Trained Immunity Reprograms Tumor-Associated Myeloid Cells to Overcome Anti-PD-1 Resistance

Trained Immunity Reprograms Tumor-Associated Myeloid Cells to Overcome Anti-PD-1 Resistance

Authors

DOI:

https://doi.org/10.63808/bm.v2i1.571

Keywords:

Trained immunity, Tumor-associated myeloid cells, Immune checkpoint inhibitors, Anti-PD-1 resistance, Tumor microenvironment, Myeloid reprogramming

Abstract

This study explores whether trained immunity can remodel the tumor immune microenvironment by reprogramming tumor-associated myeloid cells (TAMs) and reverse tumor resistance to anti-PD-1 therapy. An anti-PD-1 resistant tumor model was established, using β-glucan as a trained immunity inducer combined with anti-PD-1 antibody intervention. Single-cell RNA sequencing, flow cytometry, and metabolic analysis were employed to assess TAMs phenotype, antigen presentation capacity, and metabolic reprogramming; in vitro co-culture and in vivo CD8⁺ T cell depletion experiments were used to evaluate myeloid cell-mediated T cell activation and treatment dependency; clinical cohort analyses were combined to explore the relationship between trained immunity-related gene signatures and immune checkpoint therapy response. Trained immunity significantly reshaped TAMs transcriptional profiles and metabolic status, promoting their transformation toward a pro-inflammatory, enhanced antigen-presenting phenotype, while increasing T cell recruitment and activation and reducing immunosuppressive function. In the anti-PD-1 resistant model, trained immunity combined with anti-PD-1 significantly inhibited tumor growth and prolonged survival, with this effect dependent on CD8⁺ T cells and type I interferon signaling. Clinical cohort analysis indicated that high expression of trained immunity-related signatures was associated with better responses to immune checkpoint therapy. Trained immunity can overcome anti-PD-1 resistance by reprogramming tumor-associated myeloid cells, offering a translational potential innate immune intervention strategy to improve immune checkpoint therapy response.

References

[1] Bekkering, S., Arts, R. J. W., Novakovic, B., et al. (2018). Metabolic induction of trained immunity through the mevalonate pathway. Cell, 172(1-2), 135-146.e9. https://doi.org/10.1016/j.cell.2017.11.025

[2] Cassetta, L., & Pollard, J. W. (2018). Targeting macrophages: Therapeutic approaches in cancer. Nat Rev Drug Discov, 17(12), 887-904. https://doi.org/10.1038/nrd.2018.169

[3] DeNardo, D. G., & Ruffell, B. (2019). Macrophages as regulators of tumour immunity and immunotherapy. Nat Rev Immunol, 19(6), 369-382. https://doi.org/10.1038/s41577-019-0127-6

[4] Ding, C., Shrestha, R., Zhu, X., et al. (2023). Inducing trained immunity in pro-metastatic macrophages to control tumor metastasis. Nat Immunol, 24(2), 239-254. https://doi.org/10.1038/s41590-022-01388-8

[5] Gabrilovich, D. I., & Nagaraj, S. (2009). Myeloid-derived suppressor cells as regulators of the immune system. Nat Rev Immunol, 9(3), 162-174. https://doi.org/10.1038/nri2506

[6] Jenkins, R. W., Barbie, D. A., & Flaherty, K. T. (2018). Mechanisms of resistance to immune checkpoint inhibitors. Br J Cancer, 118(1), 9-16. https://doi.org/10.1038/bjc.2017.434

[7] Kalafati, L., Kourtzelis, I., Schulte-Schrepping, J., et al. (2020). Innate immune training of granulopoiesis promotes anti-tumor activity. Cell, 183(3), 771-785.e12. https://doi.org/10.1016/j.cell.2020.09.058

[8] Lérias, J. R., de Sousa, E., Paraschoudi, G., et al. (2020). Trained immunity for personalized cancer immunotherapy: Current knowledge and future opportunities. Front Microbiol, 10, 2924. https://doi.org/10.3389/fmicb.2019.02924

[9] Liu, G. (2026a). Tumor microenvironment: Composition, dynamic evolution, and its significance in cancer treatment. Journal of Cancer Insights, 1(1), 6. https://doi.org/10.66128/jci202601.1

[10] Liu, G. (2026b). The application of artificial intelligence in tumor immunotherapy: Integrating multi-omics data to achieve efficacy prediction and combination strategies. Applied Artificial Intelligence Research, 2(2). https://doi.org/10.65455/q72yq328

[11] Liu, G., & Wang, J. (2026). TOX phase separation locks T cells in a terminal exhausted state, and disrupting it can revive anti-tumor immunity. Biology and Medical Engineering, 2(1), 538. https://doi.org/10.63808/bm.v2i1.538

[12] Liu, G., Vasiukevich, A. M., & Wang, J. (2026f). Lactate-driven histone lactylation weakens B cell immunity in tertiary lymphoid structures. Injury and Rehabilitation Care, 2(1), 539. https://doi.org/10.63808/irc.v2i1.539

[13] Liu, G., Wang, J., & An, L. (2026b). CRISPR screen and single-cell multi-omics identify a novel immune checkpoint on tumor-infiltrating regulatory T cells. Injury and Rehabilitation Care, 2(1), 525. https://doi.org/10.63808/irc.v2i1.525

[14] Liu, G., Wang, J., & An, L. (2026c). Gut microbiota-derived butyrate induces trained immunity in intestinal macrophages to potentiate antitumor immunity. Biology and Medical Engineering, 2(1), 523. https://doi.org/10.63808/bm.v2i1.523

