Comprehensive profiling of clinical <scp>JAK</scp> inhibitors in myeloproliferative neoplasms
Bibliographic record
Abstract
Small molecule inhibitors targeting JAK2 provide symptomatic benefits for myeloproliferative neoplasm (MPN) patients and are among first-line therapeutic agents. However, despite all having potent capacity to suppress JAK-STAT signaling, they demonstrate distinct clinical profiles suggesting contributory effects in targeting other ancillary pathways. Here, we performed comprehensive profiling on four JAK2 inhibitors either FDA-approved (ruxolitinib, fedratinib, and pacritinib) or undergoing phase 3 studies (momelotinib) to better outline mechanistic and therapeutic efficacy. Across JAK2-mutant in vitro models, all four inhibitors demonstrated similar anti-proliferative phenotypes, whereas pacritinib yielded greatest potency on suppressing colony formation in primary samples, while momelotinib exhibited unique erythroid colony formation sparing. All inhibitors reduced leukemic engraftment, disease burden, and extended survival across patient-derived xenograft (PDX) models, with strongest effects elicited by pacritinib. Through RNA-sequencing and gene set enrichment analyses, differential suppressive degrees of JAK-STAT and inflammatory response signatures were revealed, which we validated with signaling and cytokine suspension mass cytometry across primary samples. Lastly, we assessed the capacity of JAK2 inhibitors to modulate iron regulation, uncovering potent suppression of hepcidin and SMAD signaling by pacritinib. These comparative findings provide insight into the differential and beneficial effects of ancillary targeting beyond JAK2 and may help guide the use of specific inhibitors in personalized therapy.
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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 | 0.000 |
| 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 | 0.001 | 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".