Outcomes of acute lymphoblastic leukemia with <i>KMT2A</i> (<i>MLL</i>) rearrangement: the MD Anderson experience
Bibliographic record
Abstract
Acute lymphoblastic leukemia (ALL) with t(4;11)(q21;q23)-KMT2A-AFF1 is associated with a poor prognosis. The impact of KMT2A rearrangements other than t(4;11) is uncertain, and the benefit of allogeneic stem cell transplantation (HSCT) is unclear. We reviewed adult patients with ALL treated at our institution from 1984 to 2019 and identified 50 out of 1102 (5%) with KMT2A rearrangement, including 42 (84%) with t(4;11)/KMT2A-AFF1 and 8 (16%) with other gene partners. The median age was 45 years (range, 18-78 years); median white blood cell count was 109.0 3 109/L (range, 0.5-1573.0). The complete remission (CR) rate was 88%, and the rate of measurable residual disease negativity by flow cytometry at CR was 41% (76% overall during follow-up). At the last follow-up, 14 patients were alive. The 5-year overall survival (OS) rate was 18% (95% confidence interval [CI], 9% to 35%), with no difference between t(4;11) and other KMT2A rearrangements (P 5 .87). In a 4-month landmark analysis, the 5-year OS rate was 32% (95% CI, 14% to 70%) in patients who underwent HSCT vs 11% (95% CI, 3-39) in others (P 5 .10). Our study confirms the poor prognosis of ALL with any KMT2A rearrangement and the role of HSCT in these patients.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".