Impact of Genetic Mutations on the Outcomes of Allogeneic Hematopoietic Cell Transplantation in Patients with Acute Myeloid Leukemia with Antecedent Myeloproliferative Neoplasm
Bibliographic record
Abstract
Purpose We previously demonstrated poor outcomes of patients with acute myeloid (AML) with antecedent myeloproliferative neoplasms (MPN) undergoing allogeneic hematopoietic stem cell transplantation (HCT) (Gupta et al, BBMT, 2019; abstract 140). In particular, we did not find any difference outcomes of patients who received transplant in remission defined as blood and bone marrow blasts Patients and Methods Of the 177 patients with post MPN AML identified the Center for International Blood and Marrow Transplant Research (CIBMTR) database, 95 (54%) had sufficient DNA to perform molecular analysis that consisted of targeted Next-Generation sequencing on 49 genes clinically relevant hematologic malignancies. Results Of the 95 patients analyzed, 54 were in remission, and 41 had active leukemia. Adverse risk cytogenetics was seen 39 patients. The most frequently mutated genes (≥10% patients) study cohort were: JAK2 (55%); TP53 (23%); ASXL1 (22%); TET2 (19%), SRSF2 (16%); DNMT3A (14%), RUNX1 (14%); CALR (13%); SF3B1 (10%). Cumulative incidence of non-relapse mortality (NRM), relapse, progression-free survival (PFS), and survival the study cohort at 5-years were 23% (95% CI 15-32), 66% (95% CI 56-75), 11% (95% CI 6-19) and 16% (95% CI 9-25). In a multivariate model, TP53 mutation status was the only factor associated with outcomes of HCT; patients with mutated TP53 mutations had inferior overall survival [RR 1.99 (95% CI 1.14-3.49)] and increased relapse [RR 2.59 (95% CI 1.41-4.74)], and inferior PFS (RR 2.18 (95% CI 1.25-3.82)]. Disease status at HCT was not associated with outcomes. Moreover, there were no differences the mutational landscape, number of mutations and variant allele frequency (VAF) between patients who underwent HCT in remission (n=54) versus those with active leukemia (n=41). Conclusions We conclude that there is minimal benefit of HCT patients with post MPN AML with mutated TP53, and novel strategies are required for these patients. We did not observe any difference clinical outcomes or mutational profile of patients undergoing transplant in remission compared to those with active leukemia questioning the clinical benefit of blast reduction strategies post MPN 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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".