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. Author manuscript; available in PMC: 2025 Jan 26.
Published in final edited form as: Nat Med. 2024 Jul 8;30(9):2667–2678. doi: 10.1038/s41591-024-03084-6

Non-relapse mortality after CAR T-cell therapy: A systematic review and meta-analysis of 7,604 lymphoma and myeloma patients

David M Cordas dos Santos 1,2,3,4,*, Tobias Tix 4,*, Roni Shouval 5,6, Anat Gafter-Gvili 7, Jean-Baptiste Alberge 1,2,3, Edward R Scheffer Cliff 1,2,8, Sebastian Theurich 4,9, Michael von Bergwelt-Baildon 4,9, Irene M Ghobrial 1,2,3, Marion Subklewe 4,9, Miguel-Angel Perales 5,6, Kai Rejeski 4,5,6,9
PMCID: PMC11765209  NIHMSID: NIHMS2044206  PMID: 38977912

Abstract

While CAR T-cell therapy represents a transformative immunotherapy, it is also associated with distinct toxicities that contribute to morbidity and mortality. In this systematic review and meta-analysis, we searched MEDLINE, Embase and CINAHL for reports of non-relapse mortality (NRM) following CAR-T in lymphoma and multiple myeloma up to March 2024. Following extraction of causes and numbers of death, we analyzed NRM point estimates using random effect models. We identified 7,604 patients across 18 clinical trials (CT) and 28 real-world studies (RWS). NRM point estimates varied across disease entities and were highest in patients with mantle cell lymphoma (10.6%), followed by multiple myeloma (8.0%), large B-cell lymphoma (6.1%), and indolent lymphoma (5.7%). Entity-specific meta-regression models for large B-cell lymphoma and multiple myeloma revealed that axicabtagene ciloleucel and ciltacabtagene autoleucel were independently associated with increased NRM point estimates, respectively. Of 574 reported non-relapse deaths, over half were attributed to infections (50.9%), followed by other malignancies (7.8%) and cardiovascular/respiratory events (7.3%). Conversely, the CAR T-cell specific side effects ICANS/neurotoxicity, CRS and HLH only represented a minority of non-relapse deaths (cumulatively 11.5%). Our findings underline the critical importance of infectious complications following CAR T-cell therapy and support the comprehensive reporting of NRM, including specific causes and long-term outcomes.

Keywords: Chimeric antigen receptor, CAR-T, non-relapse mortality, infections, secondary malignancies, meta-analysis

Introduction

Chimeric antigen receptor (CAR) T-cells directed against the B-cell antigens CD19 and BCMA are a potent immunotherapy for multiple advanced B-cell malignancies and are being actively explored for several autoimmune diseases.1–6 However, CAR T-cells display a unique spectrum of immune-related toxicities, including cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS).7–9 Clinical experience has further highlighted the role of immune effector cell-associated hematological toxicity (ICAHT), the most common high-grade toxicity in the first year following CAR T-cell infusion.10–14 Profound and prolonged neutropenia can compound the risk of severe infection together with B-cell aplasia and hypogammaglobulinemia as expected on-target/off-tumor side effects of B-cell targeting therapies.15–19 In rare cases, the profound hyperinflammatory response induced by CAR T-cells can lead to secondary hemophagocytic lymphohistiocytosis (HLH).20 Given patients’ exposure to both prior and lymphodepleting chemotherapy, secondary malignancies, particularly myeloid neoplasms, can occur, although it remains unclear whether there is a specific mechanistic link to the CAR T-cells themselves.21,22 In light of the recent investigation by the United States Food and Drug Administration (FDA) and their announcement of a class-wide black box warning, the aforementioned risk of second primary malignancies after CAR T-cell therapy has recently garnered significant attention.23,24

Collectively, CAR T-cell-related side effects can impose a considerable burden on patients with potentially long-lasting sequelae.25 To mitigate the risk of severe early toxicities (e.g. CRS or ICANS), patients often receive immunosuppressive agents such as high-dose corticosteroids that can increase patients’ susceptibility to infections and may impede therapeutic success.26 Indeed, severe toxicity is associated with an increased risk of treatment failure.27–29 Non-relapse mortality (NRM), commonly defined as death not preceded by recurrent or progressive primary malignancy, is a devastating complication of CAR T-cell therapy.30 While NRM represents a well-characterized entity in the context of autologous and allogeneic hematopoietic cell transplantation (HCT),31 comprehensive analyses of NRM across disease entities, CAR T-cell products, and treatment settings have not yet been performed, and data regarding underlying causes remains limited. In this systematic review and meta-analysis, we therefore outline the comparative incidence and causes of NRM across the spectrum of lymphomas and multiple myeloma where CD19- and BCMA-directed CAR T-cell products are currently approved.

Results

Study Cohort

We screened 3550 studies for reports of NRM in patients receiving CAR T-cell therapy. Overall, 179 full-text articles were assessed for evaluation of NRM and causes of death in CAR T-cell patients, of which 46 articles fulfilled criteria for downstream analysis (Fig. 1). This included 18 reports on clinical trials with 2015 total patients (Phase I: 2, Phase I-II: 2, Phase II: 9, Phase III: 5; Table 1)32–49, and 28 RWS comprising 5589 patients (Table 2).11,12,16,17,30,50–72 For the RWS, 11 of 28 reported on multiple CAR T-cell products including seven studies which could be divided into disease and product-specific sub-cohorts, resulting in 52 distinct study cohorts for final evaluation.

Figure 1. Study retrieval and identification for meta-analysis.

Figure 1.

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Flow diagram displaying the process for study inclusion and exclusion for the systematic review and meta-analysis of NRM following CAR T-cell therapy following the PRISMA guidelines.

