Abstract A125: Silencing Stat3 signaling in human cancers: Identifying potent small molecule inhibitors of Stat3 function.
Bibliographic record
Abstract
Abstract Stat3 is essential for transducing signals from extracellular stimuli, but also functions as a nuclear transcription factor required for regulating genes involved in proliferation, apoptosis, angiogenesis and invasion, in addition to genes encoding cytokines, chemokines and growth factors. In contrast to the transient nature of Stat3 activation in normal cells, many human cancers, including breast, prostate, ovarian, brain and multiple myeloma (MM) harbor constitutive Stat3 activity. Stat3 downstream target genes are critical to the dysregulated biological processes that promote tumor cell growth, survival and induce chemoresistance, thus targeting Stat3 signaling represents an important therapeutic target in cancer therapy. We have rationally designed and developed Stat3 inhibitors that disrupt transcriptionaly active Stat3-Stat3 homo-dimers, suppress Stat3 activation (phosphorylation), inhibit Stat3-target gene expression (c-Myc, Bcl-xL, survivin) and potently induce apoptosis in tumor cells harboring aberrant Stat3 activity. Moreover, lead compound BP-1–102, a salicylic acid containing small molecule, induced strong antitumor effects on human breast cancer (MDA-MB-231) xenografts and in MM preclinical tumor models. Most notably, given via oral gavage, BP-1–102 strongly inhibited the growth of human breast tumor xenografts, identifying it as a most potent orally bioavailable Stat3-targeting inhibitor. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr A125.
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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) | 0.000 | 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) | 0.000 | 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".