Patient-Reported Outcome (PRO)-Based Recurrent Symptomatic Deterioration
Predicts Disease Progression: Results From the ALPINE Trial
Daniel Serrano1, Jennifer R. Brown2, Mazyar Shadman3,4, Tommi Salmi5, Timothy Victor6,7, Rasika Korde6, Gisoo Barnes6
The Psychometrics Team, Sheridan, WY, USA; 2Dana-Farber Cancer Institute, Boston, MA, USA; 3Fred Hutchinson Cancer Research Center, Seattle, WA, USA; 4University of Washington, Seattle, WA, USA; 5BeiGene International GmbH, Basel, Switzerland; 6BeiGene USA, Inc., San Mateo, CA, USA; 7University of Pennsylvania, Philadelphia, PA, USARESULTS■■

PRO-based symptom endpoints are rarely associated with treatment efficacy in oncology
trials, including those conducted in patients with chronic lymphocytic leukemia/small
lymphocytic lymphoma (CLL/SLL)■ Time-to-deterioration analyses of key PRO symptoms (eg, fatigue) and functioning(eg, physical function) are routinely employed in oncology clinical trials to evaluate theeffects of treatment on a single deterioration event
However, PRO-based deterioration frequently has “transient” event times; for example,
a patient may experience multiple fatigue deteriorations over time
Therefore, transient event times are best modeled as recurrent events
Under a recurrent event process, the time to each unique deterioration is modeled, andthe overall risk of recurrent deterioration is estimated within a survival model accounting
for the correlation among recurrent deterioration events■ The objective of the current analyses was to develop a joint model to examine theassociation between time to recurrent PRO-based deterioration and disease progression(defined as PFS events) in patients enrolled in the ALPINE trial–––METHODS
Study Design and Patients■

These analyses were conducted using data from the ALPINE trial
ALPINE (BGB-3111-305; NCT03734016), a phase 3, randomized, open-label, multinational
trial of adult patients with relapsed or refractory CLL/SLL, was performed to compare theefficacy and safety of zanubrutinib with ibrutinib monotherapy1Patients were randomized 1:1 to receive zanubrutinib 160 mg orally twice daily or
ibrutinib 420 mg orally once daily until disease progression or patient withdrawal
The study was carried out in accordance with Good Clinical Practice Guidelines, theprinciples of the Declaration of Helsinki, and local laws and regulations–––

MeasuresPRO-based symptoms were assessed using the European Organisation for Research andTreatment of Cancer (EORTC) Quality of Life Questionnaire Core-30 (QLQ-C30), which isdesigned to assess the overall health-related quality of life of patients with cancer during
the past week2
Six QLQ-C30 symptom scales were analyzed: appetite, diarrhea, dyspnea, fatigue,nausea/vomiting, and pain. The QLQ-C30 was administered at baseline and cycle 1 andthen every third cycle until the end of treatment; each cycle constituted 28 days
■ Investigator-assessed PFS was analyzed as the terminal event measure■ Deterioration threshold was defined using Osoba’s Criterion3 (ie, any postbaseline changeof ≥10)
Unique recurrent symptomatic deterioration (RS-D) events from cycles 4 to 43 wereidentified using this threshold
Two deterioration events had to be separated by non-events to qualify as a unique RS-D event■–At data cutoff (September 15, 2023), the ITT population consisted of a total of 652 patients(327 received zanubrutinib and 325 received ibrutinib)
Patient demographics and baseline disease characteristics were generally balanced
across the arms
■ Fifty patients were excluded from the current analyses because they did not have any
PRO data; a total of 601 patients (zanubrutinib, n=308 [51.2%]; ibrutinib, n=293 [48.8%])
were included in the PFS joint models■ Using the QLQ-C30 fatigue domain as an example, the number of recurrent symptomatic
deterioration events ranged from 0 to 6 (Table 1)–Table 1. Number of Recurrent Fatigue Symptom Deterioration
EventsNumber of Recurrent Eventsn (%)

Cumulative n (%)

0149 (24.8)

149 (24.8)

1249 (41.4)

398 (66.1)

295 (15.8)

493 (81.9)

365 (10.8)

558 (92.7)

433 (5.5)

591 (98.2)

510 (1.7)601 (99.8)

6■

1 (0.2)

