Objectives
Survival profiles for chimeric antigen receptor T cell (CAR-T) therapies commonly exhibit a plateau that may indicate a cure. This leads to challenges predicting long-term outcomes for novel CAR-T therapies when trial data are immature. This study retrospectively analysed the accuracy of overall survival (OS) extrapolations from interim data cuts in predicting realised long-term life years (LYs) for axicabtagene ciloleucel in patients with refractory large B-cell lymphoma.
Methods
Published OS data for axicabtagene ciloleucel from successive data cuts of ZUMA-1 (median follow-ups of 15.4, 27.1 and 51.1 months) were digitised. Standard parametric, spline (1–2 knots; normal, odds and hazard) and mixture cure models (MCMs) were fitted to the first two data cuts. Statistical fit was tested using Akaike and Bayesian information criteria (AIC and BIC). Cumulative LYs were estimated for each model over a 58-month time horizon, corresponding to the longest duration of published OS data. These projected LYs were then compared to realised LYs over this period.
Results
At the earliest data cut, MCMs provided the best predictions of realised LYs, with a mean absolute difference of 6.8% between predicted and realised LYs across MCM models (range: 0.5%–10.7%). Standard parametric extrapolations considerably underestimated realised LYs (mean absolute difference: 19.2%; range: 9.9%–28.0%), whilst spline models offered a mean absolute difference of 8.3% (range: 1.4%–13.2%). Similar findings were observed for extrapolations based on the second data cut (representing 11.7 months additional follow-up), but differences between model classes were less pronounced.
Conclusions
MCMs may offer the best predictions of long-term survival for CAR-T therapies, particularly when only short-term data are available. Standard parametric models may be inappropriate to predict survival when extrapolating immature data, failing to capture the plateau in survival typical of CAR-T therapies. Further research is required to determine whether these findings are generalisable across CAR-T therapies and indications.
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