Multi-Targeted Kinase Inhibition Alleviates Mtor Inhibitor
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Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model
Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2016 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. -
Supplementary Table S1. Upregulated Genes Differentially
Supplementary Table S1. Upregulated genes differentially expressed in athletes (p < 0.05 and 1.3-fold change) Gene Symbol p Value Fold Change 221051_s_at NMRK2 0.01 2.38 236518_at CCDC183 0.00 2.05 218804_at ANO1 0.00 2.05 234675_x_at 0.01 2.02 207076_s_at ASS1 0.00 1.85 209135_at ASPH 0.02 1.81 228434_at BTNL9 0.03 1.81 229985_at BTNL9 0.01 1.79 215795_at MYH7B 0.01 1.78 217979_at TSPAN13 0.01 1.77 230992_at BTNL9 0.01 1.75 226884_at LRRN1 0.03 1.74 220039_s_at CDKAL1 0.01 1.73 236520_at 0.02 1.72 219895_at TMEM255A 0.04 1.72 201030_x_at LDHB 0.00 1.69 233824_at 0.00 1.69 232257_s_at 0.05 1.67 236359_at SCN4B 0.04 1.64 242868_at 0.00 1.63 1557286_at 0.01 1.63 202780_at OXCT1 0.01 1.63 1556542_a_at 0.04 1.63 209992_at PFKFB2 0.04 1.63 205247_at NOTCH4 0.01 1.62 1554182_at TRIM73///TRIM74 0.00 1.61 232892_at MIR1-1HG 0.02 1.61 204726_at CDH13 0.01 1.6 1561167_at 0.01 1.6 1565821_at 0.01 1.6 210169_at SEC14L5 0.01 1.6 236963_at 0.02 1.6 1552880_at SEC16B 0.02 1.6 235228_at CCDC85A 0.02 1.6 1568623_a_at SLC35E4 0.00 1.59 204844_at ENPEP 0.00 1.59 1552256_a_at SCARB1 0.02 1.59 1557283_a_at ZNF519 0.02 1.59 1557293_at LINC00969 0.03 1.59 231644_at 0.01 1.58 228115_at GAREM1 0.01 1.58 223687_s_at LY6K 0.02 1.58 231779_at IRAK2 0.03 1.58 243332_at LOC105379610 0.04 1.58 232118_at 0.01 1.57 203423_at RBP1 0.02 1.57 AMY1A///AMY1B///AMY1C///AMY2A///AMY2B// 208498_s_at 0.03 1.57 /AMYP1 237154_at LOC101930114 0.00 1.56 1559691_at 0.01 1.56 243481_at RHOJ 0.03 1.56 238834_at MYLK3 0.01 1.55 213438_at NFASC 0.02 1.55 242290_at TACC1 0.04 1.55 ANKRD20A1///ANKRD20A12P///ANKRD20A2/// -
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. -
Mouse Rps6ka5 Antibody (C-Term) Purified Rabbit Polyclonal Antibody (Pab) Catalog # AW5466
10320 Camino Santa Fe, Suite G San Diego, CA 92121 Tel: 858.875.1900 Fax: 858.622.0609 Mouse Rps6ka5 Antibody (C-term) Purified Rabbit Polyclonal Antibody (Pab) Catalog # AW5466 Specification Mouse Rps6ka5 Antibody (C-term) - Product Information Application WB,E Primary Accession Q8C050 Reactivity Mouse Host Rabbit Clonality Polyclonal Calculated MW M=97,90 KDa Isotype Rabbit Ig Antigen Source HUMAN Mouse Rps6ka5 Antibody (C-term) - Additional Information Gene ID 73086 Antigen Region All lanes : Anti-Rps6ka5 Antibody (C-term) at 850-883 1:1000 dilution Lane 1: L929 whole cell Other Names lysates Lane 2: mouse spleen lysates Lane 3: Ribosomal protein S6 kinase alpha-5, mouse thymus lysates Lysates/proteins at 20 S6K-alpha-5, 90 kDa ribosomal protein S6 µg per lane. Secondary Goat Anti-Rabbit IgG, kinase 5, Nuclear mitogen- and (H+L),Peroxidase conjugated at 1/10000 stress-activated protein kinase 1, RSK-like dilution Predicted band size : 97 kDa protein kinase, RLSK, Rps6ka5, Msk1 Blocking/Dilution buffer: 5% NFDM/TBST. Dilution WB~~1:1000 Mouse Rps6ka5 Antibody (C-term) - Background Target/Specificity This Mouse Rps6ka5 antibody is generated Serine/threonine-protein kinase that is from a rabbit immunized with a KLH required for the mitogen or stress-induced conjugated synthetic peptide between phosphorylation of the transcription factors 850-883 amino acids from the C-terminal CREB1 and ATF1 and for the regulation of the region of Mouse Rps6ka5. transcription factors RELA, STAT3 and ETV1/ER81, and that contributes to gene Format activation by histone phosphorylation and Purified polyclonal antibody supplied in PBS functions in the regulation of inflammatory with 0.09% (W/V) sodium azide. -
