DOCSLIB.ORG

Benefits of Cancerplex

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

CancerPlexSM is a comprehensive genetic assessment of a patient’s tumor that guides oncologists towards effective treatment options. What is CancerPlex? CancerPlex is a next generation DNA sequencing test for solid SM tumors. The specific coding regions of over 400 known cancer Potential outcomes from CancerPlex genes are simultaneously determined using a small amount of Identification of variants associated with response or resis- DNA extracted from tumor samples, including formalin-fixed, tance to an FDA-approved therapy for the patient’s disease. paraffin-embedded (FFPE) tissue and cell blocks from fine-needle aspirates or effusions. This analysis is performed in a single assay, Identification of variants associated with response or resis- simplifying and streamlining the test ordering process, and eliminat- tance to a therapy associated with another clinical indication. ing time consuming serial testing. Clinically actionable information unique to each patient’s tumor is consolidated into a simple report. Identification of variants associated with a therapy(s) in CancerPlex reveals missense changes, insertions and deletions, clinical development. and previously described rearrangements of ALK, RET, and ROS. Benefits of CancerPlex: CancerPlex genes were selected because of their importance in tumor The average turn-around-time for CancerPlex is under 10 biology. Analyzing more genes increases the chance of discovering business days, dramatically shortening the waiting period for actionable findings that lead to more choices for treatment. Our initial the start of treatment. results indicate that CancerPlex analysis identifies actionable findings up to 20% more frequently than previously published studies. CancerPlex ordering is simple: KEW provides all necessary shipping materials, can facilitate tissue retrieval and return with Since knowledge of tumor biology changes rapidly, CancerPlex pathologists, and manages 3rd party payment for the test. incorporates genes that are actionable today using both approved therapies and therapies in clinical development. Hot-spot analysis may miss up to 20% of clinically important mutations in some tumors. CancerPlex sequences the coding regions Contact KEW of genes, detecting novel or rare alterations that are missed by other 840 Memorial Drive testing modalities. Cambridge, MA 02139 CancerPlex reports are easy for oncologists to interpret and translate www.kewgroup.com into care decisions. KEW’s Therapeutic Pathways provide valuable For inquiries: +(1) 617-945-7922 information about the most current updates in cancer care. or [email protected] This gene list is representative of the genes analyzed