Abstract
In oncology, overall survival (OS) is the benchmark endpoint for assessing therapy effectiveness, but requires large patient cohorts and extended follow-up to attain meaningful data. The identification of reliable surrogate markers for OS may enable earlier treatment decisions and streamline clinical trial design, potentially reducing costs and accelerating access to new therapies. The objective of this study was to evaluate whether complete response (CR) could serve as an early surrogate for OS in patients with relapsed or refractory large B-cell lymphoma (r/r LBCL). This study used two data sources: a systematic literature review (SLR) with meta-analyses and patient-level real-world data from the Center for International Blood and Marrow Transplant Research (CIBMTR) registry evaluating the chimeric antigen receptor (CAR) T-cell therapy axicabtagene ciloleucel (axi-cel). Systematic searches of PubMed and EMBASE were conducted to identify clinical trials and real-world cohort studies published between January 2000 and July 2024 that reported CR rates and 1- and 2-yr OS rates in patients who received systemic treatment for r/r LBCL. Meta-analyses were stratified by treatment type, study type, median lines of prior therapy, response criteria, and high-risk status. The CIBMTR study included adult patients with r/r LBCL treated with axi-cel between October 2017 and August 2020 and collected data on CR rate, time to CR (TtCR), and OS. Multivariable analyses adjusted for key prognostic factors were conducted to identify baseline patient characteristics associated with TtCR and OS at Day 100 and Month 6 after axi-cel infusion and to determine the association between OS and CR at Day 100 and Month 6. In the SLR/meta-analysis, a total of 82 original articles were identified through PubMed and EMBASE searches and secondary references. CR rates across treatment modalities were strongly predictive of achieving OS at 1 and 2 yr after treatment in patients with r/r LBCL who were in high-risk cohorts (R2: 1 yr, 0.86; 2 yr, 0.89) or cohorts with a median of ≥3 prior therapy lines (R2: 1 yr, 0.89; 2 yr, 0.96). Of the 1251 patients included in the CIBMTR analysis, 60% (95% confidence interval [CI], 57% to 63%) achieved CR with axi-cel; 90% of initial CRs occurred within 6 mo of axi-cel infusion (median follow-up, 36.9 mo). Earlier achievement of CR was associated with older age, better Eastern Cooperative Oncology Group performance status, lower tumor burden, and prior autologous hematopoietic cell transplantation. In multivariable analyses, OS was significantly higher in patients who were in CR at Day 100 (hazard ratio [HR] 0.33; 95% CI, 0.27 to 0.40) or Month 6 (HR 0.24; 95% CI, 0.20 to 0.30) after axi-cel infusion compared with those who did not achieve CR by those time points, respectively. These results suggest that CR at Day 100 or Month 6 are viable surrogate endpoints for OS in patients who receive CAR T-cell therapies, supporting their inclusion as endpoints in clinical trial design.
Keywords: Meta-analysis, CIBMTR, R/R LBCL, Axi-cel
Graphical Abstract
INTRODUCTION
Overall survival (OS) is considered the gold standard in determining the superiority of treatment options in patients with cancer, but requires long follow-up to collect sufficient data [1–3]. Among other limitations, OS can be influenced by subsequent therapies, patient crossover between treatment arms in clinical trials, and non-cancer-related deaths, making it a challenge to attribute survival benefits to a specific treatment [1,3]. The availability of surrogate markers that are highly correlated with OS would enable earlier treatment decisions, allowing clinicians to optimize therapy and provide timely access to additional or alternative interventions if needed. In clinical trials, use of appropriate surrogate endpoints for survival could reduce costs, shorten the duration of trials, and accelerate approvals for novel therapies [1,2].
Prior studies have shown a correlation between indicators of early treatment efficacy and superior long-term outcomes [2,4,5]. Event-free survival and progression-free survival (PFS) are established, clinically relevant endpoints in lymphoma (with shorter follow-up than OS), recognized by clinicians and regulatory agencies for evaluating treatment efficacy [2,6]. In patients who received immunochemotherapy as first-line (1L) treatment for diffuse large B-cell lymphoma (DLBCL) and achieved event-free survival 24 mo after diagnosis, subsequent OS was comparable to a normal life expectancy; moreover, continued relapse monitoring beyond 24 mo had limited benefit [4]. Likewise, a pooled analysis of the Surrogate Endpoint for Aggressive Lymphoma database demonstrated that PFS at 24 mo significantly correlated with subsequent long-term OS in patients receiving 1L treatment for DLBCL [5].
Although the best evidence to date from correlative studies may support use of alternative survival endpoints as potential predictors of OS in patients with DLBCL treated in the 1L setting, less evidence exists to support application of these endpoints to patients with relapsed or refractory large B-cell lymphoma (r/r LBCL) [6]. High complete response (CR) rates and long-term OS have both been observed in patients with r/r LBCL treated with chimeric antigen receptor (CAR) T-cell therapies such as the CD19-directed CAR T-cell therapy axicabtagene ciloleucel (axi-cel) [7–10]. In the ZUMA-1 Phase 2 trial (cohorts 1 and 2), patients who achieved a CR after axi-cel therapy demonstrated improved OS compared with those who did not (with 5 yr of follow-up, the OS rate was 64% in patients who achieved CR compared with 43% among all treated patients) [9]. Although CR has been associated with improved OS in r/r LBCL, it is unknown whether it is broadly applicable as a definitive predictor of OS.
A key consideration in validating CR as a surrogate endpoint for OS is determining the optimal post-treatment landmark time points at which CR is predictive of OS. Among patients with r/r LBCL treated with axi-cel in ZUMA-1, 58% achieved CR, with 63% of those achieving CR within the first 4 wk after treatment [9]. Time to CR (TtCR) is multifactorial and may be influenced by disease-, treatment-, and patient-specific factors. Understanding CR temporal dynamics is key for optimizing treatment, improving clinical outcomes, and determining the ideal timing for next-line therapy in patients with suboptimal treatment response.
Here, we conducted a systematic literature review (SLR) with meta-analysis to evaluate whether CR is a reliable surrogate for OS (ie, at the trial level) in patients receiving different treatments for r/r LBCL, including CAR T-cell therapies such as axi-cel. Additionally, we analyzed data from the Center for International Blood and Marrow Transplant Research (CIBMTR) registry (ie, at the patient level) to identify the optimal time point at which CR best correlates with OS and to assess prognostic factors associated with TtCR following treatment with axi-cel in patients with r/r LBCL.
METHODS
Systematic Literature Review
Systematic searches of PubMed and the Excerpta Medica database (EMBASE) were conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, to identify clinical trials and real-world cohort studies published in English between January 1, 2000, and July 3, 2024, that reported CR rates and 1- and 2-yr OS or PFS rates in patients who received systemic treatment for r/r LBCL (including DLBCL, primary mediastinal large B-cell lymphoma [PMBCL], and high-grade B-cell lymphoma [HGBCL]; see Supplementary Methods for search terms). Eligible studies included those with sample size of ≥100 patients in cohort studies or 2-arm clinical trials or ≥50 patients in single-arm trials. A minimum median follow-up of 9 or 18 mo was required to support survival rates at 1 and 2 yr, respectively. Studies without response or survival data, retracted studies, duplicate reports (except for reports with different follow-up periods), and case reports were excluded. Two researchers independently screened studies based on prespecified inclusion criteria and reviewed full texts of relevant citations to identify eligible studies. Disagreements were resolved by a third reviewer.
