95%–CI
1.03 [0.70; 1.53]
1.03 [0.70; 1.53]
1.03 [0.70; 1.53]
BORT
Hjorth 2012
12.1%
Fixed effect model
12.1%
Random effects model
––
19.3%
14.6%
––
33.9%
0.70 [0.57; 0.85]
0.37 [0.27; 0.51]
0.59 [0.50; 0.70]
0.52 [0.28; 0.96]
IMiDs+mAbs
33.7%
Lonial 2015
8.4%
Dimopoulos 2016
42.1%
Fixed effect model
––
Random effects model
19.7%
18.1%
15.8%
––
53.6%
0.69 [0.57; 0.83]
0.74 [0.59; 0.93]
0.61 [0.45; 0.82]
0.69 [0.60; 0.78]
0.69 [0.60; 0.78]
Pls+IMiDs
33.7%
Stewart 2014
0.0%
Moreau 2016
12.1%
Garderet 2012
45.8%
Fixed effect model
––
Random effects model
12.5%
––
12.5%
Heterogeneity: not applicable for a single study
IMiDs+mAbs
25.3%
Lonial 2015
9.4%
Dimopoulos 2016
34.7%
Fixed effect model
––
Random effects model
2
2
Heterogeneity: I = 90.6%, T = 0.1841, P = .0011
Pls+IMiDs
Stewart 2014
Moreau 2016
Garderet 2012
Fixed effect model
Random effects model
2
28.6%
18.6%
11.7%
58.8%
––
2
Heterogeneity: I = 0%, T = 0, P = .6002
Fixed effect model
Random effects model
Prediction interval
2
100%
––
––
100%
0.67 [0.61; 0.74]
0.66 [0.54; 0.81]
[0.34; 1.27]
2
Heterogeneity: I = 73.1%, T = 0.0449, P = .0023
1
0.1
Favor ET
10
Favor ST
Hazard Ratio
W(fixed) W(random)
Study
HR
95%–CI
17.3%
––
17.3%
1.43 [0.97; 2.12]
1.43 [0.97; 2.12]
1.43 [0.97; 2.12]
25.6%
14.2%
––
39.8%
0.77 [0.61; 0.98]
0.64 [0.40; 1.02]
0.74 [0.60; 0.92]
0.74 [0.60; 0.92]
25.6%
0.0%
17.3%
––
42.9%
0.79 [0.62; 1.00]
––
100%
0.81 [0.71; 0.93]
0.83 [0.66; 1.04]
[0.40; 1.71]
Heterogeneity: not applicable for a single study
2
2
Heterogeneity: I = 0%, T = 0, P = .4789
2
0.70 [0.47; 1.03]
0.76 [0.62; 0.93]
0.76 [0.62; 0.93]
2
Heterogeneity: I = 0%, T = 0, P = .6069
Fixed effect model
Random effects model
Prediction interval
2
100%
––
2
Heterogeneity: I = 59.7%, T = 0.0389, P = .0418
0.1
1
Favor ET
10
Favor ST
Figure 2. Forest plots of comparisons between experimental treatments and standard treatments in terms of PFS and OS. Bortezomib (BORT) with or without
dexamethasone represents the standard treatment (ST) in (A) PFS and (C) OS; IMiDs represent the standard treatment in (B) PFS and (D) OS. Subgroups have been created
according to drug classes. CARF, carfilzomib; ET, experimental treatment; W, weight.
PFS analyses
Comparison of bortezomib-based regimens. The experimental treatment, when compared with bortezomib-based regimens,
significantly improved PFS by using either an FE or RE model (pooled
HRs for RE, 0.67; 95% CI, 0.58-0.78; P , .001; Figure 2A). By
using subgroup analysis, we demonstrated significant PFS benefit in
28 FEBRUARY 2017 x VOLUME 1, NUMBER 7
doublet therapy subgroups and in the subgroup for therapy with
all classes of drugs except IMiDs (pooled HRs, 0.97; 95% CI,
0.65-1.44; P 5 .880; Figure 2A; supplemental Figure 2A).
