The mutational landscape of accelerated- and blast-phase myeloproliferative neoplasms impacts patient outcomes
Bibliographic record
Abstract
Abstract There is a paucity of data regarding the impact of mutations on outcomes in accelerated-phase (AP) and blast-phase (BP) myeloproliferative neoplasms (MPNs). Moreover, it is unknown whether mutational status affects survival, as seen in chronic-phase MPNs. Therefore, we performed a retrospective analysis of all patients treated at our institution with AP/BP MPNs (N = 122; AP = 14; BP = 108) to comprehensively describe the mutational profile and correlate with clinical outcomes. Targeted sequencing with a 54-gene panel was performed. Forty-four patients were treated with intensive therapy, 27 with nonintensive therapy, and 51 with best supportive care (BSC). The most common mutation was JAK2V617F, occurring in 55% of subjects; CALR was found in 13% of patients and MPL in 6%. Thirty-two (26%) patients were triple negative. Other frequently mutated genes were ASXL1 (30%), TET2 (25%), SRSF2 (22%), RUNX1 (20%), and TP53 (17%). Mutations in 1, 2, 3, and ≥4 genes were seen in 15%, 13%, 25%, and 46% of patients, respectively. There was no difference in survival between patients treated with intensive vs nonintensive therapy, and the benefit of intensive therapy was limited to patients who were able to undergo transplantation. TP53 was the only individual mutation to correlate with shorter overall survival (hazard ratio, 1.89; P = .03). In the multivariate analysis, mutated TP53, ≥4 mutations, low albumin, increased peripheral blood blasts, ≥3 cytogenetic abnormalities, and BSC were associated with shorter survival. In conclusion, mutational data enhance the understanding of patients with AP/BP MPN who are likely to benefit from current therapeutic options.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".