Statistical Analysis for the SLR
For data extracted from the SLR, inverse of variance weighting was used for correlations and regression to predict OS or PFS rates from CR rate; traditional random-effects models were used to assess the correlation between CR rate and OS or PFS. Stratified meta-analyses were performed by (1) treatment type (chemoimmunotherapy [CIT], CAR T-cell therapy, targeted therapies), (2) study type (clinical trial, real-world evidence), (3) median lines of prior therapy, (4) treatment response assessment criteria, and (5) high-risk status (includes patients without CR to 1L therapy, patients who were ineligible or unable to proceed to hematopoietic cell transplant [HCT], or patients who did not respond to autologous HCT, CAR T-cell therapy, or 1L intensive CIT; see Supplementary Table 1 and Supplementary Methods for details). R2 values ≥0.80 or 0.60 to 0.79 were considered strong or moderate evidence of surrogacy validity, respectively.
All statistical analyses for the meta-analysis were conducted using R statistical software version 4.1.0.
An abridged protocol of the SLR and meta-analysis is provided in Supplementary Methods.
Study Design and Patient Eligibility for the CIBMTR Analysis
A prospective, non-interventional, postmarketing requirement study using the CIBMTR database infrastructure is ongoing to capture long-term effectiveness and safety data for axi-cel in patients with r/r LBCL (see Supplementary Methods). This analysis was based on secondary use of the data collected for the postmarketing requirement study. CIBMTR registry data collected up to September 25, 2023, were included in this analysis. Eligible patients were ≥18 yr old with r/r LBCL (including DLBCL, PMBCL, or HGBCL), received axi-cel between October 2017 and August 2020, and provided informed consent for participation in the CIBMTR research database.
Patients who were enrolled in clinical trials or received axi-cel in noncommercial settings and patients with follicular lymphoma, primary DLBCL of the central nervous system, Richter’s transformation, or other B-cell malignancies were excluded. Patients with prior non-transplant cellular therapy (including prior CAR T-cell therapy), disease in CR prior to infusion, incomplete baseline data, or missing data on response or time to response were also excluded. Patients with pre-existing end-organ impairments (eg, hepatic, renal, cardiac, pulmonary) were eligible.
Study Endpoints and Assessments in the CIBMTR Analysis
Effectiveness endpoints included CR rate, TtCR, PFS, relapse or progressive disease (relapse/PD), and OS. CR was evaluated by positron emission tomography or computerized tomography assessments (based on the Lugano classification [11]) obtained after axi-cel infusion and prior to any subsequent treatment given for relapse/PD. TtCR was defined as the time from infusion to the initial response of CR; postinfusion relapse/PD, subsequent anticancer treatment given for relapse/PD, or death were treated as competing risks. Patients who remained in partial response or stable disease were censored at last follow-up or subsequent HCT, whichever occurred first. PFS was defined as the time from infusion to relapse/PD or death due to any cause, whichever occurred first. Relapse/PD was defined as the time from infusion to the earliest documented relapse/PD confirmed by positron emission tomography, computerized tomography, or magnetic resonance imaging and/or clinical/hematologic assessment. PFS and relapse/PD were censored at date of subsequent HCT. OS was defined as the time from infusion to death due to any cause (patients alive at last contact date were censored, but patients were not censored for receiving subsequent treatments).
Disease assessments were performed according to routine clinical practice at each participating center rather than on a standardized schedule. As a result, the timing of response evaluations varied across patients. For each patient, the disease assessment closest to the landmark time point was used to determine response status. CR was analyzed as the best overall response to describe the highest response achieved at any time after infusion. Landmark time points were analyzed to provide a clinically informative summary of the response trajectory.
Statistical Analysis of the CIBMTR Data
Among all eligible patients, descriptive statistics were used to summarize baseline patient characteristics. The cumulative incidence of CR, referred to as TtCR, was evaluated as a time-to-event outcome using a competing risk model, with loss to follow-up censored and death before CR treated as a competing risk. Cause-specific hazard ratios (HRs) for TtCR, adjusted for baseline prognostic factors, were estimated with 95% confidence intervals (CIs) using a multivariable cause-specific Cox proportional hazards model. In contrast to simple arithmetic proportions, cumulative incidence estimates of CR incorporated both the timing of response and the occurrence of competing events, such as progression or death.
OS in patients who achieved and maintained a CR was compared with that in patients not in CR (ie, those who had not achieved a CR or had not maintained a CR) at the prespecified landmark dates of Day 100 and Months 6 and 12 after infusion and was estimated using the Kaplan–Meier method. The analysis included only patients who were alive at the corresponding landmark dates. Patients who died, relapsed/progressed, or received subsequent salvage treatment by the landmark date were not eligible for the landmark analysis. Baseline patient characteristics were summarized for each landmark subset. Adjusted HRs and 95% CIs for landmark OS, with respect to achieving and maintaining CR at the corresponding landmark dates, were estimated using a multivariable Cox proportional hazards model.
PFS and relapse/PD were analyzed in patients who were alive and had achieved and maintained a CR at the prespecified landmark dates. PFS was estimated using the Kaplan–Meier method. Cumulative incidence of relapse/PD was evaluated using the competing risk model.
For multivariable analyses, the process of variable selection involved a combination of biologically, clinically, and empirically driven approaches. Specifically, biological and clinical judgements were used to minimize the selection of two or more covariates that were highly correlated. Age at infusion and sex were forced into the model. For landmark OS data, the main variable of interest (achieving and maintaining CR at prespecified landmark dates) was also forced into the model. Other potential covariates were further selected using a stepwise process, with a P value cutoff of 0.2 for variables to enter the model and 0.05 for variables to stay in the model. Key variables for stepwise selection are described in Supplementary Methods.
All statistical tests were two-sided, with a significance level of 0.05. All analyses were conducted using SAS 9.4 (M1).
RESULTS
SLR and Meta-Analysis of CR and OS or PFS in Patients with r/r LBCL
A total of 82 eligible articles (73 studies, 83 treatment arms [from 37 clinical trials and 46 real-world studies], 13,207 patients) were identified through PubMed and EMBASE searches and secondary references (Supplementary Figure 1).
Across all studies, pooled CR rates and 1- and 2-yr OS rates in patients with r/r LBCL were 33% (95% CI, 29% to 38%), 57% (95% CI, 53% to 60%), and 43% (95% CI, 38% to 47%), respectively; these rates were highest in patients who received CAR T-cell therapies (53% [95% CI, 49% to 57%], 63% [95% CI, 60% to 67%], and 54% [95% CI, 49% to 60%], respectively) compared with CIT or targeted therapies (Supplementary Table 1). In high-risk [12–15] patient cohorts across all studies, pooled CR rates and 1- and 2-yr OS rates were 27% (95% CI, 18% to 37%), 52% (95% CI, 43% to 60%), and 34% (95% CI, 26% to 43%), respectively (Supplementary Table 1).
