Abstract
Background
Ciltacabtagene autoleucel (cilta‐cel), an anti–B‐cell maturation antigen (BCMA) chimeric antigen receptor T‐cell (CAR‐T) therapy, is approved for relapsed/refractory multiple myeloma (RRMM).
Methods
Using the Center for International Blood and Marrow Transplant Research registry, this study evaluated outcomes of frail patients receiving commercial cilta‐cel from March 2022 to December 2023. Frailty was defined by an adapted simplified score incorporating age, performance status, and comorbidities.
Results
Among 541 patients with available frailty status, 183 (33.8%) were frail and 358 (66.2%) were nonfrail. Overall response rates were comparable (frail 82.8% vs. nonfrail 88.5%). However, frail patients had inferior progression‐free survival (PFS) (12‐month PFS, 62.7% [95% confidence interval (CI), 53.6%–71.3%] vs. 75.9% [95% CI, 70.4%–81.1%]; p < .01) and overall survival (OS) (12‐month OS 72.8% [95% CI, 64.9%–80.0%] vs. 90.4% [95% CI, 86.6%–93.7%]; p < .01). Twelve‐month treatment‐related mortality in frail patients was 6.8% (95% CI, 3.4%–11.2%) versus 3.6% (95% CI, 1.8%–6.1%), p = .12. Cytokine release syndrome (grade ≥2) occurred in 22.4% of frail versus 17.9% of nonfrail patients (p = .05), and immune effector cell–associated neurotoxicity (ICANS) of any grade was reported in 32.2% versus 17.6% (p < .01). Rates of cranial nerve palsies and Parkinsonism were comparable. Prolonged cytopenia (>day 30) was more common in frail patients (30.6% vs. 21.2%; p < .01). On multivariable analysis, frailty independently predicted worse PFS (hazard ratio [HR], 1.67; 95% CI, 1.16–2.40), OS (HR, 2.46; 95% CI, 1.57–3.87), and higher odds of any‐grade ICANS (odds ratio, 2.01; 95% CI, 1.32–3.08) (all p < .01).
Conclusions
Cilta‐cel remains effective in frail RRMM, but frailty is associated with reduced survival and increased toxicity, supporting tailored CAR‐T strategies.
Keywords: CAR‐T, frailty, multiple myeloma
Short abstract
In this real‐world Center for International Blood and Marrow Transplant Research analysis of 541 patients with relapsed/refractory multiple myeloma receiving ciltacabtagene autoleucel, frail patients achieved overall response rates comparable to nonfrail patients. However, frailty was independently associated with inferior progression‐free and overall survival as well as increased neurotoxicity, emphasizing the critical need for baseline frailty assessments and tailored supportive care strategies.
INTRODUCTION
A Study of JNJ‐68284528, a Chimeric Antigen Receptor T Cell (CAR‐T) Therapy Directed Against B‐Cell Maturation Antigen (BCMA) in participants with Relapsed or Refractory Multiple Myeloma (RRMM). In the pivotal CARTITUDE‐1 trial, cilta‐cel demonstrated remarkable efficacy in heavily pretreated patients with three or more prior lines of therapy, achieving an overall response rate (ORR) of 98%, a median progression‐free survival (PFS) of 34.9 months, and a median overall survival (OS) of 61.3 months. 1 These results led to the initial US Food and Drug Administration (FDA) approval in February 2022 of this agent and it is now widely used.
Although these trials established cilta‐cel as an effective therapy for RRMM, frailty was not assessed in CARTITUDE‐1, and patients with Eastern Cooperative Oncology Group (ECOG) performance status ≥2 were excluded. Furthermore, the median age in CARTITUDE‐1 was 61 years—substantially younger than the real‐world median age of newly diagnosed multiple myeloma patients, which is 69 years. 2 Frail and older adults are consistently underrepresented in clinical trials, 3 making it challenging to generalize results to these higher‐risk populations. This may be even more important for immunotherapies like CAR‐T with unique toxicities, including cytokine release syndrome (CRS) and neurotoxicity, which could disproportionately impact frail adults.
Real‐world studies have suggested that frail or older adults receiving CAR‐T therapy for myeloma may experience comparable outcomes but higher rates of treatment‐related toxicity. 4 , 5 , 6 However, most of these data come from patients treated with a different CAR‐T construct (ide‐cel) 7 or from small cohorts (<20 patients) treated with cilta‐cel. 8 Because cilta‐cel is increasingly being used in the treatment of RRMM, there is a need to understand both the efficacy and safety of cilta‐cel among frail adults with RRMM. To our knowledge, there is no previous analysis of the outcomes of cilta‐cel specifically among frail patients.
Given this knowledge gap, we used data from the Center for International Blood and Marrow Transplant Research (CIBMTR) registry to examine the outcomes of frail patients treated with cilta‐cel. Understanding the efficacy and toxicity of cilta‐cel may allow us to better identify and develop more targeted interventions and supportive care strategies to effectively use this therapy among real‐world frail patients.
MATERIALS AND METHODS
Study design and data source
This was an observational study using data from the CIBMTR. CIBMTR is a research collaboration between the National Marrow Donor Program (NMDP) and the Medical College of Wisconsin. More than 375 medical centers worldwide submit clinical data to CIBMTR about hematopoietic cell transplant, CAR‐T cell therapy, and other cellular therapies for treatment of malignancies. All patients or their legal guardians provided informed consent for data collection and research use. These data are subject to a series of automated and manual quality checks producing high‐quality data for observational research.
