Abstract 7424: iStarTLS: Advanced detection and phenotyping of tertiary lymphoid structures
Authors: Kyung Serk Cho, Jiahui Jiang, Daiwei Zhang, Yunhe Liu, Jianfeng Chen, Rossana L. Segura, Xinmiao Yan, Guangsheng Pei, Luisa M. Soto, Yanshuo Chu, Ansam F. Sinjab, Cassian Yee, Scott Kopetz, Anirban Maitra, Andrew Futreal, Alexander Lazar, Amir A. Jazaeri, Humam Kadara, Jianjun Gao, Mingyao Li, Linghua Wang
Journal: Cancer Research (2024)

Abstract Tertiary lymphoid structures (TLSs) are clusters of immune cells formed in non-lymphoid tissues. They are often found at sites of chronic inflammation, notably within the invasive margins and the core of various solid tumors. TLSs are pivotal in mediating anti-tumor immunity. However, our understanding of TLSs in large/complex tissue contexts remains incomplete due to the lack of computational tools to effectively detect and phenotype TLSs. Recent advances in spatially resolved transcriptomics (SRT) present a broader spectrum of analytical possibilities for investigating the spatial phenotypic heterogeneity of TLSs and their interaction with stromal and cancer cells. Here, we present iStarTLS (Inferring Super-resolution Tissue ARchitecture for TLSs), a computational toolkit designed to process SRT data for TLS detection and phenotyping and showcase its performance on breast, bladder, and lung cancer samples. By effectively integrating spatial gene expression data with state-of-the-art machine learning techniques, we can substantially enhance our capabilities in TLS detection and comprehensive phenotyping. iStarTLS starts by enhancing the spatial resolution of spot-level gene expression data to near-single-cell resolution by leveraging high-resolution information provided by paired histology images. To detect TLSs and infer their cellular composition, we developed a TLS signature. Based on the high-resolution gene expression measurements and a curated reference panel of cell type-specific markers, we score cell type-specific gene signatures to obtain a cell type probability map across the whole tissue section. This map gives rise to a segmentation of key cell type components of TLSs, enabling the spatial mapping and colocalization of different cell types. Moreover, such an approach would allow us to infer the phenotypic states of cells within the TLSs, assess their cellular compositions, and discern their cellular organization in large, spatially heterogeneous tissues at a near-single-cell resolution. Notably, in conjunction with nuclei segmentation of high-resolution histology images, iStarTLS precisely maps high endothelial venules (HEVs), a key structure within TLSs often overlooked by previous studies. iStarTLS paves the way for uncovering novel mechanisms of immune-tumor interactions and designing personalized therapies targeting specific cellular components or states within TLSs. Citation Format: Kyung Serk Cho, Jiahui Jiang, Daiwei Zhang, Yunhe Liu, Jianfeng Chen, Rossana L. Segura, Xinmiao Yan, Guangsheng Pei, Luisa M. Soto, Yanshuo Chu, Ansam F. Sinjab, Cassian Yee, Scott Kopetz, Anirban Maitra, Andrew Futreal, Alexander Lazar, Amir A. Jazaeri, Humam Kadara, Jianjun Gao, Mingyao Li, Linghua Wang. iStarTLS: Advanced detection and phenotyping of tertiary lymphoid structures [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 7424.