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      Acquisition of aneuploidy drives mutant p53-associated gain-of-function phenotypes

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          Abstract

          p53 is mutated in over half of human cancers. In addition to losing wild-type (WT) tumor-suppressive function, mutant p53 proteins are proposed to acquire gain-of-function (GOF) activity, leading to novel oncogenic phenotypes. To study mutant p53 GOF mechanisms and phenotypes, we genetically engineered non-transformed and tumor-derived WT p53 cell line models to express endogenous missense mutant p53 (R175H and R273H) or to be deficient for p53 protein (null). Characterization of the models, which initially differed only by TP53 genotype, revealed that aneuploidy frequently occurred in mutant p53-expressing cells. GOF phenotypes occurred clonally in vitro and in vivo, were independent of p53 alteration and correlated with increased aneuploidy. Further, analysis of outcome data revealed that individuals with aneuploid-high tumors displayed unfavorable prognoses, regardless of the TP53 genotype. Our results indicate that genetic variation resulting from aneuploidy accounts for the diversity of previously reported mutant p53 GOF phenotypes.

          Abstract

          Previous studies report that mutant p53 proteins have gain-of-function activities and cause oncogenic phenotypes. Herein, the authors engineered two isogenic epithelial cell lines to express wild-type or missense mutant p53 or be deficient for p53 protein and show that aneuploidy drives several of the GOF phenotypes previously ascribed to mutant p53.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Fiji: an open-source platform for biological-image analysis.

            Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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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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                Author and article information

                Contributors
                j.pietenpol@vumc.org
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                31 August 2021
                31 August 2021
                2021
                : 12
                : 5184
                Affiliations
                [1 ]GRID grid.152326.1, ISNI 0000 0001 2264 7217, Department of Biochemistry, , Vanderbilt University, ; Nashville, TN USA
                [2 ]Inscripta, Inc, Boulder, CO USA
                [3 ]GRID grid.412807.8, ISNI 0000 0004 1936 9916, Vanderbilt-Ingram Cancer Center, , Vanderbilt University Medical Center, ; Nashville, TN USA
                [4 ]GRID grid.261331.4, ISNI 0000 0001 2285 7943, Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, , The Ohio State University, ; Columbus, OH USA
                [5 ]GRID grid.412807.8, ISNI 0000 0004 1936 9916, Department of Biostatistics, , Vanderbilt University Medical Center, ; Nashville, TN USA
                [6 ]GRID grid.412807.8, ISNI 0000 0004 1936 9916, Department of Medicine, , Vanderbilt University Medical Center, ; Nashville, TN USA
                [7 ]GRID grid.412807.8, ISNI 0000 0004 1936 9916, Department of Pathology, Microbiology and Immunology, , Vanderbilt University Medical Center, ; Nashville, TN USA
                Author information
                http://orcid.org/0000-0003-2769-3196
                http://orcid.org/0000-0002-7260-8723
                http://orcid.org/0000-0002-9974-1604
                http://orcid.org/0000-0001-7834-5621
                http://orcid.org/0000-0001-8951-9295
                http://orcid.org/0000-0001-9692-7393
                http://orcid.org/0000-0002-4616-6140
                http://orcid.org/0000-0003-0407-5248
                http://orcid.org/0000-0001-6268-6798
                Article
                25359
                10.1038/s41467-021-25359-z
                8408227
                34465782
                4c11b01d-9bd7-4f95-915b-dab738eb57c4
                © The Author(s) 2021

                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
                : 2 October 2020
                : 3 August 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000054, U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI);
                Award ID: CA068485
                Award ID: CA098131
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                tumour-suppressor proteins,oncogenes
                Uncategorized
                tumour-suppressor proteins, oncogenes

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