Patient eligibility and characteristics
Inclusion criteria were: 1) adults with RRMM, 2) four or more prior lines of therapy, 3) receipt of standard‐of‐care cilta‐cel infusion, and 4) completion of at least one follow‐up evaluation by day 100 after CAR‐T infusion (or earlier in cases of death). Data were collected from patients who received their infusion between March 2022 and December 2023. Patients treated with nonconforming product or within a clinical trial were excluded. High‐risk cytogenetics was defined as del(17p)/monosomy 17, t(4;14), t(14;20), or t(14;16) detected by fluorescence in situ hybridization (FISH) at any time before infusion. Extramedullary disease (EMD) was reported as present if noted organ involvement as assessed by imaging (computed tomography, magnetic resonance or positron emission tomography) before CAR‐T infusion per each center’s policy. The date of data cutoff was August 3, 2024.
Frailty definition
Frailty was defined using an adapted simplified frailty index. 9 This retrospectively applied score incorporates three domains: age, performance status, and comorbidity burden. Age was scored as: ≤75 years (0 points), 76–80 years (1 point), and >80 years (2 points). Performance status was assessed using the ECOG status: ECOG 0 (0 points), ECOG 1 (1 point), and ECOG ≥2 (2 points). For comorbidities, we used the Hematopoietic Cell Transplantation‐specific Comorbidity Index (HCT‐CI) rather than the Charlson Comorbidity Index used in the original score: HCT‐CI 0–1 (0 points) and HCT‐CI ≥2 (1 point). The total frailty score, therefore, ranged from 0 to 5. In keeping with prior work, adults with a score of 0–1 were categorized as nonfrail and those with a score ≥2 as frail.
Definitions and end points
Efficacy outcomes
The efficacy outcomes were ORR, defined per International Myeloma Working Group (IMWG) criteria, 10 and PFS, defined as the time from cilta‐cel infusion to disease progression or death, and OS, defined as the time from infusion to death from any cause.
Safety outcomes
Safety outcomes included incidence and severity of cytokine release syndrome (CRS), immune effector cell–associated neurotoxicity syndrome (ICANS), delayed neurotoxicity, immune effector cell–associated hemophagocytic lymphohistiocytosis–like syndrome (IEC‐HS), cytopenias, infections, second primary malignancies (SPMs), and treatment‐related mortality (TRM). CRS and ICANS were graded according to the American Society for Transplantation and Cellular Therapy consensus criteria. 11 Non‐ICANS neurotoxicity, including Parkinsonism and cranial nerve palsies were also recorded including time to onset and resolution. Prolonged cytopenia was defined as an absolute neutrophil count of <500 × 106/L and/or platelet count of <20 × 109/L beyond day+30. Clinically significant infections were defined as any infections requiring treatment and/or a positive COVID‐19 diagnostic test (polymerase chain reaction or antigen test). Episodes of neutropenic fever without an identifiable source and upper respiratory tract infections presumed viral but lacking pathogen confirmation were excluded. TRM was defined as death where the primary cause was other than myeloma progression.
Statistical analysis
Descriptive statistics were used to summarize patient, treatment characteristics, and outcomes. Univariable comparisons were performed using the χ2 test for categorical variables and the Wilcoxon rank‐sum test for continuous variables. For the analysis of cumulative incidence of competing risks including response (best overall response and complete response), and TRM, Gray's test was employed to compare the differences between groups. For response, competing risks included progressive disease, death, or subsequent autologous stem cell transplant. Patients with complete response continuing from time of infusion to last follow‐up were excluded from the response analysis. Survival probabilities were calculated using the Kaplan–Meier method, and survival curves were compared using the log‐rank test. Estimated probabilities are reported with 95% confidence intervals (CIs).
Cox proportional hazards regression models or logistic regression models identified the impact of frailty on ORR, complete response (CR), PFS, OS, and any‐grade ICANS. Candidate covariates included in the multivariable analysis were: frailty (frail and nonfrail), sex, race (White, Black/African American, other, not reported), ethnicity, pre‐infusion infection (yes/no), number of prior lines of therapy (4–6, 7–10, >10), prior autologous stem cell transplant (yes/no), penta‐refractory disease (yes/no), prior BCMA‐directed therapy (yes/no), elevated ferritin pre‐infusion (yes/no), bone marrow plasma cell percentage before infusion (<50%, ≥50%, unknown), lactate dehydrogenase level (normal/elevated/unknown), presence of extra‐medullary disease (EMD), high risk cytogenetics (yes/no/unknown), disease status at infusion (≥very good partial remission [VGPR], partial remission [PR], stable disease, progressive disease, unknown), absolute neutrophil count (ANC) before infusion (≥750 × 106/L vs. <750 × 106/L), platelet count before infusion (≥50 × 109/L vs. <50 × 109/L), lymphodepleting regimen (fludarabine + cyclophosphamide vs. bendamustine vs. other), and CAR‐T dose (≥0.7 million cells/kg vs. <0.7 million cells/kg), and bridging therapy (yes/no). Variables included in the frailty definition including age, ECOG PS, and comorbidities were not included. Missing covariate data were retained as separate “unknown” categories in descriptive analyses and regression models to maximize retention of patients in the analytic data set and avoid exclusions associated with complete‐case analysis. Stepwise selection was applied with a significance threshold of p = .05. The proportional hazards assumption and potential interactions were tested for all models. Hazard ratios (HRs) with 95% CIs are reported. All p values were two‐sided, with statistical significance set at p < .05. As a sensitivity analysis, multiple imputation was performed for missing covariate data using 20 imputed data sets. Full regression models including all covariates of interest were fit within each imputed data set without variable selection, and estimates were combined using Rubin’s rules. Results from the sensitivity analysis were consistent with the primary analysis. All p values were two‐sided, with statistical significance set at p < .05. All analyses were conducted using SAS (Version 9.4). All analyses were conducted using SAS (Version 9.4).
RESULTS
A total of 595 patients with RRMM received cilta‐cel during the study period. Among them, frailty status was available for 541 patients who comprised the analytic cohort. There were no significant differences in key baseline characteristics between patients included in the analysis and those excluded due to missing frailty data (Table S1). Of 541 included patients, 183 (33.8%) were categorized as frail and 358 (66.2%) as nonfrail. The median follow‐up for the entire cohort was 12 months (range, 1.1–25.4 months).
