Effect of Mutation Order on Myeloproliferative Neoplasms
Bibliographic record
Abstract
BACKGROUND: Cancers result from the accumulation of somatic mutations, and their properties are thought to reflect the sum of these mutations. However, little is known about the effect of the order in which mutations are acquired. METHODS: We determined mutation order in patients with myeloproliferative neoplasms by genotyping hematopoietic colonies or by means of next-generation sequencing. Stem cells and progenitor cells were isolated to study the effect of mutation order on mature and immature hematopoietic cells. RESULTS: The age at which a patient presented with a myeloproliferative neoplasm, acquisition of JAK2 V617F homozygosity, and the balance of immature progenitors were all influenced by mutation order. As compared with patients in whom the TET2 mutation was acquired first (hereafter referred to as "TET2-first patients"), patients in whom the Janus kinase 2 (JAK2) mutation was acquired first ("JAK2-first patients") had a greater likelihood of presenting with polycythemia vera than with essential thrombocythemia, an increased risk of thrombosis, and an increased sensitivity of JAK2-mutant progenitors to ruxolitinib in vitro. Mutation order influenced the proliferative response to JAK2 V617F and the capacity of double-mutant hematopoietic cells and progenitor cells to generate colony-forming cells. Moreover, the hematopoietic stem-and-progenitor-cell compartment was dominated by TET2 single-mutant cells in TET2-first patients but by JAK2-TET2 double-mutant cells in JAK2-first patients. Prior mutation of TET2 altered the transcriptional consequences of JAK2 V617F in a cell-intrinsic manner and prevented JAK2 V617F from up-regulating genes associated with proliferation. CONCLUSIONS: The order in which JAK2 and TET2 mutations were acquired influenced clinical features, the response to targeted therapy, the biology of stem and progenitor cells, and clonal evolution in patients with myeloproliferative neoplasms. (Funded by Leukemia and Lymphoma Research and others.).
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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 | 0.000 |
| 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".