Use of Next Generation Sequencing to Define the Origin of Primary Myelofibrosis
Bibliographic record
Abstract
Primary myelofibrosis (PMF) is a chronic myeloproliferative neoplasm (MPN) characterized by progressive bone marrow sclerosis, extra-medullary hematopoiesis, and possible transformation to acute leukemia. In the last decade, the molecular pathogenesis of the disease has been largely uncovered. Particularly, genetic and genomic studies have provided evidence of deregulated oncogenes in PMF as well as in other MPNs. However, the mechanisms through which transformation to either the myeloid or lymphoid blastic phase remain obscure. Particularly, it is still debated whether the disease has origins in a multi-potent hematopoietic stem cells or instead in a commissioned myeloid progenitor. In this study, we aimed to shed light upon this issue by using next generation sequencing (NGS) to study both myeloid and lymphoid cells as well as matched non-neoplastic DNA of PMF patients. Whole exome sequencing revealed that most somatic mutations were the same between myeloid and lymphoid cells, such findings being confirmed by Sanger sequencing. Particularly, we found 126/146 SNVs to be the e same (including JAK2V617F), indicating that most genetic events likely to contribute to disease pathogenesis occurred in a non-commissioned precursor. In contrast, only 9/27 InDels were similar, suggesting that this type of lesion contributed instead to disease progression, occurring at more differentiated stages, or maybe just represented “passenger” lesions, not contributing at all to disease pathogenesis. In conclusion, we showed for the first time that genetic lesions characteristic of PMF occur at an early stage of hematopoietic stem cell differentiation, this being in line with the possible transformation of the disease in either myeloid or lymphoid acute leukemia.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".