in CancerPlex and was created from extensive analysis of scientific literature. For detailed information on the region of interest, please contact a CancerPlex specialist at [email protected]. The highlighted genes are both sequenced and analyzed for previously described gene rearrangements. GENE LIST ABL1 BTK DDIT3 FGF23 IDH2 MAP2K2 NOTCH1 PRKDC STAG2 ABL2 BUB1B DDR1 FGF3 IGF1R MAP2K4 NOTCH2 PSIP1 STAT3 ACVR1B C11orf30 DDR2 FGF4 IGF2 MAP3K1 NOTCH4 PTCH1 STAT4 ACVR2A CARD11 DICER1 FGF6 IGF2R MAP3K7 NPM1 PTEN STK11 AFF1 CASP8 DNMT3A FGFR1 IKBKB MAPK1 NRAS PTGS2 STK36 AKT1 CBFB DOT1L FGFR2 IKBKE MAPK8 NTRK1 PTPRD SUFU AKT2 CBL DST FGFR3 IKZF1 MBD1 NTRK2 PTPRT SYK AKT3 CCND1 EDNRB FGFR4 IL2 MCL1 NTRK3 RAC1 TAF1 ALK CCND2 EGFR FH IL21R MDM2 NUMA1 RAF1 TAF1L AMER1 CCND3 EML4 FLCN IL6ST MDM4 NUP214 RALGDS TAL1 APC CCNE1 EP300 FLI1 IL7R MED12 NUP93 RARA TBX22 AR CD70 EPHA3 FLT1 INHBA MEF2B NUP98 RASA1 TCF12 ARAF CD79A EPHA5 FLT3 IRF4 MEN1 PAK3 RB1 TCF3 ARFRP1 CD79B EPHA7 FLT4 IRS2 MET PALB2 REL TCF7L1 ARID1A CDC73 EPHB1 FN1 ITGA10 MITF PARP1 RET TCF7L2 ARID2 CDH1 EPHB4 FOXA1 ITGB2 MLH1 PAX3 RHOH TCL1A ARNT CDH11 EPHB6 FOXL2 ITGB3 MMP2 PAX5 RICTOR TET1 ASCL4 CDH2 ERBB2 FOXO1 JAK1 MN1 PAX7 RNASEL TET2 ASXL1 CDH5 ERBB3 FOXP1 JAK2 MPL PAX8 RNF2 TFE3 ATM CDK12 ERBB4 G6PD JAK3 MRE11A PBRM1 RNF43 TGFBR2 ATR CDK4 ERCC1 GATA1 JUN MSH2 PBX1 ROS1 TGM7 ATRX CDK6 ERCC2 GATA2 KAT6A MSH6 PDGFRA RPS6KA2 TLR4 AURKA CDK8 ERCC3 GATA3 KAT6B MST1R PDGFRB RPTOR TNFAIP3 AURKB CDKN1A ERCC4 GDNF KDM5A MTOR PDK1 RRM1 TNFRSF14 AURKC CDKN1B ERCC5 GID4 KDM5C MTR PGAP3 RUNX1 TNK2 AXIN2 CDKN2A ERG GNA11 KDM6A MTRR PHOX2B RUNX1T1 TOP1 AXL CDKN2B ESR1 GNA13 KDR MUTYH PIK3C2B SAMD9 TP53 BAP1 CDKN2C ETS1 GNAQ KEAP1 MYC PIK3CA SDHB TP63 BARD1 CEBPA ETV1 GNAS KIAA1804 MYCL PIK3CB SETD2 TPR BCL10 CHEK1 ETV4 GPC6 KIT MYCN PIK3CD SF3B1 TRIM33 BCL11A CIC EXT1 GPR124 KLF5 MYD88 PIK3CG SGK1 TRIP11 BCL11B CREBBP EXT2 GRIK3 KLF6 MYH11 PIK3R1 SH2D1A TRRAP BCL2 CRKL EZH2 GRIN2A KLHL6 MYH9 PIK3R2 SLIT2 TSC1 BCL2L1 CRLF2 FAM46C GSK3B KMT2A NBN PIK3R3 SMAD2 TSC2 BCL2L2 CRTC1 FANCA HCAR1 KMT2D NCOA1 PIM1 SMAD4 TSHR BCL6 CSF1R FANCC HCN1 KRAS NCOA2 PKHD1 SMARCA4 TYK2 BCL9 CSMD3 FANCE HGF LAMP1 NCOA4 PLAG1 SMARCB1 UBR5 BCOR CTCF FANCF HIF1A LCK NF1 PLCG1 SMO USP9X BCORL1 CTNNA1 FANCG HLF LPP NF2 PML SOCS1 VHL BIRC2 CTNNA2 FANCL HNF1A LRP1B NFE2L2 PMS1 SOX10 WISP3 BIRC3 CTNNB1 FAS HNF4A LTF NFKB1 POT1 SOX11 WT1 BLM CUX1 FAT1 HRAS MAGEA1 NFKB2 PPARG SOX2 XPC BLNK CYLD FBXW7 HSP90AA1 MAGI1 NFKBIA PPP2R1A SOX9 XPO1 BMPR1A DAXX FGF10 HSP90AB1 MALT1 NIN PPP6C SPEN ZNF384 BRAF DCC FGF14 ICK MAML2 NKX2-1 PRDM1 SPOP ZNF521 BRIP1 DDB2 FGF19 IDH1 MAP2K1 NLRP1 PRKAR1A SRC.
Recommended publications
  • KAT6A Syndrome: Genotype-Phenotype Correlation in 76 Patients with Pathogenic KAT6A Variants

