Mixed‐phenotype acute leukemia: A cohort and consensus research strategy from the Children’s Oncology Group Acute Leukemia of Ambiguous Lineage Task Force
Bibliographic record
Abstract
BACKGROUND: Optimal chemotherapy for treating mixed-phenotype acute leukemia (MPAL) and the role of hematopoietic stem cell transplantation (HSCT) remain uncertain. Major limitations in interpreting available data are MPAL's rarity and the use of definitions other than the currently widely accepted criteria: the World Health Organization 2016 (WHO2016) classification. METHODS: To assess the relative efficacy of chemotherapy types for treating pediatric MPAL, the Children's Oncology Group (COG) Acute Leukemia of Ambiguous Lineage Task Force assembled a retrospective cohort of centrally reviewed WHO2016 MPAL cases selected from banking studies for acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). Patients were not treated in COG trials; treatment and outcome data were captured separately. The findings were then integrated with the available, mixed literature to develop a prospective trial in pediatric MPAL. RESULTS: The central review confirmed that 54 of 70 cases fulfilled WHO2016 criteria for MPAL. ALL induction regimens achieved remission in 72% of the cases (28 of 39), whereas AML regimens achieved remission in 69% (9 of 13). The 5-year event-free survival (EFS) and overall survival (OS) rates for the entire cohort were 72% ± 8% and 77% ± 7%, respectively. EFS and OS were 75% ± 13% and 84% ± 11%, respectively, for those receiving ALL chemotherapy alone without HSCT (n = 21). CONCLUSIONS: The results of the COG MPAL cohort and a literature review suggest that ALL chemotherapy without HSCT may be the preferred initial therapy. A prospective trial within the COG is proposed to investigate this approach; AML chemotherapy and/or HSCT will be reserved for those with treatment failure as assessed by minimal residual disease. Embedded biology studies will provide further insight into MPAL genomics.
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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 | 0.087 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".