Immune landscape of human prostate cancer: immune evasion mechanisms and biomarkers for personalized immunotherapy

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.

Keywords: Biomarkers; Combination therapy; Immune evasion; Immunotherapy; Prostate cancer.

Fig. 1

Immune cell abundance in the eight identified immune clusters. The distribution of lymphocyte abundance (A), cytotoxic T lymphocytes (CTL) (B), regulatory T cells (Treg) (C), and total natural killer (NK) cells (D). The asterisks indicate statistical significance compared to the normal tissues (* p-value < 0.05; ** p-value < 0.01; *** p-value < 0.001)

Fig. 2

The different IEMs in prostate cancer at different steps of the cancer-immunity cycle. After analyzing the levels of gene expression of immune-related genes in the clusters compared to adjacent normal samples, we identified the IEMs activated at different steps of the cancer-immunity cycle

Fig. 3

Classification tree with 10 predictive biomarkers of patients’ immune evasion clusters (CL) and response to immunotherapy. Cluster of differentiation 48 (CD48), Speckled 140 KDa (SP140), Kin Of IRRE Like (KIRREL), Rho-Related GTP-Binding Protein RhoB (RHOB), F-Box Protein 17 (FBXO17), Anaphase Promoting Complex Subunit 1 (ANAPC1), Epidermal growth factor receptor (EGFR), Suppressor Of Cytokine Signaling 3 (SOCS3), Arachidonate 15-Lipoxygenase (ALOX15), Ubiquitin Protein Ligase E3 Component N-Recognin 2 (UBR2)