Table 1.

Characteristics of clinical trial records

Entity Study Author Year Treatment setting Product Cohort size Number of deaths Follow-up [months] Reported NRM [%] NRM point estimate [%] Therapy Line Inclusion before/after 2020
IL ELARA Dreyling et al. 2024 Phase I-II Tisa-cel 97 5 29 NR 5.15 later after
ZUMA-5 Jacobson et al. 2022A Phase I-II Axi-cel 148 9 17.5 NR 6.08 later after
LBCL TRANSCEND NHL 001 Abramson et al. 2020 Phase I-II Liso-cel 269 7 18.8 NR 2.60 later before
BELINDA Bishop et al. 2022 Phase III Tisa-cel 155 9 40.3 NR 5.80 earlier after
ALYCANTE Houot et al. 2023 Phase I-II Axi-cel 62 7 12 NR 11.29 earlier after
TRANSFORM Kamdar et al. 2022 Phase III Liso-cel 92 5 6.2 NR 5.43 earlier after
JapicCTI-183914 Kato et al. 2023 Phase I-II Axi-cel 16 0 13.4 NR 0.00 later after
ZUMA-12 Neelapu et al. 2022 Phase I-II Axi-cel 40 2 15.9 NR 5.00 earlier after
ZUMA-1 Neelapu et al. 2023 Phase I-II Axi-cel 101 13 63.1 NR 12.87 later before
PILOT Sehgal et al. 2022 Phase I-II Liso-cel 61 4 13 NR 6.56 earlier after
ZUMA-7 Westin et al. 2023 Phase III Axi-cel 170 23 47.2 NR 13.53 earlier before
MM CRB-401 Lin et al. 2023 Phase I-II Ide-cel 62 2 18.1 NR 3.23 later before
CARTITUDE-1 Martin et al. 2023 Phase I-II Cilta-cel 97 16 28 NR 16.49 later before
CARTIFAN-1 Mi et al. 2022 Phase I-II Cilta-cel 48 9 18 NR 18.75 later after
KarMMa Munshi et al. 2021 Phase I-II Ide-cel 128 9 13.3 NR 7.03 later before
KarMMa-3 Rodriguez-Otero et al. 2023 Phase III Ide-cel 225 18 18.6 NR 8.00 earlier after
CARTITUDE-4 San-Miguel et al. 2023 Phase III Cilta-cel 176 24 15.9 NR 13.64 earlier after
MCL ZUMA-2 Wang et al. 2023A Phase I-II Brexu-cel 68 5 35.6 NR 7.35 later before
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Abbreviations: IL = indolent lymphoma (including follicular lymphoma and chronic lymphocytic leukemia), LBCL = large B-cell lymphoma, MCL = mantle cell lymphoma, MM = multiple myeloma, NR = not reported, NRM = non-relapse mortality.

Table 2.

Characteristics of real-world studies.

Entity Author Year Cohort Product Cohort size Number of deaths Follow-up [months] Reported NRM [%] NRM point estimate [%] Therapy line Inclusion before/after 2020
IL Hamilton et al. 2024 B Axi-cel 20 1 14.46 NR 5.00 later after
Berning et al. 2024 Tisa-cel+Axi-cel 172 30 8.3 NR 17.44 later after
Bethge et al. 2022 A Axi-cel 183 15 11 10.4 8.20 later after
B Tisa-cel 173 6 11 3.5 3.47 later after
Chiappella et al. 2024 Axi-cel 260 6 3 a 3.1 2.3 later after
De Philippis et al. 2024 Tisa-cel 43 0 19.1 NR 0.00 later after
Dores et al. 2021 Tisa-cel 197 8 NA NR 4.06 later before
Grana et al. 2021 Axi-cel 37 3 11 NR 8.11 later before
Hamilton et al. 2024 Axi-cel 191 13 13.77 NR 6.81 later after
Iovino et al. 2022 Axi-cel 60 3 4.8 NR 5.00 later after
Jacobson et al. 2022B Axi-cel 1297 141 12.9 3 10.87 later after
Kuhnl et al. 2022 A Tisa-cel 76 2 13.9 3.1 2.63 later after
B Axi-cel 224 19 13.9 8.7 8.48 later after
Kwon et al. 2023 A Axi-cel 152 9 9.2 7.0 5.92 later after
B Tisa-cel 155 5 9.2 4.0 3.23 later after
Lemoine et al. 2023 Tisa-cel+Axi-cel 957 48 12.4 5.0 5.02 later after
Nastoupil et al. 2020 Axi-cel 275 12 12.9 4.4 4.36 later before
Rejeski et al. 2022 A Axi-cel 17 2 3 b NR 11.76 later after
B Tisa-cel 35 1 3 b NR 2.86 later after
Riedell et al. 2022 B Tisa-cel 92 7 13.8 7.0 7.61 later before
Spanjaart et al. 2023 Axi-cel 145 11 13 5.0 7.59 later after
Trando et al. 2023 Tisa-cel+Axi-cel 66 8 16.3 NR 12.12 later after
Wudhikarn et al. 2023 Tisa-cel+Axi-cel 33 1 16 NR 3.03 later after
MM Akthar et al. 2023 Ide-cel+Cilta-cel 139 7 6.7 4.9 5.04 later after
Caillot et al. 2024 Ide-cel 24 1 15.2 NR 4.17 later after
Ferreri et al. & Hansen et al.c 2023 A Ide-cel 50 4 4.5 NR 8.00 later after
B Ide-cel 153 8 6 NR 5.23 later after
Fischer et al. 2024 Ide-cel 27 1 5.9 NR 3.70 later NR
Rejeski et al. 2023A A Ide-cel 57 3 7.9 b NR 5.26 later after
B Cilta-cel 7 1 7.9 b NR 14.29 later after
MCL Chong et al. 2024 Brexu-cel 17 4 24.5 NR 23.53 later after
Iacoboni et al. 2022 Brexu-cel 33 5 10.1 NR 15.15 later after
Rejeski et al. 2023B Brexu-cel 54 4 15.4 b NR 7.41 later after
Wang et al. 2023B Brexu-cel 168 18 14.3 9.1 10.71 later after
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a

Follow-up set to 3 months, since NRM cases only reported for this period.

b

Follow-up for whole cohort as reported in the paper.

c

Studies displayed together due to cohort overlaps.

