Comparative Effectiveness of Ciltacabtagene Autoleucel in CARTITUDE-4 Versus Real-World Physician's Choice of Therapy from the Flatiron Registry in Lenalidomide-Refractory Multiple Myeloma.

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Key Summary Points

Introduction

Therapeutic advances including proteasome inhibitors (PIs), immunomodulatory drugs (IMiDs), and monoclonal antibodies have improved patient outcomes. However, multiple myeloma (MM) remains incurable [ 1 – 7 ]. Additionally, most patients with MM will relapse on these standard therapies and have worse outcomes with each subsequent line of therapy (LOT) [ 8 – 12 ]. Lenalidomide, an IMiD, is recommended across all lines of MM therapy, including newly diagnosed and relapsed or refractory MM (RRMM) [ 13 ], but resistance has been reported [ 12 , 14 , 15 ], highlighting the need for additional effective therapies.

Currently, combination therapies of daratumumab, an anti-CD38 monoclonal antibody, with pomalidomide (IMiD) plus dexamethasone (DPd), and daratumumab with carfilzomib (PI) plus dexamethasone (DKd), as well as pomalidomide with bortezomib (PV) plus dexamethasone (PVd) are preferred regimens for the treatment of lenalidomide-refractory MM after 1–3 prior LOT, based on National Comprehensive Cancer Network (NCCN), International Myeloma Working Group (IMWG), and European Society for Medical Oncology (ESMO) guidelines [ 10 , 16 , 17 ].

Ciltacabtagene autoleucel (cilta-cel), a chimeric antigen receptor T cell (CAR-T) therapy directed against B cell maturation antigen, demonstrated superior efficacy and a manageable safety profile versus physician’s choice of standard care (PVd or DPd) in patients with lenalidomide-refractory MM after 1–3 prior LOT in the randomized, phase 3 CARTITUDE-4 study ( NCT04181827 ) [ 18 , 19 ]. At 33.6-month median follow-up, overall survival (OS) significantly improved in the cilta-cel group versus the standard-care group. Median OS was not reached in the cilta-cel group (95% confidence interval [CI] not estimable) or standard-care group (95% CI 37.75 months to not estimable; hazard ratio [HR], 0.55 [95% CI 0.39–0.79]; p = 0.0009); 30-month OS rates were 76.4% and 63.8%, respectively. Additionally, median progression-free survival (PFS) was not reached (95% CI 34.50 months to not estimable) in the cilta-cel group and was 11.79 months (95% CI 9.66–14.00) in the standard-care group (HR for progression or death, 0.29 [95% CI 0.22–0.39], protocol-specified weighted analysis); 30-month PFS rates were 59.4% and 25.7%, respectively. Adverse events (AEs) were consistent with the known safety profile of cilta-cel [ 20 ]. No new safety signals, including movement and neurocognitive AEs or cranial nerve palsy, were observed after an additional 17.7 months of follow-up [ 18 , 19 ].

Although DPd and PVd are established regimens for the treatment of lenalidomide-refractory MM after 1–3 prior LOT, the treatment choices are not exclusively restricted to these regimens [ 21 ]. Additionally, availability and access to these therapies varies worldwide; therefore, it is important to generate results comparing cilta-cel with standard-care regimens used in real-world (RW) clinical practice settings. Indirect treatment comparisons (ITCs) can be used to assess the benefits of treatments used in clinical trials relative to those used in clinical practice in the absence of head-to-head comparisons [ 22 – 24 ]. ITC methods allow for creation of an external, RW control arm representing patients who would have been randomized to physician’s choice of treatment. This RW cohort can be compared with the cilta-cel cohort in CARTITUDE-4.

In this study, we performed an ITC to evaluate the comparative effectiveness of cilta-cel in CARTITUDE-4 and RW physician’s choice of treatment from the Flatiron Health database in patients with lenalidomide-refractory MM after 1–3 prior LOT.

Methods

Data Source

CARTITUDE-4 ( NCT04181827 ) is a phase 3, randomized, open-label trial assessing the efficacy and safety of cilta-cel versus RW physician’s choice of effective standard care in patients with lenalidomide-refractory MM after 1–3 LOT, including a PI and an IMiD [ 18 , 19 ]. Patient recruitment included 81 sites in the USA, Europe, Asia, and Australia. No information was available whether a patient enrolled was also included in the Flatiron database. The primary endpoint was PFS, which was defined as the time from randomization to the first documentation of disease progression or death. Secondary endpoints included rate of complete response or better, overall response rates, minimal residual disease negativity, OS, AEs, pharmacokinetics and worsening of patient-reported symptoms as assessed by the Multiple Myeloma Symptom and Impact Questionnaire. Treatment responses and disease progression were determined according to the International Myeloma Working Group (IMWG) criteria. Time to next treatment (TTNT) was also assessed.

CARTITUDE-4 was conducted in accordance with the Declaration of Helsinki and International Council for Harmonisation guidelines for Good Clinical Practice. The independent ethics committee or institutional review board at each site approved the protocol (Supplemental Table S1 ). All patients provided written informed consent.