Terminal Event

Appetite

Statistical Analyses

Diarrhea■––––––

Terminal Event

QLQ-C30
DOMAIN

Appetite

602 (100)

Effectβ (95% CI)

PCNVG

Zanubrutinib-1.88 (-3.88 to 0.13)

0.06691.0014Time

0.02 (-0.03 to 0.08)

0.41561.0144

Zanubrutinib X time––

Table 3. Zanubrutinib vs Ibrutinib Efficacy in Three-Component
Joint Model

Diarrhea

Table 2. Zanubrutinib vs Ibrutinib Efficacy From Cycles 4 to 43 for
Recurrent Symptomatic Deterioration Events in Three-Component
Joint Model–All randomized patients in the ITT population who completed the baseline and≥1 post-baseline QLQ-C30 assessment were eligible
The analytic cohort was based on the ITT population with both PFS and RS-D event data
■ Treatment efficacy for the symptoms was evaluated using a three-component joint survival
model (treatment effect was coded as zanubrutinib vs ibrutinib with ibrutinib as thereference group) that linked the following components:
A linear mixed model for change from baseline (CFBL) in symptom scores to assess theassociation between change in symptom scores and RS-D events and disease progression
A Frailty Cox proportional hazards model for time to RS-D events
A Cox proportional hazards model for PFS (terminal event) to evaluate the RS-D event
frailty prediction of PFS■ The joint model provides a comprehensive adjustment for missing data bias
The linear mixed model directly adjusts for data missing at random
The terminal event survival models adjust the linear mixed model for data missing not atrandom■ All models were adjusted by the following stratification factors: age (<65 years vs ≥65 years),
geographic region (China vs non-China), refractory status (yes vs no), del(17p)/TP53 mutation
status (present vs absent), and cancer type (CLL vs SLL)
■ Analyses were conducted using the JMBayes2 package in R (version 4.3.2)
Model and parameter convergence were evaluated using trace and density plots and theR̂ statistic–

In the linear mixed model, treatment efficacy for zanubrutinib compared with ibrutinib wasobserved for diarrhea (-2.62 [95% CI, -4.49 to -0.67]; P=0.0089) and nausea/vomiting(-0.88 [95% CI, -1.65 to -0.10]; P=0.0251) (Table 2)

QLQ-C30
DOMAIN

Dyspnea
Progression-freesurvival
Fatigue

Nausea/vomiting

Pain-0.01 (-0.08 to 0.06)

0.72531.0091

Zanubrutinib-2.62 (-4.49 to -0.67)

0.00891.0101Time-0.04 (-0.09 to 0.02)

0.17961.0331

Zanubrutinib X time

0.04 (-0.03 to 0.11)

0.2727

1.0078

Zanubrutinib-0.47 (-2.52 to 1.66)

0.6502

1.0093Time

0.03 (-0.03 to 0.09)

0.3382

1.0259

Zanubrutinib X time

0.00 (-0.09 to 0.08)

0.9391

1.0222

Zanubrutinib-0.54 (-2.80 to 1.78)

0.65841.0052Time

0.00 (-0.06 to 0.06)

0.94531.0055

Zanubrutinib X time

0.03 (-0.05 to 0.11)

0.4491

1.0210

Zanubrutinib-0.88 (-1.65 to -0.10)

0.02511.0089Time-0.02 (-0.05 to 0.00)

0.09131.0203

Zanubrutinib X time

0.04 (0.01 to 0.08)

0.01561.0129

Zanubrutinib-1.29 (-3.50 to 1.02)

0.26561.0058Time-0.04 (-0.10 to 0.03)

0.2442

1.0135

Zanubrutinib X time

0.06 (-0.02 to 0.14)

0.14181.0181

CNVG represents convergence of parameter, based on R̂ statistic (values of 1 indicate perfect convergence). All estimates achieved acceptable
convergence. Time in this analysis is months since baseline. Significant effects are highlighted in blue. Models were adjusted for the following:region, del(17p) mutation, age >65 years, refractory status, cancer type (CLL/SLL), and baseline COA score; efficacy reference drug is ibrutinib.
CLL, chronic lymphocytic leukemia; COA, clinical outcome assessment; QLQ-C30, Quality of Life Questionnaire Core-30; SLL, small
lymphocytic lymphoma.

