The Burden of JAK2V617F Mutated Allele in Turkish Patients With Myeloproliferative Neoplasms
Bibliographic record
Abstract
BACKGROUND: Studies regarding the impact of JAK2V617F allele burden on phenotypic properties and clinical course in Philadelphia-negative myeloproliferative neoplasms (Ph-negative MPNs) have reported variable results. We aimed to analyze the association of mutated JAK2V617F allele burden with laboratory characteristics and clinical phenotype in Turkish patients (107 essential thrombocythemia (ET) and 77 primary myelofibrosis (PMF)). METHODS: Peripheral blood samples of 184 patients with Ph-negative MPNs were analyzed for JAK2V617F allele status and burden. JAK2 MutaScreen assay (Ipsogen, Luminy Biotech, Marseille, France) was used to detect the JAK2V617F status and quantitative JAK2V617F allele burdens in genomic DNA using TaqMan allelic discrimination. RESULTS: Frequency of JAK2V617F-positive patients with high mutation load (allele burden > 50%) was higher in PMF compared to ET (23.4% and 4.7%, respectively; P = 0.001). We found significant association between ET patients with high JAK2V617F allele burden and lower hemoglobin (Hgb) and hematocrit (Hct), higher LDH levels and more prevalent massive splenomegaly (P = 0.001, P = 0.001, P = 0.012 and P = 0.015, respectively). ET patients with high mutation load displayed higher prevalence of bleeding compared to low mutation load and wild-type mutational status (P = 0.003). Rate of DVT was significantly higher in ET patients with mutant allele burden in upper half compared to lower half and wild-type (P = 0.029). We observed significant association between PMF patients with high JAK2V617F allele burden and higher Hgb, Hct levels and leukocyte counts (P = 0.003, P = 0.021 and P = 0.001, respectively). CONCLUSIONS: Our study demonstrated JAK2V617F allele burden correlates with clinical features in ET and PMF. We conclude quantification of JAK2V617F mutation contributes to the workup of Ph-negative MPNs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".