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Record W3095453737 · doi:10.1182/blood-2020-137719

Matching-Adjusted Indirect Comparison (MAIC) of Lisocabtagene Maraleucel (liso-cel) Vs Axicabtagene Ciloleucel (axi-cel) and Tisagenlecleucel in Relapsed/Refractory (R/R) Large B-Cell Lymphoma (LBCL)

2020· article· en· W3095453737 on OpenAlexaff
David G. Maloney, John Kuruvilla, Christopher P. Fox, Guillaume Cartron, D. Li, Jens Hasskarl, Ashley Bonner, Yixie Zhang, Fei‐Fei Liu

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsEVERSANA (Canada)Princess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineInternal medicineOncologyPopulationCytokine release syndromeCytopeniaChimeric antigen receptorImmunotherapyCancer

Abstract

fetched live from OpenAlex

Introduction: Chimeric antigen receptor T cell therapies have shown encouraging, durable responses in patients with R/R LBCL, yet no head-to-head clinical trials comparing options exist to date. We conducted 2 separate pair-wise MAICs to compare treatment effects of liso-cel vs both axi-cel and tisagenlecleucel. Methods: MAICs were used to estimate population-adjusted relative treatment effects associated with liso-cel (TRANSCEND NHL 001 [TRANSCEND]; NCT02631044; N = 256) vs axi-cel (ZUMA-1; NCT02348216; N = 101) and vs tisagenlecleucel (JULIET; NCT02445248; N = 111). Outcomes of interest included efficacy (overall and complete response rates [ORR/CRR], overall survival [OS], and progression-free survival [PFS]) and safety (cytokine release syndrome [CRS] by Lee criteria, neurological events [NEs], aphasia, encephalopathy, infections, hypogammaglobulinemia, and prolonged cytopenia). Individual patient data (IPD) from TRANSCEND were adjusted to match the marginal distribution (eg, mean, variance) of clinical factors among patients from ZUMA-1 and JULIET. Patients from TRANSCEND were removed from the IPD set if they did not satisfy eligibility criteria specified in the comparator trial for each MAIC. IPD for patients who remained in the TRANSCEND data set were weighted using a method-of-moments propensity score model. Baseline characteristic and outcome definitions were aligned with those in ZUMA-1 or JULIET. Clinically relevant prognostic factors (identified from literature, TRANSCEND data, and 5 independent clinical experts) were adjusted collectively in a stepwise fashion by ranked order. Key matched and adjusted variables in 1 or both comparisons included: disease histology, Eastern Cooperative Oncology Group performance status (ECOG PS), central nervous system (CNS) involvement, prior allogeneic/autologous hematopoietic stem cell transplant (HSCT), tumor burden, International Prognostic Index score, response to last therapy, bulky disease, and age. Efficacy outcomes in patients without bridging therapy were evaluated; however, ZUMA-1 and TRANSCEND treatment protocols differed in bridging therapy use (not allowed in ZUMA-1) and time to product availability (median, 17 vs 24 days, respectively). Results : After aligning definitions of baseline characteristics among trials, substantial differences were noted for ECOG PS of 2, tumor burden, active CNS involvement, number of prior lines of therapy, prior allogeneic HSCT, and history of hematologic comorbidities between studies. Overall, TRANSCEND included a larger sample size and broader patient population vs comparator trials, allowing for successful MAIC adjustments. When comparing TRANSCEND to ZUMA-1, MAIC-weighted efficacy outcomes were comparable between trials: odds ratios (ORs [95% CI]) for ORR and CRR with liso-cel vs axi-cel were 0.85 (0.48-1.52) and 0.78 (0.47-1.27), respectively; hazard ratios (HRs [95% CI]) for OS and PFS were 1.15 (0.80-1.65) and 1.30 (0.96-1.77), respectively (Figure). When limited to patients without bridging therapy, differences between trials remained statistically insignificant. MAIC-weighted safety outcomes showed a favorable safety profile for liso-cel, with a statistically significant lower odds of CRS, NEs (including aphasia and encephalopathy), and infections vs axi-cel. ORs (95% CI) for all-grade and grade ≥3 CRS with liso-cel vs axi-cel were 0.06 (0.03-0.13) and 0.16 (0.06-0.47), respectively; ORs for all-grade and grade ≥3 NEs were 0.21 (0.13-0.35) and 0.31 (0.18-0.54), respectively. When comparing TRANSCEND to JULIET, liso-cel showed a statistically significant higher ORR/CRR and longer OS/PFS than tisagenlecleucel. ORs (95% CI) for ORR and CRR achieved with liso-cel vs tisagenlecleucel were 2.78 (1.63-4.74) and 2.01 (1.22-3.30), respectively; HRs (95% CI) for OS and PFS were 0.67 (0.47-0.95) and 0.65 (0.47-0.91), respectively. Adjusted safety outcomes showed generally comparable profiles with lower ORs (95% CI) for all-grade and grade ≥3 CRS with liso-cel vs tisagenlecleucel: 0.53 (0.32-0.89) and 0.10 (0.03-0.31), respectively. Conclusions : MAIC-weighted outcomes suggest that liso-cel may provide a more well-balanced overall efficacy and safety profile for the treatment of R/R LBCL, with better efficacy compared with tisagenlecleucel and better safety compared with axi-cel. Disclosures Maloney: A2 Biotherapeutics: Consultancy, Current equity holder in publicly-traded company, Honoraria; Bioline Rx: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Genentech: Consultancy, Honoraria; MorphoSys: Consultancy, Honoraria; Pharmacyclics: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Gilead Sciences: Consultancy, Honoraria; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Juno Therapeutics: Consultancy, Honoraria, Patents & Royalties: Patents are pending, but not issued, licensed, no royalties, no licensees., Research Funding. Kuruvilla:Bristol-Myers Squibb Company: Consultancy; AbbVie: Consultancy; Antengene: Honoraria; TG Therapeutics: Honoraria; Novartis: Honoraria; Pfizer: Honoraria; Seattle Genetics: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Celgene Corporation: Honoraria; Amgen: Honoraria; Roche: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria; Merck: Consultancy, Honoraria; Janssen: Honoraria, Research Funding; AstraZeneca Pharmaceuticals LP: Honoraria, Research Funding. Fox:AstraZeneca: Research Funding; Celgene: Research Funding; Sunesis: Research Funding; AbbVie: Honoraria, Research Funding; Roche: Honoraria, Research Funding; Takeda: Honoraria, Research Funding; Gilead: Honoraria, Research Funding; Adienne: Honoraria, Research Funding; Atarabio: Research Funding. Cartron:F. Hoffmann-La Roche: Consultancy, Honoraria; Abbvie: Honoraria; Sanofi: Honoraria; Celgene: Consultancy, Honoraria; Gilead: Honoraria; Jansen: Honoraria. Li:Juno Therapeutics, a Bristol-Myers Squibb Company: Current Employment; Bristol-Myers Squibb: Current equity holder in publicly-traded company. Hasskarl:Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Bonner:Eversana: Current Employment. Zhang:Eversana: Current Employment. Liu:Bristol-Myers Squibb: Current Employment, Current equity holder in publicly-traded company.

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)0.010
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.033
GPT teacher head0.278
Teacher spread0.245 · 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 designMeta-analysis
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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Citations11
Published2020
Admission routes1
Has abstractyes

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