Baseline characteristics
Baseline characteristics of the frail and nonfrail cohorts are listed in Table 1. The median age of the patients was 65.5 and 63.4 years in the frail and nonfrail cohorts, respectively. Although there were more patients ≥70 years old in the frail cohort, only four patients included in the cohort were ≥80 years old. Additionally, as expected, in the frail cohort, a higher proportion of patients had poor performance status (ECOG PS ≥2, 9.8% vs. 0% in the nonfrail cohort, p < .01) and clinically significant comorbidities (97.8% vs. 55.9%, p = .01). The list of clinically significant comorbidities is included in Table S2. The three most common comorbidities in the frail group included diabetes (20.8%), moderate pulmonary disease (24.6%), and a history of prior malignancies (23.5%) that were all approximately twice as common among frail compared to nonfrail.
TABLE 1.
Characteristics of patients by frailty status.
| Characteristic | Frail (N = 183) | Nonfrail (N = 358) | p value |
|---|---|---|---|
| Demographics, No. (%) | |||
| Age at infusion, years | <.01 a | ||
| Median (range) | 65.5 (38.1–84.3) | 63.4 (34.2–79.9) | |
| <70 | 119 (65.0) | 302 (84.3) | |
| 70–79 | 60 (32.8) | 56 (15.6) | |
| ≥80 | 4 (2.2) | 0 (0.0) | |
| Sex | .65 b | ||
| Female | 80 (43.7) | 149 (41.6) | |
| Male | 103 (56.3) | 209 (58.4) | |
| Recipient race | .07 c | ||
| White | 135 (73.8) | 280 (78.2) | |
| Black or African American | 35 (19.1) | 46 (12.8) | |
| Other/not reported | 13 (7.1) | 32 (9.0) | |
| ECOG performance status before infusion | <.01 b | ||
| ECOG 0–1 | 165 (90.2) | 358 (100) | |
| ECOG ≥2 | 18 (9.8) | 0 (0.0) | |
| Clinically significant comorbidity before infusion | .01 a | ||
| No | 4 (2.2) | 158 (44.1) | |
| Yes | 179 (97.8) | 200 (55.9) | |
| Disease and treatment details | |||
| Cytogenetic risk e | .58 c | ||
| Standard risk | 117 (63.9) | 233 (65.1) | |
| High risk | 44 (24.0) | 92 (25.7) | |
| Unknown | 22 (12.0) | 33 (9.2) | |
| Extramedullary disease e | .01 c | ||
| No | 89 (48.6) | 219 (61.2) | |
| Yes | 14 (7.7) | 29 (8.1) | |
| Unknown/not reported | 80 (43.7) | 110 (30.7) | |
| No. of prior lines of therapy | .22 a | ||
| Median (Range) | 7.0 (4.0–24.0) | 7.0 (4.0–23.0) | |
| Types of prior HCTs | .53 c | ||
| Prior auto‐HCT | 147 (80.3) | 277 (77.4) | |
| Triple‐class exposed before infusion | .32 c | ||
| No | 3 (1.6) | 10 (2.8) | |
| Yes | 152 (83.1) | 308 (86.0) | |
| Unknown | 28 (15.3) | 40 (11.2) | |
| Penta‐exposed before infusion | .33 c | ||
| No | 55 (30.1) | 121 (33.8) | |
| Yes | 100 (54.6) | 197 (55.0) | |
| Unknown | 28 (15.3) | 40 (11.2) | |
| Prior BCMA‐targeted therapy | .86 b | ||
| No | 170 (92.9) | 330 (92.2) | |
| Yes | 13 (7.1) | 28 (7.8) | |
| Disease details before infusion | |||
| Disease status before infusion | .05 c | ||
| Stringent complete remission (sCR)/CR | 2 (1.0) | 18 (5.1) | |
| VGPR | 10 (5.5) | 41 (11.5) | |
| PR | 19 (10.4) | 42 (11.7) | |
| NR/SD | 37 (20.2) | 64 (17.9) | |
| PD/relapse from CR | 114 (62.3) | 189 (52.8) | |
| Unknown/not reported | 1 (0.5) | 4 (1.1) | |
| Plasma cells in bone marrow >50% before infusion | .02 c | ||
| No | 84 (45.9) | 209 (58.4) | |
| Yes | 22 (12.0) | 28 (7.8) | |
| Not reported | 77 (42.1) | 121 (33.8) | |
| Platelets <50 × 109/L before infusion | <.01 c | ||
| No | 158 (86.3) | 335 (93.6) | |
| Yes | 25 (13.7) | 22 (6.1) | |
| Not reported | 0 (0.0) | 1 (0.3) | |
| ANC <750 × 106/L before infusion | .22 c | ||
| No | 167 (91.3) | 340 (95.0) | |
| Yes | 9 (4.9) | 10 (2.8) | |
| Not reported | 7 (3.8) | 8 (2.2) | |
| Elevated LDH before infusion | .06 c | ||
| No | 115 (62.8) | 240 (67.0) | |
| Yes | 52 (28.4) | 72 (20.1) | |
| Not reported | 16 (8.7) | 46 (12.8) | |
| CAR‐T details | |||
| Bridging therapy | .13 c | ||
| No | 39 (21.3) | 101 (28.2) | |
| Yes | 106 (57.9) | 201 (56.1) | |
| Not reported | 38 (20.8) | 56 (15.6) | |
| Lymphodepleting chemotherapy regimen | .11 c | ||
| Cyclophosphamide + fludarabine | 133 (72.7) | 288 (80.4) | |
| Bendamustine | 43 (23.5) | 56 (15.6) | |
| Other/unknown | 7 (3.8) | 14 (3.9) | |
| CAR‐T cell dose ≥0.7 million cells/kg | .15 c | ||
| No | 118 (64.5) | 257 (71.8) | |
| Yes | 62 (33.9) | 93 (26.0) | |
| Not reported | 3 (1.6) | 8 (2.2) | |
| Time from leukapheresis to infusion, days | .62 d | ||
| Median (IQR) | 63.0 (56.0–76.0) | 63.0 (56.0–71.0) | |
Abbreviations: ANC, absolute neutrophil count; auto‐HCT, autologous hematopoietic cell transplant; BCMA, B‐cell maturation antigen; CAR‐T, chimeric antigen receptor T‐cell; CR, complete response; ECOG PS, Eastern Cooperative Oncology Group performance status; HCT, hematopoietic cell transplant; IQR, interquartile range; LDH, lactate dehydrogenase; NR, no response; PD, progressive disease; PR, partial response; sCR, stringent complete response; SD, stable disease; VGPR, very good partial response.
