Real-world patterns of treatment intensity and prognostic model performance in older AML: Insights from the french ALFA-PPP cohort
Abstract
Abstract Background: In older patients (pts) with newly diagnosed (ND) acute myeloid leukemia (AML), treatment options have expanded significantly. While intensive chemotherapy (ICT) remains the standard of care for fit pts, venetoclax (VEN)-based low-intensity therapies (LIT) are increasingly used. Despite existing guidelines, the choice between ICT and LIT varies in real-life practice. Advances in understanding the prognostic role of disease genetics have led to risk models for pts receiving LIT, mostly validated in clinical trials. Their relevance in unselected real-world populations, however, remains uncertain. In this study, we analyzed patterns of treatment intensity and evaluated the performance of LIT risk models in a real-world cohort of older pts with ND-AML. Methods: We analyzed data from the ND-AML cohort of the prospective ALFA-PPP study (NCT04777916), initiated in 2022 to collect real-life clinical and biological data from 29 centers. Centralized genomic (50-gene NGS) and MRD assessments were planned for all pts. We report findings from the first 652 pts aged ≥60 years enrolled between April 2022 and June 2025. In those treated with LIT, we assessed the prognostic value of the 4-gene molecular risk signature (mPRS, PMID: 38113472), the ELN-2024 classification (PMID: 39133932), and the refined ELN (rELN)-2024 model (PMID: 39486083). Results: Among 652 pts, 315 were female and 337 male (median age 71y [60–94]; ECOG 0–2/3–4: 625/27; HCT-CI 0–2/≥3: 401/251). AML was de novo in 442 pts (68%), therapy-related in 105 (16%), post-MDS in 70 (11%), and post-MPN in 35 (5%). Among 591 evaluable pts, NPM1, FLT3, IDH1/2, sAML-like mutations (PMID: 35767897), and MR cytogenetic abnormalities were seen in 25%, 21%, 21%, 58%, and 28%, respectively. Of 652 pts, 346 (53%) received ICT (7+3 based regimen: n=187; CPX-351: n=80; other: n=7) and 306 (47%) VEN-based LIT (VEN+azacitidine: n=305; VEN+LDAC: n=1). ICT was used in 89%, 78%, 60%, and 8% of pts aged 60–65, 65–70, 70–75, and >75y, respectively. According to investigators, main reasons for choosing LIT were advanced age (n=253, 83%), comorbidities (n=79, 26%), and AML features (n=74, 24%). Analysis showed that LIT use was independently associated with older age (p<.001), ECOG>2 (p=0.02), HCT-CI≥3 (p<.001), post-MDS (p=0.001), post-MPN (p<.001), sAML-like mutations (p<.001), MR karyotype (p<.001), and TP53 mutations (p<.001). With median follow-up of 15.3m (IQR 7.2–23.8), 12-month OS was 73% (95% CI, 68–79) with ICT vs 46% (95% CI, 40–52) with LIT. Among 288 LIT pts with genetic data (129 female [45%], 159 male [55%]; median age, 77 years [60–94]; ECOG 0–2/3–4: 270/18; HCT-CI 0–2/≥3: 152/136), the mPRS classified 139 (48%) pts as favorable risk, 77 (27%) as intermediate risk, and 72 (25%) as adverse risk. According to ELN-2024, 148 (51%) pts were categorized as favorable, 79 (27%) as intermediate, and 61 (21%) as adverse. The rELN-2024 model reclassified 58 pts (20%) as favorable, 133 (46%) as intermediate, and 97 (34%) as adverse. Median OS was 10.1 months (95% CI, 7.7–12.0). Median OS by mPRS was 13.7 months (95% CI, 11.7–18.9), 10.0 (6.5–13.9), and 4.8 (3.5–7.4) for favorable, intermediate, and adverse-risk pts, respectively. According to ELN-2024, median OS was 13.1 months (11.7–15.5), 10.0 (6.5–13.9), and 4.3 (3.0–7.4), while for rELN-2024, it was 31.0 months (14.2–NA), 11.5 (8.9–14.8), and 4.4 (3.3–7.2), for the favorable, intermediate, and adverse risk group, respectively. Discriminative performance for OS showed a Harrell's c-index of 0.64 (95% CI, 0.58–0.70) for mPRS, 0.64 (0.58–0.70) for ELN-2024, and 0.70 (0.64–0.76) for rELN-2024. Compared to ELN-2024, mPRS showed no significant difference in discriminative ability (pairwise C-index difference via non-parametric bootstrap: ΔC-index = –0.01 [95% CI, –0.02–0.01]; p=0.49). rELN-2024 significantly outperformed both mPRS (ΔC-index = +0.06 [95% CI, 0.02–0.10]; p<.01) and ELN-2024 (ΔC-index = +0.06 [95% CI, 0.02–0.10]; p<.01). Conclusions: In this real-world cohort of ND-AML pts aged ≥60 years, both ICT and VEN-based LIT were commonly administered, with treatment choice guided by patient- and disease-related factors. ICT was associated with encouraging OS outcomes in older selected pts. Among the prognostic models evaluated in the LIT group, the rELN-2024 showed superior discriminative performance compared to mPRS and ELN-2024, most notably by better identifying favorable-risk pts with substantially prolonged OS.
