Direct Targeting Options for STAT3 and STAT5 in Cancer
Bibliographic record
Abstract
Signal transducer and activator of transcription (STAT)3 and STAT5 are important transcription factors that are able to mediate or even drive cancer progression through hyperactivation or gain-of-function mutations. Mutated STAT3 is mainly associated with large granular lymphocytic T-cell leukemia, whereas mutated STAT5B is associated with T-cell prolymphocytic leukemia, T-cell acute lymphoblastic leukemia and γδ T-cell-derived lymphomas. Hyperactive STAT3 and STAT5 are also implicated in various hematopoietic and solid malignancies, such as chronic and acute myeloid leukemia, melanoma or prostate cancer. Classical understanding of STAT functions is linked to their phosphorylated parallel dimer conformation, in which they induce gene transcription. However, the functions of STAT proteins are not limited to their phosphorylated dimerization form. In this review, we discuss the functions and the roles of unphosphorylated STAT3/5 in the context of chromatin remodeling, as well as the impact of STAT5 oligomerization on differential gene expression in hematopoietic neoplasms. The central involvement of STAT3/5 in cancer has made these molecules attractive targets for small-molecule drug development, but currently there are no direct STAT3/5 inhibitors of clinical grade available. We summarize the development of inhibitors against the SH2 domains of STAT3/5 and discuss their applicability as cancer therapeutics.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".