Abstract 1146: Pan-cancer characterization of cancer cell state and plasticity using spatially resolved transcriptomics
Authors: Pei Guangsheng , Cho Kyung S. , Liu Yunhe , Alejandra Serrano , Lazcano Rossana , Dai Enyu , Han Guangchun , Peng Fuduan , Zhang Daiwei , Chu Yanshuo , Sinjab Ansam F. , Jiang Jiahui , Li Mingyao , Yee Cassian , Futreal Andrew , Lazar Alex , Kadara Humam , Gao Jianjun , Soto Luisa M. , Maitra Anirban , Ajani Jaffer , Wang Linghua 
Published: March 2024

Cancer cell state heterogeneity constitutes a foundational characteristic of cancer, posing a formidable barrier to the discovery of biomarkers and the efficacy of therapeutic interventions. However, current understanding of cancer cell heterogeneity and plasticity remains limited, especially in the spatial context. Previous studies underscored that cancer cell states are not strictly governed by genetics, but rather display a remarkable degree of plasticity. Recent pan-cancer single-cell studies have unveiled dozens of recurrent cancer cell states. Nevertheless, the relationship between these cancer cell states and tumor microenvironment (TME) remains poorly understood, especially when considering the diversity across different tumor types. Recent advancements in spatial transcriptomics (ST) have paved the way for a novel approach to spatially profile cell location, organization, and interaction within the tumor microenvironment. In this study, we curated ST data (Visium, 10x Genomics) from public repositories and in-house datasets, encompassing a total of &gt;200 tissue sections across 11 cancer types. By incorporating histological annotations and inferred copy number variations, we systematically investigated the spatial heterogeneity of cancer cell states and inferred clonal architectures of cancer cell-enriched spots. Non-negative matrix factorization was applied to identify highly recurrent cancer states and transcriptional hallmarks associated with intra-tumor heterogeneity. By employing this approach, we uncover a diverse curated gene set of meta-programs. Notably, we observe a high level of reproducibility of these meta-programs when compared to the findings from single-cell level. Furthermore, we observe significant variations of cell states across different cancer types, even among two cancer subtypes within a specific tissue. We noted a unique distribution of 'hypoxia' and 'epithelial-mesenchymal transition' in the basal lineage tumor. Following this, we perform single-cell and ST co-embedding using available tools, including CellTrek and CytoSPACE. We noticed the cell type composition of tumor’s neighbor spots play a significant role in influencing tumor states, especially for myofibroblastic CAFs and the tumor state in epithelial-mesenchymal transition. These findings contribute valuable insights into the relationship between cancer cell state plasticity and interactions with the TME, offering potential targets for further exploration and therapeutic intervention.

Citation Format: Guangsheng Pei, Kyung S. Cho, Yunhe Liu, Serrano Alejandra, Rossana Lazcano, Enyu Dai, Guangchun Han, Fuduan Peng, Daiwei Zhang, Yanshuo Chu, Ansam F. Sinjab, Jiahui Jiang, Mingyao Li, Cassian Yee, Andrew Futreal, Alex Lazar, Humam Kadara, Jianjun Gao, Luisa M. Soto, Anirban Maitra, Jaffer Ajani, Linghua Wang. Pan-cancer characterization of cancer cell state and plasticity using spatially resolved 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 1146.