Original Article Bioinformatic Analysis of the Role of MNAT1 in the Progression of Lung Cancer and Its Correlation with the Prognosis of Patients
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Genetic Analysis of Retinopathy in Type 1 Diabetes
Genetic Analysis of Retinopathy in Type 1 Diabetes by Sayed Mohsen Hosseini A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Institute of Medical Science University of Toronto © Copyright by S. Mohsen Hosseini 2014 Genetic Analysis of Retinopathy in Type 1 Diabetes Sayed Mohsen Hosseini Doctor of Philosophy Institute of Medical Science University of Toronto 2014 Abstract Diabetic retinopathy (DR) is a leading cause of blindness worldwide. Several lines of evidence suggest a genetic contribution to the risk of DR; however, no genetic variant has shown convincing association with DR in genome-wide association studies (GWAS). To identify common polymorphisms associated with DR, meta-GWAS were performed in three type 1 diabetes cohorts of White subjects: Diabetes Complications and Control Trial (DCCT, n=1304), Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR, n=603) and Renin-Angiotensin System Study (RASS, n=239). Severe (SDR) and mild (MDR) retinopathy outcomes were defined based on repeated fundus photographs in each study graded for retinopathy severity on the Early Treatment Diabetic Retinopathy Study (ETDRS) scale. Multivariable models accounted for glycemia (measured by A1C), diabetes duration and other relevant covariates in the association analyses of additive genotypes with SDR and MDR. Fixed-effects meta- analysis was used to combine the results of GWAS performed separately in WESDR, ii RASS and subgroups of DCCT, defined by cohort and treatment group. Top association signals were prioritized for replication, based on previous supporting knowledge from the literature, followed by replication in three independent white T1D studies: Genesis-GeneDiab (n=502), Steno (n=936) and FinnDiane (n=2194). -
A Novel Computational Algorithm for Predicting Immune Cell Types Using Single-Cell RNA Sequencing Data
A novel computational algorithm for predicting immune cell types using single-cell RNA sequencing data By Shuo Jia A hesis submitted to the Faculty of Graduate Studies of The University of Manitoba n partial fulfillment of the requirements of the degree of MASTER OF SCIENCE Department of Biochemistry and Medical Genetics University of Manitoba Winnipeg, Manitoba, Canada Copyright © 2020 by Shuo Jia Abstract Background: Cells from our immune system detect and kill pathogens to protect our body against many diseases. However, current methods for determining cell types have some major limitations, such as being time-consuming and with low throughput rate, etc. These problems stack up and hinder the deep exploration of cellular heterogeneity. Immune cells that are associated with cancer tissues play a critical role in revealing the stages of tumor development. Identifying the immune composition within tumor microenvironments in a timely manner will be helpful to improve clinical prognosis and therapeutic management for cancer. Single-cell RNA sequencing (scRNA-seq), an RNA sequencing (RNA-seq) technique that focuses on a single cell level, has provided us with the ability to conduct cell type classification. Although unsupervised clustering approaches are the major methods for analyzing scRNA-seq datasets, their results vary among studies with different input parameters and sizes. However, in supervised machine learning methods, information loss and low prediction accuracy are the key limitations. Methods and Results: Genes in the human genome align to chromosomes in a particular order. Hence, we hypothesize incorporating this information into our model will potentially improve the cell type classification performance. In order to utilize gene positional information, we introduce chromosome-based neural network, namely ChrNet, a novel chromosome-specific re-trainable supervised learning method based on a one-dimensional 1 convolutional neural network (1D-CNN). -
