Abstract 5044: Spatial image analysis on stroma composition linking to patient prognosis, chemotherapy response, and immunotherapy resistance
Authors: Lee Sanghoon , Pressman Isaiah , Xu Hongming , Hwang Tae Hyun 
Published: June 2022

Abstract
Background: The tumor microenvironment is a biological environment comprising of heterogeneous cellular components, such as tumor, immune, and stromal cells. Stromal cells in the tumor microenvironment have been shown that they play an important role in cancer development, but how they are linked to patient prognosis, chemotherapy response, and immunotherapy resistance is not fully investigated yet. The present study sought to investigate the stroma composition in the tumor microenvironment by using spatial analysis and deep learning.
Method: We analyzed stroma composition in Whole Slide Images (WSIs) of hematoxylin and eosin-stained formalin-fixed paraffin-embedded (FFPE) sections from 4 patient cohorts: St. Mary (37 patients), Yonsei-1 (622 patients), Yonsei-2 (66 patients), and TCGA-STAD (359 patients). St. Mary was collected from St. Mary Hospital, Seoul, Republic of Korea. Yonsei-1 and Yonsei-2 were collected from Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea. Each WSI was tessellated into 256x256 um at 40X magnification and normalized using Macenko and Reinhard normalization methods to reduce the color variability of the stained tissue images. Two deep learning models (ResNet and U-Net) were used to create prediction models using Kather’s tumor dataset and a crowdsourcing stroma dataset. Models were trained over 100 epochs, using the Adam optimizer, 1e-4 learning rate, a binary loss function, and L2 regularization. Trained models were used to predict WSIs including tumor, stroma, tumor-infiltrating lymphocytes (TILs), Microsatellite instability. Spatial analysis was conducted by using Getis-Ord-Gi*, a local statistic that identifies a statistically significant hot-spot region by providing relatedness with its neighbors. Tumor hot-spot regions were computed based on z-scores in the predicted WSIs and used for understanding the spatial variation of cancer cells, recognizing statistically significant regions and their spatial relatedness.
Results: In Yonsei-1, stroma composition was significantly higher in the chemotherapy high-risk group (p-value 2.9e-09). The proportion of stroma composition is significantly different with the risk groups (p-value 0.074). In TCGA-STAD, the proportion of stroma composition is significantly different with ACTA2 and Microsatellite instability (p-value 0.028). In St. Mary, Yonsei-1, and Yonsei-2, stroma composition was significantly higher in the immunotherapy treatment non-response group (p-value 0.001).
Conclusion:These findings suggest that stroma composition in the tumor microenvironment is strongly associated with patient prognosis, chemotherapy response, and immunotherapy resistance on stomach cancer.
Citation Format: Sanghoon Lee, Isaiah Pressman, Hongming Xu, Tae Hyun Hwang. Spatial image analysis on stroma composition linking to patient prognosis, chemotherapy response, and immunotherapy resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5044.