Abstract 7412: METI: Deep profiling of tumor ecosystems by integrating cell morphology and spatial transcriptomics

Abstract

Abstract The recent progression in spatial transcriptomics (ST) offers invaluable perspectives on the structure and dynamics of cells within the tumor microenvironment (TME). Though several analytical tools exist for tasks like spatial clustering and spatially variable gene identification, most of them are general methods lacking consideration of histological features in spatial data analysis. This oversight compromises their performance and interpretability when studying the TME. Here, we introduce a computational framework, the Morphology-Enhanced Spatial Transcriptome Analysis Integrator (METI). METI provides a comprehensive framework for spatially mapping both cancer cells and various TME cells, robust stratification of cell type and transcriptional states, and cell co-localization analysis. By integrating spatial transcriptomics with cell morphology and curated gene signatures, METI deepens our insight into the molecular landscape and cellular interactions within tissues, paving the way for intricate studies of the TME and its consequential impacts. The performance of METI has been assessed using ST data derived from a range of tumor tissues, encompassing gastric, lung, and bladder cancers, and premalignant tissues. Throughout these varied tissues and conditions, METI consistently exhibited robust performance. Citation Format: Jiahui Jiang, Yunhe Liu, Jiangjiang Qin, Jingjing Wu, Jianfeng Chen, Melissa Pizzi, Rossana Segura, Kohei Yamashita, Zhiyuan Xu, Guangsheng Pei, Kyung Serk Cho, Yanshuo Chu, Ansam Sinjab, Fuduan Peng, Guangchun Han, Ruiping Wang, Xinmiao Yan, Enyu Dai, Mingyao Li, Andrew Futreal, Anirban Maitra, Alexander Lazar, Xiangdong Cheng, Humam Kadara, Jaffer Ajani, Amir Jazaeri, Jian Hu, Linghua Wang, Jianjun Gao. METI: Deep profiling of tumor ecosystems by integrating cell morphology and spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7412.