Abbreviations: LBCL = large B-cell lymphoma, MCL = mantle cell lymphoma, MM = multiple myeloma, NR = not reported, NRM = non-relapse mortality (reference period 12 months, 3 months for Jacobson et al. 2022B and Chiappella et al. 2024).

Overall, the most common entity was large B-cell lymphoma (LBCL, 5806 patients), followed by multiple myeloma (MM, 1193 patients), mantle cell lymphoma (MCL, 340 patients) and indolent lymphoma (IL, 265 patients). The number of study cohorts allocated to each CAR T-cell product was: 18 axi-cel, 9 tisa-cel, 3 liso-cel, 5 brexu-cel, 8 ide-cel and 4 cilta-cel. Five studies did not distinguish between individual CAR T-cell products when assessing NRM rates and were therefore excluded for the product-specific comparisons (Table 2). Two studies displayed substantial overlap with complementary data and were thus merged for analysis.67,69 The range of follow-up varied from 3.0 to 63.1 months.

NRM point estimates are comparable to reported NRM cumulative incidence rates

Cumulative incidence rates of NRM were not specifically reported in any of the 18 clinical trials. Furthermore, NRM data were missing in half of the RWS (14/28, 50%). For the studies reporting cumulative incidence rates, the median 1-year NRM rate was 5.0% (IQR 3.6–7.0%). Two studies reported 3-month NRM rates of 3.0% and 3.1%.52,57 To compensate for the lack of NRM data in clinical trials and some RWS, we determined NRM point estimates using random effects models based on proportions (see Online Methods). To test the reliability of these NRM point estimates, we compared them with the 1-year NRM incidence rates in RWS that did report 1-year NRM, finding no significant differences (p=0.6, Fig. S1A). The deviation between NRM point estimates and reported 1-year NRMs ranged from −2.2% to +2.6%. Since more patients die of any cause with longer follow-up times and follow-up times differed between studies, we next correlated NRM point estimates with the respective cohort follow-up times and found a weak positive correlation (Pearson r=0.26, p=0.07), predominantly driven by clinical trials (Fig. S1B). Based on these results, we proceeded with point estimates to study how the disease entity, CAR T-cell product, and treatment setting impacts NRM.

NRM point estimates vary across disease entities

Across all patients, the median follow-up was 13.4 months and was extended in CT compared to RWS (18 vs 11 months, p<0.001; Fig. S2). The overall NRM point estimate across all CAR T-cell studies was 6.8% (95% CI 5.8–8.0%), and was numerically but not significantly higher in CT compared to RWS (7.8% vs 6.3%, p=0.22; Fig. 2). Study heterogeneity was moderate-to-high (I2 = 64.5%, Fig. 2), though it was decreased when considering both disease entity and CAR T-cell product (Fig. S3–4), reflecting the underlying clinical heterogeneity of the study cohort.73 While funnel plot asymmetry suggested publication bias towards larger studies reporting on NRM (Fig. S5), we did not identify a significant risk of study bias among the included studies (Table S1). Since underlying tumor biology and previous treatments may affect non-relapse deaths, we next investigated associations between tumor entities and NRM point estimates. While follow-up was similar between tumor entities (Fig. 3A), NRM point estimates differed between the different dieases: IL (5.7%, 95% CI 3.4–9.2%), LBCL (6.1%, 95% CI 4.9–7.6%), MM (8.0%, 95% CI 5.8–11.0%), and MCL (10.6%, 95% CI 7.7–14.3%, p=0.026; Fig. 3B).

Figure 2. Forest plot of NRM point estimates across all study cohorts and stratified by treatment setting.

Figure 2.

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orest plot illustrating NRM point estimates and 95% confidence intervals using a random effects model. Studies are ordered by disease entity from top to bottom: indolent lymphoma (IL, blue), large B-cell lymphoma (LBCL, yellow), mantle cell lymphoma (MCL, grey), and multiple myeloma (MM, red). The total point estimate across all included CAR T-cell cohorts is highlighted in bold black, while the aggregated values for the treatment setting subgroups are highlighted in bold grey. Heterogeneity measures including I2 are depicted (I2 between 50% to 75% indicates moderate-to-high study heterogeneity). Abbreviation: NRM = non-relapse mortality.

Figure 3. CAR T-cell products impact NRM point estimates in a disease-specific manner.

Figure 3.

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A Bubble plot demonstrating NRM point estimates in relation to follow-up times for indolent lymphoma (IL, blue), large B-cell lymphoma (LBCL, yellow), multiple myeloma (MM, red), and mantle cell lymphoma (MCL, grey). Each bubble represents one cohort. Bubble size indicates the total number of patients per cohort. The aggregated NRM point estimate for all cohorts within one entity is encircled in black. B Depiction of aggregated NRM point estimates and 95% confidence intervals (CIs) for the different disease entities and the overall study cohort (“All”). C, E Comparison of aggregated NRM point estimates and 95% CIs across the different CAR T-cell products for LBCL (C) and MM (E). The p-values for the comparisons of NRM point estimates by disease entity and CAR T-cell product were calculated using the test for subgroup differences (random effects model). D, F Multivariable meta-regression analysis using random effects models for study features in LBCL (D) and MM (E). The integrated forest plot indicates the model estimates with 95% CI for each study variable (e.g., CAR T-cell product, treatment setting, treatment line, treatment era, follow-up time). Reference levels for the calculation of model estimates and respective p-values are depicted for each study feature. Abbreviations: NRM = non-relapse mortality; CT = clinical trials; RWS = real-world studies.

