Zanubrutinib Versus Other Bruton Tyrosine Kinase Inhibitors in Relapsed/Refractory
Chronic Lymphocytic Leukemia: A Multilevel Network Meta-Regression

P698

Mazyar Shadman*,1 Walter Bouwmeester,2 Leyla Mohseninejad,3 Sheng Xu,4 Milica Jevdjevic,2 Keri Yang,5 Rhys Williams,5 Aileen Cohen,5 Jeroen P. Jansen,6 Constantine Tam,7 Jennifer R. Brown8
1Fred Hutchinson Cancer Research Center, Seattle, Washington, USA 2PRECISIONheor, Rotterdam, South Holland, The Netherlands 3BeiGene Netherlands B.V., Schiphol, The Netherlands 4BeiGene (Shanghai) Co, Ltd, Shanghai, China
5BeiGene USA, Inc, San Mateo, CA, USA 6PRECISIONheor, San Francisco, CA, USA 7The Alfred Hospital, Melbourne, Victoria, Australia 8Dana-Farber Cancer Institute, Boston, USA(CLL) resulted in the development of Bruton tyrosine kinase inhibitors (BTKis), whichhave been investigated in randomized clinical trials (RCTs) in relapsed/refractory (R/R)
CLL. These include the ALPINE trial1 (NCT03734016; zanubrutinib vs ibrutinib) and theELEVATE-RR trial2 (NCT02477696; acalabrutinib vs ibrutinib)• Population-adjusted indirect treatment comparisons (ITC) can be conducted to estimate
relative treatment effects between different BTKis that were not studied head-to-head,
while adjusting for between-trial differences in important patient characteristics• However, commonly used ITC methods are unable to estimate the relative effect inthe true population of interest. Matching adjusted indirect comparisons (MAICs) forinstance, reweight the individual patient data (IPD) from a given trial to reflect thepopulation characteristics of a comparator trial• Zanubrutinib was associated with a more favorable PFS than acalabrutinib in apopulation as in ALPINE (Table 2). Figure 2 presents the corresponding predicted PFS
curves and the observed Kaplan-Meier curves in ALPINE• Treatment effect estimates with zanubrutinib vs ibrutinib and acalabrutinib vs ibrutinib

Zanubrutinib• To estimate the treatment effect of zanubrutinib relative to other BTKis in R/R CLL

ELEVATE-RR
Ibrutinib

Acalabrutinib

Ibrutinib

other assumptions described in the literature3, the following should be noted forthe current study:
Our analysis assumed that all relevant patient characteristics have been
incorporatedGiven the lack of IPD from the ELEVATE-RR trial, we assumed that the effectof covariates was the same in ALPINE and ELEVATE-RR (i.e., the shared effectmodifier assumption)――0.75• RCTs remain the gold standard of estimating relative treatment efficacy
0.50

Table 2. Progression-free Survival and Overall Survival per Treatment Comparison
Across Different Target Populations

Study characteristics

patients with a profile similar to the ALPINE intention-to-treat (ITT) population by meansof an ML-NMR• Treatment effects were also predicted for ELEVATE-RR-like patients
ME THO D SStudy phase anddesign

RCT phase III, open-label

RCT phase III, open-label

Country/region

Multinational

Multinational

zanubrutinib 160 mg orally 2x daily
ibrutinib 420 mg orally 1x daily

acalabrutinib 100 mg orally 2x daily
ibrutinib 420 mg orally 1x daily

Treatments• The evidence base of this analysis included ALPINE and ELEVATE-RR, connected via

their common comparator, ibrutinib, using the IPD for ALPINE and summary aggregate
level data from the publication of ELEVATE-RR1,2 (Figure 1)

Follow-up (months)• The key trial and patient characteristics at baseline are summarized in Table 1• Selection of the relevant patient characteristics to incorporate in the adjustment ofSample size

PFS: 44.2
OS: 45.8
327

aPFS: 44.2
OS: 45.6
325

aPFS: 40.9
OS: 41.1
268

Comparison

0.25

0

10

20

PFS: 40.9
OS: 41.1304050265• As IPD data was available for ALPINE but not ELEVATE-RR, it was not feasible to estimate
a different effect of covariates on hazard ratios (HRs) for zanubrutinib vs ibrutinib andacalabrutinib vs ibrutinib• Therefore, we assumed that the impact of covariates on the HRs of zanubrutinib vsAge ≥65 years, n (%)

204 (62.3)

200 (61.5)

144 (53.7)

143 (54.0)

Male, n (%)

213 (65.1)

232 (71.4)

185 (69.0)

194 (73.2)

RegionEurope, n (%)
North America, n (%)
Asia, n (%)
Other, n (%)