Overall, CR rate (as best response) was moderately predictive of OS at 1 and 2 yr after treatment with CAR T-cell therapies (R2: 1 yr, 0.55; 2 yr, 0.61), CIT (R2: 1 yr, 0.62; 2 yr, 0.69), or in cohorts with a median of 2 lines of prior therapy (R2: 1 yr, 0.63; 2 yr, 0.64), and strongly predictive in high-risk patient cohorts (R2: 1 yr, 0.86; 2 yr, 0.89) or in cohorts with a median of ≥3 lines of prior therapy (R2: 1 yr, 0.89; 2 yr, 0.96), regardless of treatment type (Table 1). CR rate was not predictive of OS in studies of targeted therapies (R2: 1 yr, 0.05; 2 yr, 0.04) or cohorts with a median of 1 line of prior therapy (R2: 1 yr, 0.38; 2 yr, 0.50; Table 1).
Table 1.
Weighted Regression Coefficients (β)* for CR Rate in Predicting OS Rate at 1 and 2 Yr after Treatment in Patients with r/r LBCL
| OS at 1 Yr | OS at 2 Yr | |||||
|---|---|---|---|---|---|---|
| N † | β (95% CI) | R 2 | N † | β (95% CI) | R 2 | |
| All | 83 | 0.59 (0.47-0.72) | 0.52 | 50 | 0.66 (0.51-0.82) | 0.61 |
| Treatment type | ||||||
| CAR T-cell | 28 | 0.62 (0.40-0.85) | 0.55 | 12 | 0.74 (0.33-1.16) | 0.61 |
| CIT | 38 | 0.89 (0.65-1.12) | 0.62 | 30 | 0.83 (0.62-1.05) | 0.69 |
| Targeted therapies‡ | 17 | 0.36 (−0.53 to 1.25) | 0.05 | 8 | 0.26 (−0.99 to 1.52) | 0.04 |
| Median lines of prior therapy | ||||||
| 1§ | 26 | 0.58 (0.27-0.89) | 0.38 | 22 | 0.67 (0.36-0.97) | 0.50 |
| 2 | 37 | 0.70 (0.51-0.88) | 0.63 | 20 | 0.66 (0.41-0.90) | 0.64 |
| 3+ | 20 | 0.64 (0.53-0.75) | 0.89 | 8 | 0.87 (0.70-1.05) | 0.96 |
| Study type | ||||||
| RWE | 46 | 0.58 (0.41-0.75) | 0.52 | 27 | 0.63 (0.38-0.87) | 0.52 |
| Clinical trial | 37 | 0.60 (0.41-0.78) | 0.55 | 23 | 0.39 (0.18-0.60) | 0.41 |
| High-risk‖ | ||||||
| Yes | 18 | 0.75 (0.59-0.91) | 0.86 | 11 | 0.74 (0.54-0.93) | 0.89 |
| No | 65 | 0.42 (0.25-0.59) | 0.28 | 39 | 0.53 (0.30-0.76) | 0.37 |
| Criteria for CR | ||||||
| Lugano 2014 [11] | 53 | 0.62 (0.52-0.72) | 0.75 | 27 | 0.72 (0.59-0.85) | 0.83 |
| Other¶ | 30 | 0.80 (0.39-1.20) | 0.36 | 23 | 0.66 (0.31-1.02) | 0.42 |
1L indicates first-line; 2L, second line; ASCT, autologous stem cell transplant; BTKi, Bruton’s tyrosine kinase inhibitor; CAR, chimeric antigen receptor; CIT, chemoimmunotherapy; CPI, checkpoint inhibitor; CR, complete response; OS, overall survival; R-CODOX-M/IVAC, cyclophosphamide, vincristine, doxorubicin, high-dose methotrexate/ifosfamide, etoposide, and high-dose cytarabine; R-EPOCH, rituximab, etoposide phosphate, prednisone, vincristine sulfate, cyclophosphamide and doxorubicin hydrochloride; R-HyperCVAD, rituximab, cyclophosphamide, vincristine, doxorubicin, dexamethasone, methotrexate, leucovorin and cytarabine; r/r LBCL, relapsed or refractory large B-cell lymphoma; RWE, real-world evidence; SCT, stem cell transplant.
Data were weighted by the inverse of the geometric average of the variances of the 2 corresponding rates (CR and OS rates at 1 or 2 yr) in the analyses.
Number of treatment arms.
Targeted therapies included polatuzumab vedotin, T-cell engager, BTKi, CPI, lenalidomide, anti-CD19, anti-CD20, or anti-CD22 monoclonal antibodies.
Of the 26 studies that reported 1-yr OS data, 20 included patients who received exactly 1 prior line of therapy; 4 of these were clinical trials that evaluated CAR T-cell therapy in the 2L setting.
Defined as any of the following: Patients without a CR to 1L therapy, patients who were not eligible or unable to proceed to SCT, or patients who did not respond to ASCT, CAR T-cell therapy, or 1L intensive CIT (R-EPOCH, R-HyperCVAD, or R-CODOX-M/IVAC).
For every 10% increase in CR rate, the predicted increase in OS rate at 1 and 2 yr in patients who received CAR T-cell therapies was 6.2% (95% CI, 4.0% to 8.5%) and 7.4% (95% CI, 3.3% to 11.6%), respectively. Similarly, predicted increases in OS rates at 1 and 2 yr based on a 10% increase in CR rate in high-risk patient cohorts were 7.5% (95% CI, 5.9% to 9.1%) and 7.4% (95% CI, 5.4% to 9.3%), respectively, and in cohorts with a median of ≥3 lines of prior therapy, predicted increases were 6.4% (95% CI, 5.3% to 7.5%) and 8.7% (95% CI, 7.0% to 10.5%), respectively (Table 1).
Similar to OS, pooled PFS rates were highest in patients treated with CAR T-cell therapies compared with those who received CIT or targeted therapies (Supplementary Table 2). CR rate was moderately predictive of PFS at 1 and 2 yr after treatment with CAR T-cell therapies (R2: 1 yr, 0.74; 2 yr, 0.65) and strongly predictive in high-risk patient cohorts (R2: 1 yr, 0.84; 2 yr, 0.83; Supplementary Table 3).
Demographic and Baseline Characteristics of Patients Treated with Axi-cel in the CIBMTR Registry
Data from 1251 patients with r/r LBCL who received axi-cel after ≥2 lines of prior therapy between October 2017 and August 2020 across 77 authorized treatment centers and who were enrolled in the CIBMTR database were included in the full analysis. The median follow-up time for all patients at the data cutoff date (September 25, 2023) was 36.9 mo (range, 0.9 to 62.2).
Demographics and baseline characteristics for all eligible patients are summarized in Table 2. Among all study patients, the median age at axi-cel infusion was 62.1 yr (range, 19.6 to 86.2), and 64% of patients were male. Prior to infusion, 5% of patients (54/1136) had an Eastern Cooperative Oncology Group performance status (ECOG PS) ≥2, 76% (917/1209) had clinically significant comorbidities [19], and 6% (55/922) had bulky disease (maximum nodal size ≥10 cm). At initial diagnosis, 81% of all patients had DLBCL and 16% had HGBCL; 26% of patients (186/729) presented with double- or triple-hit lymphoma, and 28% had histologic transformation from follicular lymphoma or marginal zone lymphoma. Patients received a median of 3 lines of prior therapy (interquartile range [IQR], 2 to 4), and 27% of patients (342/1247) received prior autologous-HCT (auto-HCT). The median time from leukapheresis to infusion was 27 days (IQR, 25 to 32), and 31% of patients received bridging therapy.