    KAT6A Syndrome: Genotype-Phenotype Correlation in 76 Patients with Pathogenic KAT6A Variants

    KAT6A Syndrome: genotype-phenotype correlation in 76 patients with pathogenic KAT6A variants. Item Type Article Authors Kennedy, Joanna;Goudie, David;Blair, Edward;Chandler, Kate;Joss, Shelagh;McKay, Victoria;Green, Andrew;Armstrong, Ruth;Lees, Melissa;Kamien, Benjamin;Hopper, Bruce;Tan, Tiong Yang;Yap, Patrick;Stark, Zornitza;Okamoto, Nobuhiko;Miyake, Noriko;Matsumoto, Naomichi;Macnamara, Ellen;Murphy, Jennifer L;McCormick, Elizabeth;Hakonarson, Hakon;Falk, Marni J;Li, Dong;Blackburn, Patrick;Klee, Eric;Babovic- Vuksanovic, Dusica;Schelley, Susan;Hudgins, Louanne;Kant, Sarina;Isidor, Bertrand;Cogne, Benjamin;Bradbury, Kimberley;Williams, Mark;Patel, Chirag;Heussler, Helen;Duff- Farrier, Celia;Lakeman, Phillis;Scurr, Ingrid;Kini, Usha;Elting, Mariet;Reijnders, Margot;Schuurs-Hoeijmakers, Janneke;Wafik, Mohamed;Blomhoff, Anne;Ruivenkamp, Claudia A L;Nibbeling, Esther;Dingemans, Alexander J M;Douine, Emilie D;Nelson, Stanley F;Arboleda, Valerie A;Newbury-Ecob, Ruth DOI 10.1038/s41436-018-0259-2 Journal Genetics in medicine : official journal of the American College of Medical Genetics Download date 30/09/2021 21:53:13 Link to Item http://hdl.handle.net/10147/627191 Find this and similar works at - http://www.lenus.ie/hse HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Genet Med Manuscript Author . Author manuscript; Manuscript Author available in PMC 2019 October 01. Published in final edited form as: Genet Med. 2019 April ; 21(4): 850–860. doi:10.1038/s41436-018-0259-2. KAT6A Syndrome: Genotype-phenotype correlation in 76 patients with pathogenic KAT6A variants A full list of authors and affiliations appears at the end of the article. Abstract Purpose: Mutations in KAT6A have recently been identified as a cause of syndromic developmental delay.
  • ONCOPANEL (Popv3)

    ONCOPANEL (Popv3)

    ONCOPANEL (POPv3) TEST INFORMATION BACKGROUND: Somatic genetic alterations in oncogenes and tumor-suppressor genes contribute to the pathogenesis and evolution of human cancers. These alterations can provide prognostic and predictive information and stratify cancers for targeted therapeutic information. We classify these alterations into five tiers using the following guidelines: Tier 1: The alteration has well-established published evidence confirming clinical utility in this tumor type, in at least one of the following contexts: predicting response to treatment with an FDA-approved therapy; assessing prognosis; establishing a definitive diagnosis; or conferring an inherited increased risk of cancer to this patient and family. Tier 2: The alteration may have clinical utility in at least one of the following contexts: selection of an investigational therapy in clinical trials for this cancer type; limited evidence of prognostic association; supportive of a specific diagnosis; proven association of response to treatment with an FDA-approved therapy in a different type of cancer; or similar to a different mutation with a proven association with response to treatment with an FDA-approved therapyin this type of cancer. Tier 3: The alteration is of uncertain clinical utility, but may have a role as suggested by at least one of the following: demonstration of association with response to treatment in this cancer type in preclinical studies (e.g., in vitro studies or animal models); alteration in a biochemical pathway that has other known, therapeutically-targetable alterations; alteration in a highly conserved region of the protein predicted, in silico, to alter protein function; or selection of an investigational therapy for a different cancer type.
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
  • Cellular and Molecular Signatures in the Disease Tissue of Early

