STAT5 Gain-of-Function Variants Promote Precursor T-Cell Receptor Activation to Drive T-Cell Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
Abstract T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive immature T-cell cancer. Hotspot mutations in JAK-STAT pathway members IL7R , JAK1 and JAK3 were analyzed in depth. However, the role of STAT5A or STAT5B mutations promoting their hyperactivation is poorly understood in the context of T-cell cancer initiation and acute leukemia progression. Importantly, the driver mutation STAT5B N642H encodes the most frequent activating STAT5 variant in T-ALL associated with poor prognosis. Here, we show that hyperactive STAT5 promotes early T-cell progenitor (ETP)-ALL-like cancer in mice and upregulated genes involved in T-cell receptor signaling (TCR), even in absence of surface TCR promoting. Importantly, these genes were also overexpressed in human T-ALL and other STAT5-dependent T-cell cancers. Moreover, human T-ALL cells were sensitive to pharmacologic inhibition by dual STAT3/5 degraders or ZAP70 tyrosine kinase blockers. Thus, we define STAT5 target genes in T-ALL that promote pre-TCR signaling mimicry. We propose therapeutic targeting using selective ZAP70 or STAT3/5 inhibitors in a subgroup of T-ALL patients with prominent IL-7R-JAK1/3-STAT5 activity. Significance We provide detailed functional characterizations of hyperactive STAT5A or STAT5B in thymic T-cell development and transformation. We found that hyperactive STAT5 transcribes T-cell-specific kinases or pre-TCR signaling hubs to promote T-ALL. Biomolecular and next-generation-sequencing methods, transgenesis and pharmacologic interference revealed that hyperactive STAT5 is a key oncogenic driver that can be targeted in T-ALL using STAT3/5 or SYK family member tyrosine kinase inhibitors. Conflict of interest The authors declare no potential conflicts of interest.
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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) | 0.002 | 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".