Pearson χ2 test.
Fisher exact test.
Fisher exact test.
Kruskal–Wallis test.
Variables could have occurred any time between diagnosis and pre‐infusion.
In the frail cohort, 44 (24.0%) patients were noted to have high‐risk cytogenetics and 14 (7.7%) had extramedullary disease. With regard to prior treatment, patients in the frail cohort had received a median of seven prior lines of therapy (range, 4–24). Of these patients, 100 (54.6%) had a history of penta‐exposure and 13 (7.1%) had prior BCMA exposure, with no statistically significant differences compared with the nonfrail cohort (Table 1). The most common prior BCMA therapies in the frail cohort were belantamab (n = 10) and teclistamab (n = 2); one patient received both belantamab and teclistamab.
Before CAR‐T infusion, 114 (62.3%) patients in the frail cohort had progressive disease compared to 189 (52.8%) in the nonfrail cohort. More patients in the frail cohort had high bone marrow disease burden (>50% plasma cells, 12.0% vs. 7.8%, p = .02) and thrombocytopenia (13.7% vs. 6.1%, p < .01). Neutropenia and elevated lactate dehydrogenase (LDH) were present in 4.9% and 28.4% of the frail cohort, respectively, with no significant differences compared to the nonfrail.
The majority of frail (57.9%) and nonfrail patients (56.1%) required bridging therapy. The most common lymphodepleting chemotherapy regimen used in >70% of patients among both frail and nonfrail patients was cyclophosphamide and fludarabine. The median time from leukapheresis to infusion among frail patients was 63 days (interquartile range [IQR], 56–76 days) with no statistical differences between the nonfrail cohort (63 days, IQR, 56–71 days, p = .62).
Efficacy outcomes
With regard to efficacy, the best ORR in the frail cohort was 82.9% compared to 88.5% in the nonfrail cohort (p = .07) as shown in Figure 1. Additionally, among frail patients, the depth of response was as follows: sCR/CR (33.1%), VGPR (38.7%), and PR (11.0%). Patients in a CR/sCR at the time of infusion and continuing to maintain CR at day 100 were excluded from this assessment. In a multivariable analysis, the presence of frailty was not associated with either ORR (odds ratio [OR], 1.03; 95% CI, 0.85–1.26, p = .74) or CR (OR, 1.17; 95% CI, 0.85–1.60, p = .33) (see Tables S3 and S4).
FIGURE 1.
ORR after cilta‐cel by frailty status. Stacked bars show PR, VGPR, and CR/sCR as proportions of each cohort; ORR (PR + VGPR + CR/sCR) is labeled above bars. cilta‐cel indicates ciltacabtagene autoleucel; CR, complete response; ORR, overall response rate; PR, partial response; sCR, stringent complete response; VGPR, very good partial response.
The median PFS of the entire cohort was not reached. The estimated 12‐month PFS in the frail cohort was 62.7% (95% CI, 53.6%–71.3%) compared to 75.9% (95% CI, 70.4%–81.1%) in the nonfrail cohort (Figure 2). In an MVA, frailty was associated with inferior PFS (HR, 1.67; 95% CI, 1.16–2.40, p < .01) as shown in Table S5.
FIGURE 2.
Progression‐free survival (PFS) after ciltacabtagene autoleucel (cilta‐cel) by frailty status.
Similarly, the 12‐month OS among frail patients was 72.8% (95% CI, 64.9%–80.0%) compared to 90.4% (95% CI, 86.6%–93.7%) in the nonfrail cohort as shown in Figure 3. In an MVA, frailty was associated with inferior OS (HR, 2.46; 95% CI, 1.57–3.87, p < .01) as shown in Table S6.
FIGURE 3.
Overall survival (OS) after ciltacabtagene autoleucel (cilta‐cel) by frailty status.
A total of 45 patients (24.6%) in the frail cohort and 37 patients (10.3%) in the nonfrail cohort died during the study follow‐up period. The most common cause of death was persistence or progressive disease in both the frail (57.8%) and nonfrail (62.2%) cohorts. Numerically, there were more patients in the frail cohort who died from infections (n = 6, 13.2% of deaths) compared to the nonfrail cohort where 3 patients (8.3% of deaths) died from infections. Similarly in the frail cohort, three patients died from immune‐mediated toxicity (CRS, n = 2; ICANS, n = 1) compared to one patient in the nonfrail cohort. The complete list of primary causes of death is included in Table S7. There were no differences in key survival outcomes between patients included and excluded from the analysis (Table S8).
Safety outcomes
Safety outcomes for the cohort are summarized in Table 2. In terms of toxicity, CRS developed in the majority of patients in both the frail (82.5%) and nonfrail cohort (79.1%), p = .36. This included grade ≥2 CRS in 22.4% of frail versus 17.9% of nonfrail patients, p = .05. In the frail cohort, the time to onset of CRS was a median of 8 days (IQR, 6–9 days) and the duration was a median of 3 days, which was not significantly different from the nonfrail cohort (median time to onset of CRS, 8 days, IQR 6–9 days, p = .85; median duration of CRS, 4 days, p = .07). On MVA, frailty was not associated with an increased risk for grade ≥2 CRS (OR, 1.23; 95% CI, 0.79–1.94, p = .36), as shown in Table S9.