Comparison of IMiD-based regimens. The experimental
treatment, when compared with IMiD-based regimens, significantly
improved PFS (pooled HRs for RE, 0.66; 95% CI, 0.54-0.81; P , .001;
META-ANALYSIS OF RELAPSED/REFRACTORY MYELOMA TRIALS
459
Table 3. Most common grade 3 to 4 adverse events analyzed in the
pairwise meta-analyses
No. of
patients
with grade
3-4 adverse
events
Adverse event subgroup
ET
CT
Overall RR
95% CI
P
BOR
280
240
1.124
0.946-1.335
.184
IMiD
208
252
0.843
0.659-1.079
.175
BOR
734
482
1.147
0.867-1.516
.337
IMiD
277
197
1.594
1.059-2.397
.025
BOR
461
251
1.672
1.198-2.334
.003
IMiD
479
462
1.023
0.807-1.296
.851
Anemia
Thrombocytopenia
Neutropenia
Peripheral neuropathy
BOR
117
127
0.686
0.425-1.107
.123
IMiD
60
36
1.118
0.527-2.374
.771
BOR
196
122
1.717
1.031-2.861
.038
IMiD
80
65
1.224
0.890-1.683
.213
BOR
199
119
1.190
0.636-2.224
.586
IMiD
55
39
1.379
0.919-2.070
.121
BOR
870
679
1.219
1.083-1.372
.001
IMiD
668
621
1.086
1.000-1.180
.050
Fatigue
Diarrhea
Serious adverse event
BOR, bortezomib; CT, conventional therapy; ET, experimental therapy.
Figure 2B). Subgroup analysis demonstrated significant PFS benefit
in the doublet therapy subgroup and in the subgroup for therapy with
all classes of drugs except bortezomib (pooled HRs, 0.78; 95% CI,
0.47-1.30; P 5 .341; Figure 2B; supplemental Figure 2B).
In our analyses, both models showed high heterogeneity, and the
prediction intervals suggest an overall nonsignificant result. Specifically, the highest level of heterogeneity was mainly a result of the
study by Hjorth et al.11 However, this study had a low weight in both
FE and RE models, and we can conclude that the addition of another
drug to either bortezomib or the IMiD backbone or the use of
carfilzomib (only in the case of bortezomib) is significantly better than
the backbone alone.
OS analyses
Comparison of bortezomib-based regimens. In our
analysis, the experimental treatment showed a trend toward benefit
in terms of OS compared with conventional treatment (pooled HRs
for RE, 0.90; 95% CI, 0.80-1.01; P 5 .06). This result was confirmed
in subgroup analyses for all classes of drugs and for doublet therapy
and single-drug therapy (Figure 2C; supplemental Figure 2C).
Comparison of IMiD-based regimens. In our analysis, the
experimental treatment showed a trend toward benefit in terms of OS
460
BOTTA et al
(pooled HRs for RE, 0.83; 95% CI, 0.66-1.44; P 5 .101). The models
showed high heterogeneity, and the very wide prediction intervals
suggest overall uncertain results. Subgroup analyses suggest again
that heterogeneity is mainly dependent on the study by Hjorth
et al.11 Indeed, each subgroup analysis showed no heterogeneity,
and we observed a significant advantage for doublet, IMiD 1 mAb,
and PI 1 IMiD groups (Figure 2D; supplemental Figure 2D). Note
that the doublet group included all studies except the study by
Hjorth et al,11 thus further underscoring the relevance of this study
in increasing the overall heterogeneity of the model.
From the previous findings, we can conclude that the addition of a
second drug to a bortezomib backbone or the use of carfilzomib
does not improve patients’ survival compared with the bortezomib
backbone alone, whereas the addition of another drug to the IMiD
backbone significantly improves patients’ OS.
RR analyses
Comparison of bortezomib-based regimens. In terms of
ORR, we found a significant advantage for the experimental
treatment (OR, 1.45; 95% CI, 1.16-1.82; P 5 .001). This advantage
was clearly evident with HDAC inhibitor, carfilzomib, and doublet
subgroups (supplemental Figure 3A-B). Differences in terms of
VGPR are shown in supplemental Figure 3C-D. In terms of CR, we
demonstrated a significant advantage for the experimental treatment
(OR, 1.84; 95% CI, 1.50-2.26; P , .001). This advantage was
evident in all subgroups except BORT 1 OTHER (supplemental
Figure 3E). As for doublet therapy and single-drug treatment
subgroups, a significant CR benefit was observed for doublet
therapy (supplemental Figure 3F).