CONCLUSIONS

Dyspnea

Progression-freesurvival

Fatigue

Nausea/vomiting

Pain

■

■

■

After predicting PFS from the risk of recurrent symptomatic
deterioration events and using a joint model to adjust for baseline
stratification factors and change from baseline in corresponding
symptoms, zanubrutinib remained superior to ibrutinib with respect
to disease progression in the ALPINE trial
Recurrent symptomatic deterioration in appetite, diarrhea, anddyspnea were leading predictors for risk of disease progression
These analyses suggest that patient reporting of deterioration inthese symptoms may indicate a need for increased clinical monitoring

DISCUSSION

The three-component joint model detected zanubrutinib efficacy in diarrhea and nausea
while simultaneously preserving zanubrutinib efficacy in PFS and demonstrating therelationship between clinical progression and recurrent deterioration in appetite, diarrhea,
and dyspnea■ These preliminary analyses provide a mechanism for modeling PRO data in clinical trials
that may help illuminate additional patient-centric therapeutic benefits
To our knowledge, this method has not previously been used for PROs in the oncology
therapeutic domain■Effect

HR (95% CI)

P

CNVG

Zanubrutinib: R-AP-DET

0.95 (0.58-1.50)

0.82761.0020

Zanubrutinib: PFS

0.55 (0.30-0.90)

0.0093

1.0376

Lin Pred = AP CFBL: R-AP-DET

1.14 (1.12-1.16)

0.00001.0040

Lin Pred = AP CFBL: PFS

0.99 (0.97-1.00)

0.0151

1.0213R-AP Det frailty: PFS

4.55 (2.25-6.87)a

0.00001.1340

Zanubrutinib: R-DI-DET

1.03 (0.64-1.64)

0.90091.0117

Zanubrutinib: PFS

0.60 (0.34-0.94)

0.0202

1.0271

Lin Pred = DI CFBL: R-DI-DET

1.16 (1.13-1.19)

0.00001.1180

Lin Pred = DI CFBL: PFS

1.00 (0.98-1.01)

0.9242

1.0067

R-DI Det frailty: PFS

3.47 (1.61-5.92)a

0.00001.0681

Zanubrutinib: R-DY-DET

1.02 (0.65-1.58)

0.91641.0093

Zanubrutinib: PFS

0.59 (0.29-0.92)

0.01891.1236

Lin Pred = DY CFBL: R-DY-DET

1.13 (1.11-1.15)

0.00001.0125Lin Pred = DY CFBL: PFS

0.99 (0.98-1.00)

0.17201.1156

R-DY Det frailty: PFS

3.73 (1.58-6.51)a

0.00001.8209

Zanubrutinib: R-FA-DET

0.98 (0.70-1.35)

0.91241.0095

Zanubrutinib: PFS

0.71 (0.51-0.95)

0.0191

1.0459Lin Pred = FA CFBL: R-FA-DET

1.10 (1.09-1.11)

0.00001.0835Lin Pred = FA CFBL: PFS

1.01 (1.00-1.01)

0.15561.0296

R-FA Det frailty: PFS

1.74 (-3.00 to 5.83)a

0.38471.7081

Zanubrutinib: R-NV-DET

0.92 (0.58-1.44)

0.73401.0238

Zanubrutinib: PFS

0.66 (0.37-0.92)

0.00871.3159Lin Pred = NV CFBL: R-NV-DET

1.28 (1.23-1.32)

0.00001.0643

Lin Pred = NV CFBL: PFS

1.01 (0.98-1.03)

0.60641.0843

R-NV Det frailty: PFS

2.63 (-2.81 to 6.94)a

0.33872.0101

Zanubrutinib: R-PA-DET

0.87 (0.64-1.16)

0.3904

1.0072aZanubrutinib: PFS

0.69 (0.48-0.93)

0.01441.0101REFERENCES

Lin Pred = PA CFBL: R-PA-DET

1.09 (1.08-1.10)

0.00001.0566

Lin Pred = PA CFBL: PFS

1.00 (0.99-1.01)

