Abstract 2418: CellClique: Dissecting tumor microenvironments at the single cell level using generative AI and spatial transcriptomics

Abstract Novel cellular therapy has the potential to change cancer treatment. However, to design new therapies requires a better understanding of the tumor microenvironment (TME) and the cellular dynamics that occur within the tumor. This requires a high resolution and subcellular view of cancer cells, immune cells while identifying the neighborhood architecture that exists between them. Spatial transcriptomics and spatial proteomics show subcellular information and can be used to identify cellular cell types and find spatial deviation between cells. However, there are a lack of tools to investigate cellular behaviors on an individual cell basis while integrating pathological information, limiting the future of spatial biology. We introduce CellClique, a tool that assists in dissecting TMEs using spatial transcriptomics with the Xenium platform and Hematoxylin and Eosin (H&E) stains. CellClique integrates both generative and analytical capabilities to provide a comprehensive view of TMEs. On the generative end, CellClique uses generative AI to model both gene expressions and cell-neighborhood interactions for cell annotation, gene expression prediction, and gene imputation. On the analytical end, CellClique offers a comprehensive four-way spatial analysis scheme aimed at 1) understanding the effects of neighboring cells on a target cell, 2) dissecting cancer-normal cell boundaries, 3) observing immune invasion mechanisms, and 4) exploring cellular subtypes with the TME. We applied CellClique to study the cellular landscape in lung and colon cancers using multiple different neighborhood analyses. We generated proximal (10 microns), local (60 microns), and distal neighborhoods (120 microns) for each cell within each tissue and found distinct expression profiles of tumor-infiltrated lymphocytes, which are functionally different from lymphocytes excluded from the tumor. Additionally, we discovered expression variation patterns in cancer-normal boundary cell communities with various cell compositions. These compositions changed significantly as distance from the boundary changed. We also compared lymphocytes at different stages of life by analyzing expression differences among lymphocytes classified with over 80% confidence, those with a 50% probability of being lymphocytes, and those with below 20% probability. We further compared how these neighborhoods changed between lung cancer and colon cancer and showed cancer specific spatial deviations and expression. Together, these methods paint a complex picture of the TME but also identifies numerous avenues for future discovery and molecular investigation. These new approaches will guide future cellular therapy and the development of new treatments. The future integration of AI and machine learning will continue to make a tremendous impact on spatial transcriptomics and our approach to treating disease. Citation Format: Sachini Weerasekara, Natasha Darras, Nicolas Fernandez, Melinda Chen, Alina Ainbinder, Colles Price. CellClique: Dissecting tumor microenvironments at the single cell level using generative AI and spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2418.