Phospho-RPS6KA5-T581 Rabbit Pab
Leader in Biomolecular Solutions for Life Science Phospho-RPS6KA5-T581 Rabbit pAb Catalog No.: AP1197 Basic Information Background Catalog No. Serine/threonine-protein kinase that is required for the mitogen or stress-induced AP1197 phosphorylation of the transcription factors CREB1 and ATF1 and for the regulation of the transcription factors RELA, STAT3 and ETV1/ER81, and that contributes to gene Observed MW activation by histone phosphorylation and functions in the regulation of inflammatory Refer to figures genes (PubMed:11909979, PubMed:12569367, PubMed:12763138, PubMed:9687510, PubMed:18511904, PubMed:9873047). Phosphorylates CREB1 and ATF1 in response to Calculated MW mitogenic or stress stimuli such as UV-C irradiation, epidermal growth factor (EGF) and 61kDa/81kDa/89kDa anisomycin (PubMed:11909979, PubMed:9873047). Plays an essential role in the control of RELA transcriptional activity in response to TNF and upon glucocorticoid, associates in Category the cytoplasm with the glucocorticoid receptor NR3C1 and contributes to RELA inhibition and repression of inflammatory gene expression (PubMed:12628924, Primary antibody PubMed:18511904). In skeletal myoblasts is required for phosphorylation of RELA at 'Ser-276' during oxidative stress (PubMed:12628924). In erythropoietin-stimulated cells, Applications is necessary for the 'Ser-727' phosphorylation of STAT3 and regulation of its WB transcriptional potential (PubMed:12763138). Phosphorylates ETV1/ER81 at 'Ser-191' and 'Ser-216', and thereby regulates its ability to stimulate transcription, which may be Cross-Reactivity important during development and breast tumor formation (PubMed:12569367). Directly Human, Mouse, Rat represses transcription via phosphorylation of 'Ser-1' of histone H2A (PubMed:15010469). Phosphorylates 'Ser-10' of histone H3 in response to mitogenics, stress stimuli and EGF, which results in the transcriptional activation of several Recommended Dilutions immediate early genes, including proto-oncogenes c-fos/FOS and c-jun/JUN (PubMed:12773393). -
Profiling Data
Compound Name DiscoveRx Gene Symbol Entrez Gene Percent Compound Symbol Control Concentration (nM) JNK-IN-8 AAK1 AAK1 69 1000 JNK-IN-8 ABL1(E255K)-phosphorylated ABL1 100 1000 JNK-IN-8 ABL1(F317I)-nonphosphorylated ABL1 87 1000 JNK-IN-8 ABL1(F317I)-phosphorylated ABL1 100 1000 JNK-IN-8 ABL1(F317L)-nonphosphorylated ABL1 65 1000 JNK-IN-8 ABL1(F317L)-phosphorylated ABL1 61 1000 JNK-IN-8 ABL1(H396P)-nonphosphorylated ABL1 42 1000 JNK-IN-8 ABL1(H396P)-phosphorylated ABL1 60 1000 JNK-IN-8 ABL1(M351T)-phosphorylated ABL1 81 1000 JNK-IN-8 ABL1(Q252H)-nonphosphorylated ABL1 100 1000 JNK-IN-8 ABL1(Q252H)-phosphorylated ABL1 56 1000 JNK-IN-8 ABL1(T315I)-nonphosphorylated ABL1 100 1000 JNK-IN-8 ABL1(T315I)-phosphorylated ABL1 92 1000 JNK-IN-8 ABL1(Y253F)-phosphorylated ABL1 71 1000 JNK-IN-8 ABL1-nonphosphorylated ABL1 97 1000 JNK-IN-8 ABL1-phosphorylated ABL1 100 1000 JNK-IN-8 ABL2 ABL2 97 1000 JNK-IN-8 ACVR1 ACVR1 100 1000 JNK-IN-8 ACVR1B ACVR1B 88 1000 JNK-IN-8 ACVR2A ACVR2A 100 1000 JNK-IN-8 ACVR2B ACVR2B 100 1000 JNK-IN-8 ACVRL1 ACVRL1 96 1000 JNK-IN-8 ADCK3 CABC1 100 1000 JNK-IN-8 ADCK4 ADCK4 93 1000 JNK-IN-8 AKT1 AKT1 100 1000 JNK-IN-8 AKT2 AKT2 100 1000 JNK-IN-8 AKT3 AKT3 100 1000 JNK-IN-8 ALK ALK 85 1000 JNK-IN-8 AMPK-alpha1 PRKAA1 100 1000 JNK-IN-8 AMPK-alpha2 PRKAA2 84 1000 JNK-IN-8 ANKK1 ANKK1 75 1000 JNK-IN-8 ARK5 NUAK1 100 1000 JNK-IN-8 ASK1 MAP3K5 100 1000 JNK-IN-8 ASK2 MAP3K6 93 1000 JNK-IN-8 AURKA AURKA 100 1000 JNK-IN-8 AURKA AURKA 84 1000 JNK-IN-8 AURKB AURKB 83 1000 JNK-IN-8 AURKB AURKB 96 1000 JNK-IN-8 AURKC AURKC 95 1000 JNK-IN-8 -
