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Who benefits from gilteritinib combination therapy amongst FLT3 <sup>mut</sup> R/R AML? Genomic insights.

2025· article· en· W4410807970 on OpenAlexaff
Akhil Rajendra, Elliot Smith, María Agustina Perusini, Kenny Tang, Eshetu G. Atenafu, Aniket Bankar, Steven M. Chan, Marta Davidson, Vikas Gupta, Dawn Maze, Mark D. Minden, Guillaume Richard‐Carpentier, Aaron D. Schimmer, Andre C. Schuh, Karen Yee, Hassan Sibai

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineCancer researchOncologyInternal medicine

Abstract

fetched live from OpenAlex

e18522 Background: The outcome of FLT3 mut R/R AML is dismal. Use of Giltertinib monotherapy results in modest benefits with an improvement in EFS (2.8 months) and OS (9.3 months). Triplet (Azacytidine, Venetoclax and Giltertinib) and doublet combinations (Venetoclax and Gilteritinib) have shown high rates of CR/CRi, mCRc and longer survival. However, on cross-trial comparison, gilteritinib combinations have similar survival to monotherapy (ADMIRAL trial). We presented our experience of FLT3 mut R/R AML, where we showed that gilteritinib combinations resulted in better mCRc and higher transplant feasibility. In this analysis we intend to look at genomic predictors which could predict survival in this population. Methods: We conducted aretrospective, single center study to evaluate the impact of co-mutations on the survival outcomes associated with gilteritinib based therapy for FLT3 mut R/R AML. Results: A total of 68 FLT3 mut R/R AML patients were treated with Giltertinib or its combinations between January 2017 and March 2024. Giltertinib monotherapy was used in 47 while 21 received combination. Combinations included Giltertinib+ Venetoclax (n = 8), Giltertinib+ Azacytidine+ Venetoclax(n = 11) and Gilteritinib plus azacytidine (n = 2). The median OS with monotherapy and combination were 5.8 months and 14.9 months, respectively ( p = 0.0958). The most common co-occurring mutations were DNMT3A (44%), NPM1 (41%), RUNX1 (19%), ASXL1 (15%) and IDH2 (15%). Notably, 21 patients (30.8%) harbored co-mutations in NPM1 and DNMT3A (termed triple-mutated).Triple-mutated patients showed significantly higher rates of CR/CRi (42.9% vs 10.6%; p = 0.007), mCRc (71.4% vs 34%; p = 0.008), and a lower probability of an EFS event (52.4 % vs 91.5%; p = 0.001) or death (42.9% vs 87.2%; p < 0.001) in comparison to non-triple mutated patients. The median OS in the triple-mutated patients was 45 months (95%CI: 4.9 - NA) in comparison to 5.6 months (95%CI: 3.6 – 7.5) in the non-triple mutated patients ( p = 0.0003). Therapy stratification revealed that the 24-month OS for triple-mutated patients treated with combination therapy and monotherapy were 60.0% and 54.5%, respectively. In contrast, the 24-month OS in the non-triple mutated cohort treated with combination therapy and monotherapy were 17.3% and 8.7% respectively. Multivariable analysis using the cox proportional hazards model, the factors positively predicting survival included achievement of mCRc, undergoing transplant and the presence of triple mutation. Exposure to azacytidine-venetoclax predicted for poorer survival. Conclusions: Patients with triple mutation ( FLT3 /NPM1/DNMT3A co-mutation) is a distinct subset which responds particularly well to FLT3-inhibitor. Future studies should explore whether therapy selection (monotherapy or combination therapy) can be decided based on the presence or absence of triple mutation.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.099
GPT teacher head0.437
Teacher spread0.339 · 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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Citations0
Published2025
Admission routes1
Has abstractyes

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