CAR T-cell products impact NRM point estimates in a disease-specific manner

Among all CAR T-cell products (Fig. S4, S6), cilta-cel had the highest overall NRM (15.2%, 95% CI 11.7–19.6%), followed by brexu-cel (10.6%, 95% CI 7.7–14.3%). Axi-cel (7.3, 95% CI 5.7–9.2%) and ide-cel (6.3%, 95% CI 4.8–8.4%) showed intermediate NRM point estimates. The lowest values were observed for tisa-cel (4.2%, 95% CI 3.1–5.6%) and liso-cel (3.8%, 95% CI 2.3–6.1%). Follow-up times were similar across CAR T-cell products (Fig. S6B).

Next, we examined the disease-specific influence of CAR T-cell product type on NRM point estimates, focusing on LBCL and MM due to the sufficient number of available studies. For LBCL, axi-cel was associated with higher NRM compared to liso-cel and tisa-cel (7.4% vs. 3.8% vs. 4.1%; p=0.004; Fig. 3C). These product-specific differences also remained consistent when considering only reported NRMs (Fig. S7). Additionally, we evaluated the impact of potential confounding factors such as follow-up time, treatment setting, treatment line and treatment era on NRM point estimates in LBCL patients (see Online Methods and Fig. S8). We did not detect a significant difference in NRM in studies evaluating the use of CD19 CAR T-cell therapy in earlier treatment lines (e.g., first or second line) versus later lines (e.g., third line and beyond) for LBCL patients (8.0% vs. 5.8%, p=0.16; Fig. S8). To examine the robustness of the CAR T-cell product and disease interaction, we performed multivariable meta-regression modeling, finding that liso-cel (p=0.021) and tisa-cel (p=0.015) were independently associated with reduced NRM when compared to axi-cel (Fig. 3D). On the other hand, none of the other meta-regression factors displayed a significant association with NRM.

In myeloma, we found that cilta-cel was associated with higher NRM compared to ide-cel (15.2% vs. 6.3%; p<0.001; Fig. 3E). In terms of study features, we observed significantly increased NRM point estimates in MM-CT compared to MM-RWS (10.4% vs. 5.5%, p=0.018, Fig. S9) and for the myeloma studies with longer follow-up (10.6 vs. 5.9%, p=0.03, Fig. S9). However, only the “CAR T-cell product” variable was retained as an independent risk factor for NRM in the multivariable meta-regression analysis (p=0.011, Fig. 3F). For both meta-regression models, estimates and p-values were stable upon permutation testing. Taken together, these results emphasize that CAR T-cell products impact NRM in a disease-specific manner.

To additionally determine the source of moderate-high heterogeneity across all studies (I2=64.5%, Fig. 2), we performed univariate meta-regression models for each of the individual variables included in the multivariable model. The individual “CAR T-cell product” variable represented the main contributor to study heterogeneity (Table S2).

Infections are the main driver of non-relapse mortality following CAR T-cell therapy

To elucidate NRM etiology, we extracted available data for all 574 reported non-relapse deaths among our total study cohort of 7604 patients. For 464/574 cases (80.8%), the underlying cause of death was specified, which we classified into one of seven groups as outlined in the methods (Fig. 4A). If the cause of death did not match any of these groups, it was classified under “other” (23/574 cases; Table S3). In 57/574 cases, the specific cause of death was reported as “unknown”, while 30/574 cases were reported as organ failure without any further information (“not otherwise specified”, NOS).

Figure 4. Distribution of causes of non-relapse deaths across treatment settings and disease entities.

Figure 4.

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A Top: donut plot displaying causes of death among the entire study cohort. Defined causes of death are shown in different colors, undefined and unclassifiable causes of death are depicted in grey colors. Additional pie charts subdivide non-relapse deaths from infections (blue tones, right), other malignancies (green tones, middle), and cardiovascular/respiratory events (red tones, left). B-C Comparison of the causes of non-relapse deaths across the treatment setting (B) and between disease entities (C). Chi-square distribution test was used for statistical testing. Abbreviations: AML = acute myeloid leukemia, CRS = cytokine release syndrome, CT = clinical trial, HLH = hemophagocytic lymphohistiocytosis, ICANS = immune effector cell-associated neurotoxicity syndrome, MDS = myelodysplastic syndrome, NOS = not otherwise specified, RW = real-world.

Notably, more than half of all reported non-relapse deaths were attributed to infections (292/574, 50.9%). The causative pathogen was not specified for the majority of fatal infections (189/292, 64.7%). Among the 103 non-relapse deaths with a reported pathogen, a substantial number succumbed to COVID-19 (55/103, 53.4%). While fungal infections are rare following CAR T-cell therapy,74 they still accounted for 20 out of 103 (19.4%) infection-related deaths with an identified pathogen. Bacterial infections were responsible for 22/103 (21.4%) infection-related deaths, while non-COVID-19 viral infections accounted for 5/103 (4.8%).

The second most common specified cause of non-relapse related death was a second malignancy other than the underlying lymphoma or myeloma (45/574, 7.8%). Over a third of these malignancy-related deaths resulted from secondary MDS/AML (15/45, 33.3%), followed by carcinoma (10/45, 22.2%), and one death from sarcoma (2.2%), while the other malignancy-related deaths were not further specified (19/45, 42.2%). MDS/AML cases were similarly distributed between entities and CAR T-cell products (Table S4). Of interest, no deaths related to T-cell malignancies were reported.

