Dual specific STAT3/5 degraders effectively block acute myeloid leukemia and natural killer/T cell lymphoma
Bibliographic record
Abstract
The transcription factors STAT3, STAT5A, and STAT5B steer hematopoiesis and immunity, but their enhanced expression and activation promote acute myeloid leukemia (AML) or natural killer/T cell lymphoma (NKCL). Current therapeutic strategies focus on blocking upstream tyrosine kinases to inhibit STAT3/5, but these kinase blockers are not selective against STAT3/5 activation and frequent resistance causes relapse, emphasizing the need for targeted drugs. We evaluated the efficacy of JPX-0700 and JPX-0750 as dual STAT3/5 binding inhibitors promoting protein degradation. JPX-0700/-0750 decreased the mRNA and protein levels of STAT3/5 targets involved in cancer survival, metabolism, and cell cycle progression, exhibiting nanomolar to low micromolar efficacy. They induced cell death and growth arrest in both AML/NKCL cell lines and primary AML patient blasts. We found that both AML/NKCL cells hijack STAT3/5 signaling through either upstream activating mutations in kinases, activating mutations in STAT3, mutational loss of negative STAT regulators, or genetic gains in anti-apoptotic, pro-proliferative, or epigenetic-modifying STAT3/5 targets. This emphasizes a vicious cycle for proliferation and survival through STAT3/5. Both JPX-0700/-0750 treatment reduced leukemic cell growth in human AML or NKCL xenograft mouse models significantly, being well tolerated by mice. Synergistic cell death was induced upon combinatorial use with approved chemotherapeutics in AML/NKCL cells.
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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 | 0.000 |
| 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".