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Record W4407403711 · doi:10.3390/curroncol32020097

Treatment-Emergent Resistance to Asciminib in Chronic Myeloid Leukemia Patients Due to Myristoyl-Binding Pocket-Mutant of BCR::ABL1/A337V Can Be Effectively Overcome with Dasatinib Treatment

2025· article· en· W4407403711 on OpenAlexvenueno aff
Péter Batár, Gabriella Mezei, Árpád Illés

Bibliographic record

VenueCurrent Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsDasatinibMyeloid leukemiaMedicineTyrosine-kinase inhibitorTyrosine kinaseImatinibPoint mutationCancer researchMutationNilotinibMutantABLInternal medicineGeneticsBiologyCancerGeneReceptor

Abstract

fetched live from OpenAlex

Despite the groundbreaking success of tyrosine kinase inhibitor therapy, the management of chronic myeloid leukemia patients is often impaired by resistance due to specific point mutations in the BCR::ABL1 oncogene. Upon classical ATP-competitive inhibitor treatment, these single nucleotide variants occur in the tyrosine kinase domain of ABL1. The novel allosteric BCR::ABL1 inhibitor asciminib was developed to treat CML patients alone or in combination to overcome or potentially prevent these treatment-emergent TKD mutations. Here, we present a case of a patient undergoing first-line asciminib therapy, and subsequently develop a specific BCR::ABL1/A337V point mutation, which resulted in asciminib resistance. Switching to second-line dasatinib treatment successfully overcame asciminib resistance and helped to achieve a deep molecular response. In case of treatment failures caused by single asciminib-specific point mutations, dasatinib therapy is a feasible choice.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.362
Teacher spread0.319 · 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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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