Methods

Study design and patients

All patients treated in France with axi-cel or tisa-cel from December 2019 to October 2021 and retrospectively included in the DESCAR-T registry sponsored by the LYSARC were considered. Data export from the registry was set on 18 October 2021. All patients with DLBCL for whom a CAR T therapy with tisa-cel or axi-cel was ordered in the setting of the European Medicines Agency approval label (that is, after at least two prior lines of treatment) were considered. Patients could be treated (1) under French Temporary Authorization for Use (ATU); (2) under post-ATU authorization; or (3) under Market Authorization covered by the French health insurance system in an approved center. All patients received a non-opposition notice letter before enrollment, according to French laws. The protocol was approved by national ethics committees and the data protection agency, and the study was undertaken in accordance with the Declaration of Helsinki. DESCAR-T is registered under the ClinicalTrials.gov identifier NCT04328298.

Outcomes

Primary outcome was PFS according to local investigator. Secondary outcomes were OS, best ORR and CRR, DOR and safety. Response was assessed according to the Lugano 2014 criteria, based on 18fluoro-deoxyglucose positron emission tomography (FDG-PET) at the approximate following timepoints: 1 month, 3 months, 6 months and 12 months after CAR T infusion33. Best response rate was considered. For all survival endpoints, survival was calculated from the date of CAR T infusion unless otherwise specified (that is, survival from CAR T order). PFS was defined from the date of CAR T infusion to the date of first documented relapse, progressive disease, date of last follow-up or death from any cause, whichever came first. OS was defined from the date of CAR T infusion or CAR T order to the date of death from any cause or the date of last follow-up. DOR was defined from the date of first response (partial or complete) to the date of first documented relapse, date of last follow-up or death from any cause, whichever came first. Hematological toxicity was graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE, version 5.0). Hematological toxicity was reported in patients without initiation of a new treatment for progression or relapse after CAR T infusion. CRS and ICANS were graded according to the consensus criteria from the American Society for Transplantation and Cellular Therapy (ASTCT)34.

Matching procedures

PSM was used to create a balanced covariate distribution between a cohort of patients treated with axi-cel and a cohort of patients treated with tisa-cel. Propensity scores were estimated using a multivariate logistic regression model with CAR T type (axi-cel versus tisa-cel) as the dependent variable. An exhaustive list of covariates was used for PSM: age (as a continuous parameter), sex, lactate dehydrogenase (LDH) level (normal versus between the upper limit of normal (ULN) and 2× ULN versus >2× ULN), C reactive protein (CRP) (dichotomized with a cutoff set at 30 mg L−1), time between last treatment and infusion (continuous), Eastern Cooperative Oncology Group (ECOG) performance status (PS) (0–1 or ≥2), Ann Arbor stage (I versus II versus III versus IV), number of prior lines of treatment before CAR T (2–4 versus >4), bridging and response to bridging (no bridge versus bridging and response (partial or complete) to bridging versus bridging and no response (stable or progressive disease)), prior SCT either autologous or allogeneic (yes versus no), bulk assessed at lymphodepletion (dichotomized with a cutoff set at 5 cm), center (all centers with fewer than 20 patients were grouped into one category) and diagnosis (DLBCL NOS or HGBCL versus transformed indolent lymphoma (tFL or tMZL)). To account for a given center experience in CAR T procedure implementation and improvement of toxicity management over time that might impact outcome (especially because some centers had access to one CAR T before the other), time between first CAR T order for that center and CAR T infusion for a given patient was also considered for PSM (as a continuous parameter). For all matching parameters except continuous variables (no missing value could be used for continuous parameters in PSM), missing data were considered as one distinct category for PSM. Of note, when survival was assessed from CAR T order instead of CAR T infusion, time intervals were calculated until or from CAR T order instead of CAR T infusion. Matching parameters are detailed in Extended Data Table 3.

Matching was performed considering a 1:1 ratio without replacement and with optimal matching applying a caliper width of the propensity score set at 0.1. Basically, a patient treated with tisa-cel was selected and then matched with a patient treated with axi-cel given the constraint that the difference between the logit (that is, the logarithm of the odds of the logistic regression that models the probability of receiving tisa-cel or axi-cel) was less than a pre-specified maximum (that is, the caliper distance).

IPTW was used as another statistical approach to allow for outcome comparison between patients treated with axi-cel and patients treated with tisa-cel. In the IPTW method, the weight for each patient is calculated by inverting the probability of receiving the treatment the patient actually receives. PSM and IPTW rely on different statistical matching approaches, provide different information and should be interpreted differently. The first one (PSM) allows for assessing average treatment effect for the treated (ATT), whereas the other (IPTW with the weighting technique used here) provides estimation of the average treatment effect (ATE). The first gives the average effect of treatment on those patients who ultimately received one CAR T versus the other, whereas the second provides the average effect of theoretically moving the entire population from receiving one CAR T to the other. For IPTW, the exact same covariates as for PSM were used for the logistic regression model to calculate the propensity of receiving one of the CAR T products versus the other. Methodology underlying propensity-score-based matched comparisons and differences with adjustment approaches have been reviewed elsewhere35.

Sensitivity analyses

Several sensitivity analyses were conducted. First, all patients with at least one missing value for at least one matching variable were removed from PSM analysis (complete case analysis). Second, a multiple imputation approach was performed using the fully conditional specification (FCS) method, allowing for different distributions across variables. Continuous variables were imputed using linear regression, whereas categorical parameters were imputed using logistic regression. All propensity score covariates and outcome (OS) were used for imputation. Ten imputed datasets were generated. A treatment effect was estimated within each imputed dataset using PSM. Estimated treatment effects from each imputed dataset were then combined into a single treatment effect using Rubin’s rule (within method). Third, a Cox bivariate model adjusting for residual aaIPI imbalance after matching was used to assess association between CAR T product and outcome (PFS and OS). Fourth, PSM was performed with a time of origin for OS set at the time of CAR T order instead of the time of CAR T infusion. Finally, to assess how robust the association between CAR T product and outcome was to potential unmeasured or uncontrolled confounding, E-value was computed36. It represents the minimum strength of association that a unique (or a set of) unmeasured confounder would need to have with both the treatment and the outcome conditional on the measured covariates to fully explain away the association between treatment (here, the CAR T product) and the outcome (here, PFS or OS). Therefore, the higher the E-value, the stronger the confounder associations must be to explain away the effect.

Statistical analysis

Survival distributions were compared using the log-rank test. Response rates were compared using the χ2 test. A two-sided P value of less than 0.05 was considered significant. No adjustment was performed for multiple testing. Two subgroup analyses according to age (≤70 years and >70 years) and tumor bulk (≤5 cm and >5 cm) were pre-planned in the statistical analysis plan. Survival curves were generated using the Kaplan–Meier estimation method. Statistical analyses were performed using SAS software version 9.3 and R version 4.2.0. The MATCH macro for PSM and the MI and MIANALYZE procedures for multiple imputation were used with SAS. The E-value package was used with R.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.