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      Tracing tumorigenesis in a solid tumor model at single-cell resolution

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          Abstract

          Characterizing the complex composition of solid tumors is fundamental for understanding tumor initiation, progression and metastasis. While patient-derived samples provide valuable insight, they are heterogeneous on multiple molecular levels, and often originate from advanced tumor stages. Here, we use single-cell transcriptome and epitope profiling together with pathway and lineage analyses to study tumorigenesis from a developmental perspective in a mouse model of salivary gland squamous cell carcinoma. We provide a comprehensive cell atlas and characterize tumor-specific cells. We find that these cells are connected along a reproducible developmental trajectory: initiated in basal cells exhibiting an epithelial-to-mesenchymal transition signature, tumorigenesis proceeds through Wnt-differential cancer stem cell-like subpopulations before differentiating into luminal-like cells. Our work provides unbiased insights into tumor-specific cellular identities in a whole tissue environment, and emphasizes the power of using defined genetic model systems.

          Abstract

          Understanding tumour development at a granular level is a challenge in solid tumours. Here, the authors provide a cell atlas across tumour development in a genetic model of salivary gland squamous cell carcinoma using single-cell transcriptome and epitope profiling.

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          Opinion: migrating cancer stem cells - an integrated concept of malignant tumour progression.

          The dissemination of tumour cells is the prerequisite of metastases and is correlated with a loss of epithelial differentiation and the acquisition of a migratory phenotype, a hallmark of malignant tumour progression. A stepwise, irreversible accumulation of genetic alterations is considered to be the responsible driving force. But strikingly, metastases of most carcinomas recapitulate the organization of their primary tumours. Although current models explain distinct and important aspects of carcinogenesis, each alone can not explain the sum of the cellular changes apparent in human cancer progression. We suggest an extended, integrated model that is consistent with all aspects of human tumour progression - the 'migrating cancer stem (MCS)-cell' concept.
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            Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors

            Understanding tumor origins and the similarities and differences between organ-specific cancers is important for determining treatment options. Young et al. generated more than 72,000 single-cell transcriptomes from healthy and cancerous human kidneys. From these data, they determined that Wilms tumor, a pediatric kidney cancer, originates from aberrant fetal cells, whereas adult kidney cancers are likely derived from a specific subtype of proximal convoluted tubular cell. Science , this issue p. [Related article:] 594 Single-cell mRNAs of normal and cancerous kidney cells reveal the cellular identity of childhood and adult tumors. Messenger RNA encodes cellular function and phenotype. In the context of human cancer, it defines the identities of malignant cells and the diversity of tumor tissue. We studied 72,501 single-cell transcriptomes of human renal tumors and normal tissue from fetal, pediatric, and adult kidneys. We matched childhood Wilms tumor with specific fetal cell types, thus providing evidence for the hypothesis that Wilms tumor cells are aberrant fetal cells. In adult renal cell carcinoma, we identified a canonical cancer transcriptome that matched a little-known subtype of proximal convoluted tubular cell. Analyses of the tumor composition defined cancer-associated normal cells and delineated a complex vascular endothelial growth factor (VEGF) signaling circuit. Our findings reveal the precise cellular identities and compositions of human kidney tumors.
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              Diffusion maps for high-dimensional single-cell analysis of differentiation data.

              Single-cell technologies have recently gained popularity in cellular differentiation studies regarding their ability to resolve potential heterogeneities in cell populations. Analyzing such high-dimensional single-cell data has its own statistical and computational challenges. Popular multivariate approaches are based on data normalization, followed by dimension reduction and clustering to identify subgroups. However, in the case of cellular differentiation, we would not expect clear clusters to be present but instead expect the cells to follow continuous branching lineages.
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                Author and article information

                Contributors
                samantha@praktiknjo.com
                wbirch@mdc-berlin.de
                rajewsky@mdc-berlin.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                20 February 2020
                20 February 2020
                2020
                : 11
                : 991
                Affiliations
                [1 ]ISNI 0000 0001 1014 0849, GRID grid.419491.0, Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology, , Max Delbrück Center for Molecular Medicine in the Helmholtz Association, ; Berlin, Germany
                [2 ]ISNI 0000 0001 2218 4662, GRID grid.6363.0, Core Unit Bioinformatics, , Berlin Institute of Health, Charité – Universitätsmedizin Berlin, ; Berlin, Germany
                [3 ]ISNI 0000 0001 1014 0849, GRID grid.419491.0, Signal Transduction in Development and Cancer, , Max Delbrück Center for Molecular Medicine in the Helmholtz Association, ; Berlin, Germany
                [4 ]GRID grid.429884.b, New York Genome Center, ; New York, NY USA
                [5 ]GRID grid.263817.9, Present Address: Southern University of Science and Technology, ; Shenzhen, China
                Author information
                http://orcid.org/0000-0002-0186-5271
                http://orcid.org/0000-0002-9116-630X
                http://orcid.org/0000-0002-5658-029X
                http://orcid.org/0000-0003-1749-3334
                http://orcid.org/0000-0002-4785-4332
                Article
                14777
                10.1038/s41467-020-14777-0
                7033116
                32080185
                e212166a-dfa0-4732-a301-ffe4f8c1ed3a
                © The Author(s) 2020

                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
                : 8 July 2019
                : 29 January 2020
                Funding
                Funded by: Chan Zuckerberg Initiative
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                cancer stem cells,computational biology and bioinformatics,proteomics,transcriptomics,oncogenesis

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