Single-Cell Analysis Reveals EP4 as a Target for Restoring T-Cell Infiltration and Sensitizing Prostate Cancer to Immunotherapy
Bibliographic record
Abstract
Abstract Purpose: Immunotherapies targeting immune checkpoint molecules have shown promising treatment for a subset of cancers; however, many “cold” tumors, such as prostate cancer, remain unresponsive. We aimed to identify a potential targetable marker relevant to prostate cancer and develop novel immunotherapy. Experimental Design: Analysis of transcriptomic profiles at single-cell resolution was performed in clinical patients' samples, along with integrated analysis of multiple RNA-sequencing datasets. The antitumor activity of YY001, a novel EP4 antagonist, combined with anti–programmed cell death protein 1 (PD-1) antibody was evaluated both in vitro and in vivo. Results: We identified EP4 (PTGER4) as expressed in epithelial cells and various immune cells and involved in modulating the prostate cancer immune microenvironment. YY001, a novel EP4 antagonist, inhibited the differentiation, maturation, and immunosuppressive function of myeloid-derived suppressor cells (MDSC) while enhancing the proliferation and anticancer functions of T cells. Furthermore, it reversed the infiltration levels of MDSCs and T cells in the tumor microenvironment by overturning the chemokine profile of tumor cells in vitro and in vivo. The combined immunotherapy demonstrated a robust antitumor immune response as indicated by the robust accumulation and activation of CD8+ cytotoxic T cells, with a significantly decreased MDSC ratio and reduced MDSC immunosuppression function. Conclusions: Our study identified EP4 as a specific target for prostate cancer immunotherapy and demonstrated that YY001 inhibited the growth of prostate tumors by regulating the immune microenvironment and strongly synergized with anti–PD-1 antibodies to convert completely unresponsive prostate cancers into responsive cancers, resulting in marked tumor regression, long-term survival, and lasting immunologic memory.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".