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ONCOLOGY, NUCLEAR MEDICINE AND TRANSPLANTOLOGY
Review Article

Tumor–Immune Interactions in Prostate Cancer: Insights from Single-Cell and Spatial Genomics

Oncology, Nuclear Medicine and Transplantology, 2(2), 2026, onmt020, https://doi.org/10.63946/onmt/18860
Publication date: Jun 26, 2026
Full Text (PDF)

ABSTRACT

Prostate cancer still remains one of the most common cancers in men worldwide, and it is a great therapeutic challenge, especially in the field of immunotherapeutics. The tumour microenvironment (TME) is immunologically “cold” in prostate cancer, and influenced by intrinsic molecular characteristics of the disease such as androgen receptor (AR) signalling, PTEN loss, and lineage plasticity towards neuroendocrine prostate cancer (NEPC). Together, these aspects inhibit antigen presentation, block the entry of cytotoxic T cells and help to establish spatially organised immunosuppressive niches, providing a rational explanation for the clinical variability and partial efficacy of immune-based therapies.
Traditional bulk genomic approaches have provided important insights into tumour biology but are unable to capture the cellular and spatial complexity of tumour–immune interactions. These developments have been spurred by recent advancements in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics, which allow to detect individual cell subpopulations within intact tumour tissues, such as exhausted T cells co-expressing PD-1, TIM-3, LAG-3 and TIGIT, immunosuppressive SPP1+ macrophages and various cancer-associated fibroblast subpopulations. These technologies have identified specific immune exclusion sites, stromal–epithelial immune silencing barriers, and therapeutic resistance and immune evasion regulatory programs in the context of prostate cancer specifically.
However, there are still many technical challenges that need to be overcome, such as the lack of patient samples and their demographic diversity, data integration, lack of spatial characterisation of bone metastases and difficulties in clinical translation. Comprehensive multi-omics atlases, AI-driven spatial pattern recognition, functional validation of potential targets and prospective clinical trials based on biomarkers are all important areas for future research. They show significant potential for the creation of better, personalized immunotherapeutic treatment for prostate cancer.

KEYWORDS

Prostate Cancer Tumour Microenvironment Tumour–Immune Interactions Single-Cell RNA Sequencing Spatial Transcriptomics Androgen Receptor Signalling PTEN Loss T-Cell Exhaustion Immunotherapy Resistance Precision Oncology

CITATION (Vancouver)

Ogboh RO, Bright E, Effa AA, Okoro BC, Anyabuoke AK. Tumor–Immune Interactions in Prostate Cancer: Insights from Single-Cell and Spatial Genomics. Oncology, Nuclear Medicine and Transplantology. 2026;2(2):onmt020. https://doi.org/10.63946/onmt/18860
APA
Ogboh, R. O., Bright, E., Effa, A. A., Okoro, B. C., & Anyabuoke, A. K. (2026). Tumor–Immune Interactions in Prostate Cancer: Insights from Single-Cell and Spatial Genomics. Oncology, Nuclear Medicine and Transplantology, 2(2), onmt020. https://doi.org/10.63946/onmt/18860
Harvard
Ogboh, R. O., Bright, E., Effa, A. A., Okoro, B. C., and Anyabuoke, A. K. (2026). Tumor–Immune Interactions in Prostate Cancer: Insights from Single-Cell and Spatial Genomics. Oncology, Nuclear Medicine and Transplantology, 2(2), onmt020. https://doi.org/10.63946/onmt/18860
AMA
Ogboh RO, Bright E, Effa AA, Okoro BC, Anyabuoke AK. Tumor–Immune Interactions in Prostate Cancer: Insights from Single-Cell and Spatial Genomics. Oncology, Nuclear Medicine and Transplantology. 2026;2(2), onmt020. https://doi.org/10.63946/onmt/18860
Chicago
Ogboh, Rita Onyebuchi, Ejembi Bright, Adebola Ayisat Effa, Blessing Chinonye Okoro, and Anthony Kosisochukwu Anyabuoke. "Tumor–Immune Interactions in Prostate Cancer: Insights from Single-Cell and Spatial Genomics". Oncology, Nuclear Medicine and Transplantology 2026 2 no. 2 (2026): onmt020. https://doi.org/10.63946/onmt/18860
MLA
Ogboh, Rita Onyebuchi et al. "Tumor–Immune Interactions in Prostate Cancer: Insights from Single-Cell and Spatial Genomics". Oncology, Nuclear Medicine and Transplantology, vol. 2, no. 2, 2026, onmt020. https://doi.org/10.63946/onmt/18860