Flatiron Health’s longitudinal database consists of de-identified patient-level electronic health record (EHR) data primarily from US community-based oncology clinics but also academic centers. The database includes an MM cohort registry from over 280 cancer clinics [ 25 ]. Flatiron Health leverages this database to develop RW data offerings on a tumor-by-tumor type basis. The Flatiron Health MM dataset includes diagnostic and treatment details captured during routine clinical care, either as structured data (e.g., International Classification of Diseases, 9th and 10th Revisions codes, laboratory values [e.g., M-protein level], medication orders, and medication administrations) or unstructured data (e.g., abstracted data obtained from EHR documents, including confirmed diagnosis, fluorescence in situ hybridization cytogenetics and karyotyping at diagnosis, and stem cell transplant). Additional derived variables include LOT and International Staging System (ISS) stage at time of diagnosis.

Patient Population

Individual patient data from CARTITUDE-4 (enrollment 10 July 2020 through 17 November 2021; data cutoff for this analysis was May 1, 2024) and de-identified EHR data derived from the Flatiron Health MM cohort registry between January 2020 and May 2024 were used to conduct adjusted comparisons between cilta-cel and RW treatment. The timeframe of the RW cohort was chosen to align with enrollment in CARTITUDE-4 and to better represent the current treatment landscape in MM.

Data from patients in the cilta-cel cohort from the CARTITUDE-4 study were compared with patients from the de-identified Flatiron database who met key CARTITUDE-4 eligibility criteria (Fig. 1 ). All patients had lenalidomide-refractory MM after 1–3 LOT, including a PI and an IMiD [ 18 , 21 ]. Refractory MM was defined as nonresponsive disease (i.e., not achieving minimal response or disease progression on therapy) with primary or salvage therapy, or disease progression within 60 days of last therapy [ 26 , 27 ].

Outcomes

Comparative effectiveness analyses included the intent-to-treat population from the CARTITUDE-4 cilta-cel cohort and all eligible patients from the RW physician’s choice of treatment cohort.

Comparative effectiveness of cilta-cel versus RW treatment was assessed by PFS, real-world PFS (RW-PFS), TTNT, and OS. Safety was not in the scope of this analysis. PFS was defined as the duration from the index date to the date of disease progression or death due to any cause, whichever occurred first. Disease progression was evaluated per IMWG criteria. For patients who had not progressed and were alive at the time of the data cutoff, data were censored at the last disease evaluation before the start of any subsequent antimyeloma therapy. PFS in the RW cohort may be underestimated as disease progression was measured by M-protein and serum-free light chain only; other measurements such as bone marrow or bone lesion assessments were lacking. Therefore, RW-PFS was assessed for both cohorts in this study. RW-PFS was defined as the duration from the index date to the date of next antimyeloma treatment, disease progression, or death, whichever comes first. For patients who had not developed any of these events, data were censored at the last disease evaluation. TTNT was defined as the time from index date to the initiation date of next antimyeloma treatment or death, whichever occurred first. Patients who were still alive and did not initiate a new LOT at the data cutoff were censored at the last date known to be alive. OS was defined as the time from the index date to the date of death. Patients who were still alive at the data cutoff were censored at the last date known to be alive. The index date (time 0) in CARTITUDE-4 for all outcomes was the date of randomization; the index date in the RW cohort was defined as the date when patients met inclusion criteria.

Statistical Analyses

To account for imbalances in baseline patient characteristics between the CARTITUDE-4 and RW cohorts, inverse probability of treatment weighting (IPTW) with average treatment effects in the treated derived from propensity scores was performed to ensure that patients in the RW cohort resembled patients in CARTITUDE-4. IPTW uses a balancing score to derive weights for the desired target population, which minimizes confounding and removes potential effect modifiers. To identify and rank prognostic factors that might influence effectiveness estimates, a clinician-driven process was used. A list of potential factors was identified a priori by consulting studies from a literature review conducted to identify clinical outcomes in patients with triple-class-exposed RRMM and was presented to a panel of clinical experts [ 24 ]. Among 15 prognostic factors, the panel prioritized five: refractory status, cytogenetic profile, ISS stage, time to progression on last regimen, and total plasmacytoma (extramedullary plasmacytomas and soft-tissue components of bone-based plasmacytomas [ 24 ]). Total plasmacytoma was not available from the Flatiron Health database and therefore was not included. Additional prognostic variables identified by the panel that were considered key covariates for the IPTW analyses were number of prior LOT, years since MM diagnosis, age, hemoglobin, and lactate dehydrogenase (LDH) levels. LDH levels were also excluded because of missingness in the Flatiron Health database, resulting in eight top-ranked variables utilized in the base case analysis: refractory status, time to progression on the last regimen, cytogenetic profile, ISS stage, number of prior LOT, years since MM diagnosis, age, and hemoglobin. Prior stem cell transplant, Eastern Cooperative Oncology Group performance status (ECOG PS), race, sex, and type of MM were also identified by the panel as key factors and, in addition to the eight variables in the base case analysis, were adjusted for in a sensitivity analysis (“fully adjusted analysis”). Population differences between CARTITUDE-4 and the RW cohort were assessed using standardized mean differences (SMDs) (small, 0 to 0.1; moderate, > 0.1 to ≤ 0.2; substantial, > 0.2) [ 28 ]. For both the CARTITUDE-4 and RW cohorts, no imputation was conducted for the missing cytogenetic data and was thus treated as a separate category; however, the actual distribution of high versus standard cytogenetic risk in each database may differ. For the RW cohort, variables with missing values (ISS stage, hemoglobin, and ECOG PS) were imputed using the last observation prior to the index date.