Application of a MYC Degradation
SCIENCE SIGNALING | RESEARCH ARTICLE CANCER Copyright © 2019 The Authors, some rights reserved; Application of a MYC degradation screen identifies exclusive licensee American Association sensitivity to CDK9 inhibitors in KRAS-mutant for the Advancement of Science. No claim pancreatic cancer to original U.S. Devon R. Blake1, Angelina V. Vaseva2, Richard G. Hodge2, McKenzie P. Kline3, Thomas S. K. Gilbert1,4, Government Works Vikas Tyagi5, Daowei Huang5, Gabrielle C. Whiten5, Jacob E. Larson5, Xiaodong Wang2,5, Kenneth H. Pearce5, Laura E. Herring1,4, Lee M. Graves1,2,4, Stephen V. Frye2,5, Michael J. Emanuele1,2, Adrienne D. Cox1,2,6, Channing J. Der1,2* Stabilization of the MYC oncoprotein by KRAS signaling critically promotes the growth of pancreatic ductal adeno- carcinoma (PDAC). Thus, understanding how MYC protein stability is regulated may lead to effective therapies. Here, we used a previously developed, flow cytometry–based assay that screened a library of >800 protein kinase inhibitors and identified compounds that promoted either the stability or degradation of MYC in a KRAS-mutant PDAC cell line. We validated compounds that stabilized or destabilized MYC and then focused on one compound, Downloaded from UNC10112785, that induced the substantial loss of MYC protein in both two-dimensional (2D) and 3D cell cultures. We determined that this compound is a potent CDK9 inhibitor with a previously uncharacterized scaffold, caused MYC loss through both transcriptional and posttranslational mechanisms, and suppresses PDAC anchorage- dependent and anchorage-independent growth. We discovered that CDK9 enhanced MYC protein stability 62 through a previously unknown, KRAS-independent mechanism involving direct phosphorylation of MYC at Ser . -
Expressed Gene Fusions As Frequent Drivers of Poor Outcomes in Hormone Receptor–Positive Breast Cancer
Published OnlineFirst December 14, 2017; DOI: 10.1158/2159-8290.CD-17-0535 RESEARCH ARTICLE Expressed Gene Fusions as Frequent Drivers of Poor Outcomes in Hormone Receptor–Positive Breast Cancer Karina J. Matissek1,2, Maristela L. Onozato3, Sheng Sun1,2, Zongli Zheng2,3,4, Andrew Schultz1, Jesse Lee3, Kristofer Patel1, Piiha-Lotta Jerevall2,3, Srinivas Vinod Saladi1,2, Allison Macleay3, Mehrad Tavallai1,2, Tanja Badovinac-Crnjevic5, Carlos Barrios6, Nuran Beşe7, Arlene Chan8, Yanin Chavarri-Guerra9, Marcio Debiasi6, Elif Demirdögen10, Ünal Egeli10, Sahsuvar Gökgöz10, Henry Gomez11, Pedro Liedke6, Ismet Tasdelen10, Sahsine Tolunay10, Gustavo Werutsky6, Jessica St. Louis1, Nora Horick12, Dianne M. Finkelstein2,12, Long Phi Le2,3, Aditya Bardia1,2, Paul E. Goss1,2, Dennis C. Sgroi2,3, A. John Iafrate2,3, and Leif W. Ellisen1,2 ABSTRACT We sought to uncover genetic drivers of hormone receptor–positive (HR+) breast cancer, using a targeted next-generation sequencing approach for detecting expressed gene rearrangements without prior knowledge of the fusion partners. We identified inter- genic fusions involving driver genes, including PIK3CA, AKT3, RAF1, and ESR1, in 14% (24/173) of unselected patients with advanced HR+ breast cancer. FISH confirmed the corresponding chromo- somal rearrangements in both primary and metastatic tumors. Expression of novel kinase fusions in nontransformed cells deregulates phosphoprotein signaling, cell proliferation, and survival in three- dimensional culture, whereas expression in HR+ breast cancer models modulates estrogen-dependent growth and confers hormonal therapy resistance in vitro and in vivo. Strikingly, shorter overall survival was observed in patients with rearrangement-positive versus rearrangement-negative tumors. Cor- respondingly, fusions were uncommon (<5%) among 300 patients presenting with primary HR+ breast cancer. -