REFERENCES

  1. Kratzer TB, Mazzitelli N, Star J, Dahut WL, Jemal A, Siegel RL. Prostate cancer statistics, 2025. CA Cancer J Clin. 2025;75(6):485–497. https://doi.org/10.3322/caac.21875
  2. Jiang Y, Wen W, Yang F, Han D, Zhang W, Qin W. Prospect of prostate cancer treatment: armed CAR-T or combination therapy. Cancers (Basel). 2022;14(4):967.https://doi.org/10.3390/cancers14040967
  3. Hatano K, Nonomura N. Genomic profiling of prostate cancer: an updated review. World J Mens Health. 2022;40(3):368-379.https://doi.org/10.5534/wjmh.210072
  4. Tiwari A, Trivedi R, Lin SY. Tumor microenvironment: barrier or opportunity towards effective cancer therapy. J Biomed Sci. 2022;29:83.https://doi.org/10.1186/s12929-022-00866-3
  5. Inayatullah M, Dwivedi AK, Tiwari VK. Advances in single-cell omics: transformative applications in basic and clinical research. Curr Opin Cell Biol. 2025;95:102548.https://doi.org/10.1016/j.ceb.2025.102548
  6. Liu Y, Dai Y, Wang L. Spatial omics at the forefront: emerging technologies, analytical innovations, and clinical applications. Cancer Cell. 2026;44(1):24-49.https://doi.org/10.1016/j.ccell.2025.11.003
  7. Kim DY, Lee J, Choi J, et al. Spatial multi-omics in precision medicine. Semin Cancer Biol. 2026;119:24-37.https://doi.org/10.1016/j.semcancer.2026.01.005
  8. Huang J, Ojo A, Tsao S, et al. Overcoming immune evasion in the prostate tumor microenvironment. Cancers (Basel). 2025;17(21):3441.https://doi.org/10.3390/cancers17213441
  9. Novysedlak R, Guney M, Al Khouri M, et al. The immune microenvironment in prostate cancer: a comprehensive review. Oncology. 2025;103(6):521-545.https://doi.org/10.1159/000542308
  10. Ansems M, Span PN. The tumor microenvironment and radiotherapy response: a central role for cancer-associated fibroblasts. Clin Transl Radiat Oncol. 2020;22:90-97.https://doi.org/10.1016/j.ctro.2020.04.001
  11. Rai V. Immune checkpoint inhibitor therapy for prostate cancer. Biomolecules. 2025;15(6):751.https://doi.org/10.3390/biom15060751
  12. Li J, Wang X, Tang X. Characteristics and therapeutic resistance mechanisms of the prostate cancer immune microenvironment. Front Pharmacol. 2026;17:1769271.https://doi.org/10.3389/fphar.2026.1769271
  13. Ortega-Batista A, Jaen-Alvarado Y, Moreno-Labrador D, et al. Single-cell sequencing: genomic and transcriptomic approaches in cancer cell biology. Int J Mol Sci. 2025;26(5):2074.https://doi.org/10.3390/ijms26052074
  14. Bhat GR, Sethi I, Sadida HQ, et al. Cancer cell plasticity: from cellular, molecular, and genetic mechanisms to tumor heterogeneity and drug resistance. Cancer Metastasis Rev. 2024;43(1):197-228.https://doi.org/10.1007/s10555-024-10172-z
  15. Liu C, Zeng F, Gao S, et al. Single-cell profiling identifies heterogeneity of the immune microenvironment in breast cancer. Front Immunol. 2025;16:1690992.https://doi.org/10.3389/fimmu.2025.1690992
  16. Nair R, Somasundaram V, Kuriakose A, et al. Deciphering T-cell exhaustion in the tumor microenvironment. Front Immunol. 2025;16:1548234.https://doi.org/10.3389/fimmu.2025.1548234
  17. Chuntova P, Chowdhury K, Varn FS. Immunomodulatory roles of myeloid cells in gliomas. In: Lim M, Weller M, editors. Immunotherapeutic Strategies for the Treatment of Glioma. Academic Press; 2022.https://doi.org/10.1016/B978-0-12-819755-4.00010-2
  18. Molla Desta G, Birhanu AG. Advancements in single-cell RNA sequencing and spatial transcriptomics. Acta Biochim Pol. 2025;72:13922.https://doi.org/10.3389/abp.2025.13922
  19. Ray U, Singh A, Samanta D, Dhar R. Gene regulatory network transitions reveal central transcription factors in lung adenocarcinoma. NPJ Syst Biol Appl. 2026;12(1):18.https://doi.org/10.1038/s41540-025-00640-9