TABLE 2.
Safety outcomes after cilta‐cel.
| Characteristic | Frail (N = 183) | Nonfrail (N = 358) | p value |
|---|---|---|---|
| CRS, No. (%) | .36 | ||
| No | 32 (17.5) | 75 (20.9) | |
| Yes | 151 (82.5) | 283 (79.1) | |
| CRS grade, No. (%) | .05 | ||
| No CRS | 32 (17.5) | 75 (20.9) | |
| Grade 1 | 110 (60.1) | 217 (60.6) | |
| Grade 2 | 27 (14.8) | 56 (15.6) | |
| Grade 3 | 8 (4.4) | 5 (1.4) | |
| Grade 4 | 3 (1.6) | 3 (0.8) | |
| Grade 5 | 3 (1.6) | 0 (0.0) | |
| Not reported | 0 (0.0) | 2 (0.6) | |
| Time from infusion to onset of CRS, days | .85 | ||
| Median (IQR) | 8.0 (6.0–9.0) | 8.0 (6.0–9.0) | |
| Duration of CRS, days | .07 | ||
| Median (range) | 3.0 (1.0–40.0) | 4.0 (1.0–34.0) | |
| ICANS, No. (%) | <.01 | ||
| No | 124 (67.8) | 295 (82.4) | |
| Yes | 59 (32.2) | 63 (17.6) | |
| Maximum ICANS grade, No. (%) | <.01 b | ||
| No neurologic impairment | 124 (67.8) | 295 (82.4) | |
| Grade 1 | 26 (14.2) | 26 (7.3) | |
| Grade 2 | 8 (4.4) | 9 (2.5) | |
| Grade 3 | 7 (3.8) | 7 (2.0) | |
| Grade 4 | 4 (2.2) | 1 (0.3) | |
| Grade 5 | 2 (1.1) | 0 (0.0) | |
| Not reported | 12 (6.6) | 20 (5.6) | |
| Time from infusion to onset of ICANS, days | .81 c | ||
| Median (IQR) | 9.0 (7.0–11.0) | 9.0 (6.0–13.0) | |
| Time from ICANS onset to resolution, days | .66 c | ||
| Median, IQR | 2.0 (1.0–5.0) | 2.0 (1.0–7.0) | |
| Non‐ICANS neurotoxicity, No. (%) | .70 a | ||
| No | 174 (95.1) | 336 (93.9) | |
| Yes | 9 (4.9) | 22 (6.1) | |
| Prolonged cytopenia at day 30, No. (%) | <.01 | ||
| No | 115 (62.8) | 276 (77.1) | |
| Yes | 56 (30.6) | 76 (21.2) | |
| IEC‐HS, No. (%) | 1.00 | ||
| No | 176 (96.2) | 345 (96.4) | |
| Yes | 7 (3.8) | 13 (3.6) | |
| Clinically significant infection, No. (%) | .47 | ||
| No | 93 (50.8) | 194 (54.2) | |
| Yes | 90 (49.2) | 164 (45.8) | |
| Second primary malignancy, No. (%) | .12 | ||
| No | 171 (93.4) | 347 (96.9) | |
| Yes | 12 (6.6) | 11 (3.1) |
Abbreviations: cilta‐cel, ciltacabtagene autoleucel; CRS, cytokine release syndrome; ICANS, immune effector cell associated neurotoxicity syndrome; IEC‐HS, immune effector cell–associated hemophagocytic lymphohistiocytosis–like syndrome; IQR, interquartile range.
Pearson χ2 test.
Fisher exact test.
Wilcoxon rank‐sum test.
The rate of ICANS (any grade) in the frail cohort was 32.2% compared to 17.6% in nonfrail cohort, p < .01. Similarly, the rates of grade ≥2 ICANS among frail was 11.5% versus 4.8% in nonfrail patients, p < .01. In the frail cohort, the time to onset of ICANS was a median of 9 days and the duration was a median of 2 days, which was not significantly different to the nonfrail cohort. On MVA, frailty was associated with significantly increased risk for developing ICANS of any grade (OR, 2.01; 95% CI, 1.32–3.08, p < .01) as shown in Table S10.
Non‐ICANS neurotoxicity was observed in nine (4.9%) frail patients and in 22 (6.1%) nonfrail patients. Cranial nerve palsies, hemiparesis, or myelitis occurred in three (1.6%) frail patients and parkinsonism occurred in five (2.7%) frail patients. Median time to onset of parkinsonism‐like symptoms was shorter in the frail group 6.5 days versus 19.5 days (p = .02). Data on duration and resolution of symptoms was limited and therefore not reported in this analysis.
There were increased rates of prolonged cytopenia (beyond day+30) among frail (n = 56, 30.6%) versus nonfrail patients (n = 76, 21.2%, p <0.01). Other toxicities including IEC‐HS were relatively uncommon, occurring in less than 4% of the cohort in both the frail and nonfrail cohorts. Rates of clinically significant infections were 49.2% in the frail cohort and 45.8% in the nonfrail cohort and did not differ significantly (p = .47). In the frail cohort, viral infections were most common (33.9%) followed by bacterial infections (21.3%). There were no statistically differences in the rates of secondary malignancy that developed in 12 (6.6%) frail patients and 11 (3.1%) nonfrail patients (p = .12). Basal cell and squamous cell carcinomas were the most common, developing in a total of nine patients across both cohorts.
Twelve‐month treatment‐related mortality in the frail cohort was 6.8% (95% CI, 3.4%–11.2%) compared to 3.6% (95% CI, 1.8%–6.1%) in the nonfrail cohort, p = .12 (Figure 4).
FIGURE 4.
Treatment‐related mortality after ciltacabtagene autoleucel (cilta‐cel) by frailty status.
