Investigating the STING Pathway to Explain Mechanisms of BCG Failures in Non-Muscle Invasive Bladder Cancer: Prognostic and Therapeutic Implications
Bibliographic record
Abstract
Background: Intravesical Bacillus Calmette Guerin (BCG) has been the gold standard immunotherapy to treat high risk non-muscle invasive bladder cancer (NMIBC) for over 40 years. Attenuation of Mycobacterium bovis for clinical use as BCG results in loss of its ability to activate the “Stimulator of Interferon Genes” (STING) pathway and potentially limits local anti-tumor immune activity and subsequent BCG responsiveness due to reduced induction of the immune cell recruiting chemokines primarily, CXCL10. We conducted the current study to determine the potential of STING pathway agonist in synergizing with BCG to enhance chemokine induction. Methods: The TICE strain of BCG (OncoTICE) was used in combination with STING agonist to determine STING pathway activation and CXCL10 production in THP-1 monocytic cell line, THP-1 defNLRP3, THP-1 dual STING knock out cells, RT112 bladder cancer cells and primary bladder epithelial cells. NanoString platform-based gene expression profiling and multiplex cytokine analysis were performed to determine induction of interferon associated genes and secreted cytokines. Results: Activation of cytosolic pattern recognition receptor and downstream IFN1 pathways demonstrated synergistic activation of STING pathway enhanced BCG induced inflammasome and STING pathway gene expression in monocytes and bladder cancer cells. The significant differences in CXCL10, CCL5, IL-8 and MIP-1a/1b amongst the knock-out cell lines confirm the convergence of these pathways following combination treatment with BCG and STING agonist. Conclusions: Findings from our study are the first evidence indicating that STING pathway activators are promising new innate immune modulators with a potential to synergize with BCG therapy in the treatment of NMIBC.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".