Estimates of comparative effectiveness were derived for both the unadjusted (cilta-cel vs RW physician’s choice of treatment prior to IPTW) and the adjusted comparison (IPTW). Treatment effect estimates, effective sample size, and covariate balance are reported for both scenarios. Sensitivity analyses evaluated the impact of alternative statistical methods and variable adjustment and were conducted using the multivariate regression and doubly robust methods [ 29 ]. First patient case weighting (i.e., the first LOT satisfying the inclusion criteria and not including patients with multiple eligible LOT) was also conducted as a sensitivity analysis. Transformations and interaction terms were not included.

For time-to-event outcomes, a weighted Cox proportional hazards model was used to derive a HR and its respective 95% CI. A weighted Kaplan–Meier method was used to derive the median time to event, each with its respective 95% CI. To account for patients with multiple index dates, the variance was estimated using a robust sandwich variance estimator specifying the patient identifier.

A significance level of 0.05 was used for all comparisons, without adjustment for multiplicity. All statistical analyses were performed with R version 4.0.3 (R Foundation for Statistical Computing, Vienna, Austria).

Results

Selection of Patients

The CARTITUDE-4 cohort included data from 208 patients randomized to the cilta-cel arm, with a median follow-up of 33.6 months (data cutoff May 1, 2024). The RW cohort included 932 patients from the de-identified Flatiron database, contributing 1445 eligible LOT between January 2020 and May 2024. Median follow-up was 23.6 months.

Overall and RW cohort weight distributions are shown in Supplementary Table S2 . Before reweighting, substantial differences (SMD > 0.20) between the CARTITUDE-4 and RW cohorts were observed for several of the baseline characteristic variables. More patients in the CARTITUDE-4 cohort had high-risk cytogenetics (59.4% vs 37.8%), defined as del17p, t[4;14], t[14;16], or gain/amp[1q] for both cohorts [ 18 , 21 ]), progressed on their last LOT > 16 months before the index treatment (51.0% vs 24.5%), had an MM diagnosis ≥ 3 years before the index treatment (50.0% vs 34.3%), and had a prior stem cell transplant (82.2% vs 30.2%). In contrast, the RW cohort had a greater proportion of patients with ISS Stage III disease (5.8% vs 16.8%), who were ≥ 65 years of age (39.4% vs 66.0%), and were refractory to a PI (49.5% vs 71.6%) (Table 1 ).

After reweighting, baseline characteristics were balanced between the two cohorts (Table 1 ). Most SMD values were < 0.10, with the maximum value of 0.10 in the base case analysis and 0.13 in the fully adjusted analysis (Table 1 ; Supplemental Fig. S1 ).

Treatment Regimens Received in RW Clinical Practice

In the RW cohort from the Flatiron database, across all eligible LOT, more than 50 treatment regimens were prescribed (Table 2 ). The most commonly prescribed treatment regimens were anti-CD38-based regimens, including DPd (16.0%), daratumumab monotherapy (8.2%), DKd (7.5%), DVd (daratumumab, bortezomib, and dexamethasone; 6.8%), DRd (daratumumab, lenalidomide, and dexamethasone; 1.8%), and Isa-Kd (isatuximab, carfilzomib, and dexamethasone; 1.5%). Of the 1445 eligible LOT, 17.7% were other drug combinations (i.e., all drugs not recommended as regimens for the treatment of MM by the NCCN, which may include any PI, IMiD, anti-CD38 monoclonal antibody, melphalan, cyclophosphamide, or selinexor).

Comparative Analysis of Efficacy Outcomes

In unadjusted analysis, patients in the cilta-cel cohort from CARTITUDE-4 showed significantly longer PFS (median not reached [NR] vs 8.1 months; HR 0.32 [95% CI 0.25–0.40), RW-PFS (median NR vs 8.1 months; HR 0.32 [95% CI 0.26–0.40]), TTNT (median NR vs 9.9 months; HR 0.35 [95% CI 0.28–0.43]), and OS (median NR vs 45.3 months; HR 0.60 [95% CI 0.43–0.82]) compared with the RW cohort (IPTW, p ≤ 0.001 for all) (Table 3 ). For the cilta-cel cohort, the estimated 24- and 30-month OS rates were 79% and 76%; for the RW cohort, the unweighted 24- and 30-month OS rates were 70% and 66%, and weighted 24- and 30-month OS rates were 70% and 65%, respectively.

