Genetic factors rather than blast reduction determine outcomes of allogeneic HCT in <i>BCR-ABL</i>–negative MPN in blast phase
Bibliographic record
Abstract
There is a limited understanding of the clinical and molecular factors associated with outcomes of hematopoietic cell transplantation (HCT) in patients with BCR-ABL-negative myeloproliferative neoplasms in blast phase (MPN-BP). Using the Center for International Blood and Marrow Transplant Research database, we evaluated HCT outcomes in 177 patients with MPN-BP. Ninety-five (54%) had sufficient DNA for targeted next-generation sequencing of 49 genes clinically relevant in hematologic malignancies. At 5 years, overall survival (OS), cumulative incidence of relapse, and nonrelapse mortality of the study cohort was 18%, 61%, and 25%, respectively. In a multivariable model, poor-risk cytogenetics was associated with inferior OS (hazard ratio [HR], 1.71; 95% CI, 1.21-2.41) due to increased relapse (HR, 1.93; 95% CI, 1.32-2.82). Transplants using mobilized peripheral blood (PB) were associated with better OS (HR, 0.60; 95% CI, 0.38-0.96). No difference in outcomes was observed in patients undergoing HCT with PB/BM blasts <5% vs those with active leukemia. Among the 95 patients with molecular data, mutation of TP53, present in 23%, was the only genetic alteration associated with outcomes. In a multivariate model, TP53-mutant patients had inferior OS (HR, 1.99; 95% CI, 1.14-3.49) and increased incidence of relapse (HR, 2.59; 95% CI, 1.41-4.74). There were no differences in the spectrum of gene mutations, number of mutations, or variant allele frequency between patients undergoing HCT with PB/BM blasts <5% vs those with active leukemia. Genetic factors, namely cytogenetic alterations and TP53 mutation status, rather than degree of cytoreduction predict outcomes of HCT in MPN-BP. No meaningful benefit of conventional HCT was observed in patients with MPN-BP and mutated TP53.
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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 | 0.001 |
| 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".