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Abstract:

Karla Paniagua, Yufang Jin, Mario Flores

We present an innovative artificial intelligence framework, TG-ME, designed to analyze the tumor microenvironment (TME) using high-resolution spatial transcriptomics datasets. The TME is a complex ecosystem composed of tumor cells, immune cells, fibroblasts, and the extracellular matrix. These elements interact dynamically in both spatial and molecular dimensions, profoundly influencing cancer progression, metastasis, and therapeutic resistance. TG-ME integrates two advanced deep learning models. First, a Transformer model is employed to analyze gene expression data, with a focus on uncovering gene–gene interactions and complex molecular dependencies. Second, a Graph Variational Autoencoder (GraphVAE) model incorporates spatial relationships within the TME, capturing how different cell types and structures are organized and interact within the tumor environment. Together, these models enable TG-ME to provide a comprehensive, multidimensional view of the TME by integrating gene expression, morphology, spatial organization, and cell-type composition.

Applied to non-small cell lung cancer (NSCLC) samples, TG-ME uncovered distinct spatial niches which are specific regions within the TME where unique biological processes are active. These niches were enriched in key cancer-related pathways such as Hypoxia, Epithelial-Mesenchymal Transition (EMT), and Interferon Signaling. Importantly, these spatial features correlated with disease severity and therapeutic resistance, suggesting their potential use as biomarkers for assessing tumor aggressiveness and predicting treatment response. TG-ME also delineated tumor–stroma borders, which play a central role in metastasis and therapy resistance, underscoring the framework’s ability to capture clinically relevant features of the TME.

Beyond NSCLC, the TG-ME framework holds broad potential for other diseases where the microenvironment plays a critical role. Many pathologies, including autoimmune diseases, chronic inflammatory conditions like Diabetic Foor Ulcer (DFU), neurodegenerative disorders, and cardiovascular disease, involve complex interactions between cells and their surrounding microenvironment that influence disease initiation, progression, and therapeutic outcomes. For example, the immune microenvironment in DFU, the glial–neuronal interactions in Alzheimer’s disease, or the stromal remodeling in fibrotic disorders could all be studied using TG-ME’s integrative approach. By capturing spatial and molecular heterogeneity across these contexts, TG-ME offers a powerful framework not only for cancer biology but also for understanding and treating a wide spectrum of diseases where the cellular microenvironment is a key determinant of pathology.

TG-ME provides a novel and versatile platform for microenvironmental analysis. Its ability to uncover spatially organized molecular programs, resolve critical tissue boundaries, and highlight prognostic features demonstrates its value in advancing precision medicine strategies across oncology and beyond.