    Cellular and Molecular Signatures in the Disease Tissue of Early

    Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of
  • Further Delineation of the Clinical Spectrum of KAT6B Disorders and Allelic Series of Pathogenic Variants

    Further Delineation of the Clinical Spectrum of KAT6B Disorders and Allelic Series of Pathogenic Variants

    ARTICLE © American College of Medical Genetics and Genomics Further delineation of the clinical spectrum of KAT6B disorders and allelic series of pathogenic variants Li Xin Zhang1, Gabrielle Lemire, MD2, Claudia Gonzaga-Jauregui, PhD3, Sirinart Molidperee, Gr. Cert1, Carolina Galaz-Montoya, MD3, David S. Liu, MD3, Alain Verloes, MD PhD4, Amelle G. Shillington, DO5, Kosuke Izumi, MD PhD6, Alyssa L. Ritter, MS LCGC7, Beth Keena, MS LCGC6, Elaine Zackai, MD7,8, Dong Li, PhD9, Elizabeth Bhoj, MD PhD7, Jennifer M. Tarpinian, MS CGC10, Emma Bedoukian, MS LCGC10, Mary K. Kukolich, MD11, A. Micheil Innes, MD12, Grace U. Ediae, BSc13, Sarah L. Sawyer, MD PhD14, Karippoth Mohandas Nair, MD15, Para Chottil Soumya, MD15, Kinattinkara R. Subbaraman, MD15, Frank J. Probst, MD PhD3,16, Jennifer A. Bassetti, MD3,16, Reid V. Sutton, MD3,16, Richard A. Gibbs, PhD3, Chester Brown, MD PhD17, Philip M. Boone, MD PhD18, Ingrid A. Holm, MD MPH18, Marco Tartaglia, PhD19, Giovanni Battista Ferrero, MD PhD20, Marcello Niceta, MD PhD19, Maria Lisa Dentici, MD19, Francesca Clementina Radio, MD PhD19, Boris Keren, MD21, Constance F. Wells, MD22, Christine Coubes, MD22, Annie Laquerrière, MD PhD23, Jacqueline Aziza, MD24, Charlotte Dubucs, MD24, Sheela Nampoothiri, MD25, David Mowat, MD26, Millan S. Patel, MD27, Ana Bracho, MD28, Francisco Cammarata-Scalisi, MD29, Alper Gezdirici, MD30, Alberto Fernandez-Jaen, MD31, Natalie Hauser, MD32, Yuri A. Zarate, MD33, Katherine A. Bosanko, MS CGC33, Klaus Dieterich, MD PhD34, John C. Carey, MD MPH35, Jessica X. Chong, PhD36,37, Deborah A. Nickerson, PhD37,38, Michael J. Bamshad, MD36,37, Brendan H. Lee, MD PhD3, Xiang-Jiao Yang, PhD39, James R.
  • Intrinsically Disordered Meningioma-1 Stabilizes the BAF Complex to Cause AML

