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      DUX4 expression in cancer induces a metastable early embryonic totipotent program

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          SUMMARY

          The transcription factor DUX4 regulates a portion of the zygotic gene activation (ZGA) program in the early embryo. Many cancers express DUX4 but it is unknown whether this generates cells similar to early embryonic stem cells. Here we identified cancer cell lines that express DUX4 and showed that DUX4 is transiently expressed in a small subset of the cells. DUX4 expression activates the DUX4-regulated ZGA transcriptional program, the subsequent 8C-like program, and markers of early embryonic lineages, while suppressing steady-state and interferon-induced MHC class I expression. Although DUX4 was expressed in a small number of cells under standard culture conditions, DNA damage or changes in growth conditions increased the fraction of cells expressing DUX4 and its downstream programs. Our demonstration that transient expression of endogenous DUX4 in cancer cells induces a metastable early embryonic stem cell program and suppresses antigen presentation has implications for cancer growth, progression, and immune evasion.

          In brief

          Smith et al. identify cancer cell lines that express the early embryonic transcription factor DUX4 and show that it is transiently expressed in a subset of cells. DUX4 expression induces the ZGA and 8C-like transcription programs as well as early lineage markers and suppresses MHC-I expression.

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          Most cited references61

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          Hallmarks of Cancer: The Next Generation

          The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.
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            Integrated analysis of multimodal single-cell data

            Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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              Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

              Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                12 October 2023
                26 September 2023
                08 September 2023
                16 October 2023
                : 42
                : 9
                : 113114
                Affiliations
                [1 ]Human Biology Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
                [2 ]Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
                [3 ]Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
                [4 ]Basic Sciences Division and Translational Science IRC, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
                [5 ]Department of Neurology, University of Washington, Seattle WA 98105, USA
                [6 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                Conceptualization, A.A.S. and S.J.T.; methodology, A.A.S., Y.N., S.R.B., D.C.H., and M.S.; software, S.R.B. and M.S.; validation, A.A.S. and Y.N.; formal analysis, A.A.S. and S.R.B.; investigation, A.A.S., Y.N., S.R.B., D.C.H., R.J.L.F.L., P.V., and S.M.M.; data curation, S.R.B; writing – original draft, A.A.S. and S.J.T.; writing – review & editing, A.A.S., S.J.T., D.C.H., S.M.M., R.J.L.F.L., M.S., S.R.B., and Y.N.; visualization, A.A.S., S.R.B., and S.J.T.; supervision, S.J.T. and S.M.M.; project administration, S.J.T, A.A.S., and S.M.M.; funding acquisition, S.J.T. and D.C.H.

                [* ]Correspondence: stapscot@ 123456fredhutch.org
                Article
                NIHMS1933912
                10.1016/j.celrep.2023.113114
                10578318
                37691147
                577624f9-875f-48dd-9514-63ff0933e8b0

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Cell biology
                Cell biology

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