59
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The stromal compartment of the tumor microenvironment consists of a heterogeneous set of tissue-resident and tumor-infiltrating cells, which are profoundly moulded by cancer cells. An outstanding question is to what extent this heterogeneity is similar between cancers affecting different organs. Here, we profile 233,591 single cells from patients with lung, colorectal, ovary and breast cancer ( n = 36) and construct a pan-cancer blueprint of stromal cell heterogeneity using different single-cell RNA and protein-based technologies. We identify 68 stromal cell populations, of which 46 are shared between cancer types and 22 are unique. We also characterise each population phenotypically by highlighting its marker genes, transcription factors, metabolic activities and tissue-specific expression differences. Resident cell types are characterised by substantial tissue specificity, while tumor-infiltrating cell types are largely shared across cancer types. Finally, by applying the blueprint to melanoma tumors treated with checkpoint immunotherapy and identifying a naïve CD4 + T-cell phenotype predictive of response to checkpoint immunotherapy, we illustrate how it can serve as a guide to interpret scRNA-seq data. In conclusion, by providing a comprehensive blueprint through an interactive web server, we generate the first panoramic view on the shared complexity of stromal cells in different cancers.

          Related collections

          Most cited references109

          • Record: found
          • Abstract: found
          • Article: not found

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            SCENIC: Single-cell regulatory network inference and clustering

            Although single-cell RNA-seq is revolutionizing biology, data interpretation remains a challenge. We present SCENIC for the simultaneous reconstruction of gene regulatory networks and identification of cell states. We apply SCENIC to a compendium of single-cell data from tumors and brain, and demonstrate that the genomic regulatory code can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Tumor mutational load predicts survival after immunotherapy across multiple cancer types

              Immune checkpoint inhibitor (ICI) treatments benefit some patients with metastatic cancers, but predictive biomarkers are needed. Findings in select cancer types suggest that tumor mutational burden (TMB) may predict clinical response to ICI.To examine this association more broadly, we analyzed the clinical and genomic data of 1662 advanced cancer patients treated with ICI, and 5371 non-ICI treated patients, whose tumors underwent targeted next-generation sequencing (MSK-IMPACT). Among all patients, higher somatic TMB (highest 20% in each histology) was associated with better OS (HR 0.52; p=1.6 ×10 −6 ). For most cancer histologies, an association between higher TMB and improved survival was observed. The TMB cutpoints associated with improved survival varied markedly between cancer types. These data indicate that TMB is associated with improved survival in patients receiving ICI across a wide variety of cancer types, but that there may not be one universal definition of high TMB.
                Bookmark

                Author and article information

                Contributors
                Diether.Lambrechts@kuleuven.vib.be
                Journal
                Cell Res
                Cell Res
                Cell Research
                Springer Singapore (Singapore )
                1001-0602
                1748-7838
                19 June 2020
                19 June 2020
                September 2020
                : 30
                : 9
                : 745-762
                Affiliations
                [1 ]VIB Center for Cancer Biology, Leuven, Belgium
                [2 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Laboratory for Translational Genetics, Department of Human Genetics, , KU Leuven, ; Leuven, Belgium
                [3 ]GRID grid.410569.f, ISNI 0000 0004 0626 3338, Department of Obstetrics and Gynaecology, , University Hospitals Leuven, ; Leuven, Belgium
                [4 ]Department of Oncology, KU Leuven, Surgical Oncology, University Hospitals Leuven, Leuven, Belgium
                [5 ]GRID grid.8767.e, ISNI 0000 0001 2290 8069, Lab of Cellular and Molecular Immunology, , Vrije Universiteit Brussel, ; Brussels, Belgium
                [6 ]Myeloid Cell Immunology Lab, VIB Center for Inflammation Research, Brussels, Belgium
                [7 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Laboratory of Molecular Digestive Oncology, Department of Oncology, , KU Leuven, ; Leuven, Belgium
                [8 ]GRID grid.410569.f, ISNI 0000 0004 0626 3338, Respiratory Oncology Unit (Pneumology) and Leuven Lung Cancer Group, , University Hospital KU Leuven, ; Leuven, Belgium
                [9 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Laboratory of Pneumology, Department of Chronic Diseases, Metabolism and Ageing, , KU Leuven, ; Leuven, Belgium
                [10 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Department of Imaging and Pathology, Laboratory of Translational Cell & Tissue Research and University Hospitals Leuven, Department of Pathology, , KU Leuven-University of Leuven, ; B-3000 Leuven, Belgium
                [11 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Laboratory of Experimental Oncology, , KU Leuven, ; Leuven, Belgium
                [12 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Laboratory for Functional Epigenetics, Department of Human Genetics, , KU Leuven, ; Leuven, Belgium
                Author information
                http://orcid.org/0000-0001-6145-045X
                http://orcid.org/0000-0003-4267-1307
                http://orcid.org/0000-0002-8772-6845
                http://orcid.org/0000-0002-3429-302X
                Article
                355
                10.1038/s41422-020-0355-0
                7608385
                32561858
                3381075c-2556-4888-8e7a-762c911028fe
                © Center for Excellence in Molecular Cell Science, CAS 2020

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 9 October 2019
                : 5 May 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 617595
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100003130, Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders);
                Award ID: G065615N
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100004040, KU Leuven (Katholieke Universiteit Leuven);
                Award ID: BOFZAP
                Award ID: BOFZAP
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100012220, Hercules Foundation;
                Funded by: FundRef https://doi.org/10.13039/501100005026, Stichting Tegen Kanker (Belgian Foundation Against Cancer);
                Funded by: the Flemish Government, Department of Economy, Science and Innovation (EWI);Flemish Supercomputer Center (VSC)
                Categories
                Article
                Custom metadata
                © Center for Excellence in Molecular Cell Science, CAS 2020

                Cell biology
                tumour immunology,cancer microenvironment,tumour heterogeneity,tumour biomarkers
                Cell biology
                tumour immunology, cancer microenvironment, tumour heterogeneity, tumour biomarkers

                Comments

                Comment on this article