198 (60.6)
52 (15.9)
49 (15.0)
28 (8.6)

191 (58.8)
59 (18.2)
45 (13.8)
30 (9.2)

NR
NR0 (0)bNR

NR
NR0 (0)bNR

ECOG PSECOG 0-1, n (%)
ECOG 2, n (%)

ibrutinib and acalabrutinib vs ibrutinib are of the same magnitude i.e., shared effectmodifiers assumption• Fixed effect ML-NMR models, which assume shared effect modifiers for both direct

Rai stage
0-II, n (%)
III-IV, n (%)
Missing, n (%)

comparisons as introduced above, were used to estimate the relative treatment effectbetween the competing interventions for progression-free survival (PFS) and overall
survival (OS) for 2 target populations: the ALPINE ITT population and the ELEVATERR population, which consisted of high-risk patients defined as having del(17p) and/ordel(11q)• Given the timing of the included trials in relation to the COVID-19 pandemic, ALPINEPFS and OS data were adjusted for death due to COVID-19• Analyses were implemented in a Bayesian framework, with parameters estimated using
Markov Chain Monte Carlo method in R (packages: rstan and loo) and Stan

4,5

Figure 1. Network Diagram of Included Trials

Acalabrutinib
ELEVATE-RR

PFSHR, 95% CrI

Time, moTreatment

Zanubrutinib

Ibrutinib

Acalabrutinib

Patient characteristics at baseline

between-trial differences was based on a review of the literature and consultation
with clinical experts and included the following treatment effect-modifiers: Rai/Binet
stage, β2-microglobulin, cytogenetic deletions/mutations (TP53, del(11q), del(17p)),
immunoglobulin heavy-chain variable (IGHV) mutation status, and the number of priortreatment lines

Ibrutinib• As with any statistical technique, ML-NMR involves some assumptions. Among― Since it had to be assumed that the impact of covariates on the HRs of zanubrutinib

Table 1. Key Trial and Patient Characteristics at Baselineto acalabrutinib and ibrutinib in a R/R CLL population akin to that of the ALPINE
trial as well as for ELEVATE-RR patients who were characterized by high-risk
cytogenetics
compared to both ibrutinib and acalalabrutinib. However, the findings were notstatistically significant, possibly due to the limitations in the number of studies
available and their sample sizes

indirect comparison were the same between the two target populations (Table 2)O BJE C TI VES

ALPINE

1.00• For both PFS and OS, the HRs of zanubrutinib vs acalabrutinib obtained with thevs ibrutinib and acalabrutinib vs ibrutinib are of the same magnitude (see Methods),
the indirect comparison estimate of zanubrutinib vs acalabrutinib is independent of
the patient characteristics in the analysis and therefore consistent across the target
populations• Our findings indicate that zanubrutinib had significantly greater PFS compared• In terms of OS, zanubrutinib also demonstrated a potential improvement in OS

were numerically different between the 2 target populations (Table 2)

ALPINE

Zanubrutinib

ALPINEacalabrutinib for the ALPINE population, but differences were not statistically significant(Table 2; Figure 3)

facilitates estimations of relative treatment effects between interventions for different
target populations based on networks of any size, and with that provides an opportunity
to overcome the common ITC limitations3
relative treatment effects can be estimated as a function of patient characteristics3

Figure 2. Prediction of Progression-Free Survival; M-spline Model Adjusted for Rai/Binet
Stage, β2-microglobulin, Cytogenetic Mutations, Immunoglobulin Heavy-chain Variable,
and the Number of Prior Line of Treatment – ALPINE Intention-to-treat Population• The analysis showed numerically favorable OS with zanubrutinib when compared to• Multilevel network meta-regression (ML-NMR) is a newly developed method that

• With ML-NMR, at the minimum, the IPD for 1 trial in the network is needed so thatC O N C LUS I O N S

Progression-free survival• Improved understanding of the disease biology of chronic lymphocytic leukemia

RE SU LTS

Figure 3. Prediction of Overall Survival; M-spline Model Adjusted for Rai/Binet stage,
β2-microglobulin, Cytogenetic Mutations, Immunoglobulin Heavy-chain Variable, andthe Number of Prior Line of Treatment – ALPINE Intention-to-treat Population
ALPINEOS
HR, 95% CrI

Population
ALPINE ITT

ELEVATE-RR

zanubrutinib vs acalabrutiniba

0.57 (0.34, 0.98)

0.57 (0.34, 0.98)

zanubrutinib vs ibrutinib

0.73 (0.55, 0.98)

0.56 (0.36, 0.86)

acalabrutinib vs ibrutinib

1.27 (0.80, 2.00)

