Abstract 1420: Image-based ROI selection for spatial transcriptomic experiments using immune checkpoint inhibitor treatment outcome prediction in gastric cancer.

Abstract Introduction: Spatial omics experiments profile only a limited number of regions of interest (ROIs) per section, making ROI selection critical. However, manual selection from tumor annotations may miss critical subregion due to the complexity of tumor structures and the limited capability of human visual processing. We recently reported S2Omics, an AI framework that selects ROIs to maximize cell-type diversity and molecular information in an outcome-agnostic manner. Here, we extend this concept to develop an image-based ROI selection method that directly incorporates immune checkpoint inhibitor (ICI) treatment outcome in gastric cancer (GC), enabling outcome-aware spatial transcriptomic experiments. Methods: We assembled 157 H authors are responsible for all content and approved the final version. Citation Format: Sunho Park, Minji Kim, Jean R. Clemenceau, Seock-Jin Chung, Eric F. Sha, Changjin Hong, Soyoung Im, Hwanil Choi, Soonyoung Lee, Jongseong Jang, Kohei Shitara, Sung Hak Lee, Jae-Ho Cheong, Tae Hyun Hwang. Image-based ROI selection for spatial transcriptomic experiments using immune checkpoint inhibitor treatment outcome prediction in gastric cancer abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1420.