USP18 Inhibition Enhances Type I Interferon Signalling and Immune Activation in the Tumour Microenvironment of Triple-Negative Breast Cancer
Bibliographic record
Abstract
Abstract Triple-negative breast cancer (TNBC) is one of the most aggressive and treatment-resistant breast cancers. Although immunotherapy has emerged as a promising treatment option, clinical benefit is limited, with only around half of patients responding, even when combined with standard chemotherapeutic agents. This limited efficacy is often attributed to immunologically “cold” tumour microenvironments (TME), which are resistant to current immunotherapies. Addressing this challenge requires approaches that can reprogram “cold” TMEs into “hot” immune-responsive states. USP18, a negative regulator of type I interferon (IFN) signalling, suppresses immune activation by removing ISG15 from target proteins and disrupting IFNAR–STAT2 interactions. Here, we show that both genetic ablation and catalytic inactivation of USP18 enhance type I IFN signalling in TNBC cells, leading to sustained STAT1/STAT2 phosphorylation. This increased IFN responsiveness promotes antigen presentation via MHC-I upregulation and increases expression of pro-apoptotic ligands such as FAS. Proteomic profiling and immunophenotyping revealed that USP18 inhibition in vivo reduces tumour growth and increases immunogenicity, accompanied by cancer-immune infiltration modulation including CD8⁺ T cells, Th1 cells, NK cells, cDC1, and pro-inflammatory M1-like macrophages. These changes reflect a shift in the TME from an immunosuppressive to an immunostimulatory state, driven by heightened and prolonged type I IFN signalling. Our findings highlight the therapeutic potential of USP18 inhibition to convert immunologically “cold” tumours into “hot” tumours, by enhancing IFN-driven immune activation and improving the efficacy of immunotherapy in TNBCs.
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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".