The impact of Down syndrome‐specific non‐malignant hematopoietic regeneration in the bone marrow on the detection of leukemic measurable residual disease
Bibliographic record
Abstract
Abstract Background Detection of measurable residual disease detection (MRD) by flow cytometry after the first course of chemotherapy is a standard measure of early response in patients with acute myeloid leukemia (AML). Myeloid leukemia associated with Down Syndrome (ML‐DS) is a distinct form of AML. Differences in steady‐state and regenerating hematopoiesis between patients with or without DS are not well understood. This understanding is essential to accurately determine the presence of residual leukemia in patients with ML‐DS. Methods A standardized antibody panel defined quantitative antigen expression in 115 follow‐up bone marrow (BM) aspirates from 45 patients following chemotherapy for ML‐DS or DS precursor B‐cell acute lymphoblastic leukemia (B‐ALL‐DS) with the “difference from normal (ΔN)” technique. When possible, FISH and SNP/CGH microarray studies were performed on sorted cell fractions. Results 93% of BM specimens submitted post chemotherapy had a clearly identifiable CD34+CD56+ population present between 0.06% and 2.6% of total non‐erythroid cells. An overlapping CD34+HLA‐DRheterogeneous population was observed among 92% of patients at a lower frequency (0.04%–0.8% of total non‐erythroid cells). In B‐ALL‐DS patients, the same CD34+CD56+ HLA‐DRheterogeneous expression was observed. FACS‐FISH/Array studies demonstrated no residual genetic clones in the DS‐specific myeloid progenitor cells. Conclusions Non‐malignant myeloid progenitors in the regenerating BM of patients who have undergone chemotherapy for either ML‐DS or B‐ALL‐DS express an immunophenotype that is different from normal BM of non‐DS patients. Awareness of this DS‐specific non‐malignant myeloid progenitor is essential to the interpretation of MRD by flow cytometry in patients with ML‐DS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".