Table 2.
Demographic, Patient, and Disease Characteristics for the Full Analysis Population from the CIBMTR Registry and for Patients at Landmark Day 100 and Month 6 after Infusion
| Characteristic | Overall (N = 1251) | Day 100 | Month 6 | ||
|---|---|---|---|---|---|
| In CR (n = 501) | Not in CR (n = 593) | In CR (n = 518) | Not in CR (n = 433) | ||
| Median age at infusion, yr (range) | 62.1 (19.6-86.2) | 63.9 (24.6-86.2) | 60.7 (19.6-86.0) | 63.8 (19.6-85.2) | 60.5 (20.8-86.2) |
| ≥65 yr, n (%) | 474 (38) | 229 (46) | 194 (33) | 229 (44) | 143 (33) |
| Male, n (%) | 805 (64) | 300 (60) | 402 (68) | 311 (60) | 282 (65) |
| Race/ethnicity, n (%) | |||||
| Non-Hispanic White | 897 (72) | 367 (73) | 419 (71) | 376 (73) | 305 (70) |
| Non-Hispanic Black | 61 (5) | 19 (4) | 31 (5) | 18 (3) | 21 (5) |
| Hispanic | 131 (10) | 51 (10) | 65 (11) | 53 (10) | 52 (12) |
| Other or unknown | 162 (13) | 64 (13) | 78 (13) | 71 (14) | 55 (13) |
| Karnofsky performance score prior to infusion, n/n (%) | |||||
| 90-100 | 513/1136 (45) | 235/447 (53) | 240/558 (43) | 244/466 (52) | 194/404 (48) |
| 80 | 388/1136 (34) | 139/447 (31) | 204/558 (37) | 151/466 (32) | 132/404 (33) |
| <80 | 235/1136 (21) | 73/447 (16) | 114/558 (20) | 71/466 (15) | 78/404 (19) |
| Not reported, n | 115 | 54 | 35 | 52 | 29 |
| ECOG PS ≥2 prior to infusion, n/n (%) | 54/1136 (5) | 6/447 (1) | 29/558 (5) | 5/466 (1) | 18/404 (4) |
| Not reported, n | 115 | 54 | 35 | 52 | 29 |
| Clinically significant comorbidities, n/n (%) | 917/1209 (76) | 362/479 (76) | 436/577 (76) | 374/495 (76) | 315/419 (75) |
| HCT-CI score prior to infusion, n/n (%) | |||||
| 0 | 371/1223 (30) | 147/486 (30) | 178/583 (31) | 155/503 (31) | 132/423 (31) |
| 1 | 250/1223 (20) | 102/486 (21) | 123/583 (21) | 108/503 (21) | 87/423 (21) |
| 2 | 156/1223 (13) | 65/486 (13) | 78/583 (13) | 69/503 (14) | 62/423 (15) |
| ≥3 | 446/1223 (36) | 172/486 (35) | 204/583 (35) | 171/503 (34) | 142/423 (34) |
| Not reported, n | 28 | 15 | 10 | 15 | 10 |
| Disease subtype at initial diagnosis, n (%) | |||||
| Diffuse LBCL | 1009 (81) | 433 (86) | 458 (77) | 438 (85) | 343 (79) |
| High-grade BCL | 204 (16)* | 59 (12) | 109 (18) | 66 (13) | 70 (16) |
| Primary mediastinal LBCL | 38 (3) | 9(2) | 26 (4) | 14 (3) | 20 (5) |
| Double- or triple-hit lymphoma at initial diagnosis, n/n (%) | 186/729 (26) | 54/274 (20) | 101/356 (28) | 62/280 (22) | 64/259 (25) |
| Not reported, n | 522 | 227 | 237 | 238 | 174 |
| Chemosensitivity prior to infusion, n/n (%) | |||||
| SD/PD prior to lymphodepletion | 848/1108 (77) | 302/416 (73) | 416/543 (77) | 304/431 (71) | 298/397 (75) |
| CR/PR prior to lymphodepletion | 260/1108 (23) | 114/416 (27) | 127/543 (23) | 127/431 (29) | 99/397 (25) |
| Untreated/unknown sensitivity, n | 143 | 85 | 50 | 87 | 36 |
| Disease stage at initial diagnosis, n/n (%) | |||||
| I | 95/1025 (9) | 37/415 (9) | 42/477 (9) | 35/432 (8) | 37/349 (11) |
| II | 160/1025 (16) | 64/415 (15) | 80/477 (17) | 74/432 (17) | 61/349 (17) |
| III | 266/1025 (26) | 108/415 (26) | 122/477 (26) | 108/432 (25) | 93/349 (27) |
| IV | 504/1025 (49) | 206/415 (50) | 233/477 (49) | 215/432 (50) | 158/349 (45) |
| Not reported, n | 226 | 86 | 116 | 86 | 84 |
| Elevated LDH at initial diagnosis, n/n (%) | |||||
| No | 151/516 (29) | 77/201 (38) | 64/261 (25) | 83/207 (40) | 49/189 (26) |
| Yes | 365/516 (71) | 124/201 (62) | 197/261 (75) | 124/207 (60) | 140/189 (74) |
| Not reported, n | 735 | 300 | 332 | 311 | 244 |
| Histologic transformation at initial diagnosis, n (%) | 346 (28)† | 142 (28) | 157 (26) | 135 (26) | 119 (27) |
| Bulky disease prior to infusion, n/n (%) | |||||
| Non-bulky (longest diameter of largest nodal mass <10 cm) | 867/922 (94) | 354/364 (97) | 407/422 (92) | 367/376 (98) | 300/327 (92) |
| Bulky (longest diameter of largest nodal mass ≥10 cm) | 55/922 (6) | 10/364 (3) | 35/422 (8) | 9/376 (2) | 27/327 (8) |
| Unknown, n | 329 | 137 | 151 | 142 | 106 |
| Median number of prior lines of therapy (IQR)‡ | 3 (2-4) | 3 (2-4) | 3 (2-4) | 3 (2-4) | 3 (2-4) |
| 1-2 prior lines, n/n (%) | 416/1250 (33) | 182/500 (36) | 205/593 (35) | 191/517 (37) | 160/433 (37) |
| ≥3 prior lines, n/n (%) | 834/1250 (67) | 318/500 (64) | 388/593 (65) | 326/517 (63) | 273/433 (63) |
| Not reported, n | 1 | 1 | 0 | 1 | 0 |
| Prior auto-HCT(s), n/n (%) | 342/1247 (27) | 173/499 (35) | 125/591 (21) | 174/515 (34) | 95/432 (22) |
| Not reported, n | 4 | 2 | 2 | 3 | 1 |
| Time from last prior auto-HCT to infusion, n/n (%) | |||||
| No prior auto-HCT | 905/1212 (75) | 326/484 (67) | 466/583 (80) | 341/499 (68) | 337/425 (79) |
| Prior auto-HCT to infusion ≤12 mo | 133/1212 (11) | 49/484 (10) | 62/583 (11) | 46/499 (9) | 49/425 (12) |
| Prior auto-HCT to infusion ≥12 mo | 174/1212 (14) | 109/484 (23) | 55/583 (9) | 112/499 (22) | 39/425 (9) |
| Not reported, n | 39 | 17 | 10 | 19 | 8 |
| Median time from leukapheresis to infusion, days (IQR) | 27 (25-32) | 27 (26-31) | 27 (25-32) | 27 (26-31) | 27 (25-32) |
| Bridging therapy (any type), n (%) | 392 (31) | 126 (25) | 204 (34) | 127 (25) | 141 (33) |
| Median follow-up, mo (range) | 36.9 (0.9-62.2) | 34.8 (0.1-58.9) | 34.3 (0.1-58.7) | 32.8 (0.2-56.2) | 31.5 (0.1-56.0) |
Auto indicates autologous; BCL, B-cell lymphoma; CIBMTR, Center for International Blood and Marrow Transplant Research; CR, complete response; ECOG PS, Eastern Cooperative Oncology Group performance status; FL, follicular lymphoma; HCT, hematopoietic cell transplantation; HCT-CI, hematopoietic cell transplantation comorbidity index; IQR, interquartile range; LBCL, large B-cell lymphoma; LDH, lactate dehydrogenase; PD, progressive disease; PR, partial response; SD, stable disease.
