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      Understanding tumor ecosystems by single-cell sequencing: promises and limitations

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      Genome Biology

      BioMed Central

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          Abstract

          Cellular heterogeneity within and across tumors has been a major obstacle in understanding and treating cancer, and the complex heterogeneity is masked if bulk tumor tissues are used for analysis. The advent of rapidly developing single-cell sequencing technologies, which include methods related to single-cell genome, epigenome, transcriptome, and multi-omics sequencing, have been applied to cancer research and led to exciting new findings in the fields of cancer evolution, metastasis, resistance to therapy, and tumor microenvironment. In this review, we discuss recent advances and limitations of these new technologies and their potential applications in cancer studies.

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          Most cited references 87

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          Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses

          To guide the design of immunotherapy strategies for patients with early stage lung tumors, we developed a multiscale immune profiling strategy to map the immune landscape of early lung adenocarcinoma lesions to search for tumor-driven immune changes. Utilizing a barcoding method that allows a simultaneous single cell analysis of the tumor, non-involved lung and blood cells together with multiplex tissue imaging to assess spatial cell distribution, we provide a detailed immune cell atlas of early lung tumors. We show that stage I lung adenocarcinoma lesions already harbor significantly altered T cell and NK cell compartments. Moreover, we identified changes in tumor infiltrating myeloid cell (TIM) subsets that likely compromise anti-tumor T cell immunity. Paired single cell analyses thus offer valuable knowledge of tumor-driven immune changes, providing a powerful tool for the rational design of immune therapies. Comparing single tumor cells with adjacent normal tissue and blood from patients with lung adenocarcinoma charts early changes in tumor immunity and provides insights to guide immunotherapy design.
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            Single-cell dissection of transcriptional heterogeneity in human colon tumors

            Cancer is often viewed as a caricature of normal developmental processes, but the extent by which its cellular heterogeneity truly recapitulates multi-lineage differentiation processes of normal tissues remains unknown. Here, we implement “single-cell PCR gene-expression analysis” (SINCE-PCR) to dissect the cellular composition of primary human normal colon and colon cancer epithelia. We show that human colon cancer tissues contain distinct cell populations whose transcriptional identities mirror those of the different cellular lineages of normal colon. By creating monoclonal tumor xenografts from injection of a single-cell (n = 1), we show that transcriptional diversity of cancer tissues is largely explained by in vivo multi-lineage differentiation, not only by clonal genetic heterogeneity. Finally, we show that perturbations in gene-expression programs linked to multi-lineage differentiation strongly associate with patient survival. Guided by SINCE-PCR data, we develop two-gene classifier systems (KRT20 vs CA1, MS4A12, CD177, SLC26A3) that predict clinical outcomes with hazard-ratios superior to pathological grade and comparable to microarray-derived multi-gene expression signatures.
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              Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor.

               Xun Xu,  Yong Hou,  Xuyang Yin (2012)
              Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and has very few mutations that are shared between different patients. To better understand the intratumoral genetics underlying mutations of ccRCC, we carried out single-cell exome sequencing on a ccRCC tumor and its adjacent kidney tissue. Our data indicate that this tumor was unlikely to have resulted from mutations in VHL and PBRM1. Quantitative population genetic analysis indicates that the tumor did not contain any significant clonal subpopulations and also showed that mutations that had different allele frequencies within the population also had different mutation spectrums. Analyses of these data allowed us to delineate a detailed intratumoral genetic landscape at a single-cell level. Our pilot study demonstrates that ccRCC may be more genetically complex than previously thought and provides information that can lead to new ways to investigate individual tumors, with the aim of developing more effective cellular targeted therapies. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                renxwise@pku.edu.cn
                zemin@pku.edu.cn
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                3 December 2018
                3 December 2018
                2018
                : 19
                Affiliations
                ISNI 0000 0001 2256 9319, GRID grid.11135.37, Beijing Advanced Innovation Centre for Genomics, Peking-Tsinghua Centre for Life Sciences, Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, , Peking University, ; Beijing, 100871 China
                Article
                1593
                10.1186/s13059-018-1593-z
                6276232
                30509292
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Funding
                Funded by: Key Technologies R&D Program (China)
                Award ID: 2016YFC0900100
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81573022, 31530036, 91742203
                Award Recipient :
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                © The Author(s) 2018

                Genetics

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