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Du Y, Shi J, Wang J, Xun Z, Yu Z, Sun H, Bao R, Zheng J, Li Z, Ye Y. Integration of Pan-Cancer Single-Cell and Spatial Transcriptomics Reveals Stromal Cell Features and Therapeutic Targets in Tumor Microenvironment. Cancer Res 2024; 84:192-210. [PMID: 38225927 DOI: 10.1158/0008-5472.can-23-1418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/14/2023] [Accepted: 11/01/2023] [Indexed: 01/17/2024]
Abstract
Stromal cells are physiologically essential components of the tumor microenvironment (TME) that mediates tumor development and therapeutic resistance. Development of a logical and unified system for stromal cell type identification and characterization of corresponding functional properties could help design antitumor strategies that target stromal cells. Here, we performed a pan-cancer analysis of 214,972 nonimmune stromal cells using single-cell RNA sequencing from 258 patients across 16 cancer types and analyzed spatial transcriptomics from 16 patients across seven cancer types, including six patients receiving anti-PD-1 treatment. This analysis uncovered distinct features of 39 stromal subsets across cancer types, including various functional modules, spatial locations, and clinical and therapeutic relevance. Tumor-associated PGF+ endothelial tip cells with elevated epithelial-mesenchymal transition features were enriched in immune-depleted TME and associated with poor prognosis. Fibrogenic and vascular pericytes (PC) derived from FABP4+ progenitors were two distinct tumor-associated PC subpopulations that strongly interacted with PGF+ tips, resulting in excess extracellular matrix (ECM) abundance and dysfunctional vasculature. Importantly, ECM-related cancer-associated fibroblasts enriched at the tumor boundary acted as a barrier to exclude immune cells, interacted with malignant cells to promote tumor progression, and regulated exhausted CD8+ T cells via immune checkpoint ligand-receptors (e.g., LGALS9/TIM-3) to promote immune escape. In addition, an interactive web-based tool (http://www.scpanstroma.yelab.site/) was developed for accessing, visualizing, and analyzing stromal data. Taken together, this study provides a systematic view of the highly heterogeneous stromal populations across cancer types and suggests future avenues for designing therapies to overcome the tumor-promoting functions of stromal cells. SIGNIFICANCE Comprehensive characterization of tumor-associated nonimmune stromal cells provides a robust resource for dissecting tumor microenvironment complexity and guiding stroma-targeted therapy development across multiple human cancer types.
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Affiliation(s)
- Yanhua Du
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- State Key Laboratory of Systems Medicine for Cancer, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Jintong Shi
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- State Key Laboratory of Systems Medicine for Cancer, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Jiaxin Wang
- Center for Immune-Related Diseases at Shanghai Institute of Immunology, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Zhenzhen Xun
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- State Key Laboratory of Systems Medicine for Cancer, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Zhuo Yu
- Hongqiao International Institute of Medicine, Shanghai Tongren Hospital, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Faculty of Basic Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Hongxiang Sun
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Rujuan Bao
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- State Key Laboratory of Systems Medicine for Cancer, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Junke Zheng
- Hongqiao International Institute of Medicine, Shanghai Tongren Hospital, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Faculty of Basic Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Zhigang Li
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Youqiong Ye
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- State Key Laboratory of Systems Medicine for Cancer, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
- Center for Immune-Related Diseases at Shanghai Institute of Immunology, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
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Ebata K, Yamashiro S, Iida K, Okada M. Building patient-specific models for receptor tyrosine kinase signaling networks. FEBS J 2021; 289:90-101. [PMID: 33755310 DOI: 10.1111/febs.15831] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 02/26/2021] [Accepted: 03/19/2021] [Indexed: 12/16/2022]
Abstract
Cancer progresses due to changes in the dynamic interactions of multidimensional factors associated with gene mutations. Cancer research has actively adopted computational methods, including data-driven and mathematical model-driven approaches, to identify causative factors and regulatory rules that can explain the complexity and diversity of cancers. A data-driven, statistics-based approach revealed correlations between gene alterations and clinical outcomes in many types of cancers. A model-driven mathematical approach has elucidated the dynamic features of cancer networks and identified the mechanisms of drug efficacy and resistance. More recently, machine learning methods have emerged that can be used for mining omics data and classifying patient. However, as the strengths and weaknesses of each method becoming apparent, new analytical tools are emerging to combine and improve the methodologies and maximize their predictive power for classifying cancer subtypes and prognosis. Here, we introduce recent advances in cancer systems biology aimed at personalized medicine, with focus on the receptor tyrosine kinase signaling network.
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Affiliation(s)
- Kyoichi Ebata
- Institute for Protein Research, Osaka University, Suita, Japan
| | - Sawa Yamashiro
- Institute for Protein Research, Osaka University, Suita, Japan
| | - Keita Iida
- Institute for Protein Research, Osaka University, Suita, Japan
| | - Mariko Okada
- Institute for Protein Research, Osaka University, Suita, Japan.,Center for Drug Design and Research, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Japan.,Institute for Chemical Research, Kyoto University, Japan
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A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data-Application to the ErbB Receptor Signaling Pathway. Cancers (Basel) 2020; 12:cancers12102878. [PMID: 33036375 PMCID: PMC7650612 DOI: 10.3390/cancers12102878] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 09/24/2020] [Accepted: 09/24/2020] [Indexed: 02/06/2023] Open
Abstract
Simple Summary Temporal signaling dynamics are important for controlling the fate decisions of mammalian cells. In this study, we developed BioMASS, a computational platform for prediction and analysis of signaling dynamics using RNA-sequencing gene expression data. We first constructed a detailed mechanistic model of early transcriptional regulation mediated by ErbB receptor signaling pathway. After training the model parameters against phosphoprotein time-course datasets obtained from breast cancer cell lines, the model successfully predicted signaling activities of another untrained cell line. The result indicates that the parameters of molecular interactions in these different cell types are not particularly unique to the cell type, and the expression levels of the components of the signaling network are sufficient to explain the complex dynamics of the networks. Our method can be further expanded to predict signaling activity from clinical gene expression data for in silico drug screening for personalized medicine. Abstract A current challenge in systems biology is to predict dynamic properties of cell behaviors from public information such as gene expression data. The temporal dynamics of signaling molecules is critical for mammalian cell commitment. We hypothesized that gene expression levels are tightly linked with and quantitatively control the dynamics of signaling networks regardless of the cell type. Based on this idea, we developed a computational method to predict the signaling dynamics from RNA sequencing (RNA-seq) gene expression data. We first constructed an ordinary differential equation model of ErbB receptor → c-Fos induction using a newly developed modeling platform BioMASS. The model was trained with kinetic parameters against multiple breast cancer cell lines using autologous RNA-seq data obtained from the Cancer Cell Line Encyclopedia (CCLE) as the initial values of the model components. After parameter optimization, the model proceeded to prediction in another untrained breast cancer cell line. As a result, the model learned the parameters from other cells and was able to accurately predict the dynamics of the untrained cells using only the gene expression data. Our study suggests that gene expression levels of components within the ErbB network, rather than rate constants, can explain the cell-specific signaling dynamics, therefore playing an important role in regulating cell fate.
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