DISCUSSION
In this CIBMTR analysis of 541 standard‐of‐care recipients of cilta‐cel, approximately one‐third of the patients were classified as frail. Frail patients experienced inferior outcomes including inferior PFS and OS and had higher rates of toxicity, particularly neurotoxicity compared to nonfrail older patients. Our data emphasize the need to assess frailty status to identify this vulnerable population for future optimization of outcomes.
In our study, the median age of the frail cohort was 65.5 years with approximately 10% having ECOG performance status ≥2 and 98% of the patients had a history of clinically significant comorbidities that represent a more functionally unfit and high comorbid population than those enrolled in CARTITUDE‐1. 12 Although the number of patients who were triple class exposed in our cohort was comparable to CARTITUDE‐1, patients in our cohort were more heavily pretreated with a median of seven prior lines compared to six in the CARTITUDE‐1 cohort. Although we do not know the exact number of patients who would have been ineligible for the CARTITUDE‐1 trial, frail patients included in our study with ECOG PS ≥2 (9.8%), prior BCMA (7.1%), thrombocytopenia (13.7%), and neutropenia (4.9%) would not have met the inclusion criteria for the trial. This is similar to other real‐world studies that have demonstrated that over 50% of the real‐world patients would not have met the eligibility criteria for the CAR‐T clinical trials. 13 , 14
With respect to efficacy, although numerically the ORR for frail older patients was lower than nonfrail older adults, frailty itself was not independently associated with ORR or complete response in an MVA. In contrast, frailty was associated with clinically and statistically meaningful decrements in time‐to‐event outcomes: 12‐month PFS of 62.7% versus 75.9% and 12‐month OS of 72.8% versus 90.4% for frail versus nonfrail patients, respectively. Additionally, in multivariable models, frailty remained independently associated with inferior PFS (HR, 1.67) and OS (HR, 2.46). Although there is a paucity of published data specifically evaluating the efficacy of cilta‐cel, particularly PFS and OS in the real‐world, smaller case series have suggested that poor performance status is an independent predictor of inferior PFS and OS among patients treated with CAR‐T therapy, 8 with other studies not showing inferior outcomes with increasing chronological age or frailty status alone on outcomes. 7 However, many of these studies had an overall smaller cohort and often included a large proportion of patients who received a different CART construct (ide‐cel), making it difficult to directly compare the results. A few additional points deserve mention here: whereas disease persistence or progression was the most common cause of death in both the frail and nonfrail cohorts, infection, immune‐toxicity, and organ failure–related deaths were more common in the frail cohort reflecting the underlying vulnerability of this population. In addition, it should be highlighted that the proportion of patients with increased disease burden was higher in the frail cohort. It is possible that in some patients, a more aggressive disease phenotype contributed to frailty.
With respect to toxicity, safety signals in this frailty subgroup were distinct. The rate of ICANS was nearly doubled in frail patients (32.2% vs. 17.6%), and frailty was independently associated with any‐grade ICANS (OR, 2.01; 95% CI, 1.32–3.08). These results are similar to previous real‐world analyses that also show increased rates of ICANS with CART therapy among frail older adults. 8 With respect to other delayed neurotoxicity, the overall numbers in the cohort are too small to do any comparative analysis; however, the overall rates of CN palsy and parkinsonism appeared comparable.
It was interesting to note that although prolonged cytopenias at day+30 in our cohort were also more common in frail patients (30.6% vs. 21.2%), there were no statistically increased rates of clinically significant infections observed. Overall, the rates of prolonged cytopenias as well as infections are similar to other case series published with ciltacel. 14 Twelve‐month treatment‐related mortality (TRM) was numerically higher in frail versus nonfrail patients (6.8% vs. 3.6%), although this difference did not reach statistical significance and was similar to other case series. 15 Similarly rates of secondary malignancies were overall low in the cohort similar to other case series 14 and not significantly different between fit and frail patients.
Overall, the strengths of our study include the large, geographically diverse cohort; standardized, prospectively collected CIBMTR data; and robust modeling that adjusted for disease status at infusion, cytopenias, marrow burden, prior BCMA‐directed therapies, and other clinically relevant covariates. Additionally, our results also highlight the importance of distinguishing chronological age from frailty. Prior CIBMTR data with ide‐cel showed that age ≥70 years alone was not associated with worse survival 7 , whereas frailty, rather than chronological age, was more closely associated with toxicity, particularly ICANS, and clinical outcomes. Additionally, this analysis also highlights product‐ and/or construct‐specific impact on outcomes as both PFS and OS were not adversely affected among older adults using ide‐cel but were impacted in our analysis among adults using cilta‐cel.
The study is, however, observational and subject to residual confounding factors (e.g., clinical decisions around referral, bridging therapy, and lymphodepletion intensity). We also selected patients who had infused product and that met quality specification requirements and therefore may have been selected for a more fit population who met the study entry criteria. Additionally, our frailty definition used an adapted simplified frailty index leveraging HCT‐CI rather than the Charlson Comorbidity Index because of data availability; although pragmatic and reproducible, this does not capture all geriatric domains (e.g., cognition and/or gait speed) and could misclassify risk particularly compared to more prospective evaluations with the IMWG frailty index 16 , 17 , 18 Finally, event adjudication for delayed neurotoxicity and infection can vary by center, and median follow‐up remains modest for late events such as second primary malignancies.
In conclusion, in the largest series to date focused on frail patients treated with cilta‐cel, frailty did not diminish the likelihood of achieving an initial response but was independently associated with shorter PFS and OS and with higher rates of ICANS. These findings, together with prior real‐world CAR‐T data, support the role of frailty assessment and targeted supportive care strategies to optimize outcomes in this vulnerable population. Prospective studies are needed to validate frailty‐adapted care pathways for cilta‐cel, including ensuring optimal disease control pre‐CART, risk‐stratified bridging strategies, cytopenia‐mitigation bundles, infection‐prevention algorithms, and standardized neurologic monitoring and management.
