Abstract 3253: A novel small-molecule inhibitor of Stat3 induces antitumor cell effects in human glioma and breast cancer cells.
Bibliographic record
Abstract
Abstract Constitutive activation of Stat3, a member of signal transducer and activator of transcription (Stat) family of proteins, occurs with a high frequency in human tumors and contributes to carcinogenesis and tumor progression. Compelling evidence shows aberrant activation of Stat3 constitutes a point of convergence in oncogenic tyrosine kinase signaling, functioning as a master regulator of events crucial for tumorigenesis and malignant progression. We present a series of novel small-molecules that abrogate constitutive activation of Stat3 in human tumors, including glioma and breast cancer. The small-molecule, SH5-07, inhibits in vitro Stat3 DNA-binding activity, with IC50 value of 3.9 μM. Further, treatment with SH5-07 induces dose-dependent inhibition of constitutively-active Stat3 in human glioma U251MG and U373MG and breast cancer MDA-MB-231 cells. Moreover, treatment with low micromolar SH5-07 of U251MG, U373MG, and MDA-MB-231 cells blocks constitutive Stat3 phosphorylation, DNA-binding, and transcriptional activities by as early as 30 minutes. Furthermore, the inhibition of aberrantly-active Stat3 by SH5-07 blocks the anchorage-dependent and independent growth, survival, migration, and invasion in vitro of human glioma and breast cancer cells. Data together indicates SH5-07 is a promising small-molecule Stat3 inhibitor and a suitable drug candidate for clinical development. Citation Format: Peibin Yue, Sina Haftchenary, Patrick T. Gunning, James Turkson. A novel small-molecule inhibitor of Stat3 induces antitumor cell effects in human glioma and breast cancer cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3253. doi:10.1158/1538-7445.AM2013-3253
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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) | 0.003 | 0.001 |
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".