Article Details
Authors (36)
Lin-Pierre Zhao
Céline Berthon
29Service d’Hématologie, Centre Hospitalier Universitaire de Lille, Hôpital Claude-Huriez, Lille, France
Lauren Fenwarth
3Laboratoire d'hématologie, CHU Lille, Lille, France, Lille, France
Stéphane de Botton
Thomas Cluzeau
15Service d’Hématologie, Centre Hospitalier Universitaire de Nice, Nice, France
Laure Farnault
28Service d’Hématologie, Assistance Publique-Hôpitaux de Marseille–Hôpital de la Conception, Marseille, France
Sylvain Chantepie
10Department of Hematology, Centre Hospitalier Universitaire de Caen, Caen, France
Pierre Arnautou
8Service d'hématologie, HIA Percy, Clamart, France, Clamart, France
Thorsten Braun
Service Hématologie, Hôpital Avicenne, AP-HP, Paris
Mathieu Leclerc
8Hôpital Henri Mondor, Service d'Hématologie Clinique et de Thérapie Cellulaire, Créteil, France
Delphine Lebon
10CHU Amiens Picardie, Unité d'Hématologie clinique et Thérapie Cellulaire, Amiens, France
Ahmad Aljijakli
13Hematology Department, Centre Hospitalier d'Argenteuil, Argenteuil, France
Isabelle Plantier
32Department of Hematology, Centre Hospitalier Roubaix, Roubaix, France
Ollivier Legrand
14Hôpital Saint-Antoine, Service d'hématologie clinique et de thérapie cellulaire, Paris, France
Juliette Lambert
19Department of Hematology, Centre Hospitalier Universitaire de Versailles, Versailles, France
Christophe Willekens
Emmanuel Raffoux
Claire Bories
5CH LENS, LENS, France
Emilie Lemasle
17Department of Hematology, Centre Henri Becquerel, Rouen, France
Kevin-James Wattebled
22Centre Hospitalier Dunkerque, Dunkerque, France, Dunkerque, France
Ambroise Marcais
Celia Salanoubat
21CH Sud Francilien, Hematology, Corbeil-Essonnes, France
Pascal Turlure
16Department of Hematology, Centre Hospitalier Universitaire de Limoges, Limoges, France
Jamilé Frayfer
30Hôpital de Meaux, Departement of Hematology, Meaux, France
Benjamin Carpentier
9St Vincent-de-Paul, GHICL, LILLE, France
Madalina Uzunov
11Sorbonne Université, AP-HP, Pitié- Salpêtrière Hospital, Department of Clinical Haematology, Paris, France
Reda Garidi
13CH Saint Quentin, Saint Quentin, France
Ioana Vaida
22Service d’Hématologie, Hôpital René-Dubos (Nord-Ouest Val-d'Oise), Pontoise, France
Renaud Buffet
14ALFA, Paris, France
Alexandra COELHO
13Centre HAYEM - IRSL, Paris, France
Karine Celli-Lebras
23Acute Leukemia French Intergroup Coordination, Paris, France
Raphaël Itzykson
6Department of Hematology, Hôpital Saint-Louis, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France
Claude Preudhomme
Unité Mixte de Recherche (UMR) 9020-UMR-S 1277-Canther-Cancer Heterogeneity, Plasticity and Resistance to Therapies, Institut de Recherche contre le Cancer de Lille, University of Lille, CNRS, Inserm, Centre Hospitalier Universitaire Lille, Lille, France
Nicolas Duployez
Unité Mixte de Recherche (UMR) 9020-UMR-S 1277-Canther-Cancer Heterogeneity, Plasticity and Resistance to Therapies, Institut de Recherche contre le Cancer de Lille, University of Lille, CNRS, Inserm, Centre Hospitalier Universitaire Lille, Lille, France
Hervé Dombret
Lionel Adès