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Enregistrement W3212419124 · doi:10.1182/blood-2021-152614

Low-Dose Dasatinib 50 Mg/Day Versus Standard-Dose Dasatinib 100 Mg/Day As Frontline Therapy in Chronic Myeloid Leukemia in Chronic Phase: A Propensity Score Analysis

2021· article· en· W3212419124 sur OpenAlexaboutno aff
Koji Sasaki, Elias Jabbour, Ghayas C. Issa, Kiran Naqvi, Jeffrey Skinner, Kristin Anderson, Sara Dellasala, Musa Yılmaz, Alessandra Ferrajoli, Prithviraj Bose, Philip A. Thompson, Yesid Alvarado, Nitin Jain, Guillermo Garcia‐Manero, Koichi Takahashi, Jan A. Burger, Gautam Borthakur, Naveen Pemmaraju, Fadi Haddad, Maria Khouri, Shilpa Paul, Sherry Pierce, Jörge E. Cortes, Hagop M. Kantarjian

Notice bibliographique

RevueBlood · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Myeloid Leukemia Treatments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDasatinibMedicineMyeloid leukemiaInternal medicinePropensity score matchingOncologyImatinib

Résumé

récupéré en direct d'OpenAlex

Abstract Background: Low-dose dasatinib was shown to be safe and effective in patients with newly diagnosed chronic myeloid leukemia in chronic phase (CML-CP). There is no randomized clinical trials to compare the outcome with the standard-dose dasatinib. The aim of this study is to compare responses and outcome of patients with newly diagnosed CML-CP treated with frontline dasatinib 50 mg/day with those who received standard-dose dasatinib 100 mg/day. Method: We analyzed 233 patients with newly diagnosed CML-CP who were treated with low-dose dasatinib 50 mg/day (N=83) or standard-dose dasatinib 100 mg/day (N=150). Responses criteria were previously defined. Failure-free survival (FFS) was calculated from the start date of therapy to the dates of treatment discontinuation for any reason except of treatment-free remission; event-free survival (EFS), to the date of any of the events while on study as defined in the IRIS study; transformation-free survival (TFS), to the date of transformation to accelerated or blast phases during study; overall survival (OS), to the date of death from any cause at any time or date of last follow-up. Patients on low-dose dasatinib who had suboptimal response by European LeukemiaNet criteria had an option to increase the dose to 100 mg/day. Propensity score analysis with 1:1 matching was performed with the nearest neighbor matching method using calipers of width equal to 0.2. Multiple imputation was performed to minimize the bias. Propensity scores were calculated with logistic regression from baseline covariates including age, spleen size by examination, white blood cell count, hemoglobin, platelet count, percentage of basophils, percentage of blasts in peripheral blood and bone marrow, the presence of clonal evolution, and Sokal risk classification to minimize difference. Results: Propensity score matching identified 77 patients in each cohort without significant baseline difference (Table 1). The overall median follow-up was 60 months: 48 months and 131 months for low-dose and standard-dose, respectively. The 12-month major molecular response (MMR) rates were 82% and 75% for low-dose and standard-dose groups, respectively (P=0.229). The cumulative incidence of molecular response (MR)4, MR4.5, and complete molecular response (CMR) rates within 1 year were higher in the low-dose dasatinib group compared with the standard-dose group (63% and 43%, 53% and 36%, and 46% and 33% for each)(P=0.009; P=0.031; P=0.060). The incidence of pleural effusion was 6% and 21% in the low-dose and standard-dose, respectively (P=0.016). The 4-year FFS rates were 89% and 77% in the low-dose dasatinib and standard-dose dasatinib, respectively (P=0.041) (Figure 1). The 4-year TFS rates were 100% and 100%, respectively (P=1.000); the 4-year EFS rates were 95% and 92%, respectively (P=0.556); the 4-year OS rates were 97% and 96%, respectively (P=0.781). Conclusions: The low-dose dasatinib is as effective as standard-dose dasatinib with less intolerance, resulting in favorable outcome. Figure 1 Figure 1. Disclosures Sasaki: Novartis: Consultancy, Research Funding; Daiichi-Sankyo: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees. Jabbour: Amgen, AbbVie, Spectrum, BMS, Takeda, Pfizer, Adaptive, Genentech: Research Funding. Issa: Syndax Pharmaceuticals: Research Funding; Novartis: Consultancy, Research Funding; Kura Oncology: Consultancy, Research Funding. Yilmaz: Pfizer: Research Funding; Daiichi-Sankyo: Research Funding. Ferrajoli: Janssen: Other: Advisory Board ; AstraZeneca: Other: Advisory Board, Research Funding; BeiGene: Other: Advisory Board, Research Funding. Bose: Incyte Corporation: Honoraria, Research Funding; Pfizer: Research Funding; Astellas: Research Funding; Constellation Pharmaceuticals: