Trained Immunity Reprograms Tumor-Associated Myeloid Cells to Overcome Anti-PD-1 Resistance
Year: 2026
Type: article
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.
Source: Biology and Medical Engineering
DOI: 10.63808/bm.v2i1.571
Institutions: Chongqing University, Taishan Polytechnic
Language: English
Subfield: Immunology
Field: Immunology and Microbiology
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W7225757948