Cardiovascular or respiratory (CVR) events were the third most common cause of NRM, resulting in 43/574 (7.3%) NRM-related deaths. The leading cause of death in the CVR group was respiratory failure (10/43, 23.3%), followed by thromboembolic events affecting the central nervous system such as stroke or ischemic brain injuries (9/43, 20.9%). Other thromboembolic events such as pulmonary embolism or ischemic colitis were responsible for 5/43 (11.6%) CVR-related deaths. Cardiac arrest led to death in an additional 8/43 cases (18.6%), while 7/43 CVR-related deaths were not further specified (16.3%).

The prototypical CAR T-cell side effects ICANS/neurotoxicity and CRS directly caused a total of 30/574 (5.2%) and 27/574 (4.7%) non-relapse deaths, respectively. In addition, 19 patients died of hemorrhage (3.3%) and 9 deaths were attributed to secondary HLH (1.6%).20

Infection-related deaths are more frequent in the real-world setting

Among deaths with a specified cause, we assessed whether the tumor entity, CAR T-cell product or treatment setting impacted the cause of death. We found that the causes of death were differently distributed between patients that were treated in clinical trials compared to the real-world setting (p=0.0004, Fig. 4B). Differences were predominantly found among the immune-related side effects (CRS, ICANS/neurotoxicity, HLH), which were responsible for 7.9% (10/127) of deaths in clinical trials compared to 16.6% (56/337) of deaths in RWS. We observed the opposite trend for cardiovascular/respiratory cases, which caused 16.5% (21/127) of deaths in clinical trials, but only 6.2% (21/337) of deaths in RWS. Fatal hemorrhages were distributed similarly (RWS: 3.9% 13/337, CT: 4.7% 6/127). Finally, infections predominated as the cause of death even more in the real-world setting (64.6% 217/337) than in clinical trials (59.1% 75/127), which also remained true even when excluding COVID-19 related deaths (Fig. S10). Overall, infections were the main determinant of NRM irrespective of the disease entity and CAR T-cell product (Fig. 4C, S11).

Discussion

In this systematic review and meta-analysis, we outline the comparative incidence and causes of NRM following CAR T-cell therapy across a spectrum of hematological malignancies. We noted increased NRM in MCL and MM patients, and with the CAR T-cell products axi-cel and cilta-cel. Infections were by far the main cause of NRM, responsible for approximately half of NRM across all disease entities. Conversely, CAR T-cell specific side effects such as CRS, ICANS/neurotoxicity and HLH, were only minor drivers of NRM.

NRM is a key piece of the puzzle that may inform the use of CAR T-cells compared with other treatment options and may guide CAR T-cell product choice. For example, in DLBCL, both axi-cel and liso-cel have been shown to have superior efficacy than salvage chemotherapy followed by autologous HCT.75,76 However, when considering the approach to patients where the evidence may be more equivocal – for example, patients whose disease relapses >12 months after frontline therapy, or patients with chemotherapy-sensitive disease – the best estimate of their NRM risk will help to inform decision-making, analogous to how transplant physicians present data to patients in diseases where allogeneic transplantation is being considered. These estimates also become increasingly important as CAR T-cells are used in earlier lines of therapy (where the competing risk of NRM is of a higher consequence), used in more indolent diseases like follicular lymphoma (where the competing risk of NRM may be higher relative to the chance of death due to disease progression), and used in patients who may not have previously been eligible for aggressive treatment.77,78 For a young, otherwise healthy patient with DLBCL relapse following multiple lines of therapy, a higher risk of NRM from CAR T-cell therapy may be acceptable as the alternative is likely death from lymphoma. Conversely, for an older, transplant-ineligible patient whose disease responds to salvage chemo- or immunotherapy, the discussion regarding whether or not to proceed with CAR T-cells is much more nuanced, and NRM would be one factor informing this discussion. Similarly, CAR T-cell related NRM will also be a factor in decision-making for patients with indolent lymphoma or myeloma deciding between other treatment options, such as bispecific antibodies, and CAR T-cells.

In our meta-analysis, we found NRM to be inherently dependent on the disease entity and CAR T-cell product. We noted increased NRM for axi-cel and cilta-cel in LBCL and MM patients, respectively, even when controlling for multiple study features in meta-regression models. However, due to the nature of this study, we were only able to control for cohort-level differences and were not able to adjust for individual patient covariates. Indeed, both host factors (e.g., performance status, comorbidities) and disease-specific factors (e.g., tumor load, systemic inflammation) could have contributed to the observed variation of NRM rates between CAR T-cell products.27,79–82 Furthermore, differences in vein-to-vein times, the use and type of bridging therapy, and lymphodepletion regimen may have played a role.58,61,83 Absent randomized comparisons between CAR T-cell products, it is difficult to know to what extent the safety profiles of CAR T-cell products differ, or whether these findings rather reflect selection bias and/or confounding by indication. Comparative analysis of NRM is additionally complicated when considering the intrinsic heterogeneity of the infusion product itself.84 While cilta-cel was associated with high NRM, there was also a lack of RWS of cilta-cel meeting inclusion criteria for this meta-analysis. Furthermore, it is worth noting that Mi and colleagues reported elevated NRM rates in the phase II CARTIFAN-1 trial,45 partly due to inadequate red blood cell and platelet transfusion product availability, raising concerns about the deliverability of CAR T-cell therapies in settings without robust supportive care systems.85

