Kinase Collection 101 Row Well TF FI NM Target Location Spec
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Gene Symbol Gene Description ACVR1B Activin a Receptor, Type IB
Table S1. Kinase clones included in human kinase cDNA library for yeast two-hybrid screening Gene Symbol Gene Description ACVR1B activin A receptor, type IB ADCK2 aarF domain containing kinase 2 ADCK4 aarF domain containing kinase 4 AGK multiple substrate lipid kinase;MULK AK1 adenylate kinase 1 AK3 adenylate kinase 3 like 1 AK3L1 adenylate kinase 3 ALDH18A1 aldehyde dehydrogenase 18 family, member A1;ALDH18A1 ALK anaplastic lymphoma kinase (Ki-1) ALPK1 alpha-kinase 1 ALPK2 alpha-kinase 2 AMHR2 anti-Mullerian hormone receptor, type II ARAF v-raf murine sarcoma 3611 viral oncogene homolog 1 ARSG arylsulfatase G;ARSG AURKB aurora kinase B AURKC aurora kinase C BCKDK branched chain alpha-ketoacid dehydrogenase kinase BMPR1A bone morphogenetic protein receptor, type IA BMPR2 bone morphogenetic protein receptor, type II (serine/threonine kinase) BRAF v-raf murine sarcoma viral oncogene homolog B1 BRD3 bromodomain containing 3 BRD4 bromodomain containing 4 BTK Bruton agammaglobulinemia tyrosine kinase BUB1 BUB1 budding uninhibited by benzimidazoles 1 homolog (yeast) BUB1B BUB1 budding uninhibited by benzimidazoles 1 homolog beta (yeast) C9orf98 chromosome 9 open reading frame 98;C9orf98 CABC1 chaperone, ABC1 activity of bc1 complex like (S. pombe) CALM1 calmodulin 1 (phosphorylase kinase, delta) CALM2 calmodulin 2 (phosphorylase kinase, delta) CALM3 calmodulin 3 (phosphorylase kinase, delta) CAMK1 calcium/calmodulin-dependent protein kinase I CAMK2A calcium/calmodulin-dependent protein kinase (CaM kinase) II alpha CAMK2B calcium/calmodulin-dependent -
GUCY2D Cone-Rod Dystrophy-6 Is a ‘Phototransduction Disease’ Triggered by Abnormal Calcium Feedback on Retinal Membrane Guanylyl Cyclase 1
This Accepted Manuscript has not been copyedited and formatted. The final version may differ from this version. Research Articles: Neurobiology of Disease GUCY2D Cone-Rod Dystrophy-6 is a ‘Phototransduction Disease’ Triggered by Abnormal Calcium Feedback on Retinal Membrane Guanylyl Cyclase 1 Shinya Sato1, Igor V. Peshenko2, Elena V. Olshevskaya2, Vladimir J. Kefalov1 and Alexander M. Dizhoor2 1Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO 63110 2Pennsylavania College of Optometry, Salus University, Elkins Park, PA 19027 DOI: 10.1523/JNEUROSCI.2985-17.2018 Received: 17 October 2017 Revised: 19 January 2018 Accepted: 24 January 2018 Published: 12 February 2018 Author contributions: S.S., I.V.P., E.V.O., and A.M.D. performed research; S.S., I.V.P., V.J.K., and A.M.D. analyzed data; V.J.K. and A.M.D. designed research; V.J.K. and A.M.D. wrote the paper. Conflict of Interest: The authors declare no competing financial interests. This work was supported by NIH grants EY11522 (AMD), EY19312, EY25696, and EY27387 (VJK), EY02687 (Washington University, Department of Ophthalmology and Visual Sciences), Pennsylvania Department of Health Formula Grant (AMD) and by Research to Prevent Blindness. Correspondence should be addressed to Co-corresponding authors: Alexander M. Dizhoor, Pennsylvania College of Optometry, Salus University, Elkins Park, PA 19027, [email protected]; Vladimir J. Kefalov, Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO 63110, [email protected] Cite as: J. Neurosci ; 10.1523/JNEUROSCI.2985-17.2018 Alerts: Sign up at www.jneurosci.org/cgi/alerts to receive customized email alerts when the fully formatted version of this article is published. -
