Small molecule STAT3/5 inhibitors exhibit therapeutic potential in acute myeloid leukemia and extra-nodal natural killer/T cell lymphoma
Bibliographic record
Abstract
Abstract The oncogenic transcription factors STAT3, STAT5A and STAT5B are essential to steer hematopoiesis and immunity, but their enhanced expression and activation drives the development or progression of blood cancers. Current therapeutic strategies focus on blocking upstream tyrosine kinases, but frequently occurring resistance often leads to disease relapse, emphasizing the need for more targeted therapies. Here we evaluate JPX-0700 and JPX-0750, which are STAT3/5-specific covalent cysteine binders that lead to growth arrest of acute myeloid leukemia (AML) and natural killer/T cell lymphoma (NKCL) cell lines in vitro and in vivo , as well as reduce cell viability of primary AML blasts ex vivo . Our non-PROTAC small molecular weight degraders selectively reduce STAT3/5 activation and total protein levels, as well as downstream target oncogene expression, exhibiting nanomolar to low micromolar efficacy. We found that both AML and NKCL cells hijack STAT3/5 signaling through either upstream activating mutations in tyrosine kinases, activating gain-of-function mutations in STAT3, mutational loss of negative STAT regulators, or genetic gains in anti-apoptotic, pro-proliferative or epigenetic-modifying STAT3/5 targets. Moreover, we have shown synergistic inhibitory action of JPX-0700 and JPX-0750 upon combinatorial use with approved chemotherapeutics (doxorubicin, daunorubicin, cytarabine), epigenetic enzyme blocker vorinostat, tyrosine kinase inhibitor cabozantinib or BCL-2 inhibitor venetoclax. Importantly, JPX-0700 or JPX-0750 treatment reduced leukemic cell growth in human AML/NKCL xenograft mouse models without adverse side effects. These potent small molecule degraders of STAT3/5 could propel further clinical development for use in AML and NKCL patients.
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 | 0.000 |
| 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".