Impact of High-Molecular-Risk Mutations on Transplantation Outcomes in Patients with Myelofibrosis
Bibliographic record
Abstract
Mutational profiling has demonstrated utility in predicting the likelihood of disease progression in patients with myelofibrosis (MF). However, there is limited data regarding the prognostic utility of genetic profiling in MF patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HCT). We performed high-throughput sequencing of 585 genes on pre-transplant samples from 101 patients with MF who underwent allo-HCT and evaluated the association of mutations and clinical variables with transplantation outcomes. Overall survival (OS) at 5 years post-transplantation was 52%, and relapse-free survival (RFS) was 51.1 % for this cohort. Nonrelapse mortality (NRM) accounted for most deaths. Patient's age, donor's age, donor type, and Dynamic International Prognostic Scoring System score at diagnosis did not predict for outcomes. Mutations known to be associated with increased risk of disease progression, such as ASXL1, SRSF2, IDH1/2, EZH2, and TP53, did not impact OS or RFS. The presence of U2AF1 (P = .007) or DNMT3A (P = .034) mutations was associated with worse OS. A Mutation-Enhanced International Prognostic Scoring System 70 score was available for 80 patients (79%), and there were no differences in outcomes between patients with high risk scores and those with intermediate and low risk scores. Collectively, these data identify mutational predictors of outcome in MF patients undergoing allo-HCT. These genetic biomarkers in conjunction with clinical variables may have important utility in guiding transplantation decision making.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".