Gemtuzumab Ozogamicin Reduces Relapse Risk in <i>FLT3</i> /ITD Acute Myeloid Leukemia: A Report from the Children's Oncology Group
Bibliographic record
Abstract
PURPOSE: Gemtuzumab ozogamicin (GO), a calicheamicin-conjugated mAb against CD33, has been used in the treatment of acute myeloid leukemia (AML). We evaluated the impact of the addition of GO to standard chemotherapy and hematopoietic stem cell transplant (HCT) in patients withFLT3/ITD. EXPERIMENTAL DESIGN: We analyzed children withFLT3/ITD-positive AML (n= 183) treated on two consecutive Children's Oncology Group AML trials (NCT00070174andNCT00372593). Outcomes were assessed forFLT3/ITD patients receiving standard chemotherapy with or without GO (GO vs. No-GO, respectively), and the impact of consolidation HCT for high-riskFLT3/ITD patients [highFLT3/ITD allelic ratio (ITD-AR)]. RESULTS: For allFLT3/ITD patients, complete remission (CR) rates for the GO versus No-GO cohorts were identical (64% vs. 64%;P= 0.98). Relapse rate (RR) after initial CR was 37% for GO recipients versus 59% for No-GO recipients (P= 0.02), disease-free survival (DFS) was similar (47% vs. 41%;P= 0.45), with higher treatment-related mortality (TRM) in GO recipients (16% vs. 0%;P= 0.008). Among high-riskFLT3/ITD patients with high ITD-AR, those who received HCT in first CR with prior exposure to GO had a significant reduction in RR (15% vs. 53%;P= 0.007), with a corresponding DFS of 65% versus 40% (P= 0.08), and higher TRM (19% vs. 7%;P= 0.08). CONCLUSIONS: CD33 targeting with HCT consolidation may be an important therapeutic strategy in high-riskFLT3/ITD AML and its efficacy and associated toxicity warrant further investigation.
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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 | 0.001 |
| 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 | 0.001 |
| 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 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".