Our analysis confirms that infectious complications represent the primary cause of NRM in CAR T-cell patients. Several factors increase the susceptibility of CAR T-cell patients to fatal infections. This includes the underlying malignancy and associated immune dysregulation, prior treatment regimens (many patients in CAR T-cell clinical trials had received many prior lines of therapy), toxicity management (particularly high-dose corticosteroids), and the combination of cellular and humoral immune suppression exerted by the CAR T-cells themselves.18,86 In our study, COVID-19 was responsible for many of the deaths that drove infection-related NRM.87,88 Importantly, many of the studies included in this meta-analysis took place during the COVID-19 pandemic (2020–2023), which may have inflated the observed NRM in our study cohort. While we did not observe a significant treatment era effect on NRM (e.g., similar NRM point estimates before and after 2020), one could hypothesize that COVID-19 related deaths were off-set by improvements in toxicity management over time.89 Additionally, patient accrual during the COVID-19 pandemic may explain the counterintuitive finding that studies examining CAR T-cells in earlier treatment lines displayed numerically higher NRM point estimates despite the preferable baseline patient risk profile (e.g., less systemic inflammation, fewer previous treatments, different disease biology).90–93 In any case, increased vigilance in infection reporting, prevention and management, including that of COVID-19, should be a focus of ongoing work given its potential to reduce NRM.94

The relatively high incidence of other malignancies, at 7.8% of all non-relapse deaths, should be interpreted within the context of the significant treatment burden carried by patients eligible for CAR T-cell therapy, including previous exposure to lenalidomide and alkylating agents such as high-dose melphalan before autologous HCT.95–97 For example, up to 15% of NRM cases after autologous HCT have been attributed to secondary malignancies.98 Supporting this hypothesis, a recent study found a high prevalence of clonal hematopoiesis of 50–60% at time of CAR T-cell infusion.99,100 Nonetheless, considering the growing body of evidence elucidating the role of inflammatory stressors for clonal expansion,101,102 the impact of CAR T-cell induced inflammatory stress on the development of secondary myeloid malignancies warrants future systematic study.

Cardiovascular and respiratory events were the third most common etiology of NRM, in line with previous studies reporting a disproportionate increase of such events in CAR T-cell treated patients.103 These findings support the early and multidisciplinary assessment of cardiovascular risk in patients receiving CAR T-cells.104 Our observation of an increased proportion of cardiovascular and respiratory events in clinical trials compared to RWS may be a consequence of differences in data capture and quality across treatment settings.105 For example, clinical trials often require repeated quality checks involving multiple stakeholders (e.g., treating physician, local clinical study investigator, local study nurse, superordinate trial center), which may result in a more accurate allocation of the cause of death. At the same time, clinical trials may also be subject to certain conflicts of interest when pharmaceutical companies function as sponsors (e.g., potential reporting bias).106

While the asymmetrical funnel plots indicated publication bias (Fig. S5), this was primarily attributable to smaller studies (n<80) without any or very low numbers of NRM-related deaths. Concomitantly, the asymmetric shape might reflect the chosen outcome rather than the presence of true publication bias. To further test robustness of our meta-analysis results, we performed multiple sensitivity analyses (Table S5). Although we detected higher NRM rates for some subgroups using a fixed compared to a random effects model, the main study findings remained stable. Similarly, applying a cut-off of 80 patients for observational studies did not substantially impact NRM point estimates, indicating that a potential publication bias regarding smaller RWS may be negligible. While we pursued extensive measures to exclude double-reporting (see Online Methods), a minimal number of cases remained ambiguous (<2% of study cohort). In a final sensitivity analysis, we demonstrate that the complete removal of studies with any potential overlap did not impact our findings.

Key limitations of this study relate to the heterogenous reporting of NRM endpoints, particularly in regard to the cumulative NRM rates, which were not reported in any of the clinical trials and only in some RWS. We also noted expected between-study heterogeneity due to the broad study inclusion criteria, which was predominantly driven by the CAR T-cell product variable as demonstrated in univariate regression models (Table S2). Furthermore, the respective timepoints of death relative to CAR T-cell infusion were often not or poorly characterized and NRM causes could not be attributed in ~15% of cases (“others” and “unknown” groups), even in clinical trial settings where patients are ideally followed closely given experimental nature.107 Additionally, reporting by categories may have been inconsistent across different studies (e.g., CRS could lead to cardiac arrest; infection may lead to organ failure and thus could have been reported in either category). However, such detailed, long-term reporting of NRM is critical to not only recognize (and mitigate) emerging side effects, but also to identify potential toxicity signals of novel CAR T-cell products. Clear definitions and guidelines for all side effects of cell therapy are useful in this regard, as was recently implemented for IEC-HS and ICAHT.20,108–110

Considering the clinical significance of fatal infections, detailed and structured reporting of infectious events should be a mandatory requirement in clinical trials. Ideally, such reporting should include infection type (e.g., viral, bacterial, fungal), whether the infection was confirmed microbiologically, organism (if known), day of infection, infection severity and site, and should further distinguish between early vs. late infections. Knowledge of the expected infection risk for each CAR T-cell product may help to guide antimicrobial prophylaxis, immunoglobulin replacement therapy and G-CSF support, particularly in high-risk patients.16,80,111 Finally, it should be emphasized that this meta-analysis focused on NRM, disregarding the differential efficacy of CAR T-cell products.112 Importantly, lower efficacy might lead to decreased NRM as more patients die due to progressive disease.

In conclusion, our analysis underscores the need for improved reporting of CAR T-cell safety, both in clinical trials and RWS. Due to the critical role of infections as the main driver of NRM across CAR T-cell products and disease entities, there is a pressing need for comprehensive evidence-based guidelines that inform infection prevention and management after CAR T-cell therapy.