Research Funding; Blueprint Medicines: Honoraria, Research Funding; CTI BioPharma: Honoraria, Research Funding; Sierra Oncology: Honoraria; Kartos Therapeutics: Honoraria, Research Funding; Novartis: Honoraria; NS Pharma: Research Funding; BMS: Honoraria, Research Funding; Celgene Corporation: Honoraria, Research Funding; Promedior: Research Funding. Thompson: Janssen: Consultancy, Honoraria; AbbVie: Other: Institution: Advisory/Consultancy, Honoraria, Research Grant/Funding; Gilead: Other: Institution: Advisory/Consultancy, Honoraria; Genentech: Other: Institution: Advisory/Consultancy, Honoraria, Research Grant/Funding; Amgen: Other: Institution: Honoraria, Research Grant/Funding; Adaptive Biotechnologies: Other: Institution: Advisory/Consultancy, Honoraria, Research Grant/Funding, Expert Testimony; Pharmacyclics: Other: Institution: Advisory/Consultancy, Honoraria, Research Grant/Funding. Alvarado: Sun Pharma: Consultancy, Research Funding; Jazz Pharmaceuticals: Research Funding; FibroGen: Research Funding; BerGenBio: Research Funding; CytomX Therapeutics: Consultancy; MEI Pharma: Research Funding; Daiichi-Sankyo: Research Funding; Astex Pharmaceuticals: Research Funding. Jain: ADC Therapeutics: Honoraria, Research Funding; Cellectis: Honoraria, Research Funding; Janssen: Honoraria; Aprea Therapeutics: Research Funding; Pfizer: Research Funding; Beigene: Honoraria; Precision Biosciences: Honoraria, Research Funding; Servier: Honoraria, Research Funding; Fate Therapeutics: Research Funding; Bristol Myers Squibb: Honoraria, Research Funding; Incyte: Research Funding; TG Therapeutics: Honoraria; Adaptive Biotechnologies: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; Genentech: Honoraria, Research Funding; AbbVie: Honoraria, Research Funding; Pharmacyclics: Research Funding. Takahashi: GSK: Consultancy; Novartis: Consultancy; Symbio Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS: Consultancy. Borthakur: GSK: Consultancy; Astex: Research Funding; Protagonist: Consultancy; University of Texas MD Anderson Cancer Center: Current Employment; ArgenX: Membership on an entity's Board of Directors or advisory committees; Ryvu: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Pemmaraju: HemOnc Times/Oncology Times: Membership on an entity's Board of Directors or advisory committees; Roche Diagnostics: Consultancy; Abbvie Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; LFB Biotechnologies: Consultancy; Cellectis S.A. ADR: Other, Research Funding; Dan's House of Hope: Membership on an entity's Board of Directors or advisory committees; DAVA Oncology: Consultancy; CareDx, Inc.: Consultancy; Incyte: Consultancy; Daiichi Sankyo, Inc.: Other, Research Funding; Springer Science + Business Media: Other; ASH Communications Committee: Membership on an entity's Board of Directors or advisory committees; Novartis Pharmaceuticals: Consultancy, Other: Research Support, Research Funding; Aptitude Health: Consultancy; Stemline Therapeutics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Samus: Other, Research Funding; Plexxicon: Other, Research Funding; ASCO Leukemia Advisory Panel: Membership on an entity's Board of Directors or advisory committees; MustangBio: Consultancy, Other; Sager Strong Foundation: Other; Celgene Corporation: Consultancy; Affymetrix: Consultancy, Research Funding; Protagonist Therapeutics, Inc.: Consultancy; Clearview Healthcare Partners: Consultancy; Blueprint Medicines: Consultancy; Bristol-Myers Squibb Co.: Consultancy; ImmunoGen, Inc: Consultancy; Pacylex Pharmaceuticals: Consultancy. Cortes: Sun Pharma: Consultancy, Research Funding; Bio-Path Holdings, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb, Daiichi Sankyo, Jazz Pharmaceuticals, Astellas, Novartis, Pfizer, Takeda, BioPath Holdings, Incyte: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Takeda: Consultancy, Research Funding. Kantarjian: Precision Biosciences: Honoraria; Immunogen: Research Funding; Jazz: Research Funding; Taiho Pharmaceutical Canada: Honoraria; Ascentage: Research Funding; Pfizer: Honoraria, Research Funding; Astra Zeneca: Honoraria; Daiichi-Sankyo: Research Funding; BMS: Research Funding; AbbVie: Honoraria, Research Funding; Astellas Health: Honoraria; Novartis: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Aptitude Health: Honoraria; NOVA Research: Honoraria; KAHR Medical Ltd: Honoraria; Ipsen Pharmaceuticals: Honoraria.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,030

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,004
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,040
Tête enseignante GPT0,318
Écart entre enseignants0,279 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2021
Routes d'admission1
Résumé présentoui

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