Structural and functional consequences of the STAT5BN642H driver mutation
Bibliographic record
Abstract
Abstract Hyper-activated STAT5B variants are high value oncology targets for pharmacologic intervention. STAT5BN642H, a frequently-occurring oncogenic driver mutation, promotes aggressive T-cell leukemia/lymphoma in patient carriers, although the molecular origins remain unclear. Herein, we emphasize the aggressive nature of STAT5BN642H in driving T-cell neoplasia upon hematopoietic expression in transgenic mice, revealing evidence of multiple T-cell subset organ infiltration. Notably, we demonstrate STAT5BN642H-driven transformation of γδ T-cells in in vivo syngeneic transplant models, comparable to STAT5BN642H patient γδ T-cell entities. Importantly, we present human STAT5B and STAT5BN642H crystal structures, which propose alternative mutation-mediated SH2 domain conformations. Our biophysical data suggests STAT5BN642H can adopt a hyper-activated and hyper-inactivated state with resistance to dephosphorylation. MD simulations support sustained interchain cross-domain interactions in STAT5BN642H, conferring kinetic stability to the mutant anti-parallel dimer. This study provides a molecular explanation for the STAT5BN642H activating potential, and insights into pre-clinical models for targeted intervention of hyper-activated STAT5B.
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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) | 0.002 | 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".