Online Methods

Study design and literature search

We included all studies that led to approval of the six commercially available CAR T-cell products (axi-cel, tisa-cel, ide-cel, cilta-cel, liso-cel, brexu-cel) and the corresponding phase I-III clinical trials and observational real-world studies (RWS). A systematic search was conducted using the MEDLINE, Embase and CINAHL (Cochrane) databases for articles published between inception and March 31, 2024 with combined keywords for each of the CAR T-cell products together with “lymphoma” or “myeloma” (see study protocol, supplementary material). Case studies, reviews, conference abstracts and meta-analyses were excluded. After screening titles and abstracts, publications were evaluated by two independent investigators (DMCDS, TT) based on the following inclusion criteria, which all needed to be met:

  1. adult cancer patients with either indolent lymphoma (IL), large B-cell lymphoma (LBCL), multiple myeloma (MM), or mantle cell lymphoma (MCL),

  2. use of CAR T-cell products approved by the Food and Drug Administration (FDA),

  3. data available on NRM or number of non-relapse deaths.

All included articles were checked for double reporting. If two studies reporting on the same patient population were identified, the study with longer follow-up was chosen. First and corresponding authors were contacted if full-text publications were not available from suitable abstracts or if additional information was needed (e.g., determination of overlap with other studies, allocation of NRM events). Cohorts displaying major overlap (≥30%) were excluded.

This study followed the PRISMA(-P) guidelines (see study protocol and PRISMA Checklist, supplemental material) and was prospectively registered to the PROSPERO database (study number: CRD42023494252).113 IRB approval was not sought as this study did not represent human participant research.

Data extraction

Data were extracted from all studies that met inclusion criteria. Data collection included date of publication, number of patients, disease entities, CAR T-cell product, time frame of patient inclusion, follow-up time, treatment line (indicated line, minimum and median number of previous lines) and treatment setting. The primary outcome was reported NRM (if reported in the studies) and the number and causes of death. NRM proportions were calculated by dividing the number of non-relapse deaths by the total patient number for each cohort. If more than one CAR T-cell product was used in a single study, the reported data was assigned to separate cohorts and evaluated accordingly.

Quality assessment

The Joanna Brigg’s Institute appraisal tool was applied to assess study bias of included articles (Table S1). Visual inspection of funnel plot asymmetry and Egger regression tests were used to assess reporting bias (Fig. S5).114,115

Statistical analysis

Data were analyzed in R (v4.3.1), using the metafor (v.4.4-0) and meta (v7.0-0) packages. NRM point estimates were determined by performing random effect meta-analyses of single proportions utilizing a generalized linear mixed model.116 The Clopper-Pearson interval was used to calculate 95% confidence intervals (95% CIs) of proportions.117 Forest plots were used to visualize outcome data for NRM. For each meta-analysis, heterogeneity of the pooled effect sizes was assessed with the Q statistic and quantified using I2, with I2 values of 25%, 50% and 75% reflecting low, moderate, and high between-study heterogeneity, respectively.118

For other comparisons, Mann-Whitney test was used to compare continuous variables between two groups and Kruskal-Wallis test was used when comparing multiple variables assuming non-normal distribution. Continuous variables were reported as median and interquartile range (IQR). To analyze the distribution of NRM-related deaths among aggregated sub-cohorts, Fisher’s exact test was used in cases of two categorical variables and Chi-squared (χ²) test in cases of more than two categorical variables. To investigate the relationship between continuous variables, we computed Pearson correlation coefficients (r).

Meta-analyses

Separate meta-analyses were performed for prespecified subgroups comparing NRM point estimates by disease entity, CAR T-cell product, and treatment setting. A second-level subgroup analysis was performed to assess the effect of the study features CAR T-cell product, follow-up time, treatment line, treatment setting, and treatment era in an entity-dependent manner (e.g., for LBCL and MM separately). The p-values for the comparisons of subgroups were calculated using the test for subgroup differences (random effects model).

Meta-regression

Multivariate meta-regressions were separately performed for LBCL and MM to examine the association between NRM point estimates and the following variables: CAR T-cell product, treatment era (e.g., inclusion before or after 01/2020), follow-up time (e.g., above and below median), treatment line (e.g., earlier vs later indication), and treatment setting (e.g., clinical trial vs. RWS). Exploratory univariate meta-regressions were conducted to estimate the contribution of individual variables to between-study heterogeneity using the R2 statistic. Meta-regressions were calculated based on random effects models using the maximum likelihood estimator.119 Individual model coefficients and respective confidence intervals were tested using the Knapp-Hartung method.120 The stability of model estimates was validated by performing permutation testing.121

Sensitivity analysis

To validate the robustness of the main findings, meta-analyses were repeated after (i) testing fixed effects models, (ii) applying a study size cutoff of 80 patients for observational RWS, and (iii) excluding studies with a potential overlap of patients.122

Cause of death analysis

To test for cause of death distributions between subgroups, causes of death were classified into one of the following groups: infection, malignancy, CRS, ICANS/neurotoxicity, cardiovascular/respiratory, hemorrhage, HLH, organ failure not otherwise specified (NOS), other, or unknown. Cardiovascular and respiratory causes of death were further classified into: stroke/ischemic brain injury, respiratory failure, cardiac arrest, cardiomyopathy, embolism, aortic dissection, or cardiovascular/respiratory NOS. The malignancy group was classified into: MDS/AML, carcinoma, sarcoma, or prior/secondary malignancy, NOS. Infections were classified into: COVID-19, bacterial, viral, fungal, or infection, NOS. Donut plots to visualize the cause of death distribution were generated using Graphpad Prism (v.10.0).

