Abstract 3684: Developing STAT3 protein inhibitors as adjuvant therapeutics: Promising synergistic effects in human cancers
Bibliographic record
Abstract
Abstract Signal Transducer and Activator of Transcription 3 (Stat3) protein is a cytosolic transcription factor that relays signals from receptors in the plasma membrane directly to the nucleus, and is routinely hyper-activated in many human cancers and diseases. STAT3 induces anti-apoptotic gene expression programs (e.g. Bcl-xL) and the over-expression of cell cycle regulators (e.g. cyclin D1) that contribute significantly to the resistance of cancer to current chemotherapeutic strategies. Since most cancer drugs aim to initiate apoptosis, tumor cells containing activated STAT3 have an intrinsic resistance to current treatment strategies. It has therefore been postulated that STAT3 inhibitors could play a significant role in the future of adjuvant cancer therapies by sensitizing human tumors to traditional chemotherapy. By examining the protein-protein interaction interface and employing computational modeling, we have thus developed a series of small molecule inhibitors of the transcriptionally active STAT3-STAT3 homo-dimer complex. Lead inhibitors showed potent anti-STAT3 activity in vitro and in tumor cell lines, as well as in malignant cells taken from leukemia patients. More specifically, these compounds displayed single digit micromolar activity against breast, prostate, pancreatic and leukemia cell lines and showed negligible cytotoxic effects on healthy cells treated with high µM concentrations of compounds. Preliminary adjuvant studies with a series of clinically relevant therapeutics have shown impressive synergistic effects in leukemia cell lines, as well as in patient tumor cells. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3684.
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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".