The Drug Sensitivity and Resistance Testing (DSRT) Approach
A phenotypic screening and machine learning platform eciently identifies triple negative breast cancer-selective and readily druggable targets Prson Gautam 1 Alok Jaiswal 1 Tero Aittokallio 1, 2 Hassan Al Ali 3 Krister Wennerberg 1,4 Identifying eective oncogenic targets is challenged by the complexity of genetic alterations in 1Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Finland cancer and their poorly understood relation to cell function and survival. There is a need for meth- Current kinome coverage of kinase inhibitors in TNBC exhibit diverse kinase dependencies MFM-223 is selectively addicted to FGFR2 2Department of Mathematics and Statistics, University of Turku, Finland 3The Miami Project to Cure Paralysis, Peggy and Harold Katz Family Drug Discovery Center, A A Sylvester Comprehensive Cancer Center, and Department of Neurological Surgery and Medicine ods that rapidly and accurately identify “pharmacologically eective” targets without the require- clinical evaluation TN Kinases MFM-223 CAL-120 MDA-MB-231 TNBC TNBC TNBC TNBC TNBC TNBC HER2+ 100 University of Miami Miller School of Medicine, Miami, FL 33136, USA. non- HER2+ FGFR1 0.97 0.00 0.00 MFM-223 BL1 BL2 M MSL IM LAR ER+, PR+ 50 ment for priori knowledge of complex signaling networks. We developed an approach that uses ma- cancerous FGFR2 56.46 0.00 0.00 CAL-120 25 4 MDA-MB-231 Biotech Research & Innovation Centre (BRIC) and Novo Nordisk Foundation Center HCC1937 CAL-85-1 CAL-120 MDA-MB-231 DU4475 CAL-148 MCF-10A SK-BR-3 BT-474 FGFR3 25.10 0.00 0.00 0 chine learning to relate results from unbiased phenotypic screening of kinase inhibitors to their bio- for Stem Cell Biology (DanStem), University of Copenhagen, Denmark HCC1599 HDQ-P1 BT-549 MDA-MB-436 MFM-223 FGFR4 0.00 0.00 0.00 MAXIS*Bk Clinical status MDA-MB-468 CAL-51 Hs578T MDA-MB-453 score chemical activity data. -
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. -
IFT88 Transports Gucy2d, a Guanylyl Cyclase, to Maintain Sensory Cilia Function in Drosophila
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.15.417840; this version posted December 15, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Werner S et al. Title: IFT88 transports Gucy2d, a guanylyl cyclase, to maintain sensory cilia function in Drosophila Authors: Sascha Werner1,6, Sihem Zitouni1,4, Pilar Okenve-Ramos1, Susana Mendonça1,5, Anje Sporbert2, Christian Spalthoff3, Martin C. Göpfert3, Swadhin Chandra Jana1,6,7, Mónica Bettencourt-Dias1,6,7 Affiliations: 1- Instituto Gulbenkian de Ciência, Rua da Quinta Grande, nº 6, 2780-156 Oeiras, Portugal. 2- Advanced Light Microscopy, Max Delbrück Centrum for Molecular Medicine Berlin in the Helmholtz Association Robert-Rössle-Straße 10, 13125 Berlin, Germany. 3- Department of Cellular Neurobiology, University of Göttingen, 37077 Göttingen, Germany. 4- Present address: Institut de Génétique Humaine (IGH) UMR 9002 CNRS, 141 Rue de la Cardonille, Montpellier, France. 5- Present address: Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208 4200-135 Porto, Portugal. 6- Correspondence should be addressed to Sascha Werner ([email protected]); Swadhin Chandra Jana ([email protected]); Mónica Bettencourt-Dias ([email protected]) 7- Shared lead authors. 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.15.417840; this version posted December 15, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. -
