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Record 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 on 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

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsDasatinibMedicineMyeloid leukemiaInternal medicinePropensity score matchingOncologyImatinib

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.040
GPT teacher head0.318
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2021
Admission routes1
Has abstractyes

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