Impact of Cooperating Myeloid Gene Mutations on Disease Progression and Survival in Japanese MPN Patients: A Multicenter Study
Bibliographic record
Abstract
In addition to driver mutations such as JAK2, CALR, and MPL, some patients with Polycythemia vera (PV), Essential thrombocythemia (ET), and Primary myelofibrosis (PMF) also have mutations common to other myeloid tumors. We conducted this study from 2018 to clarify the impact of such cooperating myeloid gene mutations on survival in real-world clinical settings. We included 622 MPN patients (PV 180, ET 360, PMF 82) from 8 hospitals who were examined for driver mutations for diagnosis and diagnosis confirmation at the University of Miyazaki from March 2007 to March 2023. 69.1% of all DNA samples were collected within 1 year from diagnosis. We investigated gain-of-function hotspot mutations in six genes (IDH1, IDH2, ASXL1, U2AF1, SF3B1, U2AF1) using Sanger sequencing. Loss-of-function mutations without hotspots (e.g., EZH2, TP53, ZRSR2, RUNX1) were not investigated. We retrospectively investigated overall survival and disease progression (progression to myelofibrosis or leukemia in PV and ET, and progression to leukemia in PMF). The median observation period was 5.1, 5.9, and 3.1 years for PV, ET, and PMF, respectively. One or more cooperating mutations were found in 9.4%, 11.7%, and 43.9% of PV, ET, and PMF cases, respectively, with ASXL1 being the most common mutation found in 5.4%, 7.3%, and 26.8% of cases. The incidence of disease progression in PV and ET patients was 0.066 and 0.098 at 10 years, respectively. Patients with cooperating mutations had a higher rate of progression than patients without these mutations (PV: 0.348 vs 0.029, p=0.0088; ET: 0.388 vs 0.060, p<0.0001). The incidence of leukemic transformation in PMF patients was 0.14 at 5 years. Patients with cooperating mutations had a higher rate of transformation compared to patients without these mutations (0.270 vs 0.051, p=0.053).The 10-year overall survival (OS)rates for patients with PV and ET were 87.6% and 85.6%, respectively. The 5-year OS rate for patients with PMF was 62.6%. Patients with cooperative mutations in PV and ET had lower 10-year OS rate than those without (PV: 58.2% vs 91.2%, p=0.02; ET: 74.7% vs 88.1%, p=0.00029).In PMF, patients with these mutations had a lower 5-year OS rates than those without (41.5% vs 80.0%, p=0.00093). We analyzed survival from the time of DNA sampling for 183 patients with PV or ET (55 PV, 128 ET) who were examined for mutations using DNA sampled more than 1 year after diagnosis. The median time from diagnosis to DNA sampling (interquartile range, IQR) was 5.3 years (IQR, 2.2-10.0 years), and the median follow-up period after DNA sampling was 5.4 years. In this patient population, patients with cooperative mutations also had a lower 5-year OS rate compared to patients without mutations (55.4% vs 93.1%, p<0.0001). In this study, genetic mutation analysis did not use NGS, resulting in lower sensitivity in mutation detection and a limited number of genes investigated. Despite these limitations, it was evident that some cooperating myeloid gene mutations had a clear negative impact on both disease progression and survival in real-world clinical settings. Our results also indicate that screening for these mutations may be of prognostic value even during the course of PV or ET. In conclusion, screening for such mutations is crucial for managing all MPN patients.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".