Comparison of IMiD-based regimens. In terms of ORR,
we demonstrated a significant advantage for the experimental treatment (OR, 2.23; 95% CI, 1.56-3.20; P , .001) across all subgroup
analyses except for bortezomib (supplemental Figure 4A-B). In
terms of VGPR, a significant difference was found in favor of
the experimental arm (pooled VGPRs, 2.49; 95% CI, 1.70-3.64;
P , .001) as confirmed for all subgroups except for the IMiD 1 mAb
group (supplemental Figure 4C-D). In terms of CR, we found a
significant advantage for the experimental treatment (OR for CR,
2.30; 95% CI, 1.29-4.10; P 5 .005; supplemental Figure 4E-F).
Altogether, our results suggest that the addition of a second drug to
the IMiD backbone or bortezomib backbone (or the use of carfilzomib)
significantly increased the probability of obtaining a highest ORR,
VGPR, or CR and that this result is more evident in patients treated
with HDAC inhibitors, anti-MM mAbs, or PIs 1 IMiDs.
Toxicity analyses
Common adverse events were similar in both meta-analyses, as
detailed in Table 3. We observed a significant difference for
thrombocytopenia in the experimental arm of IMiD meta-analysis
and a major risk of neutropenia, fatigue, and serious adverse
events in the experimental arm of bortezomib meta-analysis.
NMA
All treatments analyzed were divided into 9 groups: dexamethasone,
bortezomib, IMiDs, bortezomib 1 HDACs, bortezomib 1 mAbs,
bortezomib 1 other drugs, carfilzomib, PI 1 IMiDs, and IMiDs 1 mAbs
according to drug class (details are provided in supplemental
Table 1) or as independent treatments (except for bortezomib or
28 FEBRUARY 2017 x VOLUME 1, NUMBER 7
A
IMIDS
9
IMIDS+mAB
2
(2053)
(604)
BORT
10
(2405)
PI+IMIDS
3
(891)
3
BORT+OTHERS
(515)
4
DEX
(813)
2
(702) BORT+HDAC
1
CARF (464)
2
(328) BORT+mAB
B
PFS
IMIDS+mAB BORT+mAB
CARF
PI+IMIDS
BORT+
HDAC
BORT+
OTHERS
IMIDS
BORT
DEX
1.34
(0.25,7.14)
1.77
(0.61,5.12)
2.33
(0.54,9.99)
2.51
(0.63,9.97)
3.86
(1.70,8.78)
4.77
(1.43,15.89)
18.33
(6.55,51.35)
Safety
IMIDS+mAB IMIDS+mAB
1.34
(0.31,5.75)
BORT+mAB
4.02
BORT+mAB
(0.10,168.91)
1.00
(0.24,4.15)
1.32
(0.33,5.25)
1.74
(0.54,5.55)
1.88
(0.65,5.42)
2.88
(0.87,9.60)
3.56
(1.57,8.10)
13.68
(4.11,45.56)
CARF
3.15
12.67
(0.17,924.29) (0.08,121.23)
CARF
1.32
(0.27,6.58)
1.74
(0.42,7.20)
1.87
(0.49,7.17)
2.88
(0.67,12.36)
3.56
(1.11,11.37)
13.68
(3.19,58.69)
0.14
(0.00,8.31)
PI+IMIDS
1.31
(0.33,5.20)
1.42
(0.39,5.16)
2.18
(1.11,4.26)
2.69
(0.89,8.13)
10.33
(4.14,25.78)
BORT+
HDAC
1.08
(0.37,3.12)
1.66
(0.50,5.53)
2.05
(0.90,4.66)
7.88
(2.37,26.23)
PI+IMIDS
BORT+
HDAC
BORT+
OTHERS
1.72
(0.11,26.16)
0.43
(0.01,14.66)
22.96
13.33
5.56
1.81
(0.55,963.07) (0.05,100.00) (0.05,69.71) (0.39,456.32)
