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      TNF-α modulates genome-wide redistribution of ΔNp63α/TAp73 and NF-κB c-REL interactive binding on TP53 and AP-1 motifs to promote an oncogenic gene program in squamous cancer

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          Abstract

          The Cancer Genome Atlas (TCGA) network study of 12 cancer types (PanCancer 12) revealed frequent mutation of TP53, and amplification and expression of related TP63 isoform ΔNp63 in squamous cancers. Further, aberrant expression of inflammatory genes and TP53/p63/p73 targets were detected in the PanCancer 12 project, reminiscent of gene programs co-modulated by cREL/ΔNp63/TAp73 transcription factors we uncovered in head and neck squamous cell carcinomas (HNSCC). However, how inflammatory gene signatures and cREL/p63/p73 targets are co-modulated genome-wide is unclear. Here, we examined how inflammatory factor TNF-α broadly modulates redistribution of cREL with ΔNp63α/TAp73 complexes and signatures genome-wide in the HNSCC model UM-SCC46 using chromatin immunoprecipitation sequencing (ChIP-seq). TNF-α enhanced genome-wide co-occupancy of cREL with ΔNp63α on TP53/p63 sites, while unexpectedly promoting redistribution of TAp73 from TP53 to Activator Protein-1 (AP-1) sites. cREL, ΔNp63α, and TAp73 binding and oligomerization on NF-κB, TP53 or AP-1 specific sequences were independently validated by ChIP-qPCR, oligonucleotide-binding assays, and analytical ultracentrifugation. Function of the binding activity was confirmed using TP53, AP-1, and NF-κB specific response elements, or p21, SERPINE1 , and IL-6 promoter luciferase reporter activities. Concurrently, TNF-α regulated a broad gene network with co-binding activities for cREL, ΔNp63α, and TAp73 observed upon array profiling and RT-PCR. Overlapping target gene signatures were observed in squamous cancer subsets and in inflamed skin of transgenic mice overexpressing ΔNp63α. Furthermore, multiple target genes identified in this study were linked to TP63 and TP73 activity and increased gene expression in large squamous cancer samples from PanCancer 12 TCGA by CircleMap. PARADIGM inferred pathway analysis revealed the network connection of TP63 and NF-κB complexes through an AP-1 hub, further supporting our findings. Thus, inflammatory cytokine TNF-α mediates genome-wide redistribution of the cREL/p63/p73, and AP-1 interactome, to diminish TAp73 tumor suppressor function and reciprocally activate NF-κB and AP-1 gene programs implicated in malignancy.

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          Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin.

          Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies. Copyright © 2014 Elsevier Inc. All rights reserved.
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            Transcriptional control of human p53-regulated genes.

            The p53 protein regulates the transcription of many different genes in response to a wide variety of stress signals. Following DNA damage, p53 regulates key processes, including DNA repair, cell-cycle arrest, senescence and apoptosis, in order to suppress cancer. This Analysis article provides an overview of the current knowledge of p53-regulated genes in these pathways and others, and the mechanisms of their regulation. In addition, we present the most comprehensive list so far of human p53-regulated genes and their experimentally validated, functional binding sites that confer p53 regulation.
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              Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM

              Motivation: High-throughput data is providing a comprehensive view of the molecular changes in cancer tissues. New technologies allow for the simultaneous genome-wide assay of the state of genome copy number variation, gene expression, DNA methylation and epigenetics of tumor samples and cancer cell lines. Analyses of current data sets find that genetic alterations between patients can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients. Results: We present a novel method for inferring patient-specific genetic activities incorporating curated pathway interactions among genes. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathway's activities (e.g. internal gene states, interactions or high-level ‘outputs’) are altered in the patient using probabilistic inference. Compared with a competing pathway activity inference approach called SPIA, our method identifies altered activities in cancer-related pathways with fewer false-positives in both a glioblastoma multiform (GBM) and a breast cancer dataset. PARADIGM identified consistent pathway-level activities for subsets of the GBM patients that are overlooked when genes are considered in isolation. Further, grouping GBM patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. These findings suggest that therapeutics might be chosen that target genes at critical points in the commonly perturbed pathway(s) of a group of patients. Availability:Source code available at http://sbenz.github.com/Paradigm Contact: jstuart@soe.ucsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                8711562
                6325
                Oncogene
                Oncogene
                Oncogene
                0950-9232
                1476-5594
                15 February 2016
                2 May 2016
                3 November 2016
                07 November 2016
                : 35
                : 44
                : 5781-5794
                Affiliations
                [1 ]Tumor Biology Section, Head and Neck Surgery Branch, National Institute on Deafness and Other Communication Disorders, NIH, Bethesda, Maryland, USA
                [2 ]Orthopaedic Center, Zhujiang Hospital Guangzhou, Guangdong, China
                [3 ]Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA
                [4 ]Instituto de Química, Universidad Nacional Autónoma de México (UNAM), Circuito Exterior, Ciudad Universitaria, Mexico City, D.F. 04510, MÉXICO
                [5 ]LKS Faculty of Medicine and School of Biomedical Sciences, LKS Faculty of Medicine and Center of Genome Sciences, The University of Hong Kong, Hong Kong, China
                [6 ]Buck Institute for Research on Aging, Novato, CA
                [7 ]Department of Biomolecular Engineering, Center for Biomolecular Sciences and Engineering, University of California, Santa Cruz, Santa Cruz, CA
                [8 ]Department of Biochemistry, State University of New York at Buffalo, Center for Excellence in Bioinformatics and Life Sciences, Buffalo, New York, USA
                [9 ]Cancer Genetics Branch, National Cancer Institute, Bethesda, Maryland, USA
                [10 ]Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas
                [11 ]Biodata Mining and Discovery Section, National Institute of Arthritis, Musculoskeletal and Skin Diseases, Bethesda, Maryland, USA
                [12 ]Clinical Immunology Section, National Eye Institute, NIH, Bethesda, Maryland, USA.
                [13 ]State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
                Author notes
                [*]

                Contributed equally.

                [# ]Corresponding authors: Zhong Chen, MD, PhD, NIDCD/NIH, Bldg 10/5D55, 10 Center Drive, Bethesda, MD 20892. chenz@ 123456nidcd.nih.gov . Phone: 301-435-2073. Fax: 301-596-4643. Carter Van Waes, MD, PhD, NIDCD/NIH, Bldg 10/CRC, 4-2732, 10 Center Drive, Bethesda, MD 20892. vanwaesc@ 123456nidcd.nih.gov . Phone: 301-402-4216. Fax: 301-402-1140.
                Article
                NIHMS753191
                10.1038/onc.2016.112
                5093089
                27132513
                52a21b26-e35d-4bcf-b36b-9f513dc73714

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                Article

                Oncology & Radiotherapy
                tnf-alpha,crel,δnp63α,tap73,chip-seq
                Oncology & Radiotherapy
                tnf-alpha, crel, δnp63α, tap73, chip-seq

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