Re‐induction chemoimmunotherapy with epratuzumab in relapsed acute lymphoblastic leukemia (ALL): Phase II results from Children's Oncology Group (COG) study ADVL04P2
Bibliographic record
Abstract
BACKGROUND: Given the success of immunotherapeutic approaches in hematologic malignancies, the COG designed a phase I/II study to determine whether the addition of epratuzumab (anti-CD22) to an established chemotherapy platform improves rates of second remission (CR2) in pediatric patients with B-lymphoblastic leukemia (B-ALL) and early bone marrow relapse. PROCEDURE: Therapy consisted of three established blocks of re-induction chemotherapy. Epratuzumab (360 mg/m(2)/dose) was combined with chemotherapy on weekly × 4 (B1) and twice weekly × 4 [eight doses] (B2) schedules during the first re-induction block. Remission rates and minimal residual disease (MRD) status were compared to historical rates observed with the identical chemotherapy platform alone. RESULTS: CR2 was achieved in 65 and 66%, of the evaluable B1 (n = 54) and B2 patients (n = 60), respectively; unchanged from that observed historically without epratuzumab. Rates of MRD negativity (<0.01%) were 31% in B1 (P = 0.4128) and 39% in B2 patients (P = 0.1731), compared to 25% in historical controls. The addition of epratuzumab was well tolerated, with a similar toxicity profile to that observed with the re-induction chemotherapy platform regimen alone. CONCLUSIONS: Epratuzumab was well tolerated in combination with re-induction chemotherapy. While CR2 rates were not improved compared to historical controls treated with chemotherapy alone, there was a non-significant trend towards improvement in MRD response with the addition of epratuzumab (twice weekly for eight doses) to re-induction chemotherapy.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".