STAT5 activation promotes progression and chemotherapy-resistance in early T-cell precursor acute lymphoblastic leukemia
Bibliographic record
Abstract
Interleukin-7 (IL-7) supports the growth and chemoresistance of T-cell acute lymphoblastic leukemia (T-ALL), particularly the early T-cell precursor subtype (ETP-ALL), which frequently has activating mutations of IL-7 signaling. Signal transducer and activator of transcription (STAT5) is an attractive therapeutic target because it is almost universally activated in ETP-ALL, even in the absence of mutations of upstream activators such as the IL-7 receptor (IL-7R), Janus kinase, and Fms-like tyrosine kinase 3 (FLT3). To examine the role of activated STAT5 in ETP-ALL, we have used a Lmo2-transgenic (Lmo2Tg) mouse model in which we can monitor chemoresistant preleukemia stem cells (pre-LSCs) and leukemia stem cells (LSCs) that drive T-ALL development and relapse following chemotherapy. Using IL-7R-deficient Lmo2Tg mice, we show that IL-7 signaling was not required for the formation of pre-LSCs but essential for their expansion and clonal evolution into LSCs to generate T-ALL. Activated STAT5B was sufficient for the development of T-ALL in IL-7R-deficient Lmo2Tg mice, indicating that inhibition of STAT5 is required to block the supportive signals provided by IL-7. To further understand the role of activated STAT5 in LSCs of ETP-ALL, we developed a new transgenic mouse that enables T-cell specific and doxycycline-inducible expression of the constitutively activated STAT5B1∗6 mutant. Expression of STAT5B1∗6 in T cells had no effect alone but promoted expansion and chemoresistance of LSCs in Lmo2Tg mice. Pharmacologic inhibition of STAT5 with pimozide-induced differentiation and loss of LSCs, while enhancing response to chemotherapy. Furthermore, pimozide significantly reduced leukemia burden in vivo and overcame chemoresistance of patient-derived ETP-ALL xenografts. Overall, our results demonstrate that STAT5 is an attractive therapeutic target for eradicating LSCs in ETP-ALL.
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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".