Of the 204 patients with high-grade BCL, 18 patients lacked at least 2 of the requisite cytogenetic abnormalities (MYC, BCL2, and BCL6) for classification as double- or triple-hit lymphoma.
Original lymphoma histology: FL, mixed small-cleaved and large-cell (n = 72); FL, grade unknown (n = 62), FL, predominantly large-cell subtype (Grade IIIA, n = 50); FL, predominantly small-cleaved cell subtype (n = 40); FL, predominantly large-cell subtype (Grade IIIB, n = 26); the remaining original histology subtypes included <20 patients.
Does not include HCT or cell therapy.
Responses and TtCR in the Full Analysis Population of the CIBMTR Registry
With a median follow-up of 36.9 mo, 60% (754/1251; 95% CI, 57% to 63%) of patients achieved a CR as their best response after axi-cel infusion, regardless of when it occurred; 90% of these CRs occurred within 6 mo. The cumulative incidence of CR after infusion, which reflects the time-specific probability of CR in the presence of competing risks, was 47% (95% CI, 44% to 50%) at Day 100, 54% (95% CI, 51% to 57%) at Month 6, and 59% (95% CI, 56% to 61%) at Month 12 (Figure 1).
Figure 1.
Estimated cumulative incidence of CR and progression/death without CR in patients treated with axi-cel in the CIBMTR registry. aPostinfusion relapse/PD, subsequent anticancer treatment given for relapse/PD, or death were treated as competing risks to CR. CIBMTR indicates Center for International Blood and Marrow Transplant Research; CR, complete response; No., number; PD, progressive disease.
In multivariable analysis of TtCR, baseline variables associated with earlier CR (Figure 2) included age ≥65 yr (HR 1.47; 95% CI, 1.27 to 1.70) and prior auto-HCT (HR 1.24; 95% CI, 0.97 to 1.60 for patients who underwent auto-HCT ≤12 mo prior to infusion; HR 1.30; 95% CI, 1.06 to 1.59 for patients who underwent auto-HCT >12 mo prior to infusion). Baseline variables associated with longer TtCR (Figure 2) were ECOG PS ≥2 prior to infusion (HR 0.53; 95% CI, 0.32 to 0.88) and bulky disease prior to infusion (HR 0.52; 95% CI, 0.33 to 0.83).
Figure 2.
Multivariable analysis of baseline factors associated with TtCR in the full analysis population of patients treated with axi-cel in the CIBMTR registry. Hazard ratios >1 indicate factors associated with earlier achievement of CR; hazard ratios <1 indicate factors associated with longer TtCR. aLongest diameter of largest nodal mass <10 cm. bLongest diameter of largest nodal mass ≥10 cm. AutoHCT indicates autologous hematopoietic cell transplant; CI, confidence interval; CIBMTR, Center for International Blood and Marrow Transplant Research; CR, complete response; ECOG, Eastern Cooperative Oncology Group; HR, hazard ratio; PD, progressive disease; PR, partial response; SD, stable disease; TtCR, time to complete response.
Outcomes in Landmark Analysis Populations of the CIBMTR Registry
Demographics and baseline characteristics for patients who were alive and in follow-up at the prespecified landmark dates of Day 100 and Months 6 and 12 are summarized in Table 2 and Supplementary Table 4. Patterns of CR, partial response, and PD at landmark timepoints are presented in a Sankey diagram (Figure 3) that illustrates patient transitions between response categories and shows both best response and CR at specific landmarks. A total of 1094 patients with r/r LBCL were alive 100 days after axi-cel infusion; of these, 501 patients were in CR at Day 100, and 593 patients were not. Patients who were in CR at Day 100 had a higher median age, a higher proportion who were prior auto-HCT recipients or received their last auto-HCT >12 mo before infusion, and a lower proportion with ECOG PS ≥2, bulky disease before infusion, or elevated lactate dehydrogenase (LDH) levels at diagnosis, compared with patients not in CR at Day 100 (Table 2). The estimated 2-yr OS rates after infusion were 77% (95% CI, 73% to 81%) for patients in CR at Day 100 and 42% (95% CI, 38% to 46%) for those not in CR at Day 100 (Supplementary Figure 2A); corresponding 1-yr OS rates were 90% (95% CI, 87% to 92%) and 58% (95% CI, 54% to 62%), respectively. Among patients in CR at Day 100, the estimated 2-yr PFS rate was 65% (95% CI, 61% to 70%), with a corresponding 1-yr PFS rate of 76% (95% CI, 72% to 80%), and the estimated 2-yr cumulative incidence of relapse/PD was 25% (95% CI, 21% to 29%; Supplementary Figures 3A and 4A).
Figure 3.
Sankey diagram depicting CR, PR, and PD at landmark timepoints in the CIBMTR registry, with response outcomes at specific landmarks. CIBMTR indicates Center for International Blood and Marrow Transplant Research; CR, complete response; HCT, hematopoietic cell transplant; LTF, lost to follow-up; NR, no response; PD, progressive disease; PR, partial response.
In multivariable analyses adjusted for key prognostic factors, patients not in CR at Day 100 after infusion had significantly lower OS compared with those in CR (HR 3.05; 95% CI, 2.52 to 3.70; Figure 4A). Other factors associated with favorable OS were younger age, female sex, absence of pulmonary or diabetes comorbidities, ECOG PS <2, PMBCL disease subtype, earlier year of infusion, no bridging therapy, and chemosensitivity prior to infusion (Figure 4A).
Figure 4.
Multivariable analysis of baseline factors associated with OS in patients from the CIBMTR registry, with evaluations at (A) Day 100 and (B) Month 6 after axi-cel infusion. CI indicates confidence interval; CIBMTR, Center for International Blood and Marrow Transplant Research; CR, complete response; ECOG, Eastern Cooperative Oncology Group; HR, hazard ratio; OS, overall survival; PD, progressive disease; PR, partial response; SD, stable disease.