Supplementary Material

Supplemental Figures
Supplemental Tables
Study Protocol
Prisma Checklist

Acknowledgements

First and foremost, we acknowledge the many patients whose data were incorporated in this study as well as the research personnel that advanced this rapidly moving field. In addition, we want to thank the efforts of all first and corresponding authors who took the time to answer our queries regarding their studies. We want to particularly thank Marcelo Pasquini (CIBMTR), Sairah Ahmed (MD Anderson Cancer Center), Doris Hansen (Moffitt Cancer Center), Sergio Giralt (MSKCC) and Philipp Berning (Yale Cancer Center).

TT, DMCDS and KR received a fellowship from the School of Oncology of the German Cancer Consortium (DKTK). DMCDS received the Walter Benjamin Fellowship by a Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). KR acknowledges funding from the Else Kröner Forschungskolleg (EKFK) within the Munich Clinician Scientist Program (MCSP). This work was supported by a grant from the Bruno and Helene Jöster Foundation (to ST, KR, MS). We further acknowledge the structural support from the Bavarian Cancer Research Center (BZKF). This work was in part supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): Collaborative Research Grant SFB-TRR 388/1 2021 – 452881907 (to MS and ST), and individual research grants 451580403 (to MS) and 391587558 (to ST and MBB). RS, KR, and MAP were supported by a Memorial Sloan Kettering Cancer Center Core grant (P30 CA008748) from the National Institutes of Health/National Cancer Institute. RS was supported by an NIH-NCI K-award (K08CA282987). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. ERSC receives research funding from Arnold Ventures.

Footnotes

Competing Interests

A.G.G. Honoraria/ Consultancy: Pfizer, Bayer, Novartis, Neopharm, Roche, Medison, Sanofi, AstraZeneca, Boehringer-Ingelheim.

S.T. Honoraria/ Consultancy: Amgen, BMS/Celgene, GSK, Janssen, Pfizer, Sanofi, Takeda, Stemline and Kyowa Kirin.

M.B.B. Consultancy, Research Funding and Honoraria: MSD Sharp & Dohme, Novartis, Roche, Kite/Gilead, Bristol-Myers Squibb, Astellas, Mologen, and Miltenyi.

I.M.G. Honoraria: Celgene, Bristol-Myers-Squibb, Takeda, Amgen, Janssen and Vor Biopharma. Consultancy/Advisory: Bristol-Myers-Squibb, Novartis, Amgen, Takeda, Celgene, Cellectar, Sanofi, Janssen, Pfizer, Menarini Silicon Biosystems, Oncopeptides, The Binding Site, GSK, Abbvie, Adaptive and 10X Genomics.

M.S. receives industry research support from Amgen, BMS/Celgene, Gilead, Janssen, Miltenyi Biotec, Novartis, Roche, Seattle Genetics and Takeda and serves as a consultant/advisor to AvenCell, CDR-Life, Ichnos Sciences, Incyte Biosciences, Janssen, Miltenyi Biotec, Molecular Partners, Novartis, Pfizer and Takeda. She serves on the speakers’ bureau at Amgen, AstraZeneca, BMS/Celgene, Gilead, GSK, Janssen, Novartis, Pfizer, Roche and Takeda.

M.A.P. reports honoraria from Adicet, Allogene, Allovir, Caribou Biosciences, Celgene, Bristol-Myers Squibb, Equilium, Exevir, ImmPACT Bio, Incyte, Karyopharm, Kite/Gilead, Merck, Miltenyi Biotec, MorphoSys, Nektar Therapeutics, Novartis, Omeros, OrcaBio, Sanofi, Syncopation, VectivBio AG, and Vor Biopharma. He serves on DSMBs for Cidara Therapeutics, Medigene, and Sellas Life Sciences, and the scientific advisory board of NexImmune. He has ownership interests in NexImmune, Omeros and OrcaBio. He has received institutional research support for clinical trials from Allogene, Incyte, Kite/Gilead, Miltenyi Biotec, Nektar Therapeutics, and Novartis.

K.R. Kite/Gilead: Research Funding, Consultancy, Honoraria and travel support; Novartis: Honoraria; BMS/Celgene: Consultancy, Honoraria; Pierre-Fabre: travel support.

The remaining authors have nothing to declare. None of the mentioned conflicts of interest were related to financing of this study.

Code Availability Statement

All codes were adapted using R software, v.4.3.1 (meta package 7.0–0, metafor package 4.4–0). Data sheets were created using Microsoft Excel. The underlying R code for this study can be accessed in a GitHub repository: https://github.com/DMCDS/CART_NRM_Metaanalysis.

For further questions, please contact the corresponding author.

Inclusion and Ethics Statement

All data used in this study was previously published.

Data Availability Statement

All data needed to evaluate the conclusions in the paper are present in the manuscript and/or the Supplementary Materials. Data from primary studies are publicly available within the databases listed in the Supplementary Information. Original output data can be accessed under https://github.com/DMCDS/CART_NRM_Metaanalysis. In case of further questions, please contact the corresponding author.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Figures
NIHMS2044206-supplement-Supplemental_Figures.pdf (1.6MB, pdf)
Supplemental Tables
NIHMS2044206-supplement-Supplemental_Tables.docx (46.6KB, docx)
Study Protocol
NIHMS2044206-supplement-Study_Protocol.pdf (160.1KB, pdf)
Prisma Checklist
NIHMS2044206-supplement-Prisma_Checklist.pdf (100.8KB, pdf)

Data Availability Statement

All data needed to evaluate the conclusions in the paper are present in the manuscript and/or the Supplementary Materials. Data from primary studies are publicly available within the databases listed in the Supplementary Information. Original output data can be accessed under https://github.com/DMCDS/CART_NRM_Metaanalysis. In case of further questions, please contact the corresponding author.

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