Fusion transcript analysis reveals slower response kinetics than multiparameter flow cytometry in childhood acute myeloid leukaemia
Bibliographic record
Abstract
INTRODUCTION: Analysis of measurable residual disease (MRD) is increasingly being implemented in the clinical care of children and adults with acute myeloid leukaemia (AML). However, MRD methodologies differ and discordances in results lead to difficulties in interpretation and clinical decision-making. The aim of this study was to compare results from reverse transcription quantitative polymerase chain reaction (RT-qPCR) and multiparameter flow cytometry (MFC) in childhood AML and describe the kinetics of residual leukaemic burden during induction treatment. METHODS: In 15 children who were treated in the NOPHO-AML 2004 trial and had fusion transcripts quantified by RT-qPCR, we compared MFC with RT-qPCR for analysis of MRD during (day 15) and after induction therapy. Eight children had RUNX1::RUNX1T1, one CBFB::MYH11 and six KMT2A::MLLT3. RESULTS: When ≥0.1% was used as cut-off for positivity, 10 of 22 samples were discordant. The majority (9/10) were MRD positive with RT-qPCR but MRD negative with MFC, and several such cases showed the presence of mature myeloid cells. Fusion transcript expression was verified in mature cells as well as in CD34 expressing cells sorted from diagnostic samples. CONCLUSIONS: Measurement with RT-qPCR suggests slower response kinetics than indicated from MFC, presumably due to the presence of mature cells expressing fusion transcript. The prognostic impact of early measurements with RT-qPCR remains to be determined.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".