Clinical Effects of RUNX1 Mutations on the Outcomes of Patients with Acute Myeloid Leukemia Treated with Allogeneic Hematopoietic Stem-Cell Transplantation
Bibliographic record
Abstract
It is reported that AML with RUNX1 mutations is associated with poorer response to conventional chemotherapy, lower rates of complete remission (CR), leukemia-free survival (LFS), and overall survival (OS). We aimed to evaluate the prognostic impact of RUNX1 mutations following allogeneic hematopoietic stem cell transplantation (allo-HSCT) by comparing clinical outcomes in AML patients with and without RUNX1 mutations. We retrospectively analyzed 91 AML patients (33 RUNX1+ and 58 RUNX1−) who received their first HSCT at Peking University People’s Hospital. The median age of the cohort was 38 years (range: 6–64), with 73 patients (80%) receiving Haploidentical HSCT and 18 patients (20%) receiving sibling-matched allo-HSCT. In univariate analyses, no significant differences in survival outcomes were observed. For the RUNX1-mutation group and RUNX1-wild-type group, the 2-year cumulative incidence of relapse (CIR) was (12.6% vs. 7.6%, p = 0.472), the 2-year non-relapse mortality (NRM) rate was (9.6% vs. 7.2%, p = 0.747), the 2-year LFS was (77.8% vs. 85.2%, p = 0.426), and the 2-year OS rate was (85.9% vs. 92.7%, p = 0.397). We did not observe any negative impact of RUNX1 mutations on clinical outcomes, suggesting that allo-HSCT (especially Haplo-HSCT) may mitigate the adverse prognostic influence of RUNX1 mutations in AML.
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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".