13.02
3.23
(0.38,444.89) (0.21,49.15)
1.03
(0.03,32.07)
7.56
(0.27,208.17)
0.57
(0.04,8.61)
BORT+
OTHERS
1.54
(0.51,4.64)
1.90
(0.97,3.72)
7.30
(2.41,22.04)
IMIDS
0.90
(0.11,7.40)
0.22
(0.01,4.89)
0.07
(0.00,2.98)
0.52
(0.09,2.92)
0.04
(0.00,0.86)
0.07
(0.00,1.18)
IMIDS
1.24
(0.51,2.98)
4.75
(2.55,8.84)
BORT
5.70
(0.26,124.65)
1.42
(0.17,11.66)
0.45
(0.02,8.85)
3.31
(0.19,56.39)
0.25
(0.03,2.04)
0.44
(0.08,2.44)
6.34
(0.66,60.37)
BORT
3.84
(1.60,9.25)
DEX
0.05
(0.00,0.73)
0.01
(0.00, (0.28)
0.00
(0.00,0.17)
0.03
(0.00,0.32)
0.00
(0.00,0.05)
0.00
(0.00,0.07)
0.06
(0.01,0.28)
0.01
(0.00,0.09)
DEX
Figure 3. Network of the comparisons and comparative efficacy and tolerance results. (A) Network plot of all treatment groups evaluated in the NMA for PFS. The number
of studies that analyzed each treatment group is shown inside the circles; the overall numbers of patients included in the analysis for each group are provided in parentheses.
(B) Effect estimates of the treatment in terms of PFS (column headings being compared with row headings) and safety (row headings being compared with column headings).
PFS is reported as HR and safety is reported as RR (95% credible intervals are in parentheses). Statistically significant comparisons are shown in bold. DEX, dexamethasone.
bortezomib 1 dexamethasone regimens grouped into the “bortezomib
with or without dexamethasone” group; for thalidomide, thalidomide 1
dexamethasone and lenalidomide 1 dexamethasone were grouped into
28 FEBRUARY 2017 x VOLUME 1, NUMBER 7
the “IMiDs with or without dexamethasone” group). All regimens or
groups were evaluated for differences in PFS, OS, ORR, CR, and safety
(network plots are shown in Figure 3A; supplemental Figure 5A-B).
META-ANALYSIS OF RELAPSED/REFRACTORY MYELOMA TRIALS
461
80
2.1
60
40
20
0
100
80
40
20
0
cumulative probability of being among the
k best treatments
100
80
3.0
60
40
100
20
0
1. 2. 3. 4. 5. 6. 7. 8. 9.
1. 2. 3. 4. 5. 6. 7. 8. 9.
Rank
Rank
Rank
80
3.8
60
40
20
0
BORT+OTHERS
Probability of rank (%)
BORT+HDAC
100
100
80
4.8
60
40
20
0
100
80
5.1
60
40
20
0
1. 2. 3. 4. 5. 6. 7. 8. 9.
1. 2. 3. 4. 5. 6. 7. 8. 9.
1. 2. 3. 4. 5. 6. 7. 8. 9.
Rank
Rank
Rank
80
BORT
6.7
60
40
20
0
Probability of rank (%)
IMIDs
100
7.6
60
40
20
0
60
40
20
0
DEX
100
80
80
Probability (%)
1. 2. 3. 4. 5. 6. 7. 8. 9.
Probability of rank (%)
Probability of rank (%)
2.9
60
PI+IMIDs
Probability of rank (%)
B
CARF
Probability of rank (%)
BORT+mAB
Probability of rank (%)
Probability of rank (%)
IMIDs+mAB
100
Probability of rank (%)
A
1
100
80
2
3
4
5
6
7
8
9
9.0
Rank (k)
60
40
20
0
1. 2. 3. 4. 5. 6. 7. 8. 9.
1. 2. 3. 4. 5. 6. 7. 8. 9.
1. 2. 3. 4. 5. 6. 7. 8. 9.