0.97 (0.73, 1.29)

zanubrutinib vs acalabrutiniba

0.66 (0.30, 1.46)

0.66 (0.30, 1.46)

zanubrutinib vs ibrutinib

0.72 (0.41, 1.28)

0.52 (0.25, 1.04)

acalabrutinib vs ibrutinib

1.09 (0.50, 2.35)

0.78 (0.48, 1.24)* With the analysis, we assumed that the impact of patient characteristics on the treatment effect with zanubrutinib vs ibrutinib
and acalabrutinib vs ibrutinib are of the same magnitude; therefore, the relative treatment effect of zanubrutinib vs acalabrutinib isindependent of the patient characteristics included in the analyses.
CrI, credible interval.

R E FE R E N C E S
1.00

1. Brown JR, et al. N Engl J Med. 2023;388(4):319-332.
2. Byrd JC, et al. J Clin Oncol. 2021;39(31):3441-3452.

0-1: 320 (97.9)
2: 7 (2.1)

0-1: 312 (96.0)
2: 13 (4.0)

0-1: 247 (92.2)
2: 20 (7.5)

3. Phillippo DM, et al. J R Stat Soc Ser A Stat Soc. 2020;183(3):1189-1210.

0-1: 243 (91.7)
2: 22 (8.3)

4. Phillippo D. multinma: Bayesian Network Meta-Analysis of Individual and Aggregate
Data. 2023 doi:10.5281/zenodo.3904454, R package version 0.5.1. https://dmphillippo.
github.io/multinma/

0.9
182 (55.7)
145 (44.3)-

189 (58.2)
135 (41.5)-130 (48.5);
131 (48.9)
7 (2.6)124 (46.8);
134 (50.6)
7 (2.6)

Bulky disease at least
5 cm, n (%)

145 (44.3)

149 (45.8)

128 (47.8)

136 (51.3)

TP53 mutation and/ordel(17p), n (%)

75 (22.9)

Del(11q), n (%)

91 (27.8)

75 (23.1)
88 (27.1)

136 (50.7)
167 (62.3)

135 (50.9)
175 (66.0)

Del(17p), n (%)

45 (13.8)

50 (15.4)

121 (45.1)

120 (45.3)

TP53 mutated, n (%)

50 (15.3)

45 (13.8)

100 (37.3)

112 (42.3)

IGHV mutated, n (%)

79 (24.2)

70 (21.5)

44 (16.4)

28 (10.6)

β2-microglobulin>3.5 mg/L, n (%)

176 (53.8)

183 (56.3)

207 (77.2)

214 (80.8)>3 previous LOT, n (%)

24 (7.2)

33 (10.1)

33 (12.3)

28 (10.6)

The sample size for the ALPINE population with complete data on all relevant patient characteristics was 265 for zanubrutinib
and 251 for ibrutinib.
bConcluded based on the trial inclusion/exclusion criteria.
ECOG PS, Eastern Cooperative Oncology Group Performance Status; LOT, line of treatment; NR, not reported.

5. Stan Development Team. Stan Modeling Language Users Guide andReference Manual, 2.34. https://mc-stan.org
Overall survival

IN TR O D UCT ION

0.8

DI S C LO S UR E S
MS: Consultancy for AbbVie, Genentech, AstraZeneca, Pharmacyclics, BeiGene, BMS,MorphoSys/Incyte, Kite, Eli Lilly, Genmab, Mustang Bio, Regeneron, ADC therapeutics,
Fate Therapeutics, Janssen and MEI Pharma. Research funding from Mustang Bio, BMS,Pharmacyclics, Genentech, AbbVie, TG Therapeutics, BeiGene, AstraZeneca, Genmab,MorphoSys/incyte, Vincerx. WB: Research funding from BeiGEne. LM: Employed by andowns equity in BeiGene. Previously employed by Evidera. SX: n/a. MJ: Research funding
from BeiGene. KY: Employed by and owns equity in BeiGene. RW: Employed by andowns equity in BeiGene. AC: n/a JJ: Paid on a part time basis by Precision Medicine
Group as Chief Scientist - HEOR. CT: Research funding from Janssen, AbbVie, BeiGene.
Honararia from Janssen, AbbVie, BeiGene, LOXO, Novartis, Roche.

0.70.602040

AC K N OW L E DG E M E N TS

Time, months

aTreatment

Zanubrutinib

Ibrutinib

Acalabrutinib

This study was sponsored by BeiGene, Ltd. Editorial assistance was provided by
PRECISIONheor, and supported by BeiGene.

Presented at EHA 2024; June 13-16, 2024; Madrid, Spain/Virtual Conference