[15] Liu, G., Wang, J., & An, L. (2026e). Mitochondrial DNA sensing reshapes the tumor immune microenvironment via cooperative cGAS-STING activation and ferroptosis. Global Health Care, 2(1), 524. https://doi.org/10.63808/ghc.v2i1.524

[16] Liu, G., Wang, J., & Vasiukevich, A. M. (2026a). Overcoming anti-PD-1 resistance: Targeted STING activation reprograms tumor-associated macrophages. Global Health Care, 2(1), 540. https://doi.org/10.63808/ghc.v2i1.540

[17] Liu, G., Wang, J., & Zhu J. (2026g). Explainable spatial AI analyzes tumor-immune interactions to predict immunotherapy outcomes and identify new targets. Intelligent & Human Futures, 2(3), 510. https://doi.org/10.63808/ihf.v2i3.510

[18] Liu, G., Wang, J., & Zhu J. (2026h). Using multimodal foundational models to predict neoantigen immunogenicity and vaccine effectiveness across different tumor types. Global Health Care, 2(3), 498. https://doi.org/10.63808/ghc.v2i3.498

[19] Liu, G., Wang, J., Vasiukevich, A. M., Feng, B., & An, L. (2026d). Targeting mitochondrial metabolic reprogramming: Reversing CD8⁺ T cell exhaustion and improving cancer immunotherapy response. Biology and Medical Engineering, 2(1), 531. https://doi.org/10.63808/bm.v2i1.531

[20] Mantovani, A., Marchesi, F., Malesci, A., Laghi, L., & Allavena, P. (2017). Tumour-associated macrophages as treatment targets in oncology. Nat Rev Clin Oncol, 14(7), 399-416. https://doi.org/10.1038/nrclinonc.2016.217

[21] Netea, M. G., Domínguez-Andrés, J., Barreiro, L. B., et al. (2020). Defining trained immunity and its role in health and disease. Nat Rev Immunol, 20(6), 375-388. https://doi.org/10.1038/s41577-020-0285-6

[22] Netea, M. G., Joosten, L. A., Latz, E., et al. (2016). Trained immunity: A program of innate immune memory in health and disease. Science, 352(6284), aaf1098. https://doi.org/10.1126/science.aaf1098

[23] Netea, M. G., Quintin, J., & van der Meer, J. W. (2011). Trained immunity: A memory for innate host defense. Cell Host Microbe, 9(5), 355-361. https://doi.org/10.1016/j.chom.2011.04.006

[24] Noy, R., & Pollard, J. W. (2014). Tumor-associated macrophages: From mechanisms to therapy. Immunity, 41(1), 49-61. https://doi.org/10.1016/j.immuni.2014.06.010

[25] Pathria, P., Louis, T. L., & Varner, J. A. (2019). Targeting tumor-associated macrophages in cancer. Trends Immunol, 40(4), 310-327. https://doi.org/10.1016/j.it.2019.02.003

[26] Priem, B., van Leent, M. M. T., Teunissen, A. J., et al. (2020). Trained immunity-promoting nanobiologic therapy suppresses tumor growth and potentiates checkpoint inhibition. Cell, 183(3), 786-801.e19. https://doi.org/10.1016/j.cell.2020.09.059

[27] Quintin, J., Saeed, S., Martens, J. H. A., et al. (2012). Candida albicans infection affords protection against reinfection via functional reprogramming of monocytes. Cell Host Microbe, 12(2), 223-232. https://doi.org/10.1016/j.chom.2012.06.006

[28] Riksen, N. P., & Netea, M. G. (2021). Immunometabolic control of trained immunity. Mol Aspects Med, 77, 100897. https://doi.org/10.1016/j.mam.2020.100897

[29] Ruffell, B., & Coussens, L. M. (2015). Macrophages and therapeutic resistance in cancer. Cancer Cell, 27(4), 462-472. https://doi.org/10.1016/j.ccell.2015.02.015

[30] Saeed, S., Quintin, J., Kerstens, H. H., et al. (2014). Epigenetic programming of monocyte-to-macrophage differentiation and trained innate immunity. Science, 345(6204), 1251086. https://doi.org/10.1126/science.1251086

[31] Sharma, P., Hu-Lieskovan, S., Wargo, J. A., & Ribas, A. (2017). Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell, 168(4), 707-723. https://doi.org/10.1016/j.cell.2017.01.017

[32] Topalian, S. L., Drake, C. G., & Pardoll, D. M. (2015). Immune checkpoint blockade: A common denominator approach to cancer therapy. Cancer Cell, 27(4), 450-461. https://doi.org/10.1016/j.ccell.2015.03.001

[33] Van der Heijden, C. D. C. C., Noz, M. P., Joosten, L. A. B., Netea, M. G., Riksen, N. P., & Keating, S. T. (2018). Epigenetics and trained immunity. Antioxid Redox Signal, 29(11), 1023-1040. https://doi.org/10.1089/ars.2017.7310

[34] Xiang, X., Wang, J., Lu, D., & Xu, X. (2021). Targeting tumor-associated macrophages to synergize tumor immunotherapy. Signal Transduct Target Ther, 6(1), 75. https://doi.org/10.1038/s41392-021-00484-9

Downloads

Published

2026-11-10

Similar Articles

You may also start an advanced similarity search for this article.

Loading...