Of the 951 patients with r/r LBCL who were alive 6 mo after axi-cel infusion, 518 patients were in CR at 6 mo, and 433 patients were not (Table 2). Patients who were in CR at 6 mo had a higher median age, better response to bridging therapy, a higher proportion who received prior auto-HCT or whose last auto-HCT occurred >12 mo before infusion, and a lower proportion with ECOG PS ≥2, bulky disease before infusion, or elevated LDH levels at diagnosis, compared with patients who were not in CR at 6 mo. The estimated 2-yr OS rates after infusion were 84% (95% CI, 81% to 87%) for patients in CR at Month 6 and 44% (95% CI, 39% to 49%) for those not in CR at Month 6 (Supplementary Figure 2B); corresponding 1-yr OS rates were 97% (95% CI, 95% to 98%) and 66% (95% CI, 61% to 70%), respectively. Among patients in CR at Month 6, the estimated 2-yr PFS rate was 75% (95% CI, 71% to 79%), with a corresponding 1-yr PFS rate of 88% (95% CI, 85% to 91%), and the estimated 2-yr cumulative incidence of relapse/PD was 15% (95% CI, 12% to 19%; Supplementary Figures 3B and 4B).
In multivariable analyses adjusted for key prognostic factors, patients not in CR at 6 mo after infusion had significantly lower OS compared with those in CR (HR 4.11; 95% CI, 3.32 to 5.09; Figure 4B). Other factors associated with favorable OS were younger age, PMBCL disease subtype, fewer lines of prior therapy, and year of infusion (Figure 4B).
Baseline characteristics and outcomes for the 12-mo landmark population are described in Supplementary Results and presented in Supplementary Table 4 and Supplementary Figures 3C, 4C, and 5. Findings were consistent with those observed at earlier landmark time points.
DISCUSSION
Prior studies have demonstrated that a CR to initial therapy is generally associated with improved survival across different lymphoma subtypes [20–22]. Here, utilizing a combination of SLR/meta-analysis and real-world evidence, we demonstrated that CR is a valid surrogate for OS in patients with r/r LBCL and that 100 days or 6 mo after axi-cel infusion may be considered optimal time points at which CR reliably predicts OS in these patients.
The SLR and meta-analysis data provided study-level evidence that achievement of CR was predictive of long-term OS in certain populations of patients who received treatment for r/r LBCL, especially in high-risk patient cohorts or cohorts with a median of ≥3 lines of prior therapy. We also found evidence to suggest that CR may be a valid predictor of OS in cohorts with a median of 2 prior lines of therapy, but this relationship was not apparent in cohorts with a median of 1 prior line of therapy. Notably, in the cohort with a median of 1 prior line of therapy, outcomes were assessed before CAR T-cell therapy was approved in the 2L setting; consequently, data on 2L CAR T-cell therapy were limited. Establishing whether CR predicts long-term OS in the 2L CAR T-cell setting, where the therapeutic goal is cure, will be an important next step for advancing treatment strategies and guiding clinical decision-making. Predictive correlations between CR and OS were also observed in patients who received CAR T-cell therapies or CIT. Although an association between CR and OS was not observed in patients who received targeted therapies, this cohort comprised a relatively small and heterogeneous set of treatment arms, and the available evidence may be insufficient to establish a definitive correlation. CR was predictive of PFS in patients who received CAR T-cell therapies and those in high-risk cohorts; however, it was not predictive of PFS across the overall analysis.
TtCR is a valuable metric that identifies the most opportune time points for CR assessment. Our analyses of patient-level CIBMTR registry data indicated that, in real-world settings, 90% of patients with r/r LBCL who achieved a CR with axi-cel did so within the initial 6 mo after infusion. Moreover, we observed increases in the CR rate between multiple earlier time points, with an apparent plateau at Month 6 and a 6-mo CR rate of 54%, suggesting that patients who have not attained CR within 6 mo are unlikely to do so at a later time. These data are relevant both for patients who respond to treatment and those who do not achieve a CR, as it may be used by providers to optimize treatment regimens and subsequent interventions.
Multiple factors may influence TtCR. Beyond the choice of therapeutic regimen, additional variables, such as extent of disease burden at treatment initiation, can also affect CR kinetics. In real-world settings, patients with a diverse set of disease characteristics are treated with axi-cel. In this study, we found that older age, better ECOG PS, smaller tumor burden, and prior auto-HCT before infusion correlated with earlier achievement of CR.
Patients who were in CR at Day 100 and Month 6 after axi-cel infusion experienced significantly improved OS compared with those who were not (the risk of mortality was approximately 3 and 4.5 times greater for 100-day and 6-mo survivors not in CR, respectively, even after accounting for other clinically meaningful preinfusion factors), suggesting that achieving and maintaining a CR by 100 days or 6 mo may be valid early surrogates for long-term survival. Moreover, for patients who were in CR at 100 days or 6 mo, the estimated incidence of relapse/PD 2 yr after infusion was 25% and 15%, respectively, indicating that these events occur infrequently beyond the 6-mo landmark. PFS was higher among patients alive and in CR at later landmark dates (2-yr postinfusion PFS estimates for patients alive and in CR at 6 or 12 mo were 75% and 85%, respectively), likely reflecting the low incidence of relapse/PD beyond 6 mo.
Although clinically relevant and informative for clinical trial design, data at 12 mo did not demonstrate additional predictive value of CR for OS, potentially because most CRs occurred by 6 mo or because landmark analyses excluded patients who died before these later time points, limiting the ability to distinguish differences between groups.
Earlier landmark assessments offer greater value by providing patients with a clearer understanding of their prognosis and may be beneficial for inclusion in clinical trials. In this study, we showed that assessments made as early as Day 100 after axi-cel infusion were informative, with patients who achieved and remained in CR by this time point demonstrating significantly higher subsequent OS rates compared with those who did not. Primary factors associated with higher OS rate in these patients included more favorable patient and disease characteristics (age, ECOG PS, receipt of bridging therapy, disease subtype). Notably, across the spectrum of postinfusion landmark dates examined in this study, our analyses revealed that specific baseline patient characteristics consistently correlated with attainment of CR, including older age, non-elevated LDH levels at diagnosis, and absence of bulky disease.
Surrogate endpoints can be assessed at the patient level, where the surrogate independently predicts the final endpoint in individual patients, or at the trial/study level, where treatment effects on the surrogate reliably predict effects on the final endpoint across multiple trials [23,24]. Patient-level surrogacy alone cannot predict whether changes in the surrogate will lead to changes in the endpoint for a patient group; trial/study-level surrogacy is also necessary [23,24]. Hence, a major strength of this study is the integrated use of SLR/meta-analysis and CIBMTR real-world data to concurrently assess study-level and patient-level surrogacy, providing comprehensive validation of CR as a surrogate of survival [24]. Our study has some limitations. Certain measures of tumor burden, including the sum of product diameters and LDH levels prior to infusion, were not available in the CIBMTR registry and therefore could not be included in the analyses. Nonetheless, maximum nodal size prior to infusion was available and was used as a surrogate measure of tumor burden. The timing of disease assessments was determined by each treatment site and aggregately reported at Day 100, Month 6, and annually until Year 6; as a result, the exact TtCR may be left-censored. Moreover, in contrast to clinical trial requirements, central review of imaging is not available in the real-world setting.