  20. Zhu T, Teng X, Jiao Q, et al. T-cell exhaustion from a multiomics perspective. Clin Transl Med. 2026;16(2):e70609.https://doi.org/10.1002/ctm2.70609
  21. Tanay A, Regev A. Single cell genomics: from phenomenology to mechanism. Nature. 2017;541(7637):331-338.https://doi.org/10.1038/nature21350
  22. Trapnell C. Defining cell types and states with single-cell genomics. Genome Res. 2015;25(10):1491-1498.https://doi.org/10.1101/gr.190595.115
  23. Jovic D, Liang X, Zeng H, et al. Single-cell RNA sequencing technologies and applications. Clin Transl Med. 2022;12(3):e694.https://doi.org/10.1002/ctm2.694
  24. Li JR, Pan X, Lin Y, et al. Spatial proximity of immune cell pairs to cancer cells in the TME as biomarkers for patient stratification. Cancers (Basel). 2025;17(14):2335.https://doi.org/10.3390/cancers17142335
  25. Li J, Zhang L, Liu R, et al. CXCL12/CXCR4 axis governs Treg spatial dominance over CD8+ T cells via IL-2 sequestration. Front Immunol. 2025;16:1626708.https://doi.org/10.3389/fimmu.2025.1626708
  26. Chelebian E, Avenel C, Wahlby C. Combining spatial transcriptomics with tissue morphology. Nat Commun. 2025;16(1):4452.https://doi.org/10.1038/s41467-025-58989-8
  27. Zheng S, Wang W, Shen L, et al. Tumor battlefield within inflamed, excluded or desert immune phenotypes. Exp Hematol Oncol. 2024;13(1):80.https://doi.org/10.1186/s40164-024-00543-1
  28. Cen X, Huang X, Deng E, et al. Single-cell and spatial omics: methods and applications. MedComm. 2026;7(4):e70713.https://doi.org/10.1002/mco2.70713
  29. Wan X, Xiao J, Tam SST, et al. Integrating spatial and single-cell transcriptomics using deep generative models with SpatialScope. Nat Commun. 2023;14(1):7848. https://doi.org/10.1038/s41467-023-43629-w
  30. Wang R, Peng G, Tam PPL, Jing N. Integration of computational analysis and spatial transcriptomics in single-cell studies. Genomics Proteomics Bioinformatics. 2023;21(1):13-23.https://doi.org/10.1016/j.gpb.2022.10.004
  31. Liu B, Zhou H, Tan L, et al. Exploring treatment options in cancer: tumor treatment strategies. Signal Transduct Target Ther. 2024;9(1):175.https://doi.org/10.1038/s41392-024-01877-2
  32. Bian X, Wang W, Abudurexiti M, et al. Integration analysis of single-cell multi-omics reveals prostate cancer heterogeneity. Adv Sci. 2024;11:2305724.https://doi.org/10.1002/advs.202305724
  33. Sooi K, Walsh R, Kumarakulasinghe N, et al. Strategies to overcome immune resistance in the treatment of advanced prostate cancer. Cancer Drug Resist. 2023;6(3):656-673.https://doi.org/10.20517/cdr.2023.48
  34. Mohr AE, Ortega-Santos CP, Whisner CM, et al. Navigating challenges in multi-omics integration for personalized healthcare. Biomedicines. 2024;12(7):1496.https://doi.org/10.3390/biomedicines12071496
  35. Flores-Tellez TNJ, Baena E. Experimental challenges to modeling prostate cancer heterogeneity. Cancer Lett. 2022;524:194-205.https://doi.org/10.1016/j.canlet.2021.10.014
  36. Jiang Z, Zhang H, Gao Y, Sun Y. Multi-omics strategies for biomarker discovery in personalized oncology. Mol Biomed. 2025;6(1):115.https://doi.org/10.1186/s43556-025-00340-0
  37. Heo YJ, Hwa C, Lee GH, et al. Integrative multi-omics approaches in cancer research. Mol Cells. 2021;44(7):433-443.https://doi.org/10.14348/molcells.2021.0042
  38. Yetgin A. Revolutionizing multi-omics analysis with artificial intelligence and data processing. Quant Biol. 2025;13(3):e70002.https://doi.org/10.1002/qub2.70002
  39. Bataba E, Babcock K, Isensee KA, et al. Germline mutations and ancestry in prostate cancer. Curr Oncol Rep. 2024;26(2):175-180.https://doi.org/10.1007/s11912-024-01493-x

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