The base case analysis showed similar results, i.e., significantly improved outcomes were found in the cilta-cel cohort compared with the RW cohort (Table 3 , Figs. 2 , 3 , 4 , Supplemental Fig. S2 ). After adjustment, for patients in the cilta-cel cohort from CARTITUDE-4 compared with patients in the RW cohort, PFS (median NR vs 8.2 months; HR 0.29 [95% CI 0.22–0.36]; p < 0.001) (Supplemental Fig. S2 ), RW-PFS (median NR vs 8.2 months; HR 0.29 [95% CI 0.23–0.37]; p < 0.001) (Fig. 2 a), TTNT (median NR vs 9.4 months; HR 0.32 [95% CI 0.25–0.41]; p < 0.001) (Fig. 3 a), and OS were significantly longer (median NR vs 45.4 months; HR 0.59 [95% CI 0.41–0.84]; p = 0.003) (Fig. 4 a). All p values were based on IPTW.

Overall, results in the fully adjusted analysis were consistent with the base case analysis (Table 3 , Figs. 2 , 3 , 4 , Supplemental Fig. S2 ). After adjustment, in patients in the cilta-cel cohort from CARTITUDE-4 compared with patients in the RW cohort, PFS (median NR vs 8.3 months; HR 0.28 [95% CI 0.22–0.38]; p < 0.001) (Supplemental Fig. S2 ), RW-PFS (median NR vs 8.3 months; HR 0.29 [95% CI 0.22–0.38]; p < 0.001) (Fig. 2 b), and TTNT were significantly longer (median NR vs 9.9 months; HR 0.31 [95% CI 0.23–0.40]; p < 0.001) (Fig. 3 b). The OS was similar in patients in the cilta-cel cohort compared with patients in the RW cohort after adjustment (median NR in both cohorts; HR 0.78 [95% CI 0.52–1.16]; p = 0.222) (Fig. 4 b). All p values were based on IPTW.

Sensitivity analyses using doubly robust and multivariable regression, as well as first patient case weighting, yielded similar results (Supplemental Table S3 and S4 ).

Discussion

This ITC study compared cilta-cel from the CARTITUDE-4 study with RW physician’s choice of treatment from the Flatiron Health MM cohort, using propensity scores to align patient populations. Cilta-cel demonstrated superiority over the RW cohort for all outcomes, with similar favorable results in the fully adjusted analysis. Results from the base case analysis showed that cilta-cel reduced the risk of progression or death by 71%, reduced the risk of progression, initiating next treatment, or death by 71%, reduced the risk of initiation of the next LOT or death by 68%, and reduced the risk of death by 41% compared with the RW cohort. Results were consistent with the base case and fully adjusted analyses when doubly robust or multivariable regression were used in place of IPTW. Overall, the robustness and consistency of these findings across all analyses suggest that cilta-cel represents a promising treatment option for patients with MM in earlier LOT who are refractory to lenalidomide.

The strength of the control arm used in the present ITC study provided additional confidence in the findings. It captured the most recent innovative regimens available in clinical practice, as this analysis included patients treated from 2020 to 2024. In the unadjusted RW cohort, the median PFS, RW-PFS, TTNT, and OS were 8.1, 8.1, 9.9, and 45.3 months, respectively. Compared with the present analysis, similar results for median PFS (10.7 months) but not median OS (23.8 months) were observed in a RW, retrospective study in patients who were lenalidomide refractory [ 30 ]. In another RW cohort [ 31 ], median OS (21.5 months) was also lower than that observed in our RW cohort in lenalidomide-refractory patients with 1–3 prior LOT. However, considering that CARTITUDE-4 is an international study with study centers in the USA, Europe, Asia, and Australia, whereas the Flatiron data are from the USA only, the comparison with the other regions of the world may be different.

The current analysis was conducted in patients with lenalidomide-refractory MM. In CARTITUDE-4, the refractory status was defined prospectively using trial criteria, whereas in the RW cohort, it relied on retrospective EHR-based proxies, which might lead to overestimated refractoriness. Further, over 50 distinct treatment regimens were included in the RW cohort and patient numbers in various treatment groups (e.g., anti-CD38-based regimens or non-CD38-based therapies) were limited, resulting in insufficient data to carry out meaningful subgroup or stratified analysis. Limitations of the present analysis included the potential for residual confounding, which is not uncommon in a nonrandomized study. However, the availability of individual patient-level data from both cohorts enabled adjustment for imbalances in important prognostic factors. Although the process ensured key clinical factors were balanced between the two patient populations (i.e., one included participants in the international trial CARTITUDE-4 who received cilta-cel, the other included patients who received care from US academic and community cancer centers), total plasmacytomas and LDH levels were unavailable a priori in the de-identified database for the RW cohort. In addition, the missingness in the cytogenetic risk between the two data sources may present different risk mixes, and the actual distributions of the high-risk and standard-risk cytogenetics in each cohort were unknown. Matching on missing cytogenetic data was incomplete, albeit the same definition of high-risk cytogenetics was applied to both the cilta-cel and the RW cohorts. An external control arm from a RW data source such as the Flatiron Health database can be used to estimate the relative therapeutic effects of cilta-cel versus RW physician’s choice of treatment in clinical practice; however, estimates may be biased if there are significant differences in baseline characteristics between the patient populations. These variations in baseline characteristics can be mitigated by indirect, well-balanced analyses of nonrandomized populations across all variables potentially prognostic of outcomes. ITCs using IPTW methods are used to assess the benefits of a treatment relative to regimens used in clinical practice and ensure unbiased comparative effectiveness data that can assist physicians and payers in making decisions. IPTW methods are a robust and feasible approach due to the access of patient-level data. Another limitation is that information on response outcomes, such as overall and complete response rates, were unavailable for the RW cohort, which prevented comparative effectiveness analyses of these outcomes. Furthermore, monitoring of patients in RW databases is less rigorous and subject to greater variation than in clinical trials, where patients are regularly monitored. Lastly, PFS data quality may also be a limitation, as progression data such as bone marrow plasma cells, bone lesions, and the size of soft-tissue plasmacytomas were more likely to be missing for patients in the RW cohort than in CARTITUDE-4. To avoid underestimating progression in the RW cohort, we assessed RW-PFS, which accounts for TTNT. Results were consistent with the PFS comparison. OS data quality is not expected to be affected, as methods to assess OS have been validated for the de-identified database [ 32 ].