In summary, we have demonstrated that achievement of CR is predictive of OS in patients with r/r LBCL treated with either CAR T-cell therapy or CIT. Most complete responders achieved CR within 6 mo following treatment with axi-cel, and survival was significantly better in patients who were in CR 100 days or 6 mo after infusion compared with patients who were alive but not in CR. Inability to achieve and sustain CR by Month 6 may serve as an early marker for identifying patients who could benefit from additional intervention following CAR T-cell therapy. These findings support the assessment of CR at Day 100 or Month 6 as potential surrogate endpoints for OS, advocating for their inclusion in future clinical trials of novel therapies for LBCL.
Supplementary Material
Supplementary material associated with this article can be found in the online version at doi:10.1016/j.jtct.2026.01.010.
ACKNOWLEDGMENTS
The authors thank all investigators, coordinators, study-site personnel, and the patients and their families for participating in this study. Kite, a Gilead Company, was involved in the development of the study protocol and in data collection, analysis, and interpretation. Medical writing support was provided by Laurie Baggio, PhD, of Avalere Health, with funding provided by Kite, a Gilead Company. CIBMTR is supported primarily by the Public Health Service U24CA076518 from the National Cancer Institute (NCI), the National Heart, Lung and Blood Institute (NHLBI), and the National Institute of Allergy and Infectious Diseases (NIAID); Cellular Immunotherapy Data Resource (CIDR) U24CA233032 from NCI; 75R60222C00011 from the Health Resources and Services Administration (HRSA); and N00014-23-1-2057 and N00014-24-1-2057 from the Office of Naval Research. 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.; Adaptive Biotechnologies Corporation; ADC Therapeutics; Adienne SA; Alexion; AlloVir, Inc.; Amgen, Inc.; Astellas Pharma US; AstraZeneca; Atara Biotherapeutics; BeiGene; BioLineRX; Blue Spark Technologies; bluebird bio, inc.; Blueprint Medicines; Bristol Myers Squibb Co.; CareDx Inc.; CSL Behring; CytoSen Therapeutics, Inc.; DKMS; Editas Medicine; Elevance Health; Eurofins Viracor, DBA Eurofins Transplant Diagnostics; Gamida-Cell, Ltd.; Gift of Life Biologics; Gift of Life Marrow Registry; GlaxoSmithKline; HistoGenetics; Incyte Corporation; Iovance; Janssen Research & Development, LLC; Janssen/Johnson & Johnson; Jasper Therapeutics; Jazz Pharmaceuticals, Inc.; Karius; Kashi Clinical Laboratories; Kiadis Pharma; Kite, a Gilead Company; Kyowa Kirin; Labcorp; Legend Biotech; Mallinckrodt Pharmaceuticals; Med Learning Group; Medac GmbH; Merck & Co.; Mesoblast; Millennium, the Takeda Oncology Co.; Miller Pharmacal Group, Inc.; Miltenyi Biomedicine; Miltenyi Biotec, Inc.; MorphoSys; MSA-EDITLife; Neovii Pharmaceuticals AG; Novartis Pharmaceuticals Corporation; Omeros Corporation; OptumHealth; Orca Biosystems, Inc.; OriGen BioMedical; Ossium Health, Inc.; Pfizer, Inc.; Pharmacyclics, LLC, An AbbVie Company; PPD Development, LP; REGiMMUNE; Registry Partners; Rigel Pharmaceuticals; Sanofi; Sarah Cannon; Seagen Inc.; Sobi, Inc.; Stemcell Technologies; Stemline Technologies; STEMSOFT; Takeda Pharmaceuticals; Talaris Therapeutics; Vertex Pharmaceuticals; Vor Biopharma Inc.; Xenikos BV.
Financial Disclosure:
This study was supported by Kite, a Gilead Company. The CIBMTR study was funded by the National Cancer Institute (CIDR-NCI, U24CA233032).
Conflict of Interest Statement:
A.G. has been paid an honorarium from Kite/Gilead; has received fees for consulting or advisory role for Amgen, Atara, Bristol Myers Squibb, CRISPR Therapeutics, Kite/Gilead, and Wugen Inc; and has received research funding from Amgen, Genentech, Secura Bio, and Kite/Gilead. T.A. has been paid honoraria from Curio Biosciences, DAVA Oncology, and Targeted Oncology; has received fees for consulting or advisory role from Genmab/AbbVie, and Epizyme; and has had travel expenses paid by Curio Biosciences. J.A. has no competing financial interests. M.H. has received fees for consulting or advisory role from Kite/Gilead, Genmab, Autolus, ADC Therapeutics, Omeros, Caribou, BMS, CRISPR, and AbbVie; has been paid to participate in a speakers bureau by AstraZeneca, BeiGene, Kite/Gilead, DMC Inc, Genentech, Myeloid Therapeutics, CRISPR, and ADC Therapeutics; and has received research funding from Takeda, ADC Therapeutics, Spectrum Pharmaceuticals, and Astellas Pharma. B.L. has received fees for consulting or advisory role from Geron, Janssen, Sanofi, and Vor Biopharma. S.A. has been paid honoraria by Kite/Gilead, ADCT, Novartis, and Nektar Therapeutics; has served in a consultancy or advisory role for Myeloid, Nektar Therapeutics, and Tessa Therapeutics; has received research funding from Nektar Therapeutics, Tessa Therapeutics, Merck, Xencor, and Bristol Myers Squibb; and has served on the Data Safety Monitoring Board for Myeloid Therapeutics. S.Bh. has received fees for consulting or advisory role from Allogene. J.H.B. has received fees for consulting or advisory role from GLG Consulting; has been paid to participate in a speakers bureau by Kite/Gilead; and has received research funding from Kite/Gilead, Janssen, Regeneron, CARGO, and Genentec. C.J. has received fees for consulting or advisory role from Kite/Gilead, Novartis, BMS/Celgene, Synthekine, Ipsen, Daiichi-Sankyo, Instill Bio, ImmPACT Bio, Caribou Bio, Miltenyi Biotec, Morphosys, ADC Therapeutics, AbbVie, and Abintus Bio; and has received research funding from Kite/Gilead and Pfizer. F.L.L. has been paid honoraria from Aptitude Health, ASH, BioPHarma, Communications CARE Education, Clinical Care Options Oncology, Imedex, Society for Immunotherapy of Cancer; has received fees for consulting or advisory role from A2, Allogene, Amgen, Bluebird Bio, BMS, Calibr, Caribou, Cowen, EcoR1, Gerson Lehrman Group (GLG), Iovance, Kite/Gilead, Janssen, Legend Biotech, Novartis, Sana, Umoja, and Pfizer; has received Institutional research funding from Kite/Gilead, Allogene, CERo Therapeutics, Novartis, BlueBird Bio, 2SeventyBio, BMS, National Cancer Institute, Leukemia and Lymphoma Society; has several institutional patents held by the Moffitt Cancer Center; has had travel expenses paid by ASH and Kite/Gilead. M.U. has received fees for consulting or advisory role from Autolus Therapeutics, Stemline Therapeutics, Bristol Myers Squibb, and ADC Therapeutics. P.M.R. has received fees for consulting or advisory role from Kite/Gilead; and has received research funding from SeaGen and Genentech. M.T.H., Z-H.H., H-L.W., M.N., and S.Be. own stock in Gilead Sciences, Inc., and are employees of Kite, a Gilead Company. Z-C.L. is an employee of Kite, a Gilead company. A.S.J. owns stock in Gilead and Amgen and is an employee of Kite, a Gilead company. T.B. was previously an employee of Bristol Myers Squibb and is an employee of Kite, a Gilead company; and owned stock in Bristol Myers Squibb. M.C.P. has been paid honoraria from Kite/Gilead; has received fees for consulting or advisory role from AstraZeneca; and has received research funding from Kite/Gilead, Novartis, BMS, and Janssen.