Rank
Rank
Rank
IMIDs+mAb
PI+IMIDs
IMIDs
BORT+mAb
BORT+HDAC
BORT
CARF
BORT+OTHERS
DEX
PFS
20
0
100
80
40
20
0
80
40
100
20
0
Rank
Rank
CARF
IMIDs
BORT+HDAC
80
4.5
60
40
20
0
100
80
5.2
60
40
20
0
100
80
5.4
60
40
0
1. 2. 3. 4. 5. 6. 7. 8. 9.
1. 2. 3. 4. 5. 6. 7. 8. 9.
Rank
Rank
Rank
6.2
60
40
20
0
Probability of rank (%)
80
BORT
80
7.1
60
40
20
0
40
20
0
DEX
100
60
20
1. 2. 3. 4. 5. 6. 7. 8. 9.
100
80
1. 2. 3. 4. 5. 6. 7. 8. 9.
Probability of rank (%)
1. 2. 3. 4. 5. 6. 7. 8. 9.
Rank
100
cumulative probability of being among the
k best treatments
3.1
60
1. 2. 3. 4. 5. 6. 7. 8. 9.
BORT+OTHERS
Probability of rank (%)
2.5
60
100
Probability (%)
40
Probability of rank (%)
2.1
60
Probability of rank (%)
Probability of rank (%)
Probability of rank (%)
80
D
BORT+mAB
Probability of rank (%)
PI+IMIDs
IMIDs+mAB
100
Probability of rank (%)
C
1
100
80
2
3
8.8
4
5
6
40
0
1. 2. 3. 4. 5. 6. 7. 8. 9.
1. 2. 3. 4. 5. 6. 7. 8. 9.
Rank
Rank
Rank
CARF
BORT+OTHERS
IMIDs
BORT
BORT+mAb
BORT+HDAC
DEX
E
OS
0.894
0.572
0.834
0.636
0.563
0.675
0.458
0.516
0.441
0.562
0.417
0.597
0.284
0.344
0.201
0.006
ORR
0.813
0.662
0.537
0.758
0.681
0.505
0.737
0.711
0.431
0.443
0.342
0.443
0.092
0.264
0.081
CR
0.748
0.691
0.691
0.58
0.545
0.602
0.449
0.46
0.491
0.479
0.584
0.439
0.317
0.489
0.37
0.066
Worse
TOX
0.583
0.635
0.634
0.575
0.292
0.61
0.464
0.498
0.49
0.607
0.548
0.58
0.359
0.568
0.461
0.096
0.561
0.607
0.381
0.436
0.678
0.365
0.602
0.642
0.449
0.289
0.314
0.299
0.666
0.337
0.441
0.933
average
ranking
0.7198
1
0.6334
2
0.6154
3
0.597
4
0.5518
5
0.5514
6
0.542
7
0.529
8
0.5164
9
0.4736
10
0.4612
11
0.4514
12
0.4138
13
0.366
14
0.3474
15
0.2364
16
Better
Figure 4.
462
BOTTA et al
9
PI+IMIDs
OS
PFS
8
IMIDs+mAb
20
1. 2. 3. 4. 5. 6. 7. 8. 9.
DAR+IMiDs+DEX
CARF+IMiDs+DEX
DAR+BORT+DEX
BORT+IMiDs+DEX
ELO+IMiDs+DEX
CARF+DEX
ELO+BORT+DEX
IXA+IMiDs+DEX
BEV+BORT
PAN+BORT+DEX
VOR+BORT
PLD+BORT
IMiDs+/-DEX
SILT+BORT
BORT+/-DEX
DEX
7
Rank (k)
60
28 FEBRUARY 2017 x VOLUME 1, NUMBER 7
Each group was simultaneously compared against all other groups
through a Bayesian NMA, and efficacy results for PFS and safety
are shown in Figure 3B in terms of HRs with 95% credible intervals
(efficacy results in terms of OS, ORR, and CR are shown in
supplemental Figure 6). All groups evaluated had a significantly
lower risk for progression compared with dexamethasone, whereas
dexamethasone was found to be significantly safer than any other
group. Bortezomib 1 mAbs and carfilzomib groups were significantly better than the bortezomib alone group, whereas the PI 1
IMiD group was significantly better than IMiDs alone. Interestingly,
IMiDs 1 mAbs was the only group significantly better than both
IMiDs and bortezomib. Regarding safety, the only significant value
(except for dexamethasone) was reached by the IMiDs group, which
shows a significantly lower RR for toxicity compared with the
bortezomib 1 HDAC group.