Conclusion

In our analyses, ITC methods were used to compare patient-level data from the CARTITUDE-4 clinical study with an external control arm created from eligible patients with lenalidomide-refractory MM after 1–3 prior LOT who received RW treatment in a de-identified database. After adjusting for baseline characteristics, cilta-cel showed statistically significant superiority for PFS, RW-PFS, TTNT, and OS versus RW treatment. Sensitivity analyses supported the results of the base case analyses. On the basis of these results, cilta-cel provides superior clinical benefit compared with RW physician’s choice of conventional regimens, highlighting the value of cilta-cel as an effective therapy in patients with lenalidomide-refractory MM as early as first relapse.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 644 KB)

Acknowledgements

We thank the patients who volunteered to participate in the study, their families and caregivers, the physicians and nurses who cared for the patients and supported this clinical trial, staff members at the 81 study sites in the United States, Europe, Asia, and Australia who enrolled patients, and staff members who were involved in data collection and analysis.

Medical Writing/Editorial Assistance

Medical writing support was provided by Alana Dorfstatter, PharmD, of Eloquent, part of Envision Ignite, an Envision Medical Communications agency, a part of Envision Pharma Group, and funded by Johnson & Johnson.

Author Contributions

Cyrille Touzeau, Brea Lipe, Abdullah M Khan, Binod Dhakal, Sandhya Nair, Jianming He, João Mendes, Seina Lee, Carolina Lonardi, Ana Slaughter, Nikoletta Lendvai, Jordan M Schecter, Diana Chen, Man Zhao, Tzu-min Yeh, Xavier Leleu, Noemí Puig, Dominik Dytfeld, Elena Zamagni, Katja Weisel, Lionel Karlin, Michel Delforge, Paolo Corradini, Roberto Mina, Wilfried Roeloffzen, and Surbhi Sidana contributed to the study conception and design, material preparation, and data collection. Cyrille Touzeau, Brea Lipe, Abdullah M Khan, Binod Dhakal, Sandhya Nair, Jianming He, João Mendes, Seina Lee, Carolina Lonardi, Ana Slaughter, Nikoletta Lendvai, Jordan M Schecter, Diana Chen, Man Zhao, Tzu-min Yeh, Xavier Leleu, Noemí Puig, Dominik Dytfeld, Elena Zamagni, Katja Weisel, Lionel Karlin, Michel Delforge, Paolo Corradini, Roberto Mina, Wilfried Roeloffzen, and Surbhi Sidana performed the analysis and contributed to the development of the first and subsequent drafts of the manuscript. Cyrille Touzeau, Brea Lipe, Abdullah M Khan, Binod Dhakal, Sandhya Nair, Jianming He, João Mendes, Seina Lee, Carolina Lonardi, Ana Slaughter, Nikoletta Lendvai, Jordan M Schecter, Diana Chen, Man Zhao, Tzu-min Yeh, Xavier Leleu, Noemí Puig, Dominik Dytfeld, Elena Zamagni, Katja Weisel, Lionel Karlin, Michel Delforge, Paolo Corradini, Roberto Mina, Wilfried Roeloffzen, and Surbhi Sidana read and approved the final manuscript. Cyrille Touzeau, Brea Lipe, Abdullah M Khan, Binod Dhakal, Sandhya Nair, Jianming He, João Mendes, Seina Lee, Carolina Lonardi, Ana Slaughter, Nikoletta Lendvai, Jordan M Schecter, Diana Chen, Man Zhao, Tzu-min Yeh, Xavier Leleu, Noemí Puig, Dominik Dytfeld, Elena Zamagni, Katja Weisel, Lionel Karlin, Michel Delforge, Paolo Corradini, Roberto Mina, Wilfried Roeloffzen, and Surbhi Sidana meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.

Funding

This study (ClinicalTrials.gov identifier NCT04181827 ) was funded by Johnson & Johnson and Legend Biotech USA Inc. Funding for the journal’s Rapid Service and Open Access Fees was provided by Johnson & Johnson and Legend Biotech USA Inc.

Data Availability

The data sharing policy of Janssen Pharmaceutical Companies of Johnson & Johnson is available at https://www.janssen.com/clinical-trials/transparency . As noted on this site, requests for access to the study data can be submitted through Yale Open Data Access (YODA) Project site at http://yoda.yale.edu .