Supplementary Information Material and Methods
MCT-11-0474 BKM120: a potent and specific pan-PI3K inhibitor Supplementary Information Material and methods Chemicals The EGFR inhibitor NVP-AEE788 (Novartis), the Jak inhibitor I (Merck Calbiochem, #420099) and anisomycin (Alomone labs, # A-520) were prepared as 50 mM stock solutions in 100% DMSO. Doxorubicin (Adriablastin, Pfizer), EGF (Sigma Ref: E9644), PDGF (Sigma, Ref: P4306) and IL-4 (Sigma, Ref: I-4269) stock solutions were prepared as recommended by the manufacturer. For in vivo administration: Temodal (20 mg Temozolomide capsules, Essex Chemie AG, Luzern) was dissolved in 4 mL KZI/glucose (20/80, vol/vol); Taxotere was bought as 40 mg/mL solution (Sanofi Aventis, France), and prepared in KZI/glucose. Antibodies The primary antibodies used were as follows: anti-S473P-Akt (#9271), anti-T308P-Akt (#9276,), anti-S9P-GSK3β (#9336), anti-T389P-p70S6K (#9205), anti-YP/TP-Erk1/2 (#9101), anti-YP/TP-p38 (#9215), anti-YP/TP-JNK1/2 (#9101), anti-Y751P-PDGFR (#3161), anti- p21Cip1/Waf1 (#2946), anti-p27Kip1 (#2552) and anti-Ser15-p53 (#9284) antibodies were from Cell Signaling Technologies; anti-Akt (#05-591), anti-T32P-FKHRL1 (#06-952) and anti- PDGFR (#06-495) antibodies were from Upstate; anti-IGF-1R (#SC-713) and anti-EGFR (#SC-03) antibodies were from Santa Cruz; anti-GSK3α/β (#44610), anti-Y641P-Stat6 (#611566), anti-S1981P-ATM (#200-301), anti-T2609 DNA-PKcs (#GTX24194) and anti- 1 MCT-11-0474 BKM120: a potent and specific pan-PI3K inhibitor Y1316P-IGF-1R were from Bio-Source International, Becton-Dickinson, Rockland, GenTex and internal production, respectively. The 4G10 antibody was from Millipore (#05-321MG). -
The Role of the S6K2 Splice Isoform in Mtor/S6K Signalling and Cellular Functions
The role of the S6K2 splice isoform in mTOR/S6K signalling and cellular functions Olena Myronova A thesis submitted to the University College London in fulfilment with the requirements for the degree of Doctor of Philosophy London, November 2015 Research Department of Structural and Molecular Biology Division of Biosciences University College London Gower Street London, WC1E 6BT United Kingdom Ludwig Institute for Cancer Research 666 Third Avenue, 28th floor New York, N.Y. 10017 USA The role of the S6K2 splice isoform in mTOR/S6K signalling and cellular functions 1 Declaration I, Olena Myronova, declare that all the work presented in this thesis is the result of my own work. The work presented here does not constitute part of any other thesis. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. The work here in was carried out while I was a graduate research student at University College London, Research Department of Structural and Molecular Biology under the supervision of Professor Ivan Gout. Olena Myronova The role of the S6K2 splice isoform in mTOR/S6K signalling and cellular functions 2 Abstract Ribosomal S6 kinase (S6K) is a member of the AGC family of serine/threonine protein kinases and plays a key role in diverse cellular processes, including cell growth, survival and metabolism. Activation of S6K by growth factors, amino acids, energy levels and hypoxia is mediated by the mTOR and PI3K signalling pathways. Dysregulation of S6K activity has been implicated in a number of human pathologies, including cancer, diabetes, obesity and ageing. -
Clinical Utility of Recently Identified Diagnostic, Prognostic, And
