Post-translational modification-governed immune states in cancer immunity: Biomarker implications for checkpoint competence, tumor visibility and immunotherapy resistance (Review)
TL;DR
By organizing PTM biology around measurable state variables rather than modification class alone, the present review provides a phase-aware translational framework for candidate biomarker discovery, fit-for-purpose validation, constraint-guided stratification, and therapeutic prioritization in cancer immunotherapy.
Abstract
Immune escape and therapeutic resistance remain major obstacles to durable benefit from cancer immunotherapy, yet transcript-based or abundance-based biomarkers often fail to capture the regulatory states that determine effective immune control. Post-translational modifications (PTMs) form a dynamic protein-state layer that rapidly reshapes protein stability, trafficking, complex assembly, and signaling persistence under tumor-intrinsic and therapy-imposed stress. In the present review, a biomarker-oriented framework is proposed in which PTM biology is interpreted through three recurrent immune constraints: Checkpoint competence, tumor visibility and stress-conditioned immune-state programming. Within this framework, programmed death-ligand 1 is viewed as a protein-state biomarker problem rather than a static expression marker; tumor visibility is defined by durable antigen-presentation competence and interferon-linked reinforcement; and stress-driven immune dysfunction is interpreted through metabolite-sensitive PTM rewiring and chromatin-coupled suppressive stabilization. Rather than cataloguing PTMs comprehensively in cancer immunity, the present review focuses on five core exemplar PTM axes, glycosylation, palmitoylation, ubiquitin editing, phosphorylation and lactylation, because they repeatedly map to rate-limiting immune constraints, are supported by mechanistic evidence, and represent candidate assay-compatible or intervention-relevant state variables at differing levels of translational maturity. It is further outlined how integrated proteogenomic, immuno-peptidomic, and spatial datasets can be used to discover candidate PTM-state biomarkers, validate mechanism-proximal readouts in prespecified pretreatment and on-treatment settings, and prioritize single or co-dominant state constraints for patient stratification, pharmacodynamic monitoring, and rational combination design. By organizing PTM biology around measurable state variables rather than modification class alone, the present review provides a phase-aware translational framework for candidate biomarker discovery, fit-for-purpose validation, constraint-guided stratification, and therapeutic prioritization in cancer immunotherapy.