Declarations

Conflict of Interest

Cyrille Touzeau: Honoraria – AbbVie, Amgen, Bristol Myers Squibb/Celgene, Janssen, Pfizer, Sanofi, Takeda; consulting or advisory role – AbbVie, Amgen, Bristol Myers Squibb/Celgene, Janssen, Pfizer, Takeda; research funding – Sanofi (Inst); travel, accommodations, expenses – Johnson & Johnson/Janssen, Pfizer, Sanofi. Brea Lipe: Consulting or advisory role – AbbVie, GlaxoSmithKline, Janssen, Karyopharm Therapeutics, Pfizer, Sanofi; research funding – Amgen. Abdullah M Khan: Speakers’ bureau – Amgen, Sanofi; research funding – Bristol Myers Squibb, Sanofi; travel, accommodations, expenses – Bristol Myers Squibb. Binod Dhakal: Consulting or advisory role – Amgen, Arcellx, Genentech/Roche, GlaxoSmithKline, Janssen Oncology, Natera, Pfizer, Sanofi, Pfizer, Takeda; speakers bureau – Janssen Oncology; honoraria – Celgene, GlaxoSmithKline, Karyopharm Therapeutics, Sanofi; research funding – Amgen (Inst), Arcellx (Inst), CARsgen Therapeutics (Inst), Bristol Myers Squibb/Celgene (Inst), GlaxoSmithKline, Janssen Oncology (Inst), Sanofi (Inst). Man Zhao: Employment – Haisco Pharmaceutical (I), IQVIA, Janssen; research funding – Haisco Pharmaceutical (I); patents, royalties, other intellectual property – Haisco Pharmaceutical (I). Xavier Leleu: Honoraria – Bristol Myers Squibb, Takeda. Noemí Puig: Honoraria – Amgen, Celgene, Janssen, Pfizer, Sanofi, Takeda, The Binding Site; consulting or advisory role – Amgen, Celgene, Janssen, Pfizer, Sanofi, Takeda, The Binding Site; speakers bureau – Celgene; research funding – Amgen, Celgene, Janssen, Pfizer, Takeda; travel, accommodations, expenses – Amgen, Celgene, Janssen, Pfizer, Takeda, The Binding Site. Dominik Dytfeld: Honoraria – Amgen, Bristol Myers Squibb, GlaxoSmithKline, Janssen, Pfizer; consulting or advisory role – Amgen, Bristol Myers Squibb, GlaxoSmithKline, Janssen, Pfizer; research funding – Bristol Myers Squibb, Janssen; travel, accommodations, expenses – Amgen, Janssen, Bristol Myers Squibb, Pfizer. Elena Zamagni: Honoraria and advisory role – Amgen, Bristol Myers Squibb, GlaxoSmithKline, Janssen, Menarini-Stemline, Oncopeptides, Pfizer, Sanofi. Katja Weisel: Honoraria – AbbVie, Adaptive Biotechnologies, Amgen, Bristol Myers Squibb, Celgene, GlaxoSmithKline, Janssen (Inst), Janssen-Cilag, Karyopharm Therapeutics, Menarini, Novartis, Oncopeptides, Pfizer, Roche, Sanofi, Stemline Therapeutics, Takeda; consulting or advisory role – Adaptive Biotechnologies, Amgen, Bristol Myers Squibb, Celgene, GlaxoSmithKline, Janssen-Cilag, Karyopharm Therapeutics, Menarini, Oncopeptides, Roche, Sanofi, Takeda; research funding – AbbVie (Inst), Amgen (Inst), Bristol Myers Squibb/Celgene (Inst), Celgene (Inst), GlaxoSmithKline (Inst), Janssen-Cilag (Inst), Sanofi (Inst); travel, accommodations, expenses – Amgen, Bristol Myers Squibb, Celgene, GlaxoSmithKline, Janssen-Cilag, Menarini, Takeda. Lionel Karlin: Honoraria – AbbVie, Amgen, Celgene, Johnson & Johnson, Takeda; consulting or advisory role – Amgen, Celgene, Johnson & Johnson, Takeda; travel, accommodations, expenses – Amgen, Johnson & Johnson. Michel Delforge: Honoraria – Amgen, Bristol Myers Squibb, GlaxoSmithKline, Johnson & Johnson; consulting or advisory role – Johnson & Johnson. Paolo Corradini: Honoraria – AbbVie, Amgen, Celgene, Gilead/Kite, Janssen, Novartis, Roche, Sanofi, Takeda; consulting or advisory role – AbbVie, ADC Therapeutics, Amgen, BeiGene, Celgene, Daiichi Sankyo, Gilead/Kite, GlaxoSmithKline, Incyte, Janssen, Kyowa Kirin, Nerviano Medical Science, Novartis, Roche, Sanofi, Takeda. Roberto Mina: Honoraria – AbbVie, Amgen, Bristol Myers Squibb, Janssen Pharmaceuticals, Sanofi; consulting or advisory role – Bristol Myers Squibb, GlaxoSmithKline, Janssen-Cilag EMEA, Janssen Pharmaceuticals, Pfizer, Takeda. Wilfried Roeloffzen: Honoraria – Amgen (Inst), Fondation Sanofi Espoir (Inst), Janssen Biotech (Inst); consulting or advisory role – Bristol Myers Squibb (Inst); travel, accommodations, expenses – AbbVie. Surbhi Sidana: Consulting or advisory role – AbbVie, BiolineRx, Bristol Myers Squibb/Celgene, Genentech/Roche, Janssen, Kite/Gilead, Legend Biotech, Pfizer, Regeneron, Sanofi; research funding – AbbVie (Inst), Allogene Therapeutics (Inst), Bristol Myers Squibb/Celgene (Inst), Janssen (Inst), Magenta Therapeutics (Inst), Novartis (Inst). Sandhya Nair, Seina Lee, Carolina Lonardi, and Diana Chen: Employment – Johnson & Johnson. Jianming He, João Mendes, Ana Slaughter, Nikoletta Lendvai, Jordan M Schecter, and Tzu-min Yeh: Employment – Johnson & Johnson; stock and other ownership interests – Johnson & Johnson.