The Cryoelectron Microscopy Structure of the Human CDK-Activating Kinase
The cryoelectron microscopy structure of the human CDK-activating kinase Basil J. Grebera,b,1,2, Juan M. Perez-Bertoldic, Kif Limd, Anthony T. Iavaronee, Daniel B. Tosoa, and Eva Nogalesa,b,d,f,2 aCalifornia Institute for Quantitative Biosciences (QB3), University of California, Berkeley, CA 94720; bMolecular Biophysics and Integrative Bio-Imaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720; cBiophysics Graduate Group, University of California, Berkeley, CA 94720; dDepartment of Molecular and Cell Biology, University of California, Berkeley, CA 94720; eQB3/Chemistry Mass Spectrometry Facility, University of California, Berkeley, CA 94720; and fHoward Hughes Medical Institute, University of California, Berkeley, CA 94720 Edited by Seth A. Darst, Rockefeller University, New York, NY, and approved August 4, 2020 (received for review May 14, 2020) The human CDK-activating kinase (CAK), a complex composed of phosphoryl transfer (11). However, in addition to cyclin binding, cyclin-dependent kinase (CDK) 7, cyclin H, and MAT1, is a critical full activation of cell cycle CDKs requires phosphorylation of the regulator of transcription initiation and the cell cycle. It acts by T-loop (9, 12). In animal cells, these activating phosphorylations phosphorylating the C-terminal heptapeptide repeat domain of are carried out by CDK7 (13, 14), itself a cyclin-dependent ki- the RNA polymerase II (Pol II) subunit RPB1, which is an important nase whose activity depends on cyclin H (14). regulatory event in transcription initiation by Pol II, and it phos- In human and other metazoan cells, regulation of transcription phorylates the regulatory T-loop of CDKs that control cell cycle initiation by phosphorylation of the Pol II-CTD and phosphor- progression. -
Gene Expression Profile of THZ1-Treated Nasopharyngeal Carcinoma Cell Lines Indicates Its Involvement in the Inhibition of the Cell Cycle
460 Original Article Gene expression profile of THZ1-treated nasopharyngeal carcinoma cell lines indicates its involvement in the inhibition of the cell cycle Lijuan Gao1,2,3#, Shuang Xia4#, Kunyi Zhang1,2,3#, Chengguang Lin1,2,3, Xuyu He4, Ying Zhang4 1Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, China; 2State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, China; 3Department of Radiation Oncology, Cancer Center, Sun Yat-sen University, Guangzhou, China; 4Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China Contributions: (I) Conception and design: L Gao, S Xia, K Zhang; (II) Administrative support: X He, Y Zhang; (III) Provision of study materials or patients: L Gao, S Xia, K Zhang; (IV) Collection and assembly of data: C Lin; (V) Data analysis and interpretation: C Lin, X He, Y Zhang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. #The authors contributed equally to this work. Correspondence to: Xuyu He; Ying Zhang. Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd road, Guangzhou 510080, China. Email: [email protected]; [email protected]. Background: The aim of this study was to identify downstream target genes and pathways regulated by THZ1 in nasopharyngeal carcinoma (NPC). Methods: The gene expression profile of GSE95750 in two NPC cell lines, untreated group and treated with THZ1 group, was analyzed. -
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. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
Cytotoxic Effects and Changes in Gene Expression Profile