0.54001.0086

R-PA Det frailty: PFS

2.32 (-0.31 to 4.77)a

0.07001.0210

CNVG represents convergence of parameter, based on R̂ statistic (values of 1 indicate perfect convergence). All estimates achieved acceptable
convergence. Time in this analysis is months since baseline. Significant effects are highlighted blue. Models were adjusted for the following: region,del(17p) mutation, age >65 years, refractory status, cancer type (CLL/SLL), and baseline COA score; efficacy reference drug is ibrutinib.
aAssociation parameter and not HR.
CLL, chronic lymphocytic leukemia; COA, clinical outcome assessment; R-AP-DET, recurrent appetite deterioration; R-DI-DET, recurrent diarrhea
deterioration; R-DY-DET, recurrent dyspnea deterioration; R-FA-DET, recurrent fatigue deterioration; R-NV-DET, recurrent nausea/vomiting
deterioration; R-PA-DET, recurrent pain deterioration; SLL, small lymphocytic lymphoma.

–Joint Model

Patients were censored if they experienced no recurrent events or disease progression byend of study■ Convergence plots for the joint model indicated satisfactory convergence of the Bayesian
integral-based marginalization (Figure 1A and 1B)■

Figure 1. Convergence Density (A) and Convergence Trace (B)for the Joint Model
ADensity

INTRODUCTION

In the recurrent event models for symptomatic deterioration, after adjusting for PFS, CFBL
in corresponding symptoms, and stratification factors, there was no difference between
treatment arms in risk of RS-D events (Table 3)
As expected, increasing CFBL in all symptoms (appetite, diarrhea, dyspnea, fatigue,nausea/vomiting, pain) was associated with increased risk of RS-D events, irrespectiveof treatment (Table 3). This is reflected in the linear predictor coefficient (eg, for appetite:
Lin Pred = AP CFBL: R-AP-DET)■ In the PFS model, after adjusting for recurrent symptomatic deterioration, CFBL incorresponding symptoms, and stratification factors, zanubrutinib treatment was associatedwith statistically significant reduction in the risk of investigator-assessed PFS events whencompared with ibrutinib (Table 3; PFS HRs after adjusting for symptoms of appetite:
0.55, P=0.0093; diarrhea: 0.60, P=0.0202; dyspnea: 0.59, P=0.0189; fatigue: 0.71, P=0.0191;
nausea/vomiting: 0.66, P=0.0087; pain: 0.69, P=0.0144)■ Increasing RS-D events for appetite, diarrhea, and dyspnea were strongly associated withrisk of PFS, irrespective of treatment (Table 3)■ Convergence for the dyspnea frailty prediction exhibited incomplete convergence (R̂=1.82)■0.30.2

Chaina1230.10.0−4−2Value02B2Value

1Chaina1230−2−401000

Iteration

2000

3000

Chains are sampling elements for Markov chains, autocorrelated samples from a posterior distribution.

1. Brown et al. N Engl J Med. 2023;388(4):319-332.
2. Aaronson et al. J Natl Cancer Inst. 1993;85(5):365-376.
3. Osoba et al. J Clin Oncol. 1998;16(1):139-144.

DISCLOSURES

Jennifer Brown has served as a consultant for AbbVie, Acerta/AstraZeneca, Alloplex Biotherapeutics, BeiGene, Bristol Myers Squibb/Juno/Celgene, Catapult,Genentech/Roche, Grifols Worldwide Operations, Hutchmed, iOnctura, Janssen, Kite, Loxo/Lilly, MEI Pharma, Merck Sharp & Dohme, Numab Therapeutics, Pfizer,and Pharmacyclics; and has received research funding from BeiGene, Gilead, iOnctura, Loxo/Lilly, MEI Pharma, Verastem/SecuraBio, and TG Therapeutics.

FUNDING

This study was funded by BeiGene USA, Inc.

ACKNOWLEDGMENTS

We would like to thank the investigators, site support staff, and especially the patients, for participating in this study. Medical writing support, under thedirection of the authors, was provided by Jason Allaire, PhD, of Generativity Solutions Group and was funded by BeiGene USA, Inc. Editorial support, under
the direction of the authors, was provided by Thai Cao, MS, and Smitha Reddy, PhD, of Envision Pharma Inc., and was funded by BeiGene.
Presenting author: jennifer_brown@dfci.harvard.edu (Jennifer Brown)

Poster presented at the European Hematology Association (EHA) Hybrid Congress; 13-16 June 2024; Madrid, Spain