Ethical Approval

CARTITUDE-4 was conducted in accordance with the Declaration of Helsinki and International Conference on Harmonisation Guidelines for Good Clinical Practice. The independent ethics committee or institutional review board at each site approved the protocol (Supplemental Table S1 ). All patients provided written informed consent.

Footnotes

Prior Presentation : This manuscript is based on work that was previously presented at the 2024 European Haematology Association Hybrid Congress; June 13–16, 2024; Madrid, Spain, and online. Touzeau C, et al. Comparative effectiveness of ciltacabtagene autoleucel from the CARTITUDE-4 trial vs real-world physician’s choice of therapy from the Flatiron Registry in lenalidomide-refractory multiple myeloma (#P967).

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References

1. Dimopoulos MA, Chen C, Spencer A, et al. Long-term follow-up on overall survival from the MM-009 and MM-010 phase III trials of lenalidomide plus dexamethasone in patients with relapsed or refractory multiple myeloma. Leukemia. 2009;23:2147–52.

2. Dimopoulos MA, Moreau P, Palumbo A, et al. Carfilzomib and dexamethasone versus bortezomib and dexamethasone for patients with relapsed or refractory multiple myeloma (ENDEAVOR): a randomised, phase 3, open-label, multicentre study. Lancet Oncol. 2016;17:27–38.

3. Miguel JS, Weisel K, Moreau P, et al. Pomalidomide plus low-dose dexamethasone versus high-dose dexamethasone alone for patients with relapsed and refractory multiple myeloma (MM-003): a randomised, open-label, phase 3 trial. Lancet Oncol. 2013;14:1055–66.

4. Orlowski RZ, Nagler A, Sonneveld P, et al. Randomized phase III study of pegylated liposomal doxorubicin plus bortezomib compared with bortezomib alone in relapsed or refractory multiple myeloma: combination therapy improves time to progression. J Clin Oncol. 2007;25:3892–901.

5. Raab MS, Cavo M, Delforge M, et al. Multiple myeloma: practice patterns across Europe. Br J Haematol. 2016;175:66–76.

6. San-Miguel JF, Hungria VT, Yoon SS, et al. Panobinostat plus bortezomib and dexamethasone versus placebo plus bortezomib and dexamethasone in patients with relapsed or relapsed and refractory multiple myeloma: a multicentre, randomised, double-blind phase 3 trial. Lancet Oncol. 2014;15:1195–206.

7. Usmani SZ, Nahi H, Plesner T, et al. Daratumumab monotherapy in patients with heavily pretreated relapsed or refractory multiple myeloma: final results from the phase 2 GEN501 and SIRIUS trials. Lancet Haematol. 2020;7:e447–55.

8. Dhakal B, Einsele H, Potluri R, et al. P-240: Real-world assessment of treatment patterns and outcomes in patients with lenalidomide-refractory relapsed multiple myeloma from the SEER-Medicare database. Clin Lymphoma Myeloma Leuk. 2022;22:S167.

9. Dhakal B, Einsele H, Potluri R, et al. P899: Real-world assessment of treatment patterns and outcomes in patients with lenalidomide-refractory relapsed/refractory multiple myeloma from the optum database. HemaSphere. 2022;6:790–1.

10. Dimopoulos MA, Moreau P, Terpos E, et al. Multiple myeloma: EHA-ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Hemasphere. 2021;5:e528.

11. Rodriguez-Lobato LG, Pereira A, Fernández de Larrea C, et al. Real-world data on survival improvement in patients with multiple myeloma treated at a single institution over a 45-year period. Br J Haematol. 2022;196:649–59.

12. van de Donk N. Sequencing multiple myeloma therapies with and after antibody therapies. Hematology Am Soc Hematol Educ Program. 2020;2020:248–58.

13. REVLIMID (lenalidomide) [Prescribing information]. Summit, NJ. Celgene Corporation, 2013.

14. de Arriba de la Fuente F, Montes Gaisán C, de la Rubia Comos J. How to manage patients with lenalidomide-refractory multiple myeloma. Cancers (Basel). 2022;15:155.

