High activation of STAT5A drives peripheral T-cell lymphoma and leukemia
Bibliographic record
Abstract
Recurrent gain-of-function mutations in the transcription factors STAT5A and much more in STAT5B were found in hematopoietic malignancies with the highest proportion in mature T- and natural killer-cell neoplasms (peripheral T-cell lymphoma, PTCL). No targeted therapy exists for these heterogeneous and often aggressive diseases. Given the shortage of models for PTCL, we mimicked graded STAT5A or STAT5B activity by expressing hyperactive Stat5a or STAT5B variants at low or high levels in the hematopoietic system of transgenic mice. Only mice with high activity levels developed a lethal disease resembling human PTCL. Neoplasia displayed massive expansion of CD8+ T cells and destructive organ infiltration. T cells were cytokine-hypersensitive with activated memory CD8+ T-lymphocyte characteristics. Histopathology and mRNA expression profiles revealed close correlation with distinct subtypes of PTCL. Pronounced STAT5 expression and activity in samples from patients with different subsets underline the relevance of JAK/STAT as a therapeutic target. JAK inhibitors or a selective STAT5 SH2 domain inhibitor induced cell death and ruxolitinib blocked T-cell neoplasia in vivo. We conclude that enhanced STAT5A or STAT5B action both drive PTCL development, defining both STAT5 molecules as targets for therapeutic intervention.
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 | 0.000 | 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) | 0.002 | 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".