Improved Overall Survival with Enasidenib Compared with Standard of Care Among Patients with Relapsed or Refractory Acute Myeloid Leukemia and IDH2 Mutations: A Propensity Score Matching Analysis Using Data from the AG221-C-001 Trial and Two Data Sources from France and Germany
Bibliographic record
Abstract
Introduction: Enasidenib is approved for the treatment of patients with relapsed/refractory (R/R) acute myeloid leukemia (AML) with an isocitrate dehydrogenase-2 mutation (mIDH2+) in the USA. To compare the relative effectiveness of enasidenib with standard of care (SoC) in terms of overall survival (OS) among patients with mIDH2+ R/R AML who are ineligible for hematopoietic cell transplantation (HCT), a propensity score matching (PSM) analysis was performed using data from the phase 1/2 AG221-C-001 single-arm trial and a real-world chart review study of patients from France (France chart review [FCR] study) and historical data from the AML Study Group (AMLSG) database. Methods: Individual patient data (IPD) were obtained from the phase 1/2 AG221-C-001 trial for enasidenib (100 mg/day) and from the FCR study and AMLSG database for SoC. Data from the FCR study and AMLSG database were combined to create a single pooled SoC group. Based on clinician feedback and data availability, 6 clinically important covariates (history of HCT before baseline, age, number of prior lines of AML therapy at baseline, cytogenetic risk at baseline, history of myelodysplastic syndromes [MDS], Eastern Cooperative Oncology Group performance status) were used for propensity score calculation and matching. Patients from the 2 groups were matched using nearest neighbor 1:1 matching with a caliper of 0.2 standard deviations (SDs) of the logit transform of the propensity score. A comparison of means, SDs, and standardized mean differences (SMDs) between treatment groups was conducted for each covariate to assess the balance between the pre- and post-match populations. Hazard ratios (HRs) were estimated using doubly robust Cox proportional hazard models that adjusted for the aforementioned covariates. Sensitivity analyses were conducted using alternative matching algorithms, weighting methods, and covariates. Additional sensitivity analyses excluding patients with early events (landmark analyses) were conducted. Results: Before matching, considerable differences existed between the enasidenib (N = 195) and SoC (N = 258) groups (i.e. SMDs > 0.10 were observed for nearly all covariates), and OS was numerically in favor of enasidenib (HR 0.82, 95% confidence interval [CI] 0.66-1.01). After matching, the enasidenib and SoC groups (N = 144 per group) were mostly well balanced (SMDs for all covariates except for prior MDS were < 0.10), and enasidenib was associated with significantly longer OS than SoC (HR 0.61, 95% CI 0.47-0.80). The median OS was 8.8 months (95% CI 7.5-10.7) for enasidenib and 4.4 months (95% CI 3.5-6.1) for SoC (Figure). Sensitivity analyses (i.e., analyses using alternative matching algorithms, weighting methods and covariates, and landmark analyses) delivered results consistent with the primary analysis. Conclusions: The results of this study suggest that enasidenib may prolong survival compared with SoC for patients with mIDH2+ R/R AML who are ineligible for HCT. The incorporation of 2 separate data sources (i.e. the FCR study and AMLSG database) into a combined SoC group increases the generalizability and robustness of these findings. Additional studies should aim to validate these findings using data sources from other countries and assess the comparative efficacy of enasidenib with SoC for other clinically important outcomes. Disclosures De Botton: Syros: Consultancy; Servier: Consultancy; Janssen: Consultancy; Daiichi: Consultancy; AbbVie: Consultancy; Agios: Consultancy, Research Funding; Celgene Corporation: Consultancy, Speakers Bureau; Forma: Consultancy, Research Funding; Novartis: Consultancy; Pfizer: Consultancy; Pierre Fabre: Consultancy; Bayer: Consultancy; Astellas: Consultancy. Brandwein:Roche: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; Jazz Pharma: Consultancy, Honoraria; Otsuka: Honoraria. Stein:Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees; Daiichi Sankyo, Inc.: Membership on an entity's Board of Directors or advisory committees; Bioline: Membership on an entity's Board of Directors or advisory committees; Genentech: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; PTC Therapeutics: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees; Astellas Pharma US, Inc: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees. Wei:Celgene: Honoraria, Research Funding; Walter and Eliza Hall Institute: Other: former employee, Patents & Royalties: receives a fraction of its royalty stream related to venetoclax; Genentech: Honoraria; Amgen: Honoraria, Research Funding; Pfizer: Honoraria; Servier: Honoraria, Research Funding; Macrogenics: Honoraria; AbbVie: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Astellas: former employee, Honoraria; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria. Weber:Celgene Corporation: Research Funding. Pigneux:Roche: Honoraria; Pfizer: Honoraria; Daichi: Honoraria; Amgen: Honoraria; Jazz: Honoraria; Astellas: Honoraria; Abbvie: Honoraria; F. Hoffmann-La Roche Ltd: Honoraria; Novartis: Honoraria. Boissel:NOVARTIS: Consultancy. Paschka:Pfizer: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sunesis: Membership on an entity's Board of Directors or advisory committees; BMS: Other: Travel expenses, Speakers Bureau; Amgen: Other: Travel expenses; Novartis: Membership on an entity's Board of Directors or advisory committees, Other: Travel expenses, Speakers Bureau; Abbvie: Other: Travel expenses; Takeda: Other: Travel expenses; Astellas: Membership on an entity's Board of Directors or advisory committees; Agios: Membership on an entity's Board of Directors or advisory committees; Janssen: Other: Travel expenses; Jazz: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Otsuka: Membership on an entity's Board of Directors or advisory committees; Astex: Membership on an entity's Board of Directors or advisory committees, Travel expenses; Celgene: Membership on an entity's Board of Directors or advisory committees, Other: Travel expenses, Speakers Bureau. Döhner:Daiichi: Honoraria; Jazz: Honoraria; Novartis: Honoraria; Celgene: Honoraria; Janssen: Honoraria; CTI Biopharma: Consultancy, Honoraria. Nehme:Celgene Corporation: Employment, Equity Ownership. Frattini:Celgene Corporation: Employment, Equity Ownership. Marion-Gallois:Celgene Corporation: Employment. Wang:Celgene International: Employment, Equity Ownership. Cameron:Cornerstone Research Group: Employment, Equity Ownership. Siddiqui:Celgene: Consultancy; Cornerstone Research Group: Employment. Qadeer:Cornerstone Research Group: Employment; Celgene Corporation: Consultancy. Döhner:Pfizer: Research Funding; Roche: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding; Bristol Myers Swuibb: Research Funding; Astex: Consultancy, Honoraria; Arog: Research Funding; Astellas: Consultancy, Honoraria; Celgene Corporation: Consultancy, Honoraria, Research Funding; Jazz: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".