Enhanced Efficacy of Aurora Kinase Inhibitors in G2/M Checkpoint Deficient TP53 Mutant Uterine Carcinomas Is Linked to the Summation of LKB1-AKT-p53 Interactions.

Uterine carcinoma (UC) is the most common gynecologic malignancy in the United States. TP53 mutant UCs cause a disproportionate number of deaths due to limited therapies for these tumors and the lack of mechanistic understanding of their fundamental vulnerabilities. Here we sought to understand the functional and therapeutic relevance of TP53 mutations in UC. We functionally profiled targetable TP53 dependent DNA damage repair and cell cycle control pathways in a panel of TP53 mutant UC cell lines and patient-derived organoids. There were no consistent defects in DNA damage repair pathways. Rather, most models demonstrated dependence on defective G2/M cell cycle checkpoints and subsequent upregulation of Aurora kinase-LKB1-p53-AKT signaling in the setting of baseline mitotic defects. This combination makes them sensitive to Aurora kinase inhibition. Resistant lines demonstrated an intact G2/M checkpoint, and combining Aurora kinase and WEE1 inhibitors, which then push these cells through mitosis with Aurora kinase inhibitor-induced spindle defects, led to apoptosis in these cases. Overall, this work presents Aurora kinase inhibitors alone or in combination with WEE1 inhibitors as relevant mechanism driven therapies for TP53 mutant UCs. Context specific functional assessment of the G2/M checkpoint may serve as a biomarker in identifying Aurora kinase inhibitor sensitive tumors

Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects

Copy number variants (CNVs) have been strongly implicated in the genetic etiology of schizophrenia (SCZ). However, genome-wide investigation of the contribution of CNV to risk has been hampered by limited sample sizes. We sought to address this obstacle by applying a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. A global enrichment of CNV burden was observed in cases (OR=1.11, P=5.7×10−15), which persisted after excluding loci implicated in previous studies (OR=1.07, P=1.7 ×10−6). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 ×10−11) and neurobehavioral phenotypes in mouse (OR = 1.18, P= 7.3 ×10−5). Genome-wide significant evidence was obtained for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. Suggestive support was found for eight additional candidate susceptibility and protective loci, which consisted predominantly of CNVs mediated by non-allelic homologous recombination

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Schizophrenia-associated somatic copy-number variants from 12,834 cases reveal recurrent NRXN1 and ABCB11 disruptions

While germline copy-number variants (CNVs) contribute to schizophrenia (SCZ) risk, the contribution of somatic CNVs (sCNVs)—present in some but not all cells—remains unknown. We identified sCNVs using blood-derived genotype arrays from 12,834 SCZ cases and 11,648 controls, filtering sCNVs at loci recurrently mutated in clonal blood disorders. Likely early-developmental sCNVs were more common in cases (0.91%) than controls (0.51%, p = 2.68e−4), with recurrent somatic deletions of exons 1–5 of the NRXN1 gene in five SCZ cases. Hi-C maps revealed ectopic, allele-specific loops forming between a potential cryptic promoter and non-coding cis-regulatory elements upon 5′ deletions in NRXN1. We also observed recurrent intragenic deletions of ABCB11, encoding a transporter implicated in anti-psychotic response, in five treatment-resistant SCZ cases and showed that ABCB11 is specifically enriched in neurons forming mesocortical and mesolimbic dopaminergic projections. Our results indicate potential roles of sCNVs in SCZ risk

Replicated methylation changes associated with eczema herpeticum and allergic response.

