A Review of Two Regulatory Approved Anti-CD19 CAR T-Cell Therapies in Diffuse Large B-Cell Lymphoma: Why Are Indirect Treatment Comparisons Not Feasible?

Abstract

Anti-CD19 chimeric antigen receptor (CAR) T-cell therapies can be effective for diffuse large B-cell lymphoma (DLBCL), a cancer with limited treatment options and poor outcomes, particularly for patients with relapsed or refractory (r/r) disease. Axicabtagene ciloleucel (axi-cel) and tisagenlecleucel (tisa-cel) are CAR T-cell therapies approved by regulatory bodies for certain patients with r/r DLBCL on the basis of demonstrated treatment effects in their pivotal single-arm trials, ZUMA-1 and JULIET, respectively. In the absence of head-to-head trials, the question of whether a valid indirect treatment comparison (ITC) between axi-cel and tisa-cel could be performed using existing evidence is of interest to patients, physicians, payers, and other stakeholders. This article addresses that question by summarizing the current evidence from clinical trials and real-world studies and discussing the challenges and limitations of potential analytical approaches associated with an ITC. Two ITC approaches attempting to adjust for cross-trial heterogeneity between ZUMA-1 and JULIET, matching-adjusted indirect comparison and regression-prediction model analysis, were evaluated. After evaluating the current clinical trial data and real-world evidence, and present and prior ITC analyses of axi-cel and tisa-cel, the authors conclude that a valid comparative analysis is not currently feasible. The substantial differences (e.g., timing of leukapheresis and enrollment, use of bridging chemotherapy [90% in JULIET vs. 0% in ZUMA-1], lymphodepleting regimens) between the two trials' designs and patient populations preclude a robust and reliable ITC. No other approaches are able to account for such differences. The current real-world data are still too immature to be used for ITCs. Thus, drawing conclusions from such ITCs should be avoided to prevent misinforming treatment choices or limiting patient access to effective treatment options. Additional data from ongoing or future real-world studies with appropriate statistical analyses are needed to provide insights into the comparative effectiveness and safety of these two treatments.

Keywords: Anti-CD19 chimeric antigen receptor T-cell therapies; Axicabtagene ciloleucel; Diffuse large B-cell lymphoma; Indirect treatment comparison; Tisagenlecleucel.

Fig. 1

Patient journey from screening to CAR T-cell infusion for JULIET and ZUMA-1. The figure was developed by the authors based on trial protocols of JULIET and ZUMA-1 and was validated by clinical experts. axi-cel  axicabtagene ciloleucel, CAR T-cell chimeric antigen receptor T-cell, tisa-cel  tisagenlecleucel

Fig. 2

Observed tisa-cel OS, observed axi-cel OS, and adjusted tisa-cel OS based on the MAIC and CAR-T prediction model¹⁻⁴. axi-cel axicabtagene ciloleucel, CAR-T chimeric antigen receptor T-cell therapy, ITC indirect treatment comparison, LDC lymphodepleting chemotherapy, MAIC matching-adjusted indirect comparison, OS overall survival, tisa-cel tisagenlecleucel. ¹Adjusted tisa-cel OS from the CAR-T prediction model: expected OS for tisa-cel assuming that tisa-cel had treated patients with similar patient characteristics as those from ZUMA-1. The prediction model was built based on tisa-cel patient-level data from JULIET. Due to the small number of events for several key predictors (e.g., only 11 out of 115 patients did not receive bridging chemotherapy in JULIET), this method was not reliable. ²Adjusted tisa-cel OS from the MAIC: expected OS for tisa-cel among patients who had similar patient characteristics to those from ZUMA-1. Due to missingness of important effect modifiers (i.e., use of bridging chemotherapy and LDC regimens), this method was also not reliable. ³Substantial differences (i.e., enrollment, bridging chemotherapy usage, LDC regimens, etc.) between JULIET and ZUMA-1 preclude any reliable ITC being conducted; if conducted, two ITC methods (MAIC and CAR-T prediction model) provide contradictory conclusions as shown in the figure. ⁴The proportional hazards assumption was not rejected in any Cox models in the analyses