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Record W3096057643 · doi:10.1182/blood-2020-136122

Allogeneic Transplant Can Abrogate the Relapse Risk in the Patients with Detectable Measureable Residual Disease By Multicolor Flow-Cytometry at the Time of Assessment of Acute Myeloid Leukemia Patients in First Remission

2020· article· en· W3096057643 on OpenAlexaff
Muhned Alhumaid, Georgina S. Daher-Reyes, Arjun Law, Auro Viswabandya, Armin Gerbitz, Fotios V. Michelis, Igor Novitzky‐Basso, Jeffrey H. Lipton, Jonas Mattson, Ivan Pašić, Rajat Kumar, Wilson Lam, Zeyad Al‐Shaibani, Hassan Sibai, Dennis Dong Hwan Kim

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineCumulative incidenceInternal medicineHazard ratioMinimal residual diseaseProportional hazards modelOncologyMyeloid leukemiaMultivariate analysisLeukemiaTransplantationConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: Acute myeloid leukemia (AML) is a highly heterogeneous group of diseases with variable response to therapy. Several factors have a prognostic impact for an outcome. Despite intensive chemotherapy and hematopoietic stem cell transplant (HCT), a significant proportion of patients eventually relapse, indicating that morphological assessment is not adequate due to limitations in sensitivity, requiring a better tool for assessment of remission. METHODS: A retrospective analysis was performed in AML patients who achieved first complete remission (CR1) and the outcomes compared according to the performance of HCT, and multi-color flow cytometry (MFC)-based measurable residual disease (MRD) status (defined as negative if patients achieved 0.1% or less) assessed at the time of CR1. In order to take account of the time interval from the MFC-MRD assessment to HCT, we applied a Mantel-Byar test for overall (OS) and relapse-free survival (RFS), considering time-to-HCT as a time-dependent covariate, while Simon and Makuch plot was used. Time-dependent Cox proportional hazard models were applied for multivariate analysis. Cumulative incidence of relapse (CIR) and non-relapse mortality (NRM) where evaluated using Fine-Gray model. RESULTS: A total of 435 patients diagnosed with AML and treated with induction chemotherapy between 2015 and 2018, of whom 380 patients (87%) achieved remission, were included. MFC-MRD was assessed in 336 patients in CR1 (77%), out of 380 patients who achieved CR1, and 200 patients (53%) proceeded to HCT. We evaluated OS, RFS, CIR and NRM according to MFC-MRD status in those patients who had negative MRD (MRDneg; n=218, 65%) vs. those with MRD (MRDpos; n=118, 35%). The OS at 2 years was 67.0% vs.40.7% (p≤0.001), RFS at 2 years was 8.7% vs. 40.6% (p≤0.001), CIR 26.9% vs.21.1% but with borderline significance (p=0.08), and NRM 32.5% vs. 20.2% with borderline significance (p=0.057). In patients who achieved CR, we compared OS, CIR, NRM and RFS between the HCT group (n=200) vs. those who did not undergo HCT (no-HCT; n=235). Between the 2 groups, the OS at 2 years was 55.7% vs. 47.2% (p=0.004); CIR 9.7% vs. 34.6% (p≤0.001); NRM 40.9% vs. 12.6% (p≤0.001). There was no difference in RFS: 49.4% vs. 52.8% (p=0.505). There was no difference in the time interval from the MFC-MRD assessment to HCT between the groups (MRDpos vs MRDneg) with a median of 96 days in overall patients who received HCT (p=0.31). In the overall population, when HCT was accounted as a time-dependent covariate, we failed to observe any difference of OS (HR 1.23; p=0.19) or RFS (HR 1.09; p=0.60) between the HCT vs. no-HCT groups. Then, we compared the OS, RFS, CIR, and NRM between the HCT vs no-HCT groups confined to the subgroups of patients with MFC-MRDneg vs MFC-MRDpos, separately. In the MFC-MRDpos subgroup, patients who underwent HCT did better: OS 54.8% in HCT vs. 25.5% in no-HCT (HR 0.52; p≤0.001) and RFS 48.7% vs. 24.1% (HR 0.45; p≤0.001). However, in the MFC-MRDneg subgroup, similar outcomes were noted between the HCT vs no-HCT groups in terms of OS 60.8% vs. 70.7% (HR 1.27, p=0.32), RFS 51.6% vs. 62.4% (HR 1.25; p=0.46) (Fig 1). With respect to the cause of treatment failure according to treatment modality (HCT vs no-HCT) and MFC-MRD status, Fig 2 revealed different patterns of relapse vs NRM between the HCT and the no-HCT groups. In the MFC-MRDneg subgroup, HCT group showed a higher NRM over the no-HCT group (38.0% vs 8.7%; HR 2.08; p≤0.001), while relapse risk was lower in the HCT group (10.4% vs 29.3%; p≤0.001). In the MFC-MRDpos subgroup, relapse incidence was strikingly different in favor of HCT (9.5% vs 50.0%; HR, p≤0.001). Conclusion: These findings suggest that in AML patients HCT could abrogate the relapse risk in patients who are MFC-MRDpos at the time of remission assessment, while the benefit from HCT was minimal in the subgroup that are MFC-MRDneg. Further study is strongly warranted to reach a clearer conclusion with a larger number of cohorts. Disclosures Lipton: Bristol-Myers Squibb: Honoraria; Takeda: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Ariad: Consultancy, Research Funding.

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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)0.001
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.009
GPT teacher head0.238
Teacher spread0.229 · 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
Published2020
Admission routes1
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