Small-molecule MMP2/MMP9 inhibitor SB-3CT modulates tumor immune surveillance by regulating PD-L1
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint blockade (ICB) therapy has demonstrated considerable clinical benefit in several malignancies, but has shown favorable response in only a small proportion of cancer patients. Recent studies have shown that matrix metalloproteinases (MMPs) are highly associated with the microenvironment of tumors and immune cells. However, it is unknown whether MMPs are involved in immunotherapy. METHODS: Here, we used integrative analysis to explore the expression landscape of the MMP family and its association with immune features across multiple cancer types. We used T cell cytotoxicity-mediated tumor killing assay to determine the co-cultured T cell activity of SB-3CT, an MMP2/9 inhibitor. We then used in vitro assays to examine the regulating roles of SB-3CT on PD-L1. We further characterized the efficacy of SB-3CT, in combination with anti-PD-1 and/or anti-CTLA4 treatment in mouse models with melanoma and lung cancer. RESULTS: Our computational analysis demonstrated a strong association between MMP2/9 and immune features. We demonstrated that inhibition of MMP2/9 by SB-3CT significantly reduced the tumor burden and improved survival time by promoting anti-tumor immunity. Mechanistically, we showed that SB-3CT treatment significantly diminished both mRNA and protein levels of PD-L1 in cancer cells. Pre-clinically, SB-3CT treatment enhanced the therapeutic efficacy of PD-1 or CTLA-4 blockade in the treatment of both primary and metastatic tumors. CONCLUSIONS: Our study unraveled novel molecular mechanisms regarding the regulation of tumor PD-L1 and provided a novel combination therapeutic strategy of SB-3CT and ICB therapy to enhance the efficacy of immunotherapy.
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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) | 0.001 | 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".