    Intrinsically Disordered Meningioma-1 Stabilizes the BAF Complex to Cause AML

    Article Intrinsically disordered Meningioma-1 stabilizes the BAF complex to cause AML Graphical abstract Authors Simone S. Riedel, Congcong Lu, Hongbo M. Xie, ..., Gerd A. Blobel, Benjamin A. Garcia, Kathrin M. Bernt Correspondence [email protected] In brief Meningioma-1 (MN1) translocations result in overexpression of MN1 through enhancer hijacking, or expression of an MN1 fusion protein. MN1 is an intrinsically disordered polyQ protein. MN1 overexpression is sufficient to cause malignant transformation via over- stabilization of the BAF complex at critical enhancers. Highlights d MN1 translocations in AML result in MN1 overexpression due to enhancer hijacking d MN1 interacts with the myeloid progenitor BAF complex d MN1 over-stabilizes the BAF complex at critical enhancers d Overexpression of the polyQ protein MN1 is sufficient to cause AML Riedel et al., 2021, Molecular Cell 81, 1–17 June 3, 2021 ª 2021 Elsevier Inc. https://doi.org/10.1016/j.molcel.2021.04.014 ll Please cite this article in press as: Riedel et al., Intrinsically disordered Meningioma-1 stabilizes the BAF complex to cause AML, Molecular Cell (2021), https://doi.org/10.1016/j.molcel.2021.04.014 ll Article Intrinsically disordered Meningioma-1 stabilizes the BAF complex to cause AML Simone S. Riedel,1 Congcong Lu,2 Hongbo M. Xie,3 Kevin Nestler,1 Marit W. Vermunt,4 Alexandra Lenard,1 Laura Bennett,5 Nancy A. Speck,5 Ichiro Hanamura,6 Julie A. Lessard,7 Gerd A. Blobel,4,8 Benjamin A. Garcia,2 and Kathrin M. Bernt1,8,9,* 1Division of Pediatric Oncology, Children’s Hospital
  • 1714 Gene Comprehensive Cancer Panel Enriched for Clinically Actionable Genes with Additional Biologically Relevant Genes 400-500X Average Coverage on Tumor

    1714 Gene Comprehensive Cancer Panel Enriched for Clinically Actionable Genes with Additional Biologically Relevant Genes 400-500X Average Coverage on Tumor

    xO GENE PANEL 1714 gene comprehensive cancer panel enriched for clinically actionable genes with additional biologically relevant genes 400-500x average coverage on tumor Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 HOXA11 IL6R KAT5 ACVR2A
  • Metastatic Adrenocortical Carcinoma Displays Higher Mutation Rate and Tumor Heterogeneity Than Primary Tumors

    Metastatic Adrenocortical Carcinoma Displays Higher Mutation Rate and Tumor Heterogeneity Than Primary Tumors

    ARTICLE DOI: 10.1038/s41467-018-06366-z OPEN Metastatic adrenocortical carcinoma displays higher mutation rate and tumor heterogeneity than primary tumors Sudheer Kumar Gara1, Justin Lack2, Lisa Zhang1, Emerson Harris1, Margaret Cam2 & Electron Kebebew1,3 Adrenocortical cancer (ACC) is a rare cancer with poor prognosis and high mortality due to metastatic disease. All reported genetic alterations have been in primary ACC, and it is 1234567890():,; unknown if there is molecular heterogeneity in ACC. Here, we report the genetic changes associated with metastatic ACC compared to primary ACCs and tumor heterogeneity. We performed whole-exome sequencing of 33 metastatic tumors. The overall mutation rate (per megabase) in metastatic tumors was 2.8-fold higher than primary ACC tumor samples. We found tumor heterogeneity among different metastatic sites in ACC and discovered recurrent mutations in several novel genes. We observed 37–57% overlap in genes that are mutated among different metastatic sites within the same patient. We also identified new therapeutic targets in recurrent and metastatic ACC not previously described in primary ACCs. 1 Endocrine Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. 2 Center for Cancer Research, Collaborative Bioinformatics Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. 3 Department of Surgery and Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA. Correspondence and requests for materials should be addressed to E.K. (email: [email protected]) NATURE COMMUNICATIONS | (2018) 9:4172 | DOI: 10.1038/s41467-018-06366-z | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-06366-z drenocortical carcinoma (ACC) is a rare malignancy with types including primary ACC from the TCGA to understand our A0.7–2 cases per million per year1,2.
  • SUPPLEMENTARY NOTE Co-Activation of GR and NFKB