We determined which group had the highest probability of being
the most effective in term of PFS, OS, ORR, CR, and safety
(supplemental Figure 7A). The IMiD 1 mAB group achieved the
highest probability of being the most effective in term of PFS, OS, and
ORR, whereas the carfilzomib group reported the highest probability
of achieving CR. The bortezomib 1 HDAC group had the highest
probability of inducing a grade 3 to 4 toxicity in MM patients.
However, ranking the treatment solely on the basis of the probability
of being the best does not account for the uncertainty in the relative
treatment effects and may lead to overinterpretation of the results.
Thus, we evaluated the probability of each group being at each rank
(rank probabilities) and summarized them in a rankogram together
with the median rank. We also evaluated the cumulative probability of
each group being among the k ranks (k ranges from 1 to 9 in our
analysis) together with the SUCRA.22,23,43
Figure 4A-B clearly shows that, regarding PFS, the IMiD 1 mAb
group has the highest probability of being the most effective and
that almost certainly it is among the 4 best treatments. It should be
noted, however, that the bortezomib 1 mAb and carfilzomib groups
are only slightly inferior to the best-ranked treatment and that the
magnitude of this difference is very difficult to estimate.
In term of OS, the scenario is slightly different (Figure 4C-D). The
IMiD 1 mAb group is again the highest ranked treatment and has
a more than 90% probability of being among the 4 best treatments.
In this case, however, the PI 1 IMiD group performed similarly, followed by the bortezomib 1 mAb group. Thus, there is considerable
uncertainty regarding the ranking of the carfilzomib group, which has
similar probabilities of being at each of the 9 ranks. We then
estimated SUCRA values for PFS, OS, ORR, CR, and safety and
calculated an average value to rank all the groups included in the
analysis by using a multiparametric approach (supplemental Figure 7B).
This analysis revealed IMiD 1 mAb, PI 1 IMiD, bortezomib 1 mAb,
and carfilzomib as the best 4 options for RRMM patients, but we
should take into account that the carfilzomib group, while achieving
the highest score in term of CR, seems to be inferior to other groups in
term of OS and toxicity. Moreover, when examining the rank probabilities
(supplemental Figure 7C), the addition of a second drug to an IMiD
backbone (IMiD 1 mAb and PI 1 IMiD together) accounts for an
impressive 70% probability of being the best regimen in term of OS.
Finally, we investigated which regimen, among all regimens included
in the NMA, scores as the overall best regimen. To find the answer,
we determined the probability of being the best, cumulative rank
probabilities, and SUCRA values for PFS, OS, ORR, CR, and
safety and estimated an average value to rank all the treatment
options included in the analysis (Figure 4E; supplemental Figure 8).
According to average SUCRA values, the daratumumab 1
lenalidomide-dexamethasone regimen (within the IMiD 1 mAB
group) achieved the highest score (average SUCRA, 0.7198)
followed by carfilzomib 1 lenalidomide-dexamethasone (average
SUCRA, 0.6334) and daratumumab 1 bortezomib-dexamethasone
(average SUCRA, 0.6154). These results were further confirmed
by the fact that the daratumumab 1 lenalidomide-dexamethasone
regimen reported the highest probability of being the best regimen in
terms of PFS (44.56%), OS (27.57%), and ORR (22.26%), with a
very low probability of being the worst regimen in terms of grade 3 to
4 toxicities (3.70%) (supplemental Figure 8A). Again, it is relevant that
4 of the 5 best regimens in our ranking include the addition of a
second drug to an IMiD backbone, and that 3 of the 5 best regimens
include the new anti-MM mAbs daratumumab and elotuzumab.
No significant inconsistency or loop-specific heterogeneity were
found in our NMA as shown by the inconsistency factor plots in
supplemental Figure 9A (based on the 95% CI of the logRoR
crossing the null value of 0).