15. Kulig P, Milczarek S, Bakinowska E, Szalewska L, Baumert B, Machalinski B. Lenalidomide in multiple myeloma: review of resistance mechanisms, current treatment strategies and future perspectives. Cancers (Basel). 2023;15:963.

16. Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) for Multiple Myeloma V.1.2025. © National Comprehensive Cancer Network, Inc. 2025. All rights reserved. Accessed March 31, 2025. To view the most recent and complete version of the guideline, go online to NCCN.org. NCCN makes no warranties of any kind whatsoever regarding their content, use or application and disclaims any responsibility for their application or use in any way.

17. Moreau P, Kumar SK, San Miguel J, et al. Treatment of relapsed and refractory multiple myeloma: recommendations from the International Myeloma Working Group. Lancet Oncol. 2021;22:e105–18.

18. San-Miguel J, Dhakal B, Yong K, et al. Cilta-cel or standard care in lenalidomide-refractory multiple myeloma. N Engl J Med. 2023;389:335–47.

19. Mateos M-V, San-Miguel J, Dhakal B, et al. Overall survival with ciltacabtagene autoleucel versus standard of care in lenalidomide-refractory multiple myeloma: phase 3 CARTITUDE-4 study update. Presented at IMS; September 25–28, 2024; Rio de Janeiro, Brazil.

20. Janssen Biotech Inc. CARVYKTI® (ciltacabtagene autoleucel) suspension for intravenous infusion [Prescribing information]. Horsham, PA: Janssen Biotech, Inc., 2024. https://www.fda.gov/media/156560/download?attachment . Accessed April 1, 2025.

21. Dhakal B, Einsele H, Schecter JM, et al. Real-world treatment patterns and outcomes in relapsed/refractory multiple myeloma (1–3 prior lines): Flatiron database. Blood Adv. 2024;8:5062–71.

22. Shah N, Mojebi A, Ayers D, et al. Indirect treatment comparison of idecabtagene vicleucel versus conventional care in triple-class exposed multiple myeloma. J Comp Eff Res. 2022;11:737–49.

23. Weisel K, Krishnan A, Schecter JM, et al. Matching-adjusted indirect treatment comparison to assess the comparative efficacy of ciltacabtagene autoleucel in CARTITUDE-1 versus belantamab mafodotin in DREAMM-2, selinexor-dexamethasone in STORM Part 2, and melphalan flufenamide-dexamethasone in HORIZON for the treatment of patients with triple-class exposed relapsed or refractory multiple myeloma. Clin Lymphoma Myeloma Leuk. 2022;22:690–701.

24. Martin T, Krishnan A, Yong K, et al. Comparative effectiveness of ciltacabtagene autoleucel in CARTITUDE-1 versus physician’s choice of therapy in the Flatiron Health multiple myeloma cohort registry for the treatment of patients with relapsed or refractory multiple myeloma. EJHaem. 2022;3:97–108.

25. Ma X, Long L, Moon S, Adamson BJS, Baxi SS. Comparison of population characteristics in real-world clinical oncology databases in the US: Flatiron Health, SEER, and NPCR. medRxiv. 2020. 10.1101/2020.03.16.20037143.

26. Rajkumar SV, Harousseau JL, Durie B, et al. Consensus recommendations for the uniform reporting of clinical trials: report of the International Myeloma Workshop Consensus Panel 1. Blood. 2011;117:4691–5.

27. Usmani S, Ahmadi T, Ng Y, et al. Analysis of real-world data on overall survival in multiple myeloma patients with ≥3 prior lines of therapy including a proteasome inhibitor (PI) and an immunomodulatory drug (IMiD), or double refractory to a PI and an IMiD. Oncologist. 2016;21:1355–61.

28. Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009;28:3083–107.

29. Funk MJ, Westreich D, Wiesen C, Stürmer T, Brookhart MA, Davidian M. Doubly robust estimation of causal effects. Am J Epidemiol. 2011;173:761–7.

30. Kastritis E, Ntanasis-Stathopoulos I, Theodorakakou F, et al. Characteristics and outcomes of patients with relapsed/refractory multiple myeloma after exposure to lenalidomide in first line of therapy: a single center database review in Greece. Clin Lymphoma Myeloma Leuk. 2024;24:468–77.

31. Hajek R, Sliwka H, Stork M, et al. Patient characteristics and survival outcomes of lenalidomide exposed non-refractory vs lenalidomide refractory multiple myeloma patients in the HONEUR Federated Data Network. Blood. 2022;140(Supplement 1):7200–2.

32. Curtis MD, Griffith SD, Tucker M, et al. Development and validation of a high-quality composite real-world mortality endpoint. Health Serv Res. 2018;53:4460–76.

Associated Data

Supplementary Materials

Supplementary file1 (PDF 644 KB)

Data Availability Statement

The data sharing policy of Janssen Pharmaceutical Companies of Johnson & Johnson is available at https://www.janssen.com/clinical-trials/transparency . As noted on this site, requests for access to the study data can be submitted through Yale Open Data Access (YODA) Project site at http://yoda.yale.edu .