Abstract 2426: TMEseg: Connecting histopathology with spatial transcriptomics through tumor microenvironment segmentation for lung cancer

Abstract The tumor microenvironment (TME) plays a key role in lung cancer progression. Spatial transcriptomics offers insights into tumor heterogeneity but relies on integration with histological features to decode tumor transcriptomic programs, which is challenging due to TME complexity. To overcome this, we developed AI models that accurately identify 10 tissue types from histology images, streamlining TME analysis. We used public annotations to train AI models, DeepLabV3+ and Segformer, for TME segmentation. The training set included hematoxylin&eosin-stained slides from breast and lung cancers in TCGA, combined with newly curated annotations from TCGA and four internal slides of surgically resected tumors, totaling 3403 patches (breast: 3110; lung: 293) at 0.44 μm/pixel. The testing set included 2000 patches from lung cancer slides at 1 μm/pixel. The models were trained to classify 10 tissue types: tumor, stroma, inflammatory, necrosis/hemorrhage, adipose, bronchi epithelium, vessel, macrophage area, alveoli, and muscle. For comparison, predictions of adipose, vessel, and muscle were grouped into stroma, while inflammatory and macrophage areas were grouped as immune. Model performance was evaluated using the Dice score. We further validated the models using spot-level manual annotations from Visium data for four lung cancer samples. Pathologists labeled spots as tumor, stroma, immune aggregates, macrophage, bronchi epithelium, vessel, and alveoli using Loupe Browser. The type with the highest number of predicted pixels by AI within a spot-based patch was assigned as the predicted label. Concordance was assessed using Fleiss's kappa. At pixel level, Segformer outperformed DeepLabV3+, achieving an average Dice score of 0.856 and 0.809. At spot level, kappa index between pathologist and Segformer was 0.28-0.66, showing fair (n=2) to good (n=1) agreement, consistent with the concordance between pathologist and DeepLabV3+ (0.34-0.722). Kappa index between Segformer and DeepLabV3+ showed good (n=3) to perfect (n=1) agreements (0.677-0.842). Interestingly, the first annotated sample showed good agreement between the pathologist and AI models (0.66-0.722), whereas the last annotated sample demonstrated only fair agreement (0.28-0.343). Notably, this discrepancy was not observed when comparing Segformer and DeepLabV3+ directly, implying a high reproducibility for AI models. In terms of efficiency, the pathologist required about five hours in theory to annotate a slide containing around 12000 spots, while AI models, running on an A100 GPU, completed the segmentation in about 20 minutes. Our study shows the effectiveness and efficiency of AI models in recognizing the TME from histology images in lung cancer. These models streamline the integration of tissue histopathological features with Visium assay and facilitate the discovery of image-derived biomarkers. Citation Format: Xiaoxi Pan, Maria E. Salvatierra, Caner Ercan, Lakshmi Kakarala, Wei Lu, Ou Shi, Idania C. Lubo Julio, Ignacio I. Wistuba, Luisa M. Solis Soto, Yinyin Yuan. TMEseg: Connecting histopathology with spatial transcriptomics through tumor microenvironment segmentation for lung cancer [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 2426.