Supplementary Material DNA Methylation in Inflammatory Pathways Modifies the Association Between BMI and Adult-Onset Non- Atopic
Supplementary Material DNA Methylation in Inflammatory Pathways Modifies the Association between BMI and Adult-Onset Non- Atopic Asthma Ayoung Jeong 1,2, Medea Imboden 1,2, Akram Ghantous 3, Alexei Novoloaca 3, Anne-Elie Carsin 4,5,6, Manolis Kogevinas 4,5,6, Christian Schindler 1,2, Gianfranco Lovison 7, Zdenko Herceg 3, Cyrille Cuenin 3, Roel Vermeulen 8, Deborah Jarvis 9, André F. S. Amaral 9, Florian Kronenberg 10, Paolo Vineis 11,12 and Nicole Probst-Hensch 1,2,* 1 Swiss Tropical and Public Health Institute, 4051 Basel, Switzerland; [email protected] (A.J.); [email protected] (M.I.); [email protected] (C.S.) 2 Department of Public Health, University of Basel, 4001 Basel, Switzerland 3 International Agency for Research on Cancer, 69372 Lyon, France; [email protected] (A.G.); [email protected] (A.N.); [email protected] (Z.H.); [email protected] (C.C.) 4 ISGlobal, Barcelona Institute for Global Health, 08003 Barcelona, Spain; [email protected] (A.-E.C.); [email protected] (M.K.) 5 Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain 6 CIBER Epidemiología y Salud Pública (CIBERESP), 08005 Barcelona, Spain 7 Department of Economics, Business and Statistics, University of Palermo, 90128 Palermo, Italy; [email protected] 8 Environmental Epidemiology Division, Utrecht University, Institute for Risk Assessment Sciences, 3584CM Utrecht, Netherlands; [email protected] 9 Population Health and Occupational Disease, National Heart and Lung Institute, Imperial College, SW3 6LR London, UK; [email protected] (D.J.); [email protected] (A.F.S.A.) 10 Division of Genetic Epidemiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; [email protected] 11 MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, W2 1PG London, UK; [email protected] 12 Italian Institute for Genomic Medicine (IIGM), 10126 Turin, Italy * Correspondence: [email protected]; Tel.: +41-61-284-8378 Int. -
R Graphics Output
TNF signaling pathway all genes sink node genes ● ●● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ●●● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ●● ●●● ●●● ●●● ● ● ● ●●● ● ●●● ● maximum = 3 maximum = 2 2 1 s> 10 0 <log −1 −2 2 1 x a m s 0 10 log −1 −2 group 1 group 2 group 3 group 4 group 5 group 6 group 7 group 8 TNF_signaling_pathway genes with data CASP10 CCL2 CCL5 CCL20 CASP7 FADD CXCL1 CXCL1 CXCL1 CASP3 CASP8 CXCL5 CXCL10 CX3CL1 CSF1 CSF2 BIRC2 ITCH CFLAR FAS IL18R1 JAG1 MAP3K5 IL1B IL6 IL15 LIF LTA TAB1 MAP2K7 MAPK8 FOS BCL3 NFKBIA SOCS3 TNFAIP3 TRAF1 TRAF2 MAP3K7 TNF TNFRSF1A TRADD RIPK1 MAP2K3 MAPK14 CEBPB MAP3K8 NFKB1 RPS6KA4 CREB3 BAG4 MAP3K14 MAP2K1 MAPK1 FOS JUN JUNB MMP3 MMP9 MMP14 IKBKB NFKBIA CHUK EDN1 VEGFC IKBKG NFKB1 NOD2 RIPK1 RIPK3 MLKL PGAM5 ICAM1 SELE VCAM1 DNM1L PGAM5 PTGS2 PIK3CA AKT3 NFKB1 MAP3K14 CHUK NFKBIA TRAF1 LTA TNFRSF1B TRAF2 BIRC2 RIPK1 MAP2K3 MAPK14 TRAF3 DAB2IP MAPK8 JUN IRF1 IFNB1 TNF_signaling_pathway sink nodes CASP10 CCL2 CCL5 CCL20 CASP7 FADD CXCL1 CXCL1 CXCL1 CASP3 CASP8 CXCL5 CXCL10 CX3CL1 CSF1 CSF2 BIRC2 ITCH CFLAR FAS IL18R1 JAG1 MAP3K5 IL1B IL6 IL15 LIF LTA TAB1 MAP2K7 MAPK8 FOS BCL3 NFKBIA SOCS3 TNFAIP3 TRAF1 TRAF2 MAP3K7 TNF TNFRSF1A TRADD -