BackgroundAlthough epigenetic mechanisms are important risk factors for allergic disease, few studies have evaluated DNA methylation differences associated with atopic dermatitis (AD), and none has focused on AD with eczema herpeticum (ADEH+). We will determine how methylation varies in AD individuals with/without EH and associated traits. We modeled differences in genome-wide DNA methylation in whole blood cells from 90 ADEH+, 83 ADEH-, and 84 non-atopic, healthy control subjects, replicating in 36 ADEH+, 53 ADEH-, and 55 non-atopic healthy control subjects. We adjusted for cell-type composition in our models and used genome-wide and candidate-gene approaches.ResultsWe replicated one CpG which was significantly differentially methylated by severity, with suggestive replication at four others showing differential methylation by phenotype or severity. Not adjusting for eosinophil content, we identified 490 significantly differentially methylated CpGs (ADEH+ vs healthy controls, genome-wide). Many of these associated with severity measures, especially eosinophil count (431/490 sites).ConclusionsWe identified a CpG in IL4 associated with serum tIgE levels, supporting a role for Th2 immune mediating mechanisms in AD. Changes in eosinophil level, a measure of disease severity, are associated with methylation changes, providing a potential mechanism for phenotypic changes in immune response-related traits

Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects

Copy number variants (CNVs) have been strongly implicated in the genetic etiology of schizophrenia (SCZ). However, genome-wide investigation of the contribution of CNV to risk has been hampered by limited sample sizes. We sought to address this obstacle by applying a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. A global enrichment of CNV burden was observed in cases (odds ratio (OR) = 1.11, P = 5.7 x 10(-15)), which persisted after excluding loci implicated in previous studies (OR = 1.07, P = 1.7 x 10(-6)). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 x 10(-11)) and neurobehavioral phenotypes in mouse (OR = 1.18, P = 7.3 x 10(-5)). Genome-wide significant evidence was obtained for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. Suggestive support was found for eight additional candidate susceptibility and protective loci, which consisted predominantly of CNVs mediated by nonallelic homologous recombination

Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects

Copy number variants (CNVs) have been strongly implicated in the genetic etiology of schizophrenia (SCZ). However, genome-wide investigation of the contribution of CNV to risk has been hampered by limited sample sizes. We sought to address this obstacle by applying a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. A global enrichment of CNV burden was observed in cases (odds ratio (OR) = 1.11, P = 5.7 x 10(-15)), which persisted after excluding loci implicated in previous studies (OR = 1.07, P = 1.7 x 10(-6)). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 x 10(-11)) and neurobehavioral phenotypes in mouse (OR = 1.18, P = 7.3 x 10(-5)). Genome-wide significant evidence was obtained for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. Suggestive support was found for eight additional candidate susceptibility and protective loci, which consisted predominantly of CNVs mediated by nonallelic homologous recombination

Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations

OLIVEIRA, Ricardo Riccio. Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil. Michelle Daya1, Nicholas Rafaels1, Tonya M. Brunetti1, Sameer Chavan1, Albert M. Levin2, Aniket Shetty1, Christopher R. Gignoux1, Meher Preethi Boorgula1, Genevieve Wojcik 3, Monica Campbell1, Candelaria Vergara 4, Dara G. Torgerson5, Victor E. Ortega6, Ayo Doumatey7, Henry Richard Johnston8, Nathalie Acevedo9, Maria Ilma Araujo10, Pedro C. Avila 11, Gillian Belbin12, Eugene Bleecker13, Carlos Bustamante3, Luis Caraballo9, Alvaro Cruz14, Georgia M. Dunston15, Celeste Eng5, Mezbah U. Faruque16, Trevor S. Ferguson 17, Camila Figueiredo18, Jean G. Ford19, Weiniu Gan20, Pierre-Antoine Gourraud21, Nadia N. Hansel4, Ryan D. Hernandez22, Edwin Francisco Herrera-Paz 23,24, Silvia Jiménez9, Eimear E. Kenny12, Jennifer Knight-Madden17, Rajesh Kumar25, Leslie A. Lange1, Ethan M. Lange1, Antoine Lizee21, Pissamai Maul26, Trevor Maul26, Alvaro Mayorga27, Deborah Meyers13, Dan L. Nicolae28, Timothy D. O’Connor29, Ricardo Riccio Oliveira30, Christopher O. Olopade31, Olufunmilayo Olopade28, Zhaohui S. Qin 32, Charles Rotimi 7, Nicolas Vince 21, Harold Watson33, Rainford J. Wilks17, James G. Wilson34, Steven Salzberg 35, Carole Ober36, Esteban G. Burchard22, L. Keoki Williams37, Terri H. Beaty 38, Margaret A. Taub39, Ingo Ruczinski39, CAAPA, Rasika A. Mathias4 & Kathleen C. Barnes1, Ayola Akim Adegnika40, Ganiyu Arinola41, Ulysse Ateba-Ngoa40, Gerardo Ayestas23, Hilda Bjarnadóttir42, Adolfo Correa 43, Said Omar Leiva Erazo23, Marilyn G. Foreman44, Cassandra Foster4, Li Gao4, Jingjing Gao45, Leslie Grammer11, Mark Hansen46, Tina Hartert47, Yijuan Hu32, Iain Königsberg1, Kwang-Youn A. Kim 48, Pamela Landaverde-Torres23, Javier Marrugo49, Beatriz Martinez49, Rosella Martinez23, Luis F. Mayorga23, Delmy-Aracely Mejia-Mejia50, Catherine Meza49, Solomon Musani43, Shaila Musharoff3, Oluwafemi Oluwole28, Maria Pino-Yanes 5, Hector Ramos23, Allan Saenz23, Maureen Samms-Vaughan51, Robert Schleimer11, Alan F. Scott52, Suyash S. Shringarpure3, Wei Song29, Zachary A. Szpiech 22, Raul Torres 53, Gloria Varela23, Olga Marina Vasquez54, Francisco M. De La Vega3, Lorraine B. Ware47 & Maria Yazdanbakhsh 5. 1Department of Medicine, University of Colorado Denver, Aurora, CO 80045, USA. 2Department of Public Health Sciences, Henry Ford Health System, Detroit, MI 48202, USA. 3Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA. 4Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA. 5Department of Medicine, University of California San Francisco, San Francisco, CA 94143, USA. 6Center for Human Genomics and Personalized Medicine, Wake Forest School of Medicine, Winston-Salem 27157, USA. 7Center for Research on Genomics & Global Health, National Institutes of Health, Bethesda, MD 20892, USA. 8Department of Human Genetics, Emory University, Atlanta, GA 30322, USA. 9Institute for Immunological Research, Universidad de Cartagena, Cartagena 130000, Colombia 10Immunology Service, Universidade Federal da Bahia, Salvador 401110170, Brazil. 11Department of Medicine, Northwestern University, Chicago, IL 60611, USA. 12Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 13Department of Medicine, University of Arizona College of Medicine, Tucson, AZ 85724, USA. 14Universidade Federal da Bahia, Salvador 401110170, Brazil. 15Department of Microbiology, Howard University College of Medicine, Washington, DC 20059, USA. 16National Human Genome Center, Howard University College of Medicine, Washington, DC 20059, USA. 17Caribbean Institute for Health Research, The University of the West Indies, Kingston 00007, Jamaica. 18Departamento de Biorregulacao, Universidade Federal da Bahia, Salvador 401110170, Brazil. 19Department of Medicine, Einstein Medical Center, Philadelphia, PA 19141, USA. 20National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA. 21Université de Nantes, INSERM, Centre de Recherche en Transplantation et Immunologie, UMR, 1064ATIP-Avenir, Equipe 5, Nantes, France. 22Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94143, USA. 23Facultad de Medicina, Universidad Católica de Honduras, San Pedro Sula 21102, Honduras. 24Universidad Tecnológica Centroamericana (UNITEC), Facultad de Ciencias Médicas, Tegucigalpa, Honduras. 