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Barboy O, Bercovich A, Li H, Eyal-Lubling Y, Yalin A, Shapir Itai Y, Abadie K, Zada M, David E, Shlomi-Loubaton S, Katzenelenbogen Y, Jaitin DA, Gur C, Yofe I, Feferman T, Cohen M, Dahan R, Newell EW, Lifshitz A, Tanay A, Amit I. Modeling T cell temporal response to cancer immunotherapy rationalizes development of combinatorial treatment protocols. NATURE CANCER 2024; 5:742-759. [PMID: 38429414 DOI: 10.1038/s43018-024-00734-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 01/19/2024] [Indexed: 03/03/2024]
Abstract
Successful immunotherapy relies on triggering complex responses involving T cell dynamics in tumors and the periphery. Characterizing these responses remains challenging using static human single-cell atlases or mouse models. To address this, we developed a framework for in vivo tracking of tumor-specific CD8+ T cells over time and at single-cell resolution. Our tools facilitate the modeling of gene program dynamics in the tumor microenvironment (TME) and the tumor-draining lymph node (tdLN). Using this approach, we characterize two modes of anti-programmed cell death protein 1 (PD-1) activity, decoupling induced differentiation of tumor-specific activated precursor cells from conventional type 1 dendritic cell (cDC1)-dependent proliferation and recruitment to the TME. We demonstrate that combining anti-PD-1 therapy with anti-4-1BB agonist enhances the recruitment and proliferation of activated precursors, resulting in tumor control. These data suggest that effective response to anti-PD-1 therapy is dependent on sufficient influx of activated precursor CD8+ cells to the TME and highlight the importance of understanding system-level dynamics in optimizing immunotherapies.
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Affiliation(s)
- Oren Barboy
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Akhiad Bercovich
- Department of Computer Science and Applied Mathematics and Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Hanjie Li
- Department of Synthetic Immunology, Shenzhen Institutes of Advanced Technology, Shenzhen, China
| | - Yaniv Eyal-Lubling
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - Adam Yalin
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Yuval Shapir Itai
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Kathleen Abadie
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Mor Zada
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Eyal David
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Shir Shlomi-Loubaton
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | | | - Diego Adhemar Jaitin
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Chamutal Gur
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
- The Hebrew University, Jerusalem, Israel
| | - Ido Yofe
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Tali Feferman
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Merav Cohen
- Department of Clinical Microbiology and Immunology, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Rony Dahan
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Evan W Newell
- Fred Hutchinson Cancer Research Center, University of Washington, Seattle, WA, USA
| | - Aviezer Lifshitz
- Department of Computer Science and Applied Mathematics and Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Amos Tanay
- Department of Computer Science and Applied Mathematics and Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
| | - Ido Amit
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel.
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2
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Ren Z, Ren Y, Liu P, Xu H. Cytokine expression patterns: A single-cell RNA sequencing and machine learning based roadmap for cancer classification. Comput Biol Chem 2024; 109:108025. [PMID: 38335854 DOI: 10.1016/j.compbiolchem.2024.108025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/22/2023] [Accepted: 02/02/2024] [Indexed: 02/12/2024]
Abstract
Cytokines are small protein molecules that exhibit potent immunoregulatory properties, which are known as the essential components of the tumor immune microenvironment (TIME). While some cytokines are known to be universally upregulated in TIME, the unique cytokine expression patterns have not been fully resolved in specific types of cancers. To address this challenge, we develop a TIME single-cell RNA sequencing (scRNA-seq) dataset, which is designed to study cytokine expression patterns for precise cancer classification. The dataset, including 39 cancers, is constructed by integrating 684 tumor scRNA-seq samples from multiple public repositories. After screening and processing, the dataset retains only the expression data of immune cells. With a machine learning classification model, unique cytokine expression patterns are identified for various cancer categories and pioneering applied to cancer classification with an accuracy rate of 78.01%. Our method will not only boost the understanding of cancer-type-specific immune modulations in TIME but also serve as a crucial reference for future diagnostic and therapeutic research in cancer immunity.
