LAUSR

The development of immunotherapy drugs, such as immune checkpoint inhibitors (ICIs) has changed the environment of cancer treatment tremendously by providing efficacious therapeutic options for many cancer patients. However, only a minority of patients experience durable clinical benefit and increasing evidence has linked the efficacy of ICIs to tumor cell heterogeneity, the complex tumor immune microenvironment and their interactions, which remains poorly understood, particularly in the tissue context. Recent advances in spatially resolved transcriptomics (SRT) has provided great opportunity to better understand spatial tumor-immune interactions. In this study, we obtained public and in-house SRT data on a total of 136 tissue sections across 11 different cancer types, representing to date, the largest collection of SRT on human cancer. Sample and spot-level quality filters were applied, batch effects were assessed and properly handled. High-quality SRT data were pre-processed uniformly to comprehensively interrogate the spatial heterogeneity and architectures of the 3 major compartments (malignant, stroma, and immune) as well as relationships among them. Transcriptome data was integrated with region- and/or spot-level annotations from pathologists. For regions enriched with malignant cells, we inferred somatic copy number alterations, clonal structure of tumor cells, and profiled the transcriptional hallmarks of intra-tumor heterogeneity (ITH) including a number of curated gene sets and meta-programs, and systematically characterized tumor cell heterogeneity under the spatial modality. We observed a great variation in aneuploidy levels and transcriptome profiles within and across patients and cancer types and notably in molecular processes regulating tumor cell responses to stress, hypoxia and interferon signals and other key processes such as epithelial-mesenchymal transition. In addition, we performed cell-type deconvolution analysis using available tools including RCTD and cell2location, based on expression of curated cell-type specific gene signatures, we inferred levels of immune infiltration in each tissue section in both tumor core and invasive edges, and classified tumors into “cold”, “warm”, “hot”, and “mixed” immune phenotypes. We further quantified the abundance and spatial distribution of stromal cells and key TME structures such as tertiary lymphoid structures and lympho-myeloid aggregates, as well as their spatial neighbors with oncogenic features which revealed multiple interesting interplay patterns. Together, this study provide novel insights into our improved understanding of spatial tumor heterogeneity and tumor-immune interactions and revealed potential exploitable targets, and great resource for the community. Citation Format: Guangsheng Pei, Jingjing Wu, Enyu Dai, Yunhe Liu, Guangchun Han, Jian Hu, Fuduan Peng, Kyung S. Cho, Jiahui Jiang, Daiwei Zhang, Ansam F. Sinjab, Boyu Zhang, Shumei Song, Junya Fujimoto, Luisa M. Solis Soto, Anirban Maitra, Jaffer Ajani, Mingyao Li, Humam Kadara, Linghua Wang. Pan-cancer characterization of tumor-immune interactions using spatially resolved transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6766.