AUTHOR CONTRIBUTIONS
Hira Mian: Conceptualization; investigation; methodology; validation; writing—original draft; writing—review and editing. Muhammad Salman Faisal: Conceptualization; methodology. Tiening Chen: Conceptualization; formal analysis; methodology; validation. Ruta Brazauskas: Formal analysis. Temitope Oloyede: Investigation. Nausheen Ahmed: Investigation. Aimaz Afrough: Investigation. Larry D. Anderson: Investigation. Rahul Banerjee: Investigation. Jesus Berdeja: Investigation. Aram Bidikian: Investigation. Jakob Devos: Formal analysis; investigation. Binod Dhakal: Investigation. Ajoy Dias: Investigation. Danai Dima: Investigation. Yvonne A. Efebera: Investigation. Lohith Gowda: Investigation. Doris K. Hansen: Investigation. Hamza Hashmi: Investigation. Heather J. Landau: Investigation. Lazaros Lekakis: Investigation. Abu‐Sayeef Mirza: Investigation. Ravi Narra: Investigation. Krina Patel: Investigation. Ashley E. Rosko: Investigation. Mark Schroeder: Investigation. Surbhi Sidana: Investigation. Saad Usmani: Investigation. Marcelo C. Pasquini: Formal analysis; project administration. Taiga Nishihori: Investigation. Othman Salim Akhtar: Conceptualization; investigation; methodology; formal analysis; writing—review and editing. Meera Mohan: Conceptualization; methodology; validation. All authors approved the final version and are accountable for all aspects of the work.
CONFLICT OF INTEREST STATEMENT
Hira Mian receives honoraria from Bristol‐Myers Squibb, Pfizer, Janssen, Amgen, Takeda, GSK, FORUS, and Sanofi, and research funding from Pfizer and Janssen. Nausheen Ahmed receives research funding from Kite/Gilead and Pfizer and has consulting or advisory roles with Bristol‐Myers Squibb, Kite/Gilead, Invivyd Bio, Legend Biotech, Johnson & Johnson, Autolus, and ADC Therapeutics; she is also founder of Cortex (nonprofit) and a board member of USMIRC. Aimaz Afrough receives research funding from AbbVie, Regeneron Pharmaceuticals, K36‐Therapeutics, Janssen, and Adaptive Biotech, and honoraria from Karyopharm, Bristol‐Myers Squibb, Janssen, Sanofi, and Pfizer. Larry D. Anderson receives consulting fees and honoraria from Pfizer, Bristol‐Myers Squibb, BeiGene, Sanofi, Amgen, Karyopharm, Celgene, Prothena, Cellectar, Johnson & Johnson, GSK, and AbbVie, and research funding from Bristol‐Myers Squibb, BeiGene, Johnson & Johnson, GSK, and AbbVie. Rahul Banerjee reports consulting for AbbVie, Adaptive Biotech, Bristol‐Myers Squibb, Caribou Biosciences, Genentech, Gilead/Kite, GSK, Janssen, Karyopharm, Legend Biotech, Pfizer, Poseida Therapeutics, Sanofi, and SparkCures, and research funding from AbbVie, Bristol‐Myers Squibb, Janssen, Novartis, Pack Health, Prothena, and Sanofi. Binod Dhakal receives honoraria from Natera, GlaxoSmithKline, Menarini, Kite, and Janssen; consulting for Genentech, Caribou, Johnson & Johnson, Karyopharm, Bristol‐Myers Squibb, Janssen, Arcellx, and Pfizer; and research funding from Caribou, Ichnos, Kite, Carsgen, Bristol‐Myers Squibb, Janssen, Arcellx, and Pfizer. Danai Dima reports consulting for Karyopharm and Caribou Biosciences. Yvonne A. Efebera receives honoraria from Pfizer, GSK, Kite, Janssen, Sanofi, Bristol‐Myers Squibb, Orca, and Oncopeptides, and research funding via Pharmacyclics (Alliance) and GSK (Alliance). Doris K. Hansen receives research funding from Bristol‐Myers Squibb, Janssen, Kite Pharma, Karyopharm, and Adaptive Biotech, and reports consulting or advisory roles with Bristol‐Myers Squibb, Janssen, Legend Biotech, Pfizer, Kite Pharma/Gilead Sciences, AstraZeneca, and Karyopharm. Hamza Hashmi reports consulting for Bristol‐Myers Squibb, Sanofi, and Janssen. Abu‐Sayeef Mirza reports consulting for Bristol‐Myers Squibb. Krina Patel reports consulting for Bristol‐Myers Squibb, Janssen, Pfizer, Arcellx, and Karyopharm Therapeutics; research funding from Bristol‐Myers Squibb, Poseida Therapeutics, Takeda, Janssen, Cellectis, Nektar, AbbVie/Genentech, Precision Biosciences, and Allogene Therapeutics; and travel support from Bristol‐Myers Squibb. Surbhi Sidana reports consulting for Janssen, Bristol‐Myers Squibb, Legend Biotech, Magenta Therapeutics, Sanofi, Pfizer, Takeda, Kite, AbbVie, Regeneron, BioLineRx, and Genentech/Roche, and research funding from Janssen, Magenta Therapeutics, Allogene Therapeutics, Novartis, and Bristol‐Myers Squibb. Saad Usmani reports consulting or advisory roles with GSK, Takeda, SecuraBio, Amgen, Seagen, Johnson & Johnson/Janssen, Genentech, Sanofi, Gilead, Oncopeptides, Bristol‐Myers Squibb/Celgene, SkylineDX, AbbVie, EdoPharma, Gracell Therapeutics, TeneoBio, and AstraZeneca, and research funding from Janssen, Takeda, SecuraBio, Amgen, Seagen, Johnson & Johnson/Janssen, Sanofi, Gilead, Oncopeptides, Bristol‐Myers Squibb/Celgene, SkylineDX, AbbVie, EdoPharma, Gracell Therapeutics, and TeneoBio. Marcelo C. Pasquini receives honoraria from Gilead and research funding from Kite/Gilead, Janssen, Bristol‐Myers