Toxicology in Vitro 34 (2016) 309–320 Contents lists available at ScienceDirect Toxicology in Vitro journal homepage: www.elsevier.com/locate/toxinvit Fusarium mycotoxin enniatin B: Cytotoxic effects and changes in gene expression profile Martina Jonsson a,⁎,MarikaJestoib, Minna Anthoni a, Annikki Welling a, Iida Loivamaa a, Ville Hallikainen c, Matti Kankainen d, Erik Lysøe e, Pertti Koivisto a, Kimmo Peltonen a,f a Chemistry and Toxicology Research Unit, Finnish Food Safety Authority (Evira), Mustialankatu 3, FI-00790 Helsinki, Finland b Product Safety Unit, Finnish Food Safety Authority (Evira), Mustialankatu 3, FI-00790 Helsinki, c The Finnish Forest Research Institute, Rovaniemi Unit, P.O. Box 16, FI-96301 Rovaniemi, Finland d Institute for Molecular Medicine Finland (FIMM), University of Helsinki, P.O. Box 20, FI-00014, Finland e Plant Health and Biotechnology, Norwegian Institute of Bioeconomy, Høyskoleveien 7, NO -1430 Ås, Norway f Finnish Safety and Chemicals Agency (Tukes), Opastinsilta 12 B, FI-00521 Helsinki, Finland article info abstract Article history: The mycotoxin enniatin B, a cyclic hexadepsipeptide produced by the plant pathogen Fusarium,isprevalentin Received 3 December 2015 grains and grain-based products in different geographical areas. Although enniatins have not been associated Received in revised form 5 April 2016 with toxic outbreaks, they have caused toxicity in vitro in several cell lines. In this study, the cytotoxic effects Accepted 28 April 2016 of enniatin B were assessed in relation to cellular energy metabolism, cell proliferation, and the induction of ap- Available online 6 May 2016 optosis in Balb 3T3 and HepG2 cells. The mechanism of toxicity was examined by means of whole genome ex- fi Keywords: pression pro ling of exposed rat primary hepatocytes. -
Análise Integrativa De Perfis Transcricionais De Pacientes Com
UNIVERSIDADE DE SÃO PAULO FACULDADE DE MEDICINA DE RIBEIRÃO PRETO PROGRAMA DE PÓS-GRADUAÇÃO EM GENÉTICA ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas Ribeirão Preto – 2012 ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas Tese apresentada à Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo para obtenção do título de Doutor em Ciências. Área de Concentração: Genética Orientador: Prof. Dr. Eduardo Antonio Donadi Co-orientador: Prof. Dr. Geraldo A. S. Passos Ribeirão Preto – 2012 AUTORIZO A REPRODUÇÃO E DIVULGAÇÃO TOTAL OU PARCIAL DESTE TRABALHO, POR QUALQUER MEIO CONVENCIONAL OU ELETRÔNICO, PARA FINS DE ESTUDO E PESQUISA, DESDE QUE CITADA A FONTE. FICHA CATALOGRÁFICA Evangelista, Adriane Feijó Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas. Ribeirão Preto, 2012 192p. Tese de Doutorado apresentada à Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo. Área de Concentração: Genética. Orientador: Donadi, Eduardo Antonio Co-orientador: Passos, Geraldo A. 1. Expressão gênica – microarrays 2. Análise bioinformática por module maps 3. Diabetes mellitus tipo 1 4. Diabetes mellitus tipo 2 5. Diabetes mellitus gestacional FOLHA DE APROVAÇÃO ADRIANE FEIJÓ EVANGELISTA Análise integrativa de perfis transcricionais de pacientes com diabetes mellitus tipo 1, tipo 2 e gestacional, comparando-os com manifestações demográficas, clínicas, laboratoriais, fisiopatológicas e terapêuticas. -
SNM1A (DCLRE1A) (NM 001271816) Human Tagged ORF Clone Product Data
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for RC235137 SNM1A (DCLRE1A) (NM_001271816) Human Tagged ORF Clone Product data: Product Type: Expression Plasmids Product Name: SNM1A (DCLRE1A) (NM_001271816) Human Tagged ORF Clone Tag: Myc-DDK Symbol: DCLRE1A Synonyms: PSO2; SNM1; SNM1A Vector: pCMV6-Entry (PS100001) E. coli Selection: Kanamycin (25 ug/mL) Cell Selection: Neomycin ORF Nucleotide >RC235137 representing NM_001271816 Sequence: Red=Cloning site Blue=ORF Green=Tags(s) TTTTGTAATACGACTCACTATAGGGCGGCCGGGAATTCGTCGACTGGATCCGGTACCGAGGAGATCTGCC GCCGCGATCGCC ATGTTAGAAGACATTTCCGAAGAAGACATTTGGGAATACAAATCTAAAAGAAAACCAAAACGAGTTGATC CAAATAATGGCTCTAAAAATATTCTAAAATCTGTTGAAAAAGCAACAGATGGAAAATACCAGTCAAAACG GAGTAGAAACAGAAAAAGAGCCGCAGAAGCTAAAGAGGTGAAGGACCATGAAGTGCCCCTTGGAAATGCA GGTTGTCAGACTTCTGTTGCTTCTAGTCAGAATTCAAGTTGTGGAGATGGTATTCAGCAGACCCAAGACA AGGAAACTACTCCAGGAAAACTCTGTAGAACTCAAAAAAGCCAACACGTGTCCCCAAAGATACGTCCAGT TTATGATGGATACTGTCCAAATTGCCAGATGCCTTTTTCCTCATTGATAGGGCAGACACCTCGATGGCAT GTTTTTGAATGTTTGGATTCTCCACCACGCTCTGAAACAGAGTGTCCTGATGGTCTTCTGTGTACCTCAA CCATTCCTTTTCATTACAAGAGATACACTCACTTCCTGCTAGCTCAAAGCAGGGCTGGTGATCATCCTTT TAGCAGCCCATCACCTGCGTCAGGTGGCAGTTTCAGTGAGACTAAGTCAGGCGTCCTTTGTAGCCTTGAG GAAAGATGGTCTTCGTATCAGAACCAAACTGATAACTCGGTTTCAAATGATCCCTTATTGATGACACAGT ATTTTAAAAAGTCTCCGTCTCTGACTGAAGCCAGTGAAAAGATTTCTACTCATATCCAAACATCCCAACA AGCTCTACAATTTACAGATTTTGTTGAGAATGACAAACTAGTGGGAGTTGCTTTGCGTCTTGCAAACAAC -
