Abstract B18: Development of human STING agonists for prostate cancer immunotherapy
Bibliographic record
Abstract
Abstract Recent studies have identified the host STING pathway as a critical mechanism of innate immune sensing of cancer, that drives type-I interferons (IFNs) production and promotes aggressive antitumor responses. Thus, STING agonists could be candidates for testing as stimulants for anticancer immune activity. Although DMXAA binds and activates mouse STING, it cannot activate human STING (hSTING). This species specificity is thought to be the reason that DMXAA showed dramatic effect against solid tumor in rodent models, but failed in human clinical trials. In this project, we aim to identify novel chemical compounds as agonists of hSTING. By combining in silico screening and in vitro assays, we have discovered two novel chemical compounds that activate hSTING. Direct binding of our compounds with hSTING was confirmed by Surface plasmon resonance (SPR) analysis. We demonstrated that our hSTING agonist activated interferon signaling pathway in monocytic human THP-1 cells, which express endogenous hSTING. Further, our compound itself is not cytotoxic to 22Rv1 prostate cancer cells, but the spent supernatants of the THP-1 cells exposed to our compound are cytotoxic to 22Rv1 cells. Chemical optimization of initial active compounds is in progress. We aim to generate compound candidates as potent hSTING agonists for in vivo evaluation in rodent models. Note: This abstract was not presented at the conference. Citation Format: Jian Hui Wu. Development of human STING agonists for prostate cancer immunotherapy. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Targeting the Vulnerabilities of Cancer; May 16-19, 2016; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(1_Suppl):Abstract nr B18.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".