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Affiliation(s)
- Zhixiang Ren
- Peng Cheng Laboratory, Shenzhen, Guangdong Province 518055, China
| | - Yiming Ren
- Peng Cheng Laboratory, Shenzhen, Guangdong Province 518055, China
| | - Pengfei Liu
- School of Computer Science and Engineering, Sun Yatsen University, Guangzhou, Guangdong Province 528406, China
| | - Huan Xu
- School of Public Health, Anhui University of Science and Technology, Hefei, Anhui Province 231131, China; Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism and Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
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3
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Deng Y, Chen P, Xiao J, Li M, Shen J, Qin S, Jia T, Li C, Chang A, Zhang W, Liu H, Xue R, Zhang N, Wang X, Huang L, Chen D. SCAR: Single-cell and Spatially-resolved Cancer Resources. Nucleic Acids Res 2024; 52:D1407-D1417. [PMID: 37739405 PMCID: PMC10767865 DOI: 10.1093/nar/gkad753] [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: 06/08/2023] [Revised: 08/16/2023] [Accepted: 09/05/2023] [Indexed: 09/24/2023] Open
Abstract
Advances in sequencing and imaging technologies offer a unique opportunity to unravel cell heterogeneity and develop new immunotherapy strategies for cancer research. There is an urgent need for a resource that effectively integrates a vast amount of transcriptomic profiling data to comprehensively explore cancer tissue heterogeneity and the tumor microenvironment. In this context, we developed the Single-cell and Spatially-resolved Cancer Resources (SCAR) database, a combined tumor spatial and single-cell transcriptomic platform, which is freely accessible at http://8.142.154.29/SCAR2023 or http://scaratlas.com. SCAR contains spatial transcriptomic data from 21 tumor tissues and single-cell transcriptomic data from 11 301 352 cells encompassing 395 cancer subtypes and covering a wide variety of tissues, organoids, and cell lines. This resource offers diverse functional modules to address key cancer research questions at multiple levels, including the screening of tumor cell types, metabolic features, cell communication and gene expression patterns within the tumor microenvironment. Moreover, SCAR enables the analysis of biomarker expression patterns and cell developmental trajectories. SCAR also provides a comprehensive analysis of multi-dimensional datasets based on 34 state-of-the-art omics techniques, serving as an essential tool for in-depth mining and understanding of cell heterogeneity and spatial location. The implications of this resource extend to both cancer biology research and cancer immunotherapy development.
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Affiliation(s)
- Yushan Deng
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Peixin Chen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Cam-Su Genomic Resource Center, Medical College of Soochow University, Suzhou 215123, China
| | - Jiedan Xiao
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Mengrou Li
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Jiayi Shen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Peninsula Cancer Research Center, School of Basic Medical Sciences, Binzhou Medical University, Yantai 264003, China
| | - Siying Qin
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Tengfei Jia
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Changxiao Li
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Ashley Chang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Wensheng Zhang
- Cam-Su Genomic Resource Center, Medical College of Soochow University, Suzhou 215123, China
- Peninsula Cancer Research Center, School of Basic Medical Sciences, Binzhou Medical University, Yantai 264003, China
| | - Hebin Liu
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Ruidong Xue
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University Health Science Center & Translational Cancer Research Center, Peking University First Hospital, Beijing 100191, China
| | - Ning Zhang
- Peking University-Yunnan Baiyao International Medical Research Center, Peking University Health Science Center & Translational Cancer Research Center, Peking University First Hospital, Beijing 100191, China
| | - Xiangdong Wang
- Zhongshan Hospital, Department of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai 200000, China
| | - Li Huang
- The Future Laboratory, Tsinghua University, Beijing 100084, China
| | - Dongsheng Chen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
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Pirrotta S, Masatti L, Corrà A, Pedrini F, Esposito G, Martini P, Risso D, Romualdi C, Calura E. signifinder enables the identification of tumor cell states and cancer expression signatures in bulk, single-cell and spatial transcriptomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.07.530940. [PMID: 36945491 PMCID: PMC10028855 DOI: 10.1101/2023.03.07.530940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Over the last decade, many studies and some clinical trials have proposed gene expression signatures as a valuable tool for understanding cancer mechanisms, defining subtypes, monitoring patient prognosis, and therapy efficacy. However, technical and biological concerns about reproducibility have been raised. Technical reproducibility is a major concern: we currently lack a computational implementation of the proposed signatures, which would provide detailed signature definition and assure reproducibility, dissemination, and usability of the classifier. Another concern regards intratumor heterogeneity, which has never been addressed when studying these types of biomarkers using bulk transcriptomics. With the aim of providing a tool able to improve the reproducibility and usability of gene expression signatures, we propose signifinder, an R package that provides the infrastructure to collect, implement, and compare expression-based signatures from cancer literature. The included signatures cover a wide range of biological processes from metabolism and programmed cell death, to morphological changes, such as quantification of epithelial or mesenchymal-like status. Collected signatures can score tumor cell characteristics, such as the predicted response to therapy or the survival association, and can quantify microenvironmental information, including hypoxia and immune response activity. signifinder has been used to characterize tumor samples and to investigate intra-tumor heterogeneity, extending its application to single-cell and spatial transcriptomic data. Through these higher-resolution technologies, it has become increasingly apparent that the single-sample score assessment obtained by transcriptional signatures is conditioned by the phenotypic and genetic intratumor heterogeneity of tumor masses. Since the characteristics of the most abundant cell type or clone might not necessarily predict the properties of mixed populations, signature prediction efficacy is lowered, thus impeding effective clinical diagnostics. Through signifinder, we offer general principles for interpreting and comparing transcriptional signatures, as well as suggestions for additional signatures that would allow for more complete and robust data inferences. We consider signifinder a useful tool to pave the way for reproducibility and comparison of transcriptional signatures in oncology.
