Concordance of two approaches in monitoring of minimal residual disease in B-precursor acute lymphoblastic leukemia: Fusion transcripts and leukemia-associated immunophenotypes
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Real-time quantitative polymerase chain reaction (RQ-PCR) for fusion transcripts and flow cytometry for leukemia-specific markers are widely used for minimal residual disease (MRD) detection in acute lymphoblastic leukemia, but the relation between the results of either method is unclear. METHODS: Mononucleated cells from 108 bone marrow samples collected from 55 B-precursor acute lymphoblastic leukemia patients (30 with t(12;21)/ETV6-RUNX1, 16 with t(9;22)/BCR-ABL1 and nine with t(1;19)/TCF3-PBX1) were examined in tandem by RQ-PCR and six-color flow cytometry. RESULTS: MRD results were concordant in 91 of the 108 paired samples (84.2%; K=0.690); 49 samples were MRD-negative while 42 were MRD-positive by both methods, with < 1 log difference in positive MRD estimates in 39 samples (92.9%). Of the 17 discordant samples, 16 were MRD-positive by RQ-PCR but MRD-negative by flow cytometry; the opposite was true in one sample. Kappa value/concordance was 0.690/85.0% (n = 60) for ETV6-RUNX1, 0.842/93.3% (n = 15) for TCF3-PBX1, and 0.535/78.8% (n = 33) for BCR-ABL1. Specific immunophenotypic abnormalities were more prevalent in each genetic subgroup, such as CD38 underexpression, CD58 overexpression, and CD34 overexpression in ETV6-RUNX1, TCF3-PBX1, and BCR-ABL1, respectively. CONCLUSION: In most follow-up samples, MRD estimates by two methods are in agreement, especially in patients with TCF3-PBX1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".