    SUPPLEMENTARY NOTE Co-Activation of GR and NFKB

    SUPPLEMENTARY NOTE Co-activation of GR and NFKB alters the repertoire of their binding sites and target genes. Nagesha A.S. Rao1*, Melysia T. McCalman1,*, Panagiotis Moulos2,4, Kees-Jan Francoijs1, 2 2 3 3,5 Aristotelis Chatziioannou , Fragiskos N. Kolisis , Michael N. Alexis , Dimitra J. Mitsiou and 1,5 Hendrik G. Stunnenberg 1Department of Molecular Biology, Radboud University Nijmegen, the Netherlands 2Metabolic Engineering and Bioinformatics Group, Institute of Biological Research and Biotechnology, National Hellenic Research Foundation, Athens, Greece 3Molecular Endocrinology Programme, Institute of Biological Research and Biotechnology, National Hellenic Research Foundation, Greece 4These authors contributed equally to this work 5 Corresponding authors E-MAIL: [email protected] ; TEL: +31-24-3610524; FAX: +31-24-3610520 E-MAIL: [email protected] ; TEL: +30-210-7273741; FAX: +30-210-7273677 Running title: Global GR and NFKB crosstalk Keywords: GR, p65, genome-wide, binding sites, crosstalk SUPPLEMENTARY FIGURES/FIGURE LEGENDS AND SUPPLEMENTARY TABLES 1 Rao118042_Supplementary Fig. 1 A Primary transcript Mature mRNA TNF/DMSO TNF/DMSO 8 12 r=0.74, p< 0.001 r=0.61, p< 0.001 ) 2 ) 10 2 6 8 4 6 4 2 2 0 Fold change (mRNA) (log Fold change (primRNA) (log 0 −2 −2 −2 0 2 4 −2 0 2 4 Fold change (RNAPII) (log2) Fold change (RNAPII) (log2) B chr5: chrX: 56 _ 104 _ DMSO DMSO 1 _ 1 _ 56 _ 104 _ TA TA 1 _ 1 _ 56 _ 104 _ TNF TNF Cluster 1 1 _ Cluster 2 1 _ 56 _ 104 _ TA+TNF TA+TNF 1 _ 1 _ CCNB1 TSC22D3 chr20: chr17: 25 _ 33 _ DMSO DMSO 1 _ 1 _ 25 _ 33 _ TA TA 1 _ 1 _ 25 _ 33 _ TNF TNF Cluster 3 1 _ Cluster 4 1 _ 25 _ 33 _ TA+TNF TA+TNF 1 _ 1 _ GPCPD1 CCL2 chr6: chr22: 77 _ 35 _ DMSO DMSO 1 _ 77 _ 1 _ 35 _ TA TA 1 _ 1 _ 77 _ 35 _ TNF Cluster 5 Cluster 6 TNF 1 _ 1 _ 77 _ 35 _ TA+TNF TA+TNF 1 _ 1 _ TNFAIP3 DGCR6 2 Supplementary Figure 1.
  • Retinoblastoma Binding Protein 4 Maintains Cycling Neural Stem Cells and Prevents DNA Damage and Tp53-Dependent Apoptosis in Rb1 Mutant Neural Progenitors Laura E

    Retinoblastoma Binding Protein 4 Maintains Cycling Neural Stem Cells and Prevents DNA Damage and Tp53-Dependent Apoptosis in Rb1 Mutant Neural Progenitors Laura E

    Genetics, Development and Cell Biology Genetics, Development and Cell Biology Publications 9-25-2018 Retinoblastoma binding protein 4 maintains cycling neural stem cells and prevents DNA damage and Tp53-dependent apoptosis in rb1 mutant neural progenitors Laura E. Schultz-Rogers Iowa State University Maira P. Almeida Iowa State University, [email protected] Wesley a. Wierson Iowa State University Marcel Kool Hopp Children’s Cancer Center at the NCT (KiTZ) MFoallourwa MthicsGr andail additional works at: https://lib.dr.iastate.edu/gdcb_las_pubs IowaP Satrate of U ntheiversitCya,nc mmcgrer Baiiol@ilogasyt aCteommon.edu s, and the Genetics and Genomics Commons The ompc lete bibliographic information for this item can be found at https://lib.dr.iastate.edu/ gdcb_las_pubs/208. For information on how to cite this item, please visit http://lib.dr.iastate.edu/ howtocite.html. This Article is brought to you for free and open access by the Genetics, Development and Cell Biology at Iowa State University Digital Repository. It has been accepted for inclusion in Genetics, Development and Cell Biology Publications by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Retinoblastoma binding protein 4 maintains cycling neural stem cells and prevents DNA damage and Tp53-dependent apoptosis in rb1 mutant neural progenitors Abstract Retinoblastoma-binding protein 4 (Rbbp4) is a WDR adaptor protein for multiple chromatin remodelers implicated in human oncogenesis. Here we show Rbbp4 is overexpressed in zebrafish rb1-embryonal brain tumors and is upregulated across the spectrum of human embryonal and glial brain cancers. We demonstrate in vivo Rbbp4 is essential for zebrafish neurogenesis and has distinct roles in neural stem and progenitor cells.
  • Microrna Regulation of Epigenetic Modifiers in Breast Cancer

