<i>JAK2</i> <i>V617F</i> mutation and associated chromosomal alterations in primary and secondary myelofibrosis and post-HCT outcomes
Bibliographic record
Abstract
JAK2 V617F is the most common driver mutation in primary or secondary myelofibrosis for which allogeneic hematopoietic cell transplantation (HCT) is the only curative treatment. Knowledge of the prognostic utility of JAK2 alterations in the HCT setting is limited. We identified all patients with MF who received HCT between 2000 and 2016 and had a pre-HCT blood sample (N = 973) available at the Center of International Blood and Marrow Transplant Research biorepository. PacBio sequencing and single nucleotide polymorphism-array genotyping were used to identify JAK2V617F mutation and associated mosaic chromosomal alterations (mCAs), respectively. Cox proportional hazard models were used for HCT outcome analyses. Genomic testing was complete for 924 patients with MF (634 primary MF [PMF], 135 postpolycythemia vera [PPV-MF], and 155 postessential thrombocytopenia [PET-MF]). JAK2V617F affected 562 patients (57.6% of PMF, 97% of PPV-MF, and 42.6% of PET-MF). Almost all patients with mCAs involving the JAK2 region (97.9%) were JAK2V617-positive. In PMF, JAK2V617F mutation status, allele burden, or identified mCAs were not associated with disease progression/relapse, nonrelapse mortality (NRM), or overall survival. Almost all PPV-MF were JAK2V617F-positive (97%), with no association between HCT outcomes and mutation allele burden or mCAs. In PET-MF, JAK2V617F high mutation allele burden (≥60%) was associated with excess risk of NRM, restricted to transplants received in the era of JAK inhibitors (2013-2016; hazard ratio = 7.65; 95% confidence interval = 2.10-27.82; P = .002). However, allele burden was not associated with post-HCT disease progression/relapse or survival. Our findings support the concept that HCT can mitigate the known negative effect of JAK2V617F in patients with MF, particularly for PMF and PPV-MF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".