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Record W4405038423 · doi:10.1182/blood-2024-200937

Matching-Adjusted Indirect Comparison (MAIC) of Lisocabtagene Maraleucel (liso-cel) Versus Axicabtagene Ciloleucel (axi-cel) for Second-Line (2L) Treatment of Patients (pts) with Refractory/Early Relapsed (R/R) Large B-Cell Lymphoma (LBCL): Update with 34 Months of Liso-Cel Follow-up

2024· article· en· W4405038423 on OpenAlexaff
Jeremy S. Abramson, Manali Kamdar, Fei Fei Liu, Alessandro Crotta, Alessandro Previtali, S. Klijn, Pearl Wang, Yixie Zhang, Ashley Bonner, Matthew A. Lunning

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsEVERSANA (Canada)
Fundersnot available
KeywordsRefractory (planetary science)MedicineInternal medicineSecond line treatmentNuclear medicineGastroenterologyOncologyPhysicsChemotherapy

Abstract

fetched live from OpenAlex

Background: Two CAR T cell therapies, liso-cel and axi-cel, demonstrated superior efficacy over salvage chemotherapy and autologous transplant as 2L therapy in transplant-intended pts with high-risk R/R LBCL, yet no head-to-head comparisons have been performed. A previous MAIC in the 2L setting with a median follow-up of 17.5 mo for liso-cel and 24.9 mo for axi-cel showed comparable efficacy and more favorable safety outcomes for liso-cel with lower rates of all-grade and grade ≥ 3 cytokine release syndrome (CRS) and neurological events (NEs) (Abramson JS, et al. Blood 2022). Here, we present updated results with long-term follow-up for liso-cel and axi-cel. Methods: MAICs were used to estimate population-adjusted relative treatment effects associated with liso-cel for event-free survival (EFS), PFS, ORR, and CR rate (TRANSFORM; NCT03575351; N = 184; data cutoff date: October 2023) vs axi-cel (ZUMA-7; NCT03391466; N = 359; data cutoff date: January 2023) and safety (TRANSFORM, n = 183; ZUMA-7, n = 338). Pts were excluded from the TRANSFORM data set if they did not meet ZUMA-7 eligibility criteria (ie, matching). Individual pt data (IPD) for pts remaining in the TRANSFORM data set were weighted using a method-of-moments propensity score model to match the marginal distribution (ie, mean, variance) of clinical factors among pts from ZUMA-7 (ie, adjustment). Baseline characteristics and outcome measures were revised to align with those defined in ZUMA-7. Efficacy comparisons were anchored through the common comparator, standard of care (SOC; with similar protocol-defined salvage chemotherapy regimens in both trials, followed by high-dose chemotherapy and autologous transplant in responders). Hazard ratios (HRs) were used to compare time-to-event outcomes (EFS, PFS), and odds ratios were used to compare binary outcomes (ORR, CR rate, safety). Selection and rank ordering of the treatment effect modifiers were guided by analysis of the TRANSFORM IPD and clinical experts. Factors to match (ie, pts from TRANSFORM were removed) and adjust (ie, pts from TRANSFORM were reweighted) for efficacy and safety comparisons were reported previously (Abramson JS, et al. Blood 2022). Safety comparisons were unanchored due to the absence of CAR T cell-associated toxicities in the SOC arms. Bridging chemotherapy was allowed in TRANSFORM but not in ZUMA-7; it was not possible to adjust for this factor given sample size constraints. Results: Median study follow-up time was 33.9 mo for liso-cel and 47.2 mo for axi-cel. Efficacy outcomes were comparable between therapies in the unmatched/unadjusted comparison. For liso-cel vs axi-cel, respectively, median (95% CI) EFS was 29.5 mo (9.5‒not reached [NR]) vs 8.3 mo (4.5‒15.8) with HR (95% CI) of 0.94 (0.60‒1.46), and median (95% CI) PFS was 29.5 mo (10.3‒NR) vs 14.7 mo (5.4‒43.5) with HR of 0.90 (95% CI, 0.56‒1.47). Median ORR was 87% vs 83% with odds ratio (95% CI) of 1.41 (0.58‒3.40), and CR rate was 74% vs 65% with odds ratio (95% CI) of 0.95 (0.44‒2.03). After matching with ZUMA-7 for pt eligibility, the TRANSFORM sample size was 158; matching and adjusting for the selected effect modifiers resulted in an effective sample size of 80 for the primary efficacy scenario comparisons (sample size for ZUMA-7 and median efficacy values for axi-cel were unchanged). After matching and adjustment, efficacy outcomes remained comparable between therapies. Median (95% CI) EFS for liso-cel was NR (6.21‒NR) with HR (95% CI) of 0.75 (0.43‒1.33), and median (95% CI) PFS was NR (9.4-NR) with HR (95% CI) of 0.68 (0.37‒1.23). ORR was 85% with odds ratio (95% CI) of 1.63 (0.60‒4.44), and CR rate was 68% with odds ratio (95% CI) of 0.94 (0.40‒2.22). For safety, MAIC results demonstrated lower odds ratios (95% CI) of grade ≥ 3 serious treatment-emergent adverse events (TEAEs; 0.49 [0.27‒0.90]), CRS (any grade, 0.09 [0.04‒0.18]; grade ≥ 3, 0.09 [0.01‒0.75]), and NEs (any grade, 0.08 [0.03‒0.18]; grade ≥ 3, 0.21 [0.06‒0.68]) for liso-cel vs axi-cel. Conclusions: Results from this updated MAIC of liso-cel and axi-cel for the 2L treatment of R/R LBCL showed comparable efficacy, with more favorable safety outcomes for liso-cel. Liso-cel demonstrated a better safety profile with lower rates of grade ≥ 3 serious TEAEs and lower rates of all-grade and grade ≥ 3 CRS and NEs compared with axi-cel.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.007
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.281
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2024
Admission routes1
Has abstractyes

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