Modern Pathology (2017) 30, 1338–1366 1338 © 2017 USCAP, Inc All rights reserved 0893-3952/17 $32.00 Clinical utility of recently identified diagnostic, prognostic, and predictive molecular biomarkers in mature B-cell neoplasms Arantza Onaindia1, L Jeffrey Medeiros2 and Keyur P Patel2 1Instituto de Investigacion Marques de Valdecilla (IDIVAL)/Hospital Universitario Marques de Valdecilla, Santander, Spain and 2Department of Hematopathology, MD Anderson Cancer Center, Houston, TX, USA Genomic profiling studies have provided new insights into the pathogenesis of mature B-cell neoplasms and have identified markers with prognostic impact. Recurrent mutations in tumor-suppressor genes (TP53, BIRC3, ATM), and common signaling pathways, such as the B-cell receptor (CD79A, CD79B, CARD11, TCF3, ID3), Toll- like receptor (MYD88), NOTCH (NOTCH1/2), nuclear factor-κB, and mitogen activated kinase signaling, have been identified in B-cell neoplasms. Chronic lymphocytic leukemia/small lymphocytic lymphoma, diffuse large B-cell lymphoma, follicular lymphoma, mantle cell lymphoma, Burkitt lymphoma, Waldenström macroglobulinemia, hairy cell leukemia, and marginal zone lymphomas of splenic, nodal, and extranodal types represent examples of B-cell neoplasms in which novel molecular biomarkers have been discovered in recent years. In addition, ongoing retrospective correlative and prospective outcome studies have resulted in an enhanced understanding of the clinical utility of novel biomarkers. This progress is reflected in the 2016 update of the World Health Organization classification of lymphoid neoplasms, which lists as many as 41 mature B-cell neoplasms (including provisional categories). Consequently, molecular genetic studies are increasingly being applied for the clinical workup of many of these neoplasms. In this review, we focus on the diagnostic, prognostic, and/or therapeutic utility of molecular biomarkers in mature B-cell neoplasms. -
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 . -
The Number of Genes
Table S1. The numbers of KD genes in each KD time The number The number The number The number Cell lines of genes of genes of genes of genes (96h) (120h) (144h) PC3 3980 3822 128 1725 A549 3724 3724 0 0 MCF7 3688 3471 0 1837 HT29 3665 3665 0 0 A375 3826 3826 0 0 HA1E 3801 3801 0 0 VCAP 4134 34 4121 0 HCC515 3522 3522 0 0 Table S2. The predicted results in the PC3 cell line on the LINCS II data id target rank A07563059 ADRB2 48 A12896037 ADRA2C 91 A13021932 YES1 77 PPM1B;PPP1CC;PPP2CA; A13254067 584;1326;297;171;3335 PTPN1;PPP2R5A A16347691 GMNN 2219 PIK3CB;MTOR;PIK3CA;PIK A28467416 18;10;9;13;8 3CG;PIK3CD A28545468 EHMT2;MAOB 14;67 A29520968 HSPB1 1770 A48881734 EZH2 1596 A52922642 CACNA1C 201 A64553394 ADRB2 155 A65730376 DOT1L 3764 A82035391 JUN 378 A82156122 DPP4 771 HRH1;HTR2C;CHRM3;CH A82772293 2756;2354;2808;2367 RM1 A86248581 CDA 1785 A92800748 TEK 459 A93093700 LMNA 1399 K00152668 RARB 105 K01577834 ADORA2A 525 K01674964 HRH1;BLM 31;1314 K02314383 AR 132 K03194791 PDE4D 30 K03390685 MAP2K1 77 K06762493 GMNN;APEX1 1523;2360 K07106112 ERBB4;ERBB2;EGFR 497;60;23 K07310275 AKT1;MTOR;PIK3CA 13;12;1 K07753030 RGS4;BLM 3736;3080 K08109215 BRD2;BRD3;BRD4 1413;2786;3 K08248804 XIAP 88 K08586861 TBXA2R;MBNL1 297;3428 K08832567 GMNN;CA12 2544;50 LMNA;NFKB1;APEX1;EH K08976401 1322;341;3206;123 MT2 K09372874 IMPDH2 232 K09711437 PLA2G2A 59 K10859802 GPR119 214 K11267252 RET;ALK 395;760 K12609457 LMNA 907 K13094524 BRD4 7 K13662825 CDK4;CDK9;CDK5;CDK1 34;58;13;18 K14704277 LMNA;BLM 1697;1238 K14870255 AXL 1696 K15170068 MAN2B1 1756 K15179879 -
Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1.