Multiomic immune microenvironment signatures associated with response and resistance to immune checkpoint blockade across solid tumours

Abstract

Abstract The treatment of many solid tumours has been revolutionized by immune checkpoint inhibitors (ICIs), although clinical results are still quite erratic and unexpected. The intricate biology that controls treatment outcomes is not captured by conventional biomarkers like PD-L1 expression, tumour mutational burden, and microsatellite instability, which offer limited predictive accuracy. A growing body of research indicates that the tumour immune microenvironment (TME), which reflects dynamic interactions between immune cells, stromal elements, cytokine networks, metabolic signals, and tumour-intrinsic pathways, is crucial in determining responsiveness or resistance to ICIs. A deeper understanding of these interactions has been made possible by advances in multi-omic technologies, such as genomics, transcriptomics, epigenomics, proteomics, metabolomics, spatial profiling, and single-cell analyses. These technologies have also identified important immune-microenvironment signatures linked to therapeutic success or failure. Clinical results are influenced by several TME immunophenotypes, T-cell activation and exhaustion stages, antigen-presentation ability, myeloid-derived suppression, stromal barriers, metabolic reprogramming, and interferon signaling. The development of composite biomarkers that provide more precise and biologically based prediction of ICI response is currently supported by emerging multi-omic and spatial techniques. This review highlights the fundamental processes of primary and acquired resistance, summarizes the current understanding of multi-omic immune-microenvironment signatures across major solid tumours, and addresses recent translational developments that are propelling the next generation of predictive biomarkers. This review proposes a framework for improving patient stratification and informing precision immunotherapy across diverse solid tumour contexts by integrating mechanistic insights with emerging technological platforms.