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A Standard, 3-Tube, 10-Color Flow Cytometry Panel for Acute Myeloid Leukemia Diagnosis Can be Used for MRD Detection at Day 30 after Induction, and Is Predictive of Early Relapse

2014· article· en· W2344778610 on OpenAlexaff
Naheed Alam, Anne Tierens, Karen Yee, Aaron D. Schimmer, Vikas Gupta, Mark D. Minden, Matthew D. Seftel, Anna Porwit, Andre C. Schuh

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreLeukemia & Lymphoma Society of Canada
Fundersnot available
KeywordsMedicineMinimal residual diseaseMyeloid leukemiaInternal medicineCD33Induction chemotherapyAcute leukemiaLeukemiaImmunophenotypingOncologyCD34GastroenterologySurgeryImmunologyFlow cytometryChemotherapyStem cellBiology

Abstract

fetched live from OpenAlex

Abstract Introduction: As acute myeloid leukemia (AML) is a highly heterogeneous disease, strategies are needed to follow patients after induction and to predict relapse, so that second line treatments can be initiated early. Since assessment of remission status by morphology has known limitations, minimal residual disease (MRD) detection by flow cytometry (FC) has been used to evaluate remission status. In most publications, individually-tailored, patient-specific panels were applied at follow up. We evaluated MRD using a standard, 3-tube, 10-color antibody panel, which was applied at diagnosis and at day 30 post induction as the routine practice at Princess Margaret Cancer Center (PMCC). Methods: Patients who received front line induction chemotherapy for AML at PMCC from November 2012 to June 2013 and who were alive at Day 30 and able to undergo bone marrow aspiration were evaluated. All patients have had at least one year of follow-up. The leukemia associated immunophenotype was determined at diagnosis by a standard panel [AML1: CD65 FITC, CD13 PE, CD14 ECD, CD33 PC5.5, CD34 PC7, CD117 APC, CD7 A700, CD11b A750, CD16 PB, AML2: CD36 FITC, CD64 PE, CD56 ECD, CD33 PC5.5, CD34 PC7, CD123 APC, CD19 A700, CD38 A750, HLA-DR PB, CD45 KO, AML3: CD71 FITC, CD11c PE, CD4 ECD, CD33 PC5.5, CD34 PC7, CD2 APC, CD10 A700, CD235a A750, CD15 PB, CD45 KO with NaviosTM flow cytometer and KaluzaTM analysis software (Beckman Coulter)]. The analysis protocol was created individually for each patient to follow-up aberrant phenotypes detected at diagnosis. At follow up, 250x103 events were acquired, allowing a sensitivity level of 0.05%. A level of <0.1% cells with the leukemia-associated phenotype was used as the cutoff for MRD positivity, following previously published data. Results: The characteristics of 51 patients who were followed are summarized in Table 1. The median age of patients was 52 (range 18-81) years. Overall survival of all patients at one year was 70%. The cumulative incidence of relapse at one year was 22% (95% CI 8.0-34.1). Overall, 30 (59%) patients were positive for MRD by FC at day 30 after induction chemotherapy. Of these, 14 (47%) relapsed and 1 (3%) died in remission. Of 21 (41%) patients who were negative for MRD at day 30, only 4 (19%) relapsed, giving an odds ratio 3.72 [(95%CI 1.009-13.702), p-value 0.04]. Fifteen patients positive for MRD at day 30 remained in remission after 1 year. Of these, five (33%) were good risk patients with either inv(16) or t(8;21) abnormalities. Seven patients (47%) underwent allogeneic hematopoietic cell transplantation and remain in remission. The remaining three patients (20%) remain in remission without further intervention. Conclusion: MRD monitoring using standard, 3-tube, 10-color FC at day 30 after induction chemotherapy in AML is predictive of early relapse in standard and poor cytogenetic risk groups. In good risk cytogenetics patients, however, MRD positivity does not appear to be predictive. Table 1: Patient Characteristics n=51 Age at diagnosis, median (range), years 52 (18-81) AML classification (WHO 2008) Acute myeloid leukemia with inv(16)(p13.1q22) or t(16;16)(p13.1;q22), CBFB/MYH11 4 (8%) Acute myeloid leukemia with t(9;11)(p22;q23);MLLT3-MLL 1 (2%) Acute myeloid leukemia, t(8;21)(q22;q22) RUNX1-RUNX1T1 5 (10%) Acute myeloid leukemia with myelodysplasia-related changes 8 (15%) Acute myeloid leukemia not otherwise specified 12 (23%) Acute myeloid leukemia, NPM1 mutated, FLT3 ITD 9 (18%) Acute myeloid leukemia, NPM1 mutated, FLT3 TKD 2 (4%) Acute myeloid leukemia, NPM1 mutated, FLT3 negative 10 (20%) Cytogenetic Risk Good 5 (10%) Standard 39 (76%) Poor 7 (14%) WBC count at diagnosis, median (range) 16.7 (0.4-237) Hemoglobin at diagnosis, median (range) 88 (50-131) Platelet count at diagnosis, median (range) 36 (5-340) Neutrophil count at diagnosis, median (range) 1.4 (0-34.9) Peripheral blasts at diagnosis, median (range) 4.72 (0-197) Bone marrow blasts at diagnosis, median (range) 51.5 (20-95) Performance status 0-1 51 (100%) MRD + ve 30 (59%) MRD - ve 21(41%) Allogeneic HCT 16 (31%) Disclosures Yee: Roche: Research Funding. Gupta:Incyte Corporation: Consultancy, Research Funding; Novartis: Consultancy, Honoraria, Research Funding. Minden:Celgene: Honoraria. Porwit:Beckman-Coulter: Speakers Bureau.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.018
GPT teacher head0.276
Teacher spread0.258 · 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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Published2014
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