TGF-β1 protein trap AVID200 beneficially affects hematopoiesis and bone marrow fibrosis in myelofibrosis
Bibliographic record
Abstract
Myelofibrosis (MF) is a progressive chronic myeloproliferative neoplasm characterized by hyperactivation of JAK/STAT signaling and dysregulation of the transcription factor GATA1 in megakaryocytes (MKs). TGF-β plays a pivotal role in the pathobiology of MF by promoting BM fibrosis and collagen deposition and by enhancing the dormancy of normal hematopoietic stem cells (HSCs). In this study, we show that MF-MKs elaborated significantly greater levels of TGF-β1 than TGF-β2 and TGF-β3 to a varying degree, and we evaluated the ability of AVID200, a potent TGF-β1/TGF-β3 protein trap, to block the excessive TGF-β signaling. Treatment of human mesenchymal stromal cells with AVID200 significantly reduced their proliferation, decreased phosphorylation of SMAD2, and interfered with the ability of TGF-β1 to induce collagen expression. Moreover, treatment of MF mononuclear cells with AVID200 led to increased numbers of progenitor cells (PCs) with WT JAK2 rather than mutated JAK2V617F. This effect of AVID200 on MF PCs was attributed to its ability to block TGF-β1-induced p57Kip2 expression and SMAD2 activation, thereby allowing normal rather than MF PCs to preferentially proliferate and form hematopoietic colonies. To assess the in vivo effects of AVID200, Gata1lo mice, a murine model of MF, were treated with AVID200, resulting in the reduction in BM fibrosis and an increase in BM cellularity. AVID200 treatment also increased the frequency and numbers of murine progenitor cells as well as short-term and long-term HSCs. Collectively, these data provide the rationale for TGF-β1 blockade, with AVID200 as a therapeutic strategy for patients with MF.
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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 | 0.000 | 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 | 0.001 |
| 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".