Abstract 1353: Characterizing biological functional changes in cancer tumor microenvironment by integrating histology-based gene imputation with scRNA-seq foundation models

Abstract Background and Aim: Spatially resolved transcriptomics (SRT) offers insights into tumor microenvironments (TME), mapping gene and cell activities across tissue regions. While systems biology methods utilize SRT data to characterize tissue and cell neighborhood variations, high costs and limited scalability hinder large-scale applications and clinical correlations. In contrast, histological imaging (HI) provides accessible, high-resolution morphological insights with clinical context. Existing HI-based gene expression imputation methods rely on small datasets and cover a limited gene fraction, leaving much of the transcriptome unexplored. This study develops a robust framework for imputing spatially resolved gene expression profiles from HI data by integrating HI-based imputation with scGPT - a foundation models trained on single-cell RNA-seq (scRNA-seq) data. We hypothesize HI data can impute genes linked to TME, tissue structure, and cell types, while scGPT infers additional genes using gene and cell type-specific relationships. Methods: The proposed framework merges an advanced HI-based gene imputation algorithm with scGPT to predict spatially resolved full-transcriptome profiles. The HI-based model first imputes 500-1,000 tissue structure- and cell type-dependent genes. We enhanced existing methods by (1) incorporating cross-pixel multi-attentions for spatial adjustments, (2) enabling residual connections to preserve early features, and (3) introducing a composite loss function for spatial coherence, gene-gene interaction preservation, and distribution alignment. Tissue annotations provide contextual information that enhances model predictivity. scGPT refines and expands predictions into whole gene profiles, leveraging its robust architecture to capture complex molecular relationships. Results: The framework demonstrates strong performance across unseen specimens, achieving low mean squared error (0.38) and high correlations (0.79) while mitigating batch effects. Spatial heatmaps capture tissue heterogeneity, with tumor, immune, and stromal boundaries aligning well with annotations. Gene Enrichment analysis shows the model's capability to capture immune, stromal, and tumor compartments, though challenges remain for underrepresented genes. Integrating scGPT generates accurate and meaningful whole-genome profiles. This enables implementation of systems biology models for transcriptomics - including pathway, metabolism, stemness and CNV analysis - into HI data analysis. Conclusion: This framework bridges histology and transcriptomics by combining advanced imputation algorithms with powerful capabilities of scGPT, enabling scalable, spatially resolved gene expression mapping. Its robust extrapolation transforms understanding of tissue biology and the TME complexity. Citation Format: Paveethran Swaminathan, Pengtao Dang, Haiqi Zhu, Zheng An, Xin Lu, Zhi Li, Yue Fang, Min Yang, Yuhui Wei, Tingbo Guo, Xinyu Zhou, Xiao Wang, Jia Wang, Chi Zhang, Sha Cao. Characterizing biological functional changes in cancer tumor microenvironment by integrating histology-based gene imputation with scRNA-seq foundation models [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 1353.