Dual STAT3/STAT5 inhibition as a novel treatment strategy in T-prolymphocytic leukemia
Bibliographic record
Abstract
T-prolymphocytic leukemia (T-PLL) is a rare, aggressive T-cell malignancy with poor outcomes and an urgent need for new therapeutic approaches. Integrating genomic data and new transcriptomic profiling, we identified recurrent JAK/STAT mutations (predominantly in JAK3 and STAT5B) as hallmarks in a cohort of 335 T-PLL cases. In line, transcriptomic and protein analyses revealed constitutive JAK/STAT activation in virtually all samples. Consequently, we explored the anti-leukemic potential of dual STAT3/STAT5 non-PROTAC degraders in T-PLL, with JPX-1244 as our lead substance. JPX-1244 efficiently and selectively induced cell death in primary T-PLL samples, including those resistant to conventional therapies, by blocking STAT3 and STAT5 phosphorylation and by inducing their degradation. The extent of STAT3/STAT5 degradation directly correlated with cytotoxicity. RNA-sequencing confirmed the treatment-related downregulation of STAT5 target genes. While JAK/STAT mutations did not predict responses to pharmacologic STAT3/STAT5 degradation, elevated expression of TOX, PAK6, and SPINT1 were associated with drug sensitivity. In subsequent combination screenings, cladribine, venetoclax, and azacytidine emerged as most effective combination partners of STAT3/STAT5 degraders, even in low-responding T-PLL samples, all synergistically reducing STAT5 phosphorylation. These findings highlight dual STAT3/STAT5 inhibition, particularly in combination with hypomethylating and BCL2-targeting agents, as a promising interventional approach in T-PLL, warranting further investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".