Expressed Gene Fusions As Frequent Drivers of Poor Outcomes in Hormone Receptor–Positive Breast Cancer
Published OnlineFirst December 14, 2017; DOI: 10.1158/2159-8290.CD-17-0535 RESEARCH ARTICLE Expressed Gene Fusions as Frequent Drivers of Poor Outcomes in Hormone Receptor–Positive Breast Cancer Karina J. Matissek1,2, Maristela L. Onozato3, Sheng Sun1,2, Zongli Zheng2,3,4, Andrew Schultz1, Jesse Lee3, Kristofer Patel1, Piiha-Lotta Jerevall2,3, Srinivas Vinod Saladi1,2, Allison Macleay3, Mehrad Tavallai1,2, Tanja Badovinac-Crnjevic5, Carlos Barrios6, Nuran Beşe7, Arlene Chan8, Yanin Chavarri-Guerra9, Marcio Debiasi6, Elif Demirdögen10, Ünal Egeli10, Sahsuvar Gökgöz10, Henry Gomez11, Pedro Liedke6, Ismet Tasdelen10, Sahsine Tolunay10, Gustavo Werutsky6, Jessica St. Louis1, Nora Horick12, Dianne M. Finkelstein2,12, Long Phi Le2,3, Aditya Bardia1,2, Paul E. Goss1,2, Dennis C. Sgroi2,3, A. John Iafrate2,3, and Leif W. Ellisen1,2 ABSTRACT We sought to uncover genetic drivers of hormone receptor–positive (HR+) breast cancer, using a targeted next-generation sequencing approach for detecting expressed gene rearrangements without prior knowledge of the fusion partners. We identified inter- genic fusions involving driver genes, including PIK3CA, AKT3, RAF1, and ESR1, in 14% (24/173) of unselected patients with advanced HR+ breast cancer. FISH confirmed the corresponding chromo- somal rearrangements in both primary and metastatic tumors. Expression of novel kinase fusions in nontransformed cells deregulates phosphoprotein signaling, cell proliferation, and survival in three- dimensional culture, whereas expression in HR+ breast cancer models modulates estrogen-dependent growth and confers hormonal therapy resistance in vitro and in vivo. Strikingly, shorter overall survival was observed in patients with rearrangement-positive versus rearrangement-negative tumors. Cor- respondingly, fusions were uncommon (<5%) among 300 patients presenting with primary HR+ breast cancer. -
Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients with Stable Coronary Heart Disease
Supplementary Online Content Ganz P, Heidecker B, Hveem K, et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA. doi: 10.1001/jama.2016.5951 eTable 1. List of 1130 Proteins Measured by Somalogic’s Modified Aptamer-Based Proteomic Assay eTable 2. Coefficients for Weibull Recalibration Model Applied to 9-Protein Model eFigure 1. Median Protein Levels in Derivation and Validation Cohort eTable 3. Coefficients for the Recalibration Model Applied to Refit Framingham eFigure 2. Calibration Plots for the Refit Framingham Model eTable 4. List of 200 Proteins Associated With the Risk of MI, Stroke, Heart Failure, and Death eFigure 3. Hazard Ratios of Lasso Selected Proteins for Primary End Point of MI, Stroke, Heart Failure, and Death eFigure 4. 9-Protein Prognostic Model Hazard Ratios Adjusted for Framingham Variables eFigure 5. 9-Protein Risk Scores by Event Type This supplementary material has been provided by the authors to give readers additional information about their work. Downloaded From: https://jamanetwork.com/ on 10/02/2021 Supplemental Material Table of Contents 1 Study Design and Data Processing ......................................................................................................... 3 2 Table of 1130 Proteins Measured .......................................................................................................... 4 3 Variable Selection and Statistical Modeling ........................................................................................