用三组学解码肿瘤微环境：在同一切片上对蛋白-蛋白相互作用、RNA和蛋白标志物进行自动化空间分析

Understanding the complexity of the tumor microenvironment (TME) requires simultaneous insight into multiple biological domains. Studying the molecular interactions that govern intercellular and intracellular signaling in combination with the analysis of cell phenotypes and transcriptional states, can guarantee an improved comprehension of key pathways driving tumor growth or the response of anti-cancer therapies. Protein-protein interactions (PPIs), such as PD-1/PD-L1, are central to immune evasion and targets of important immunotherapies. Despite the success of PD-1/PD-L1 and other checkpoint inhibitors, patient stratification for these therapies has been challenging, and marker expression alone has shown to not fully capture functional engagement or predict an efficient drug response. Here, we present a fully automated workflow that combines three omics layers on the same tissue section: protein-protein proximity, RNA, and protein expression, enabling a more comprehensive view of cellular interplay in cancer and modeling of treatment outcomes. The multiomics assay runs on the COMET™ platform. It allows the co-detection of: (i) RNA profiling via RNAscope™ HiPlex Pro, (ii) Protein expression through sequential immunofluorescence (seqIF™, PMID: 37813886), (iii) Protein-protein proximity detection using oligonucleotide-conjugated secondary antibody pairs and RNAscope™ amplification chemistry. Proximity signals are interpreted as probabilistic indicators of molecular interactions and supported by multiple controls to ensure their specificity: from the colocalization of seqIF™ signals to negative controls run on the same section. In this study, we demonstrated that it is possible to combine the detection of proximity signals for the analysis of intercellular interactions controlling anti-tumoral immune responses, alongside protein markers for cell phenotyping and RNA targets for functional markers and cell activation status. In detail, across multiple human FFPE tumor samples, PD-1/PD-L1 interaction was detected in combination with multiple proteins, for immune and stromal phenotyping, and RNA transcripts responsible for the expression of key secreted molecules like cytokines and chemokines. Furthermore, we showed that multiple iterative cycles allow the detection of more than one PPI on the same FFPE section. Tumor progression, immune evasion, and therapy resistance are not driven by single molecules but by complex interaction networks among proteins, signaling pathways, and cellular types. This automated spatial multiomics workflow incorporating protein-protein proximity as a third dimension, can help reveal how these processes are regulated. By combining RNA, protein, and proximity data, the assay offers a powerful approach to support biomarker discovery and improved patient stratification.