25Department of Pediatrics, Northwestern University, Chicago, IL 60611, USA. 26Genetics and Epidemiology of Asthma in Barbados, The University of the West Indies, Chronic Disease Research Centre, Jemmots Lane, St. Michael BB11115, Barbados. 27Centro de Neumologia y Alergias, San Pedro Sula 21102, Honduras. 28Department of Medicine, University of Chicago, Chicago, IL 60637, USA. 29Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA. 30Laboratório de Patologia Experimental, Centro de Pesquisas Gonçalo Moniz, Salvador 40296-710, Brazil. 31Department of Medicine and Center for Global Health, University of Chicago, Chicago, IL 60637, USA. 32Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA. 33Faculty of Medical Sciences, The University of the West Indies, Queen Elizabeth Hospital, Bridgetown, St. Michael BB11000, Barbados. 34Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA. 35Departments of Biomedical Engineering and Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA. 36Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA. 37Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI 48202, USA. 38Department of Epidemiology, Bloomberg School of Public Health, JHU, Baltimore, MD 21205, USA. 39Department of Biostatistics, Bloomberg School of Public Health, JHU, Baltimore, MD 21205, USA. These authors contributed equally: Rasika A. Mathias, Kathleen C. Barnes.40Centre de Recherches Médicales de Lambaréné, BP:242, Lambaréné 13901, Gabon. 41Department of Chemical Pathology, University of Ibadan, Ibadan 900001, Nigeria. 42Faculty of Medicine, University of Iceland, 101 Reykjavík, Iceland. 43Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA. 44Pulmonary and Critical Care Medicine, Morehouse School of Medicine, Atlanta, GA 30310, USA. 45Data and Statistical Sciences, AbbVie, North Chicago, IL 60064, USA. 46Illumina, Inc., San Diego, CA 92122, USA. 47Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA. 48Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA. 49Instituto de Investigaciones Immunologicas, Universidad de Cartagena, Cartagena 130000, Colombia. 50Facultad de Ciencias de la Salud, Universidad Tecnológica Centroamericana (UNITEC), San Pedro Sula 21102, Honduras. 51Department of Child Health, The University of the West Indies, Kingston 00007, Jamaica. 52Department of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA. 53Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, CA 94158, USA. 54Centro Medico de la Familia, San Pedro Sula 21102, Honduras. 55Department of Parasitology, Leiden University Medical Center, Leiden 02333, NetherlandsSubmitted by Ana Maria Fiscina Sampaio ([email protected]) on 2019-03-25T16:18:22Z No. of bitstreams: 1 Daya M Association study in African-admixed...2019.pdf: 1446713 bytes, checksum: ec386d63089da2ac2c2f15c4ef98f264 (MD5)Approved for entry into archive by Ana Maria Fiscina Sampaio ([email protected]) on 2019-03-25T16:36:07Z (GMT) No. of bitstreams: 1 Daya M Association study in African-admixed...2019.pdf: 1446713 bytes, checksum: ec386d63089da2ac2c2f15c4ef98f264 (MD5)Made available in DSpace on 2019-03-25T16:36:07Z (GMT). No. of bitstreams: 1 Daya M Association study in African-admixed...2019.pdf: 1446713 bytes, checksum: ec386d63089da2ac2c2f15c4ef98f264 (MD5) Previous issue date: 2019Múltipla - ver em NotasAsthma is a complex disease with striking disparities across racial and ethnic groups. Despite its relatively high burden, representation of individuals of African ancestry in asthma genome-wide association studies (GWAS) has been inadequate, and true associations in these underrepresented minority groups have been inconclusive. We report the results of a genome-wide meta-analysis from the Consortium on Asthma among African Ancestry Populations (CAAPA; 7009 asthma cases, 7645 controls). We find strong evidence for association at four previously reported asthma loci whose discovery was driven largely by non-African populations, including the chromosome 17q12-q21 locus and the chr12q13 region, a novel (and not previously replicated) asthma locus recently identified by the Trans-National Asthma Genetic Consortium (TAGC). An additional seven loci reported by TAGC show marginal evidence for association in CAAPA. We also identify two novel loci (8p23 and 8q24) that may be specific to asthma risk in African ancestry populations