For exploratory purposes, we performed the same NMA by using
a frequentist approach. All regimens were ranked by using the
P-score according to the method recently described by Rücker
et al.20 We observed similar results in terms of all variables
(supplemental Figure 9B) and the daratumumab 1 lenalidomidedexamethasone regimen was again found to be the highest scoring
regimen (average P-score, 0.733).
Discussion
The aim of our study was to systematically review and compare all
therapeutic options available for RRMM, taking into account the high
activity of regimens that included novel agents such as HDAC inhibitors
or mAbs. Indeed, although most of these agents have undergone randomized clinical trials, a clear understanding of their relative efficacy
is still lacking, and in the absence of predictive biomarkers, treatment
choice is exclusively based on patient characteristics (prior treatment, absolute refractoriness of the disease, comorbidities, and the
patient’s ability to tolerate the regimen) and tumor-specific characteristics (disease burden, risk status, cytogenetics, and score according to the International Scoring System [ISS] and Revised ISS)
characteristics. Thus, ranking the value of all these opportunities
might be of help in designing therapeutic algorithms to be validated
in prospective clinical trials.
In this study, we performed 2 traditional pairwise meta-analyses and
an NMA. Indeed, although pairwise meta-analysis can offer a robust
Figure 4. Ranking of treatments based on NMA results. (A,C) Distribution of the probabilities of being at each rank, together with mean rank. Cumulative ranking probabilities
for each treatment evaluated in term of (B) PFS and (D) OS. (E) All of the SUCRA values for each regimen in regard to PFS, OS, ORR, CR, and toxicity (TOX; in this case, the higher
the SUCRA, the safer the regimen is for patients). An average SUCRA and the average ranking are provided. BEV, bevacizumab; DAR, daratumumab; DEX, dexamethasone;
ELO, elotuzumab; IXA, ixazomib; PAN, panobinostat; PLD, pegylated liposomal doxorubicin; SILT, siltuximab; VOR, vorinostat.
28 FEBRUARY 2017 x VOLUME 1, NUMBER 7
META-ANALYSIS OF RELAPSED/REFRACTORY MYELOMA TRIALS
463
tool for evaluating the strength of evidence derived from direct
comparative studies, Bayesian NMA seems to be the best tool for
investigating the strength of evidence for regimens that have not
undergone direct comparison.
The pairwise meta-analyses showed that a 3-drug combination that
includes bortezomib or lenalidomide reduces the risk of disease
progression and increases the probability of achieving at least a
partial response, even at the cost of greater toxicity compared with
doublet therapy. However, only IMiD-based regimens plus mAbs or
PIs demonstrated a clear advantage in terms of OS. To overcome the
limitation of the lack of direct comparisons, we used the NMA
approach and grouped all available regimens into 9 subgroups. In our
analysis, the IMiD 1 mAb group achieved the highest probability of
being the most effective regimen for RRMM in term of PFS followed
by the bortezomib 1 mAb and the carfilzomib group with very close
SUCRA scores; OS followed closely with the PI 1 IMiD and
bortezomib 1 mAb groups. When considering specific regimens, we
observed that the daratumumab 1 lenalidomide-dexamethasone
regimen reached the highest SUCRA values in term of PFS, OS,
and ORR with a good safety profile, ranking as the average best
treatment. We also observed that a regimen that adds a second
drug to an IMiD backbone has a 70% probability of being the
best treatment option and that, among the 5 regimens (of 16
evaluated) scoring the highest average SUCRA, 4 included an
IMiD backbone.
Thus, the results of our pairwise comparison indicate that whenever
possible, a 3-drug regimen must be preferred over a 2-drug regimen,
and our NMA further suggests that this regimen should have an
IMiD backbone (preferentially combined with a new anti-MM mAb
or alternatively with a PI). Furthermore, we observed that the combination of an HDAC inhibitor or pegylated liposomal doxorubicin
with bortezomib significantly increases the toxicity of the treatment
without adding additional benefit compared with regimens based on
PIs 1 IMiDs, carfilzomib, or mAbs. It is clear that these regimens
should be reserved for selected very fit MM patients. Furthermore,
a correlation between activity and efficacy outcomes was not
clearly demonstrated. Although the highest CR rates were attained
with carfilzomib or PI-based 3-drug regimens, the 4 regimens that
included elotuzumab and daratumumab ranked highest in terms of
OS. These data are consistent with clinical outcomes observed in
other tumors undergoing immunotherapy-based regimens44-48 and
are supported by recent experimental findings on MM pathogenesis.
