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Immune Microenvironment and Response in Prostate Cancer Using Large Population Cohorts

ABSTRACT: Immune microenvironment of prostate cancer (PCa) is implicated in disease progression. However, previous studies have not fully explored PCa immune microenvironment. This study used ssGSEA algorithm to explore expression levels of 53 immune terms in a combined PCa cohort (eight cohorts; 1,597 samples). The top 10 immune terms were selected based on the random forest analysis and used for immune-related risk score (IRS) calculation. Furthermore, we explored differences in clinical and genomic features between high and low IRS groups. An IRS signature based on the 10 immune terms showed high prediction potential for PCa prognosis. Patients in the high IRS group showed significantly higher percentage of immunotherapy response factors, implying that IRS is effective in predicting immunotherapy response rate. Furthermore, consensus clustering was performed to separate the population into three IRSclusters with different clinical outcomes. Patients in IRScluster3 showed the worst prognosis and highest immunotherapy response rate. On the other hand, patients in IRScluster2 showed better prognosis and low immunotherapy response rate. In addition, VGLL3, ANPEP, CD38, CCK, DPYS, CST2, COMP, CRISP3, NKAIN1, and F5 genes were differentially expressed in the three IRSclusters. Furthermore, CMap analysis showed that five compounds targeted IRS signature, thioridazine, trifluoperazine, 0175029-0000, trichostatin A, and fluphenazine. In summary, immune characteristics of PCa tumor microenvironment was explored and an IRS signature was constructed based on 10 immune terms. Analysis showed that this signature is a useful tool for prognosis and prediction of immunotherapy response rate of PCa.

SUBMITTER: Ren X

PROVIDER: S-EPMC8585452 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Prostate cancer is usually considered as immune "cold" tumor with poor immunogenic response and low density of tumor-infiltrating immune cells, highlighting the need to explore clinically actionable strategies to sensitize prostate cancer to immunotherapy. In this study, we investigated whether docetaxel-based chemohormonal therapy induces immunologic changes and potentiates checkpoint blockade immunotherapy in prostate cancer. Methods: We performed transcriptome and histopathology analysis to characterize the changes of prostate cancer immune microenvironment before and after docetaxel-based chemohormonal therapy. Furthermore, we investigated the therapeutic benefits and underlying mechanisms of chemohormonal therapy combined with anti-PD1 blockade using cellular experiments and xenograft prostate cancer models. Finally, we performed a retrospective cohort analysis to evaluate the antitumor efficacy of anti-PD1 blockade alone or in combination with docetaxel-based chemotherapy. Results: Histopathology assessments on patient samples confirmed the enrichment of tumor-infiltrating T cells after chemohormonal therapy. Moreover, we found that docetaxel activated the cGAS/STING pathway in prostate cancer, subsequently induced IFN signaling, resulting in lymphocytes infiltration. In a xenograft mouse model, docetaxel-based chemohormonal therapy prompted the intratumoral infiltration of T cells and upregulated the abundance of PD1 and PD-L1, thereby sensitizing mouse tumors to the anti-PD1 blockade. To determine the clinical significance of these results, we retrospectively analyzed a cohort of 30 metastatic castration-resistant prostate cancer patients and found that docetaxel combined with anti-PD1 blockade resulted in better prostate-specific antigen progression-free survival when compared with anti-PD1 blockade alone. Conclusions: Our study demonstrates that docetaxel activates the antitumoral immune response and facilitates T cell infiltration in a cGAS/STING-dependent manner, providing a combination immunotherapy strategy that would improve the clinical benefits of immunotherapy.

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Immune Microenvironment and Response in Prostate Cancer Using Large Population Cohorts

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