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      Identification of a core TP53 transcriptional program with highly distributed tumor suppressive activity

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          Abstract

          The tumor suppressor TP53 is the most frequently mutated gene product in human cancer. Close to half of all solid tumors carry inactivating mutations in the TP53 gene, while in the remaining cases, TP53 activity is abrogated by other oncogenic events, such as hyperactivation of its endogenous repressors MDM2 or MDM4. Despite identification of hundreds of genes regulated by this transcription factor, it remains unclear which direct target genes and downstream pathways are essential for the tumor suppressive function of TP53. We set out to address this problem by generating multiple genomic data sets for three different cancer cell lines, allowing the identification of distinct sets of TP53-regulated genes, from early transcriptional targets through to late targets controlled at the translational level. We found that although TP53 elicits vastly divergent signaling cascades across cell lines, it directly activates a core transcriptional program of ∼100 genes with diverse biological functions, regardless of cell type or cellular response to TP53 activation. This core program is associated with high-occupancy TP53 enhancers, high levels of paused RNA polymerases, and accessible chromatin. Interestingly, two different shRNA screens failed to identify a single TP53 target gene required for the anti-proliferative effects of TP53 during pharmacological activation in vitro. Furthermore, bioinformatics analysis of thousands of cancer genomes revealed that none of these core target genes are frequently inactivated in tumors expressing wild-type TP53. These results support the hypothesis that TP53 activates a genetically robust transcriptional program with highly distributed tumor suppressive functions acting in diverse cellular contexts.

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          Ferroptosis as a p53-mediated activity during tumour suppression.

          Although p53-mediated cell-cycle arrest, senescence and apoptosis serve as critical barriers to cancer development, emerging evidence suggests that the metabolic activities of p53 are also important. Here we show that p53 inhibits cystine uptake and sensitizes cells to ferroptosis, a non-apoptotic form of cell death, by repressing expression of SLC7A11, a key component of the cystine/glutamate antiporter. Notably, p53(3KR), an acetylation-defective mutant that fails to induce cell-cycle arrest, senescence and apoptosis, fully retains the ability to regulate SLC7A11 expression and induce ferroptosis upon reactive oxygen species (ROS)-induced stress. Analysis of mutant mice shows that these non-canonical p53 activities contribute to embryonic development and the lethality associated with loss of Mdm2. Moreover, SLC7A11 is highly expressed in human tumours, and its overexpression inhibits ROS-induced ferroptosis and abrogates p53(3KR)-mediated tumour growth suppression in xenograft models. Our findings uncover a new mode of tumour suppression based on p53 regulation of cystine metabolism, ROS responses and ferroptosis.
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            Mutational heterogeneity in cancer and the search for new cancer genes

            Major international projects are now underway aimed at creating a comprehensive catalog of all genes responsible for the initiation and progression of cancer. These studies involve sequencing of matched tumor–normal samples followed by mathematical analysis to identify those genes in which mutations occur more frequently than expected by random chance. Here, we describe a fundamental problem with cancer genome studies: as the sample size increases, the list of putatively significant genes produced by current analytical methods burgeons into the hundreds. The list includes many implausible genes (such as those encoding olfactory receptors and the muscle protein titin), suggesting extensive false positive findings that overshadow true driver events. Here, we show that this problem stems largely from mutational heterogeneity and provide a novel analytical methodology, MutSigCV, for resolving the problem. We apply MutSigCV to exome sequences from 3,083 tumor-normal pairs and discover extraordinary variation in (i) mutation frequency and spectrum within cancer types, which shed light on mutational processes and disease etiology, and (ii) mutation frequency across the genome, which is strongly correlated with DNA replication timing and also with transcriptional activity. By incorporating mutational heterogeneity into the analyses, MutSigCV is able to eliminate most of the apparent artefactual findings and allow true cancer genes to rise to attention.
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              GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers

              We describe methods with enhanced power and specificity to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth. By separating SCNA profiles into underlying arm-level and focal alterations, we improve the estimation of background rates for each category. We additionally describe a probabilistic method for defining the boundaries of selected-for SCNA regions with user-defined confidence. Here we detail this revised computational approach, GISTIC2.0, and validate its performance in real and simulated datasets.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                October 2017
                October 2017
                : 27
                : 10
                : 1645-1657
                Affiliations
                [1 ]Linda Crnic Institute for Down Syndrome, University of Colorado Anschutz Medical Campus, Aurora, Colorado 80045, USA;
                [2 ]Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, Colorado 80045, USA;
                [3 ]Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, Boulder, Colorado 80203, USA;
                [4 ]Centre for Integrative Biology (CIBIO), University of Trento, 38123 Trento, TN, Italy;
                [5 ]Howard Hughes Medical Institute, Chevy Chase, Maryland 20815-6789, USA
                Author notes
                Author information
                http://orcid.org/0000-0001-7838-302X
                http://orcid.org/0000-0003-0485-3927
                http://orcid.org/0000-0003-2725-0205
                http://orcid.org/0000-0001-9048-1941
                Article
                9509184
                10.1101/gr.220533.117
                5630028
                28904012
                4a720d29-8bc2-4882-ab84-f169b664118b
                © 2017 Andrysik et al.; Published by Cold Spring Harbor Laboratory Press

                This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 4 February 2017
                : 22 August 2017
                Page count
                Pages: 13
                Funding
                Funded by: National Institutes of Health (NIH) , open-funder-registry 10.13039/100000002;
                Award ID: R01 CA117907
                Funded by: National Science Foundation (NSF) , open-funder-registry 10.13039/100000001;
                Award ID: MCB-1243522/MCB-1627615
                Funded by: NIH , open-funder-registry 10.13039/100000002;
                Award ID: 1F32CA199716-01
                Funded by: Mary Miller & Charlotte Fonfara-LaRose Down Syndrome & Leukemia Research
                Funded by: Howard Hughes Medical Institute , open-funder-registry 10.13039/100000011;
                Funded by: NIH , open-funder-registry 10.13039/100000002;
                Funded by: National Cancer Institute (NCI) , open-funder-registry 10.13039/100000054;
                Award ID: P30 CA046934
                Categories
                Research

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