Supplementary Materials
Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine -
Cell Cycle–Regulated Phosphorylation of P220 by Cyclin E/Cdk2 in Cajal Bodies Promotes Histone Gene Transcription
Downloaded from genesdev.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press Cell cycle–regulated phosphorylation of p220NPAT by cyclin E/Cdk2 in Cajal bodies promotes histone gene transcription Tianlin Ma,1,8 Brian A. Van Tine,4,5,8 Yue Wei,2 Michelle D. Garrett,6 David Nelson,7 Peter D. Adams,7 Jin Wang,1,3 Jun Qin,1,3 Louise T. Chow,4 and J. Wade Harper1,2,9 1Department of Biochemistry and Molecular Biology, 2Department of Molecular Physiology and Biophysics, 3Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030, USA; 4Department of Biochemistry and Molecular Genetics, 5Department of Pathology, University of Alabama, Birmingham, Alabama 35294, USA; 6CRC Centre for Cancer Therapeutics, Institute of Cancer Research, Sutton, SM2 5NG, United Kingdom; 7Fox Chase Cancer Center, Philadelphia, Pennsylvania 19111, USA Cyclin E/Cdk2 acts at the G1/S-phase transition to promote the E2F transcriptional program and the initiation of DNA synthesis. To explore further how cyclin E/Cdk2 controls S-phase events, we examined the subcellular localization of the cyclin E/Cdk2 interacting protein p220NPAT and its regulation by phosphorylation. p220 is localized to discrete nuclear foci. Diploid fibroblasts in Go and G1 contain two p220 foci, whereas S- and G2-phase cells contain primarily four p220 foci. Cells in metaphase and telophase have no detectable focus. p220 foci contain cyclin E and are coincident with Cajal bodies (CBs), subnuclear organelles that associate with histone gene clusters on chromosomes 1 and 6. Interestingly, p220 foci associate with chromosome 6 throughout the cell cycle and with chromosome 1 during S phase. -
Pharmacodynamic Effects of Seliciclib, an Orally Administered
Cancer Therapy: Clinical Pharmacodynamic Effects of Seliciclib, an Orally Administered Cell Cycle Modulator, in Undifferentiated Nasopharyngeal Cancer Wen-Son Hsieh,4 Ross Soo,1Bee-Keow Peh,2 Thomas Loh,4 Difeng Dong,3 Donny Soh,5 Lim-SoonWong,3,5 Simon Green,6 Judy Chiao,6 Chun-Ying Cui,1Yo k e -F o n g L a i , 4 Soo-Chin Lee,1Benjamin Mow,1 Richie Soong,2 Manuel Salto-Tellez,2 and Boon-Cher Goh1, 2 Abstract Purpose: Cell cycle dysregulation resulting in expression of antiapoptotic genes and uncontrolled proliferation is a feature of undifferentiated nasopharyngeal carcinoma. The pharmacodynamic effects of seliciclib, a cyclin-dependent kinase (CDK) inhibitor, were studied in patients with nasopharyngeal carcinoma. Experimental Design: Patients with treatment-naI«ve locally advanced nasopharyngeal carci- noma received seliciclib at 800 mg or 400 mg twice daily on days1 to 3 and 8 to12.Paired tumor samples obtained at baseline and on day 13 were assessed by light microscopy, immunohisto- chemistry, and transcriptionalprofiling using real-time PCR low-density array consisting of apanel of 380 genes related to cell cycle inhibition, apoptosis, signal transduction, and cell proliferation. Results: At 800 mg bd, one patient experienced grade 3 liver toxicity and another had grade 2 vomiting; no significant toxicities were experienced in 13 patients treated at 400 mg bd. Seven of fourteen evaluable patients had clinical evidence of tumor reduction. Some of these responses were associated with increased tumor apoptosis, necrosis, and decreases in plasma EBV DNA posttreatment. Reduced protein expression of Mcl-1, cyclin D1, phosphorylated retinoblastoma protein pRB (T821), and significant transcriptional down-regulation of genes related to cellular proliferation and survival were shown in some patients posttreatment, indicative of cell cycle modulation by seliciclib, more specifically inhibition of cdk2/cyclin E, cdk7/cyclin H, and cdk9/cyclinT.