Indeed, the encouraging results of immunotherapy are, to a degree,
not surprising when taking into account that MM is a malignancy
strongly dependent on microenvironment features, including inflammation and immunosuppression events.49-51 In such a scenario, it
is likely that new immunotherapeutic agents will quickly become
available for RRMM patients. Immune checkpoint inhibitors such as
programmed cell death protein 1 and programmed death-ligand
1 blocking antibodies have shown promising preliminary results
in small phase 1/2 trials that demonstrate the benefit of IMiDs in
combination with pembrolizumab; several phase 2/3 trials with
agents of this class are currently ongoing.52,53
Unfortunately, these novel drugs are costly, thus leading to sustainability concerns for health care systems. It is therefore imperative to
prioritize treatment schedules that have demonstrated improvement
in survival and/or quality of life and to develop and implement new
methodologies of comparative analysis such as NMA for constructing
464
BOTTA et al
decision-making therapeutic algorithms. This information might benefit
health technology assessment by providing a novel benchmark
(NMA) ranking for cost-effectiveness. One of the major strengths of
our analysis is the simultaneous comparison of the results from all
published trials, thus achieving information regarding hierarchy even
in the absence of direct comparisons.
However, our work has limitations. First, data were retrieved from
published studies rather than from individual patients’ records.
Second, and most important, potential biases can be produced
by the heterogeneity of the agents and patient populations
included in the analysis. An optimal meta-analysis in this setting
should compare studies that are similar in terms of patient
characteristics (such as number of prior regimens, percentages
of IMiD and PI refractoriness, age, MM risks) and treated with the
same regimens in the same treatment line, which cannot be done
with current evidence. In addition, no information is provided on
the best sequencing of the combinations that were investigated.
Finally, this work is intrinsically heuristic in nature and should
therefore be considered a snapshot of current evidence, taking
into account that some of these regimens are candidates for firstline therapy.
To the best of our knowledge, this is the first NMA on RRMM that
includes all regimens currently evaluated in randomized trials in
MM. Our findings suggest that a 3-drug regimen containing the
lenalidomide-dexamethasone backbone, preferentially combined
with anti-MM mAbs daratumumab or elotuzumab, has the highest
probability of being ranked as the best treatment in this setting,
underlying the role of immunotherapy in MM. Prospective randomized
trials are eagerly awaited to clarify the optimal sequencing of
treatments for MM.
Acknowledgments
This work was supported by the Italian Association for Cancer
Research (AIRC) with “Special Program for Molecular Clinical
Oncology–5 per mille” No. 9980, 2010/15 (Principal Investigator
[PI]: P. Tassone); “5 per 1000 Molecular Clinical Oncology
Extension Program” No. 9980, 2016/18 (PI: P. Tassone); and
partially by “Innovative Immunotherapeutic Treatments of Human
Cancer” Multi Unit Regional No. 16695 (cofinanced by AIRC
and the CARICAL foundation), 2015/18 (PI: P. Tassone).
Authorship
Contribution: C.B. and D.C. designed the research, conducted the
literature search, performed data extraction, conducted the statistical analysis, interpreted the results, and wrote the manuscript; N.S.
reviewed the analysis, assessed methodologic quality, and wrote the
manuscript; M.C. and T.G. reviewed the analysis; M.R., P. Tagliaferri
and P. Tassone reviewed the protocol, assessed methodologic
quality, reviewed and edited the manuscript; and all authors approved the final version of the manuscript.
Conflict-of-interest disclosure: The authors declare no competing financial interests.
ORCID profiles: C.B., 0000-0002-1522-4504.
Correspondence: Pierfrancesco Tassone, Department of Experimental and Clinical Medicine, Magna Graecia University, Salvatore
Venuta University Campus, Viale Europa, 88100 Catanzaro, Italy;
e-mail: [email protected].
28 FEBRUARY 2017 x VOLUME 1, NUMBER 7
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