Deep Molecular Response Rate in Chronic Phase Chronic Myeloid Leukemia: Eligibility to Discontinuation Related to Time to Response and Different Frontline TKI in the Experience of the Gimema Labnet CML National Network.

Chronic myeloid leukemia Molecular response Outcome Treatment free remission Tyrosine kinase inhibitors

Journal

Clinical lymphoma, myeloma & leukemia
ISSN: 2152-2669
Titre abrégé: Clin Lymphoma Myeloma Leuk
Pays: United States
ID NLM: 101525386

Informations de publication

Date de publication:
07 Sep 2024
Historique:
received: 14 07 2024
revised: 28 08 2024
accepted: 29 08 2024
medline: 26 9 2024
pubmed: 26 9 2024
entrez: 25 9 2024
Statut: aheadofprint

Résumé

In the last decade, TKIs improved the overall survival (OS) of chronic myeloid leukemia (CML) patients who achieved a deep and sustained molecular response (DMR, defined as stable MR4 and MR4.5). Those patients may attempt therapy discontinuation. In our analysis, we report the differences in eligibility criteria due to time of response and different TKI used as frontline treatment analyzed in a large cohort of CP-CML patients. Data were exported by LabNet CML, a network founded by GIMEMA in 2014. The network standardized and harmonized the molecular methodology among 51 laboratories distributed all over Italy for the diagnosis and molecular residual disease (MRD) monitoring. Out of 1777 patients analyzed, 774 had all evaluable timepoints (3, 6, and 12 months). At 3 months, 40 patients obtained ≥MR4: of them 14 (3.6%) with imatinib, 8 (5.8%) with dasatinib, and 18 (7.4%) with nilotinib (P = .093); at 6 months, 146 patients were in MR4: 42 (11%) with imatinib, 38 (28%) with dasatinib, and 66 (27%) with nilotinib (P < .001). At 12 months, 231 patients achieved a DMR: 85 (22%) with imatinib, 55 (40%) with dasatinib and 91 (38%) with nilotinib (P < .001). Achieving at least ≥MR2 at 3 months, was predictive of a DMR at any timepoint of observation: with imatinib 67% versus 30% of patients with <MR2, with dasatinib 66% versus 28% of patients with <MR2, and with nilotinib 75% versus 30% of patients with < MR2 (P < .001). At the same time point, achieving at least ≥MR3 is even more predictive of a DMR at any timepoint: 89% versus 38% of patients with <MR3 with imatinib (P < .001), 84% versus 40% of patients with <MR3 with dasatinib (P < .001), and 89% versus 49% of patients with <MR3 with nilotinib (P < .001). Of 908 patients who reached a DMR, 461 (51%) lost it: the loss of response after >2 years was significant for patients who at 3 months had ≥MR2 (18% vs. 9.9% of pts with <MR2, P = .038). In conclusion, reaching ≥MR2 and a MR3 at 3 months it seems predictive of a DMR at any time point. Considering the prerequisite for a discontinuation with a sustained DMR only a minority of patients can be eligible for the discontinuation, regardless the frontline treatment received.

Sections du résumé

BACKGROUND BACKGROUND
In the last decade, TKIs improved the overall survival (OS) of chronic myeloid leukemia (CML) patients who achieved a deep and sustained molecular response (DMR, defined as stable MR4 and MR4.5). Those patients may attempt therapy discontinuation. In our analysis, we report the differences in eligibility criteria due to time of response and different TKI used as frontline treatment analyzed in a large cohort of CP-CML patients.
METHODS METHODS
Data were exported by LabNet CML, a network founded by GIMEMA in 2014. The network standardized and harmonized the molecular methodology among 51 laboratories distributed all over Italy for the diagnosis and molecular residual disease (MRD) monitoring.
RESULTS RESULTS
Out of 1777 patients analyzed, 774 had all evaluable timepoints (3, 6, and 12 months). At 3 months, 40 patients obtained ≥MR4: of them 14 (3.6%) with imatinib, 8 (5.8%) with dasatinib, and 18 (7.4%) with nilotinib (P = .093); at 6 months, 146 patients were in MR4: 42 (11%) with imatinib, 38 (28%) with dasatinib, and 66 (27%) with nilotinib (P < .001). At 12 months, 231 patients achieved a DMR: 85 (22%) with imatinib, 55 (40%) with dasatinib and 91 (38%) with nilotinib (P < .001). Achieving at least ≥MR2 at 3 months, was predictive of a DMR at any timepoint of observation: with imatinib 67% versus 30% of patients with <MR2, with dasatinib 66% versus 28% of patients with <MR2, and with nilotinib 75% versus 30% of patients with < MR2 (P < .001). At the same time point, achieving at least ≥MR3 is even more predictive of a DMR at any timepoint: 89% versus 38% of patients with <MR3 with imatinib (P < .001), 84% versus 40% of patients with <MR3 with dasatinib (P < .001), and 89% versus 49% of patients with <MR3 with nilotinib (P < .001). Of 908 patients who reached a DMR, 461 (51%) lost it: the loss of response after >2 years was significant for patients who at 3 months had ≥MR2 (18% vs. 9.9% of pts with <MR2, P = .038).
CONCLUSION CONCLUSIONS
In conclusion, reaching ≥MR2 and a MR3 at 3 months it seems predictive of a DMR at any time point. Considering the prerequisite for a discontinuation with a sustained DMR only a minority of patients can be eligible for the discontinuation, regardless the frontline treatment received.

Identifiants

pubmed: 39322541
pii: S2152-2650(24)01802-0
doi: 10.1016/j.clml.2024.08.009
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 Elsevier Inc. All rights reserved.

Auteurs

Massimo Breccia (M)

Department of Translational and Precision Medicine, Università Sapienza, Rome, Italy. Electronic address: breccia@bce.uniroma1.it.

Rosalba Cucci (R)

GIMEMA Data Center, Rome, Italy.

Giovanni Marsili (G)

GIMEMA Data Center, Rome, Italy.

Fausto Castagnetti (F)

Department of Hematology, Istituto Seragnoli, Università di Bologna, Bologna, Italy.

Sara Galimberti (S)

Department of Hematology, Università di Pisa, Pisa, Italy.

Barbara Izzo (B)

Department of Hematology, Università Federico II, Napoli, Italy.

Federica Sorà (F)

Department of Hematology, Università Cattolica, Roma, Italy.

Simona Soverini (S)

Department of Hematology, Istituto Seragnoli, Università di Bologna, Bologna, Italy.

Monica Messina (M)

GIMEMA Data Center, Rome, Italy.

Alfonso Piciocchi (A)

GIMEMA Data Center, Rome, Italy.

Massimiliano Bonifacio (M)

Department of Hematology, Università di Verona, Verona, Italy.

Daniela Cilloni (D)

Department of Hematology, Università di Torino, Torino, Italy.

Alessandra Iurlo (A)

Department of Hematology, Policlinico di Milano, Milano, Italy.

Giovanni Martinelli (G)

Department of Hematology, Istituto Seragnoli, Università di Bologna, Bologna, Italy.

Gianantonio Rosti (G)

Department of Hematology, Istituto Amadori, Meldola, Italy.

Fabio Stagno (F)

Department of Hematology, Università di Messina, Messina, Italy.

Paola Fazi (P)

GIMEMA Data Center, Rome, Italy.

Marco Vignetti (M)

GIMEMA Data Center, Rome, Italy.

Fabrizio Pane (F)

Department of Hematology, Università Federico II, Napoli, Italy.

Classifications MeSH