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INTERNATIONAL CONGRESS OF

CancerGenetics

• Spatial Transcriptomic Signatures of ICI Resistance in Solid Tumors: Cellular Niches, Cell–Cell Interactions, and Computational Biomarkers

• Rahil Nasari Fard ,^1,* Mohammadreza Mahdipour,²
  1. Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran
  2. Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran
  
  
  
• Introduction: The development of immune checkpoint inhibitors (ICIs) that target PD-1, DL1, and CTLA-4 tumors has made them an effective treatment option for various types of solid tumor. Even so, many patients develop primary resistance and may develop secondary resistance after a positive clinical response. Recent discoveries suggest that the resistance is not solely determined by changes in tumor cells' genomics and transcriptional processes, but also by modifications in their organization and function within the TME. Although bulk transcriptomic analysis provides some spatial information, single-cell RNA sequencing does not provide the tissue context in its entirety. The use of spatial transcriptomics as an alternative method preserves the spatial arrangement of molecular signals within whole-mount tissues, enabling the study of cellular niches, cell-cell interactions, and highly organized transcriptional programs linked to immunotherapy response and resistance. This review focuses on recent pipeline developments in spatial transcriptomics and bioinformatics for investigating the spatial factors behind immunotherapy resistance in solid tumor models.
• Methods: In order to carry out the literature review, a structured method was adopted with the objective of finding studies that have looked at spatial transcriptomic and other spatial omics methods in relation to ICI response and resistance in solid tumors. The review centers on four main areas: the spatial arrangement of tumor and immune cells, the identification of cellular niches linked with resistance, the characterization of cell–cell communication networks, and the computational discovery of predictive biomarkers. Special attention is given to studies that combine spatial transcriptomics with single-cell RNA sequencing and other molecular techniques. The computational methods taken into account in this review are spatial clustering, cell-type annotation and deconvolution, the detection of spatially variable genes, analysis of cellular neighborhoods, inference of ligand–receptor interactions, gene-program analysis, the use of machine learning, and multi-omics integration.
• Results: The available evidence shows that spatial organization supplies information which cannot fully be obtained just by looking at the number of cells present. There are clear examples of immune inflation, immune exclusion, and immune deserts that are linked to different levels of antitumor immunity and to different responses to treatment. Spatially limited groups of dysfunctional T cells, immunosuppressive myeloid cells, cancer-associated stromal cells, and tumor cells with altered transcriptional states can result in cellular microenvironments that are resistant and that play a role in immune evasion. By combining single-cell and spatial transcriptomic datasets it is possible to produce a high-resolution map of these cell states and to identify genes that change in a spatial manner as well as coordinated transcriptional programs associated with treatment response. A computational analysis of ligand–receptor interactions also shows possible mechanisms of communication between the tumor, immune, and stromal compartments. In a number of solid tumors, several common features, such as immune suppression, impaired infiltration of immune cells, and local cell–cell interactions, occur repeatedly, even though the specific molecular signatures and spatial architectures may vary considerably depending on the type of tumor and the tissue context. These results indicate that spatially resolved molecular features could serve as candidate biomarkers for telling apart tumors that are sensitive from those that are resistant.
• Conclusion: Spatial transcriptomics is a useful computational and biological method for gaining an understanding of the tissue-level mechanisms that lead to resistance to immune checkpoint inhibitors; since it links molecular data with spatial information, it allows for the detection of cellular niches that are either resistant or dysfunctional in their immune states as well as of the intercellular signaling networks which cannot be identified by conventional transcriptomic methods. When combined with single-cell sequencing, machine learning, digital pathology, and spatial multi-omics, it can also help in the discovery of biomarkers and in patient stratification. Future research should focus on establishing standard analytical procedures, validating the results in different cohorts, ensuring that the findings are reproducible and not dependent on the platform employed, and carrying out prospective clinical validation in order to assess whether spatial biomarkers can be turned into reliable tools for precision immuno-oncology.
• Keywords: Computational Biomarkers, Spatial Transcriptomics, Tumor Microenvironment, Immunotherapy Resistance