Evidence map›Paper›PMID 40930226›Full record

ArticleTransplantation and cellular therapy2025

Validation of Cure Assumptions when Analyzing ZUMA-7 Follow-up Data of Axicabtagene Ciloleucel and Standard of Care Therapy in Second-Line Relapsed/Refractory Large B-cell Lymphoma.

Miguel-Angel Perales, Olalekan O Oluwole, Anik R Patel, Markqayne D Ray, Yael A Rodriguez-Guadarrama, Nathaniel J Smith, Rob Blissett, Yin Yang, Frederick L Locke

Abstract read
In one paragraph

Article in Transplantation and cellular therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Miguel-Angel PeralesAdult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York; Department of Medicine, Weill Cornell Medical College, New York, New York. Electronic address: peralesm@mskcc.org.
Olalekan O OluwoleVanderbilt University Medical Center, Nashville, Tennessee.
Anik R PatelKite, A Gilead Company, Santa Monica, California.
Markqayne D RayKite, A Gilead Company, Santa Monica, California.
Yael A Rodriguez-GuadarramaMaple Health Group, LLC, New York, New York.
Nathaniel J SmithMaple Health Group, LLC, New York, New York.
Rob BlissettMaple Health Group, LLC, New York, New York.
Yin YangKite, A Gilead Company, Santa Monica, California.
Frederick L LockeH. Lee Moffitt Cancer Center, Tampa, Florida.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

backgroundZUMA-7 is the largest randomized controlled trial (RCT) for chimeric antigen receptor (CAR) T-cell therapy, which compared axicabtagene ciloleucel (axi-cel) to historical standard of care (HSoC) for the treatment of relapsed or refractory diffuse large B-cell lymphoma (R/R DLBCL). Both arms of ZUMA-7 contained potentially curative treatments; however, differences in the treatment completion rate and timing to receive definitive treatment led to differences in the extent and timing of cure. Mixture cure modeling (MCM) has been suggested as a superior method in designing and powering clinical studies of curative therapies but also for extrapolation of long-term outcomes in simulation studies. The aim of this study was to evaluate the appropriateness of MCM in second line (2L) therapy for DLBCL by analyzing extrapolations from other cost-effectiveness analyses; to use data cuts of ZUMA-7 trial to validate MCMs and describe differences that may have arisen due to treatment specific timing and extent of cure.

methodsA targeted literature review (TLR) was conducted to identify cost-effectiveness modeling studies that have used ZUMA-7 clinical trial data to extrapolate survival of axi-cel over the long-term. Overall survival (OS) individual patient-level data from the ZUMA-7 trial for axi-cel and historical HSoC from both primary event-free survival (EFS; median follow-up: 24.9 months) and primary OS cutoff (median follow-up: 47.2 months) were used in this analysis. Three intermediate datasets were simulated to compare differences in cure fractions and accuracy of extrapolation predictions among data cutoffs. Differences between trial arms were evaluated.

resultsThe TLR demonstrated the extrapolation using ZUMA-7 OS data cutoff provided lower difference margins with respect to long-term observed data, than external extrapolations. Within the models tested, MCM provided the best fit based on the percentage inaccuracy of extrapolations which ranged 2%-27% in the axi-cel group. Based on observed primary EFS cutoff, cure fractions ranged in both treatment arms (axi-cel: 24%-54% and HSoC: 35%-49%). The range of cure fractions narrowed following extrapolations performed on the more mature observed OS cutoff (axi-cel: 50%-54% and HSoC: 41%-50%).

conclusionReplicating prior findings, MCM methods provide the more accurate estimate of long-term OS compared with standard parametric modeling for patients with R/R DLBCL treated with CAR T-cell therapy. However, these findings also highlight the importance of obtaining appropriate clinical benchmarking to ensure clinical plausibility and face validity of long-term extrapolations intended to informed cost-effectiveness analysis and inform decision making.

Indexed as

Antigens, CD19Biological ProductsImmunotherapy, AdoptiveLymphoma, Large B-Cell, DiffuseStandard of CareFollow-Up StudiesHumansAntigens, CD19axicabtagene ciloleucelBiological ProductsChimeric antigen receptor T-cellLarge B-cell lymphomaMixture-cure modelSurvival extrapolation

Identifiers

PMID40930226
PMCPMC13213531

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.