    Microrna Regulation of Epigenetic Modifiers in Breast Cancer

    cancers Review MicroRNA Regulation of Epigenetic Modifiers in Breast Cancer Brock Humphries 1,* , Zhishan Wang 2 and Chengfeng Yang 2,3,* 1 Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, MI 48109; USA 2 Department of Toxicology and Cancer Biology, College of Medicine, University of Kentucky, Lexington, KY 40536, USA 3 Center for Research on Environment Disease, College of Medicine, University of Kentucky, Lexington, KY 40536; USA * Correspondence: [email protected] (B.H.); [email protected] (C.Y.) Received: 24 May 2019; Accepted: 24 June 2019; Published: 27 June 2019 Abstract: Epigenetics refers to the heritable changes in gene expression without a change in the DNA sequence itself. Two of these major changes include aberrant DNA methylation as well as changes to histone modification patterns. Alterations to the epigenome can drive expression of oncogenes and suppression of tumor suppressors, resulting in tumorigenesis and cancer progression. In addition to modifications of the epigenome, microRNA (miRNA) dysregulation is also a hallmark for cancer initiation and metastasis. Advances in our understanding of cancer biology demonstrate that alterations in the epigenome are not only a major cause of miRNA dysregulation in cancer, but that miRNAs themselves also indirectly drive these DNA and histone modifications. More explicitly, recent work has shown that miRNAs can regulate chromatin structure and gene expression by directly targeting key enzymes involved in these processes. This review aims to summarize these research findings specifically in the context of breast cancer. This review also discusses miRNAs as epigenetic biomarkers and as therapeutics, and presents a comprehensive summary of currently validated epigenetic targets in breast cancer.
  • 1,25-Dihydroxyvitamin D3-Induced Genes in Osteoblasts

    1,25-Dihydroxyvitamin D3-Induced Genes in Osteoblasts

    1,25-DIHYDROXYVITAMIN D3-INDUCED GENES IN OSTEOBLASTS: UNCOVERING NEW FUNCTIONS FOR MENINGIOMA 1 AND SEMAPHORIN 3B IN SKELETAL PHYSIOLOGY by XIAOXUE ZHANG Submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Thesis advisor: Paul N. MacDonald Department of Pharmacology CASE WESTERN RESERVE UNIVERSITY May 2009 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of _____________________________________________________ candidate for the ______________________degree *. (signed)_______________________________________________ (chair of the committee) ________________________________________________ ________________________________________________ ________________________________________________ ________________________________________________ ________________________________________________ (date) _______________________ *We also certify that written approval has been obtained for any proprietary material contained therein. I dedicate this thesis to my mother and father for their lifelong love, encouragement and sacrifice TABLE OF CONTENTS Table of Contents ii List of Tables iii List of Figures iv Acknowledgements vii Abbreviations x Abstract xiii Chapter I Introduction 1 Chapter II Meningioma 1 (MN1) is a 1,25-dihydroxyvitamin D3- 44 induced transcription coactivator that promotes osteoblast proliferation, motility, differentiation, and function Chapter III Semaphorin 3B (SEMA3B) is a 1,25- 108 dihydroxyvitamin D3-induced gene in osteoblasts that promotes