Squibb, and Novartis. Othman Salim Akhtar reports advisory board participation for Sanofi and Janssen, and consulting for Immix Biopharma. Meera Mohan is supported by the Advancing a Healthier Wisconsin CTSI KL2 award, receives institutional research funding from Sanofi, Bristol‐Myers Squibb, and Celgene, and reports consulting for Sanofi, Bristol‐Myers Squibb/Celgene, Pfizer, Janssen Scientific Affairs, Legend Biotech, and Adaptive Biotech. Jesus Berdeja reports consulting fees from AstraZeneca, Bristol‐Myers Squibb, Eli Lilly and Company, GlaxoSmithKline, Johnson and Johnson, Kite Pharma, Inc, Kyowa Kirin Co., Ltd, Pfizer, Regeneron, and Roche; and grant and/or contract funding from AstraZeneca, Bristol‐Myers Squibb, C4 Therapeutics, Caribou Biosciences, Inc, Eli Lilly and Company, Genentech, GlaxoSmithKline, Ichnos, Johnson and Johnson, k36 Therapeutics, Karyopharm Therapeutics, Pfizer, Sanofi Pasteur Biologics LLC, and Karyopharm Therapeutics. Ajoy Dias reports stock holdings with ARK Genomic Revolution, Editas Medicine, Novo Nordisk, OPKO Health Inc (OPK), US Medical Device, and Vanguard Health Care Fund. Hira Mian reports fees for professional activities from AbbVie, Amgen, Bristol‐Myers Squibb, F. Hoffmann‐La Roche, GlaxoSmithKline, Janssen Pharmaceuticals, Pfizer, and Sanofi; and consulting fees from Regeneron. Ashley Rosko reports fees for professional activities from Transplantation and Cellular Therapy, Clinical Care Options, LLC, Curio Science, Dava Oncology, LP, Eastern Area Health Education Center, IMIDEX Great Debates and Updates, MD Education, National Comprehensive Cancer Network, Physicians Education Resource MJH Life Sciences, and Sanofi; and fees for travel from the International Myeloma Foundation. The other authors declare no conflicts of interest.
Supporting information
Supplementary Material
ACKNOWLEDGMENTS
The authors acknowledge the patients who participated in this retrospective study, along with their families and caregivers, the physicians and nurses who provided care, the staff members at the CIBMTR, and the personnel involved in data collection at all participating centers. Center for International Blood and Marrow Transplant Research (CIBMTR) is supported primarily by the Public Health Service U24CA076518 from the National Cancer Institute (NCI), the National Heart, Lung and Blood Institute (NHLBI), the National Institute of Allergy and Infectious Diseases (NIAID), and 75R60222C00011 from the Health Resources and Services Administration (HRSA). Additional federal support is provided by U01AI184132 from the National Institute of Allergy and Infectious Diseases (NIAID); and UG1HL174426 from the National Heart, Lung and Blood Institute (NHLBI). Support is also provided by the Medical College of Wisconsin, NMDP, Gateway for Cancer Research, Pediatric Transplantation and Cellular Therapy Consortium, and from the following commercial entities: AbbVie, Actinium Pharmaceuticals, Inc, Adaptimmune LLC, Adaptive Biotechnologies Corporation, ADC Therapeutics, Adienne SA, Alexion, AlloVir, Inc, Amgen, Inc, Astellas Pharma US, AstraZeneca, Atara Biotherapeutics, Autolus Limited, Beam, BeiGene, BioLineRX, Blue Spark Technologies, Blueprint Medicines, Bristol‐Myers Squibb Co., CareDx Inc, CSL Behring, CytoSen Therapeutics, Inc, DKMS, Eurofins Viracor, DBA Eurofins Transplant Diagnostics, Gamida‐Cell, Ltd, Genetix, Gift of Life Biologics, Gift of Life Marrow Registry, HistoGenetics, ImmunoFree, Incyte Corporation, Iovance, Janssen Research & Development, LLC, Janssen/Johnson & Johnson, Japan Hematopoietic Cell Transplantation Data Center, Jasper Therapeutics, Jazz Pharmaceuticals, Inc, Karius, Kashi Clinical Laboratories, Kiadis Pharma, Kite, a Gilead Company, Kyowa Kirin International plc, Labcorp, Legend Biotech, Mallinckrodt Pharmaceuticals, Med Learning Group, Medac GmbH, Medexus, Merck & Co., Mesoblast, Inc, Millennium, the Takeda Oncology Co., Miller Pharmacal Group, Inc, Miltenyi Biotec, Inc, MorphoSys, MSA‐EDITLife, Neovii Pharmaceuticals AG, Novartis Pharmaceuticals Corporation, Omeros Corporation, Orca Biosystems, Inc, OriGen BioMedical, Ossium Health, Inc, Pfizer, Inc, Pharmacyclics, LLC, an AbbVie company, Pierre Fabre Pharmaceuticals, PPD Development, LP, Registry Partners, Rigel Pharmaceuticals, Sanofi, Sarah Cannon, Seagen Inc, Servier, Sobi, Inc, Sociedade Brasileira de Terapia Celular e Transplante de Medula Óssea (SBTMO), Stemcell Technologies, Stemline Technologies, STEMSOFT, Syndax, Takeda Pharmaceuticals, Talaris Therapeutics, Tscan Therapeutics, Vertex Pharmaceuticals, Vor Biopharma Inc, and Xenikos BV.
Contributor Information
Hira Mian, Email: mianh@mcmaster.ca.
Othman Salim Akhtar, Email: oakhtar@mcw.edu.
DATA AVAILABILITY STATEMENT
Research data are not shared.
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Supplementary Materials
Supplementary Material
Data Availability Statement
Research data are not shared.