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      Single-cell transcriptome reveals cellular hierarchies and guides p-EMT-targeted trial in skull base chordoma

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          Abstract

          Skull base chordoma (SBC) is a bone cancer with a high recurrence rate, high radioresistance rate, and poorly understood mechanism. Here, we profiled the transcriptomes of 90,691 single cells, revealed the SBC cellular hierarchies, and explored novel treatment targets. We identified a cluster of stem-like SBC cells that tended to be distributed in the inferior part of the tumor. Combining radiated UM-Chor1 RNA-seq data and in vitro validation, we further found that this stem-like cell cluster is marked by cathepsin L ( CTSL), a gene involved in the packaging of telomere ends, and may be responsible for radioresistance. Moreover, signatures related to partial epithelial–mesenchymal transition (p-EMT) were found to be significant in malignant cells and were related to the invasion and poor prognosis of SBC. Furthermore, YL-13027, a p-EMT inhibitor that acts through the TGF-β signaling pathway, demonstrated remarkable potency in inhibiting the invasiveness of SBC in preclinical models and was subsequently applied in a phase I clinical trial that enrolled three SBC patients. Encouragingly, YL-13027 attenuated the growth of SBC and achieved stable disease with no serious adverse events, underscoring the clinical potential for the precision treatment of SBC with this therapy. In summary, we conducted the first single-cell RNA sequencing of SBC and identified several targets that could be translated to the treatment of SBC.

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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              Integrating single-cell transcriptomic data across different conditions, technologies, and species

              Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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                Author and article information

                Contributors
                ggj@zju.edu.cn
                zhaoyaohs@vip.sina.com
                Journal
                Cell Discov
                Cell Discov
                Cell Discovery
                Springer Nature Singapore (Singapore )
                2056-5968
                20 September 2022
                20 September 2022
                2022
                : 8
                : 94
                Affiliations
                [1 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, , Fudan University, ; Shanghai, China
                [2 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, , Fudan University, ; Shanghai, China
                [3 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Center for Stem Cell and Regenerative Medicine, , Zhejiang University School of Medicine, ; Hangzhou, Zhejiang China
                [4 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Department of Oncology, Huashan Hospital, Shanghai Medical College, , Fudan University, ; Shanghai, China
                [5 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Department of Pathology, Huashan Hospital, Shanghai Medical College, , Fudan University, ; Shanghai, China
                [6 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Department of Plastic and Reconstructive Surgery, Shanghai Institute of Precision Medicine, Shanghai Ninth People’s Hospital, , Shanghai Jiao Tong University School of Medicine, ; Shanghai, China
                [7 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Department of Radiology, Huashan Hospital, Shanghai Medical College, , Fudan University, ; Shanghai, China
                [8 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, , Fudan University Cancer Center, ; Shanghai, China
                [9 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, , Fudan University, ; Shanghai, China
                [10 ]GRID grid.22069.3f, ISNI 0000 0004 0369 6365, Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, ; Shanghai, China
                [11 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Neurosurgical Institute of Fudan University, ; Shanghai, China
                [12 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, National Clinical Research Center for Aging and Medicine, Huashan Hospital, , Fudan University, ; Shanghai, China
                Author information
                http://orcid.org/0000-0001-6448-2588
                http://orcid.org/0000-0001-6418-8370
                http://orcid.org/0000-0002-1716-4621
                Article
                459
                10.1038/s41421-022-00459-2
                9489773
                36127333
                0ae428e7-2a5a-4373-80b3-fa47ebb2ddd2
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 January 2022
                : 19 August 2022
                Funding
                Funded by: the China Pituitary Adenoma Specialist Council (CPASC), the National High Technology Research and Development Program of China (863 program, 2014AA020611), the Chang Jiang Scholars Program, the National Program for Support of Top-Notch Young Professionals, the National Science Fund for Distinguished Young Scholars (81725011) and Clinical Research Plan of SHDC (SHDC2020CR2004A)
                Funded by: the National Natural Science Funds of China (81802495), the Shanghai Sailing Program (18YF1403400), Shanghai Chenguang Scholar (19CG08)
                Funded by: the National Natural Science Funds of China (81802496), the Shanghai Sailing Program (18YF1402700)
                Categories
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                Custom metadata
                © The Author(s) 2022

                bone cancer,cancer genomics,targeted therapies
                bone cancer, cancer genomics, targeted therapies

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