Abstract 747: Deciphering tumor ecosystems at super-resolution from spatial transcriptomics

Abstract

Abstract The tumor microenvironment (TME) contains networks of cells and structures that surround tumor cells. Cell populations in the TME, including their abundance, composition, and spatial location are critical determinants of the occurrence, growth, and metastasis of a tumor. A comprehensive analysis of the multiple exchanges between tumor cells and their TME is essential for understanding the underlying mechanisms of tumor growth and response to anti-cancer therapies. Recent advances in spatially resolved transcriptomics (SRT) techniques have enabled gene expression profiling while preserving location information in tissues, which innovates a promising avenue to study the TME in a spatial context. With the power of SRT, we aim to provide a detailed annotation of tumor structure and different lymphocytes by integrating gene expression information and cell morphology features in the complemented high-resolution histology image obtained from the same tissue section. A major challenge that hinders gene expression and histology integration in SRT data is the relatively low resolution of gene expression data compared to pixel-resolution histology images. As gene expression is only measured in discrete spots that are separated by tissue gaps, a large proportion of the tissue area remains unmeasured (e.g., >50% in 10x Visium). The incomplete coverage of gene expression in ST has prevented the deciphering of detailed TME structures such as the tertiary lymphoid structure (TLS).To overcome this challenge, we present TESLA (Tumor Edge Structure and Lymphocyte multi-level Annotation), a machine learning framework that integrates gene expression and histology image in SRT to investigate the detailed strictures in TME. TESLA first fills in the gene expression for unmeasured areas and generates gene expression images in the same resolution as the histology image. Next, TESLA integrates the gene images with the histology image to annotate different tumor/TME cell types at pixel resolution. Some specific tumor-infiltrating lymphocytes structures, such as TLSs, can also be detected by colocalization analysis of different lymphocytes. TESLA is also able to characterize high-resolution cellular and molecular spatial structures of tumor by separating tumor into different subtype regions and elucidating differential transcriptome programs. The detailed multi-level annotations performed by TESLA provide a comprehensive understanding of the spatial context and the nature of cellular heterogeneity of the TME. Citation Format: Jian Hu, Kyle Coleman, Edward B. Lee, Humam Kadara, Linghua Wang, Mingyao Li. Deciphering tumor ecosystems at super-resolution from spatial transcriptomics [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 747.