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Record W4389235051 · doi:10.1182/blood-2023-190694

Prognostic Role of Multiparameter Flow Cytometry-Based Measurable Residual Disease Assessment in Acute Myeloid Leukemia Patients with FMS3-like Tyrosine Kinase-3 Internal Tandem Duplication (FLT3-ITD)

2023· article· en· W4389235051 on OpenAlexafffund
Josephine Anne Lucero, Aniket Bankar, Marta Davidson, Guillaume Richard‐Carpentier, Aaron D. Schimmer, Andre C. Schuh, Dawn Maze, Karen Yee, Mark D. Minden, Steven M. Chan, Jonas Mattsson, Rajat Kumar, Vikas Gupta, José‐Mario Capo‐Chichi, Tracy Stockley, Hassan Sibai, Anne Tierens, Dennis Dong Hwan Kim

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
FundersTakeda CanadaMerck CanadaAstellas PharmaCelgeneSierra OncologyCTI BiopharmaGilead SciencesJazz PharmaceuticalsTeva Pharmaceutical IndustriesLeukemia and Lymphoma SocietyAgios PharmaceuticalsBristol-Myers SquibbServierAmgen
KeywordsNPM1OncologyMedicineMyeloid leukemiaInternal medicineMinimal residual diseaseCumulative incidenceHazard ratioProportional hazards modelMultivariate analysisLog-rank testSurvival analysisLeukemiaBiologyTransplantationConfidence intervalGeneticsGene

Abstract

fetched live from OpenAlex

Introduction Measurable residual disease (MRD) monitoring is predictive in acute myeloid leukemia (AML). Assessment of FMS3-like tyrosine kinase-3 in-frame internal tandem duplications (FLT3-ITD) is usually performed at diagnosis by polymerase chain reaction (PCR). Due to its relative lower sensitivity of 2%, FLT3-ITD PCR is not routinely used during response assessment. Next-generation sequencing is similarly limited by technical difficulties in capturing tandem duplications using the short base pair-based sequencing method. Long base pair-based sequencing has been reported, but its use is limited by financial restrictions. Multiparameter flow cytometry (MFC) can be a useful tool for MRD monitoring in this AML subtype until such time that molecular techniques for detecting FLT3-ITD MRD are optimized. Patients and methods The study evaluated the outcomes of FLT3-ITD mutated AML patients diagnosed and treated from 2018 to 2022 at Princess Margaret Cancer Centre. We compared outcomes according to MFC-MRD post-induction and FLT3-ITD allele frequency (AF) status at diagnosis. MRD cut-off was 0.1%. Data were locked as of June 30, 2023. Clinical outcomes evaluated include overall survival (OS) and relapse-free survival (RFS). The cumulative incidence of relapse (CIR) and non-relapse mortality (NRM) were calculated considering competing risk. The Kaplan-Meier method using a log-rank test and a multivariate Cox proportional hazard model was used for analyses, while the Gray test and Fine-Grey model were used for uni- and multivariate analysis for CIR and NRM. Results A total of 111 patients with a mean age of 63.5 years were included, of whom 90 received treatment. Secondary AML accounted for 12.7% of patients. Risk stratification according to European LeukemiaNet (ELN) 2022 was favorable in 2 (1.8%), intermediate in 67 (60.4%), and adverse in 42 patients (37.8%). Nucleophosmin 1 (NPM1) co-mutation was observed in 55 patients (49.5%). Seventy-nine patients (87.8%) could be assessed for overall response, including 69 (76.7%) who achieved complete remission (CR) or CR with incomplete count recovery (CRi). Of these, 54 achieved first CR/CRi (CR1) with 1 induction cycle. MFC-MRD data were available in 61 patients, of whom 44 (72.1%) were MRD negative, while 17 (27.9%) were MRD positive. With a median follow-up of 437 days, 50 patients (45%) were still alive. Median OS and RFS were 3.42 years and 1.05 years, respectively. Among patients who achieved CR1, post-induction MFC-MRD positivity correlated with an inferior OS (HR 2.35 [1.06-5.26], p=0.037) and a trend for a shorter RFS (HR 2.08 [0.99-4.35], p=0.052). We examined the impact of FLT3-ITD AF at diagnosis on long-term outcomes. By applying a binary recursive partitioning method, the cut-off of FLT3-ITD AF with the best risk stratification power for RFS, was defined at 54.6%. The group with a higher FLT3-ITD AF showed inferior OS (HR 1.86 [1.01-3.44], p=0.047) and RFS (HR 1.91 [1.09-3.33], p=0.023). Taking together FLT3-ITD AF at diagnosis and MFC-MRD status at CR1, patients with low FLT3-ITD AF and negative MRD had the highest OS rate of 86.2% at 12 months (p=0.023), and the highest RFS at 72.4% (p=0.096), while the corresponding values for low FLT3-ITD AF/positive MRD patients were 87.5% and 50%, respectively. In contrast, those with high FLT3-ITD AF/negative MRD had an OS of 72.7% and a RFS of 45.5%, while those with high FLT3-ITD AF/positive MRD showed the lowest OS (16.7%) and the shortest RFS (16.7%). There was no statistical difference in CIR and NRM among groups. Multivariate analysis with stepwise selection was performed, considering age at diagnosis, ELN 2022 risk, MFC-MRD at CR1, FLT3-ITD AF at diagnosis, cytogenetics, and NPM1 co-mutation. Predictive factors for OS were MFC-MRD post-induction (HR 2.49 [1.11-5.61], p=0.027) and FLT3-ITD AF (HR 2.29 [1.03-5.11], p=0.043). For RFS, age at diagnosis (HR 1.03 [1.00-1.06], p=0.038) and FLT3-ITD AF (HR 2.40 [1.15-5.01], p=0.019) were predictive, while MFC-MRD was not significant (HR 1.59 [0.72-3.53], p=0.25). Conclusion Our data demonstrate that MFC-based MRD assessment is feasible in AML with FLT3-ITD. Although better outcomes are expected in patients with a lower FLT3-ITD AF, patients who failed to achieve MRD negativity at CR1 showed inferior outcomes. The presence of both poor risk factors, a high AF of FLT3-ITD at diagnosis and MRD positivity at CR1, correlated with the worst treatment outcomes.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.274
Teacher spread0.261 · 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
Published2023
Admission routes2
Has abstractyes

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