Long-Term Subgroup Analyses from Azacitidine Vs. Decitabine in Unfit Newly Diagnosed Acute Myeloid Leukemia Patients: Results from the Pethema Registry
Bibliographic record
Abstract
Abstract INTRODUCTION The hypomethylating agents (HMAs), decitabine (DEC) and azacitidine (AZA), have made it possible to treat more elderly patients with acute myeloid leukemia (AML). Both HMAs have demonstrated efficacy in monotherapy and in combination with targeted therapies. However, there is little direct comparative data on AZA and DEC in first-line treatment, and we do not know which group of patients might benefit from each drug. Results of the full analysis set (FAS) were presented previously (Labrador J, et al. ASH 2020). Here, we report long-term clinical efficacy from prespecified patient subgroup analyses. METHODS We conducted a retrospective study to compare real-life clinical outcomes between AZA and DEC in patients with AML ineligible for intensive chemotherapy included in the PETHEMA registry, and analyzed clinical variables associated with response and overall survival (OS) between AZA and DEC. RESULTS A total of 626 patients were included for the FAS between 2006 and 2019. 487 (78%) received AZA and 139 (22%) received DEC. Baseline characteristics were comparable in both groups, except for the percentage of bone marrow blasts (44% vs. 34% in the DEC group compared to AZA, p=0.010). In the FAS, there was no difference in the CR, CR/CRi or ORR (CR/RCi + PR) rate: 18%, 20.5% and 32% with AZA vs. 23%, 25% and 39.5% with DEC (p=0.20, p=0.27 and p=0.12). In the subgroup analysis, DEC was associated with higher CR/CRi rate than AZA in patients with ECOG ≥ 2 (95% CI: 0.088 - 0.801), bone marrow blast count < 50% (95% CI: 0.293 - 0.965), secondary AML (95% CI: 0.223 - 0.918) and adverse cytogenetics (95% CI: 0.171 - 0.857) (Figure 1A). DEC was associated with higher ORR rate than AZA in patients with ECOG ≥ 2 (95% CI: 0.116 - 0.782), leukocytes < 10 x10 9/L (95% CI: 0.321 - 0.920) and bone marrow blasts < 50% (95% CI: 0.321 - 0.920) (Figure 1B) 120 days-mortality was 25.4% after AZA and 27.1% after DEC, p=0.70. Patients who did not achieve at least a PR had significantly higher 120-day mortality with both HMAs (OR 8.85 and 8.22 for AZA and DEC, respectively). In the subgroup analysis, patients with leukocytes ≥ 10 x10 9/L (95% CI: 1.069 - 4.157) and those with estimated glomerular filtration rate (eGFR) ≥ 45 mL/min/1.73m 2 (95% CI: 1.249 - 4.664) had higher 120-day mortality with DEC than with AZA (Figure 1C) With a median follow-up of 12 months, median OS was 10.4 months (95% CI: 9.2 - 11.7) for AZA vs. 8.8 months (95% CI: 6.7 - 11.0) for DEC (p = 0.455). The subgroup analysis revealed that patients ≥ 80 years (95%: CI 1.005 - 2.341), with leukocytes ≥ 10 x10 9/L (95% CI 1.039 - 2.062), platelet count <20 x10 9/L (95% CI: 1.150 - 3.422) and those with eGFR ≥ 45 mL/min/1.73m 2 (95% CI: 1.040 - 2.059) did benefit for treatment with AZA compared to DEC (Figure 1D). CONCLUSIONS Our study provides real-life data on the outcomes of AML patients treated with AZA compared to DEC in a large retrospective cohort with long-term follow-up. In addition, we identify for the first time some baseline characteristics that could benefit from AZA or DEC in terms of responses, 120-day mortality and OS. These findings could help us to choose the most appropriate HMA in monotherapy or for the development of new combinations. Figure 1 Figure 1. Disclosures de la Fuente: Novartis: Research Funding; Abbie: Consultancy, Speakers Bureau; Incyte: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau. Tormo: Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Jazz Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Pérez-Simón: Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Montesinos: Forma Therapeutics: Consultancy; Tolero Pharmaceutical: Consultancy; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Sanofi: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Incyte: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Daiichi Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Glycomimetics: Consultancy; Agios: Consultancy; Stemline/Menarini: Consultancy; Teva: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Astellas Pharma, Inc.: Consultancy, Honoraria, Other: Advisory board, Research Funding, Speakers Bureau.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".