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Stromal-myeloid-lymphoid crosstalk in immune evasion and immunotherapy resistance in solid tumors
Badar Raza1, Muhammad Usama1, Qingshuang Cai2
1Department of Synthetic Biology, School of Life Sciences, Anhui Agricultural University, Hefei 230036, China.
Abstract:
Despite the remarkable advances in cancer immunotherapy, immune evasion, relapse, and immunotherapy resistance remain major challenges in the fight against solid tumors. These limitations cannot be fully attributed to tumor-intrinsic changes; the interplay between stromal, myeloid, and lymphoid (SML) compartments in the tumor microenvironment (TME) strongly influences tumor response. This review summarizes the current understanding of SML crosstalk and how multicellular interactions control immune exclusion, suppression and immunotherapy resistance. We provide a narrative overview of the recent literature to discuss the spatial organization and functional states of stromal, myeloid, and lymphoid populations and the mechanisms involving chemokine-mediated trafficking, cytokine signaling, extracellular matrix remodeling, antigen presentation defects, metabolic competition, hypoxia, and immune checkpoint pathways. The evidence suggests that SML crosstalk creates interconnected regulatory circuits in which stromal remodeling fosters myeloid suppression and lymphoid dysfunction, which in turn increases tumor resistance to therapy. These networks are involved in primary and acquired resistance to immune checkpoint blockade and resistance to adoptive cell therapy, cancer vaccines and oncolytic virus therapy. Still, the evidence is limited by significant tumor heterogeneity, reliance on preclinical studies, and insufficient spatial and longitudinal validation as well as by the lack of standardization of biomarkers predictive of treatment outcomes. Future studies should focus on spatial multi-omics, functional experimental models and prospective clinical studies to delineate causal SML interactions and develop clinically relevant biomarkers. A more comprehensive understanding of SML ecosystem dynamics could provide a basis for more accurate patient stratification and appropriate combination therapies in cancer immunotherapy.