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Affiliation(s)
| | - Laura Masatti
- Department of Biology, University of Padua, Padua, Italy
| | - Anna Corrà
- Department of Biology, University of Padua, Padua, Italy
| | | | - Giovanni Esposito
- Immunology and Molecular Oncology Diagnostic Unit of The Veneto Institute of Oncology IOV – IRCCS, Padua, Italy
| | - Paolo Martini
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Davide Risso
- Department of Statistical Sciences, University of Padua, Italy
| | | | - Enrica Calura
- Department of Biology, University of Padua, Padua, Italy
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5
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Christensen E, Luo P, Turinsky A, Husić M, Mahalanabis A, Naidas A, Diaz-Mejia JJ, Brudno M, Pugh T, Ramani A, Shooshtari P. Evaluation of single-cell RNAseq labelling algorithms using cancer datasets. Brief Bioinform 2022; 24:6965910. [PMID: 36585784 PMCID: PMC9851326 DOI: 10.1093/bib/bbac561] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 09/19/2022] [Accepted: 11/01/2022] [Indexed: 01/01/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) clustering and labelling methods are used to determine precise cellular composition of tissue samples. Automated labelling methods rely on either unsupervised, cluster-based approaches or supervised, cell-based approaches to identify cell types. The high complexity of cancer poses a unique challenge, as tumor microenvironments are often composed of diverse cell subpopulations with unique functional effects that may lead to disease progression, metastasis and treatment resistance. Here, we assess 17 cell-based and 9 cluster-based scRNA-seq labelling algorithms using 8 cancer datasets, providing a comprehensive large-scale assessment of such methods in a cancer-specific context. Using several performance metrics, we show that cell-based methods generally achieved higher performance and were faster compared to cluster-based methods. Cluster-based methods more successfully labelled non-malignant cell types, likely because of a lack of gene signatures for relevant malignant cell subpopulations. Larger cell numbers present in some cell types in training data positively impacted prediction scores for cell-based methods. Finally, we examined which methods performed favorably when trained and tested on separate patient cohorts in scenarios similar to clinical applications, and which were able to accurately label particularly small or under-represented cell populations in the given datasets. We conclude that scPred and SVM show the best overall performances with cancer-specific data and provide further suggestions for algorithm selection. Our analysis pipeline for assessing the performance of cell type labelling algorithms is available in https://github.com/shooshtarilab/scRNAseq-Automated-Cell-Type-Labelling.
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Affiliation(s)
| | | | - Andrei Turinsky
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada
| | - Mia Husić
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada
| | - Alaina Mahalanabis
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada
| | - Alaine Naidas
- Children’s Health Research Institute, Lawson Research Institute, London, ON, Canada
- Department of Pathology and Lab Medicine, University of Western Ontario, London, ON, Canada
| | | | - Michael Brudno
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Trevor Pugh
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Arun Ramani
- Centre for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada
| | - Parisa Shooshtari
- Corresponding author: Parisa Shooshtari, Department of Pathology and Lab Medicine, University of Western Ontario, London, ON, Canada. Tel.: +1 (519) 685-8500 x55427. E-mail:
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Mahalanabis A, Turinsky A, Husic M, Christensen E, Luo P, Naidas A, Brudno M, Pugh T, Ramani A, Shooshtari P. Evaluation of Single-cell RNA-seq Clustering Algorithms on Cancer Tumor Datasets. Comput Struct Biotechnol J 2022; 20:6375-6387. [DOI: 10.1016/j.csbj.2022.10.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/19/2022] [Accepted: 10/20/2022] [Indexed: 11/03/2022] Open
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