Footnotes
Ethics Approval Statement: The study protocol for the postmarketing requirement was overseen by the Institutional Review Board of the NMDP (formerly known as the National Marrow Donor Program); all patients provided written informed consent.
Data availability:
Gilead Sciences shares anonymized individual participant data upon request or as required by law or regulation with qualified external researchers based on submitted curriculum vitae and reflecting non-conflict of interest. The request proposal must also include a statistician. Approval of such requests is at Gilead Science’s discretion and is dependent on the nature of the request, the merit of the research proposed, the availability of the data, and the intended use of the data. Data requests should be sent to datasharing@gilead.com.
REFERENCES
- 1.Delgado A, Guddati AK. Clinical endpoints in oncology—a primer. Am J Cancer Res. 2021;11(4):1121–1131. [PMC free article] [PubMed] [Google Scholar]
- 2.Zhu J, Yang Y, Tao J, et al. Association of progression-free or event-free survival with overall survival in diffuse large B-cell lymphoma after immunochemotherapy: a systematic review. Leukemia. 2020;34(10):2576–2591. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Kilickap S, Demirci U, Karadurmus N, Dogan M, Akinci B, Sendur MAN. Endpoints in oncology clinical trials. J BUON. 2018;23(7):1–6. [PubMed] [Google Scholar]
- 4.Maurer MJ, Ghesquieres H, Jais JP, et al. Event-free survival at 24 months is a robust end point for disease-related outcome in diffuse large B-cell lymphoma treated with immunochemotherapy. J Clin Oncol. 2014;32(10):1066–1073. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Shi Q, Schmitz N, Ou FS, et al. Progression-free survival as a surrogate end point for overall survival in first-line diffuse large B-cell lymphoma: an individual patient-level analysis of multiple randomized trials (SEAL). J Clin Oncol. 2018;36(25):2593–2602. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Assouline S, Wiesinger A, Spooner C, Jovanovic J, Schlueter M. Validity of event-free survival as a surrogate endpoint in haematological malignancy: review of the literature and health technology assessments. Crit Rev Oncol Hematol. 2022;175:103711. [DOI] [PubMed] [Google Scholar]
- 7.YESCARTA ® (axicabtagene ciloleucel) [prescribing information]. Santa Monica, CA: Kite Pharma, Inc.; 2025. [Google Scholar]
- 8.Locke FL, Ghobadi A, Jacobson CA, et al. Long-term safety and activity of axicabtagene ciloleucel in refractory large B-cell lymphoma (ZUMA-1): a single-arm, multicentre, phase 1-2 trial. Lancet Oncol. 2019;20(1):31–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Neelapu SS, Jacobson CA, Ghobadi A, et al. Five-year follow-up of ZUMA-1 supports the curative potential of axicabtagene ciloleucel in refractory large B-cell lymphoma. Blood. 2023;141(19):2307–2315. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Neelapu SS, Locke FL, Bartlett NL, et al. Axicabtagene ciloleucel CAR T-cell therapy in refractory large B-cell lymphoma. N Engl J Med. 2017;377(26):2531–2544. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Cheson BD, Fisher RI, Barrington SF, et al. Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification. J Clin Oncol. 2014;32(27):3059–3068. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Alarcon Tomas A, Fein JA, Fried S, et al. Outcomes of first therapy after CD19-CAR-T treatment failure in large B-cell lymphoma. Leukemia. 2023;37(1):154–163. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Ayers EC, Margolis D, Landsburg DJ. Real world outcomes in patients with relapsed/refractory diffuse large B-cell lymphoma receiving palliative intent therapies. Clin Lymphoma Myeloma Leuk. 2020;20(10):661–667. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Crump M, Neelapu SS, Farooq U, et al. Outcomes in refractory diffuse large B-cell lymphoma: results from the international SCHOLAR-1 study. Blood. 2017;130(16):1800–1808. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Jacobson CA, Locke FL, Ma L, et al. Real-world evidence of axicabtagene ciloleucel for the treatment of large B cell lymphoma in the United States. Transplant Cell Ther. 2022;28(9):581.e1–581.e8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Cheson BD, Pfistner B, Juweid ME, et al. Revised response criteria for malignant lymphoma. J Clin Oncol. 2007;25(5):579–586. [DOI] [PubMed] [Google Scholar]
- 17.Cheson BD, Horning SJ, Coiffier B, et al. Report of an international workshop to standardize response criteria for non-Hodgkin’s lymphomas. NCI Sponsored International Working Group. J Clin Oncol. 1999;17(4):1244. [DOI] [PubMed] [Google Scholar]
- 18.Harris NL, Jaffe ES, Stein H, et al. A revised European-American classification of lymphoid neoplasms: a proposal from the international lymphoma study group. Blood. 1994;84(5):1361–1392. [PubMed] [Google Scholar]
- 19.Sorror ML, Maris MB, Storb R, et al. Hematopoietic cell transplantation (HCT)-specific comorbidity index: a new tool for risk assessment before allogeneic HCT. Blood. 2005;106(8):2912–2919. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Bommier C, Zucca E, Chevret S, et al. Early complete response as a validated surrogate marker in extranodal marginal zone lymphoma systemic therapy. Blood. 2024;143(5):422–428. [DOI] [PubMed] [Google Scholar]
- 21.Coiffier B, Thieblemont C, Van Den Neste E, et al. Long-term outcome of patients in the LNH-98.5 trial, the first randomized study comparing rituximab-CHOP to standard CHOP chemotherapy in DLBCL patients: a study by the Groupe d’Etudes des Lymphomes de l’Adulte. Blood. 2010;116(12):2040–2045. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Pels H, Juergens A, Schirgens I, et al. Early complete response during chemotherapy predicts favorable outcome in patients with primary CNS lymphoma. Neuro Oncol. 2010;12(7):720–724. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Buyse M, Saad ED, Burzykowski T, Regan MM, Sweeney CS. Surrogacy beyond prognosis: the importance of “Trial-Level” surrogacy. Oncologist. 2022;27(4):266–271. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Mittal A, Kim MS, Dunn S, Wright K, Gyawali B. Frequently asked questions on surrogate endpoints in oncology-opportunities, pitfalls, and the way forward. EClinicalMedicine. 2024;76:102824. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Gilead Sciences shares anonymized individual participant data upon request or as required by law or regulation with qualified external researchers based on submitted curriculum vitae and reflecting non-conflict of interest. The request proposal must also include a statistician. Approval of such requests is at Gilead Science’s discretion and is dependent on the nature of the request, the merit of the research proposed, the availability of the data, and the intended use of the data. Data requests should be sent to datasharing@gilead.com.