| Summary: | Abstract Background Despite recent advances in cancer immunotherapy, the efficacy of these therapies for the treatment of human prostate cancer patients is low due to the complex immune evasion mechanisms (IEMs) of prostate cancer and the lack of predictive biomarkers for patient responses. Methods To understand the IEMs in prostate cancer and apply such understanding to the design of personalized immunotherapies, we analyzed the RNA-seq data for prostate adenocarcinoma from The Cancer Genome Atlas (TCGA) using a combination of biclustering, differential expression analysis, immune cell typing, and machine learning methods. Results The integrative analysis identified eight clusters with different IEM combinations and predictive biomarkers for each immune evasion cluster. Prostate tumors employ different combinations of IEMs. The majority of prostate cancer patients were identified with immunological ignorance (89.8%), upregulated cytotoxic T lymphocyte-associated protein 4 (CTLA4) (58.8%), and upregulated decoy receptor 3 (DcR3) (51.6%). Among patients with immunologic ignorance, 41.4% displayed upregulated DcR3 expression, 43.26% had upregulated CTLA4, and 11.4% had a combination of all three mechanisms. Since upregulated programmed cell death 1 (PD-1) and/or CTLA4 often co-occur with other IEMs, these results provide a plausible explanation for the failure of immune checkpoint inhibitor monotherapy for prostate cancer. Conclusion These findings indicate that human prostate cancer specimens are mostly immunologically cold tumors that do not respond well to mono-immunotherapy. With such identified biomarkers, more precise treatment strategies can be developed to improve therapeutic efficacy through a greater understanding of a patient’s immune evasion mechanisms.
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