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Targeted Next Generation Sequencing Reveals Actionable Variants As Potential Therapeutic Targets In Vulvar Melanoma: A Case Study Analysis

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International Journal of Gynecological Cancer 2025年第2期35卷

作者： Simona Maria Fragomeni Alex Federico Alice Dal Miglio Angelo Minucci Alessia Perrucci Luciano Giaco' Mina Karimi Giulia Bravetti Tina Pasciuto Ernesto Rossi Giovanni Schinzari Ketty Peris Alessando Di Stefani Angela Santoro Valentina Iacobelli Camilla Nero Giovanni Scambia Giorgia Garganese Unità di Ginecologia Oncologica Dipartimento Scienze della Salute della Donna del Bambino e di Sanità Pubblica Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Sezione Ginecologia e Ostetricia Dipartimento Universitario Scienze della Vita e Sanità Pubblica Università Cattolica del Sacro Cuore Rome Italy Genomics Research Core facility Gemelli Science and Technology Park (G-STeP) Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Bioinformatics Research Core Facility Gemelli Science and Technology Park (G-STeP) Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Data Collection Core Facility Gemelli Science and Technology Park (G-STeP) Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Medical Oncology Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Medical Oncology Università Cattolica del Sacro Cuore Rome Italy UOC di Dermatologia Dipartimento di Scienze Mediche e Chirurgiche Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Dipartimento di Medicina e Chirurgia Traslazionale Università Cattolica del Sacro Cuore Rome Italy Unità Operativa Complessa Anatomia Patologica Generale Dipartimento di Scienze Della Salute Della Donna del Bambino e di Sanità Pubblica Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Unità operativa di Chirurgia degli Organi Genitali Esterni Femminili Divisione di Ginecologia Oncologica Dipartimento Scienze della Salute della Donna del Bambino e di Sanità Pubblica Fondazione Policlinico Universitario A. Gemelli IRCCS； Gemelli Women Health Center for Digital and Personalized Medicine Sezione Ginecologia e Ostetricia Dipartimento Universitario Scienze della Vita e Sanità Pubblica Università Cattolica del Sacro Cuore Rome Italy Gemelli Women Health Center for Digital and Personalized Medicine Dipartimento Scienze della Vita e Sanità Pubblica all’Università Cattolica del Sacro Cuore Rome Italy

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Genomics and epidemiology for gastric adenocarcinomas (GE4GAC): a Brazilian initiative to study gastric cancer

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Applied Cancer Research 2019年第1期39卷 1-4页

作者： Bartelli, Thais F. Senda de Abrantes, Lais L. Freitas, Helano C. Thomas, Andrew M. Silva, Jordana M. Albuquerque, Gabriela E. Araújo, Luiza F. Branco, Gabriela P. de Amorim, Maria G. Serpa, Marianna S. Takenaka, Isabella K. T. M. Souza, Deborah T. Monção, Lucas O. Moda, Bruno S. Valieris, Renan Defelicibus, Alexandre Borges, Rodrigo Drummond, Rodrigo D. Alves, Francisco I. A. Santos, Monize N. P. Bobrovnitchaia, Irina G. Elhaik, Eran Coelho, Luiz G. V. Khayat, André Demachki, Samia Assumpção, Paulo P. Santiago, Karina M. Torrezan, Giovana T. Carraro, Dirce M. Peres, Stela V. Calsavara, Vinícius F. Burbano, Rommel Nóbrega, Calebe R. Baladão, Graziela P. P. Pereira, Ana C. C. Gatti, Camila M. Fagundes, Marcela A. Araújo, Marília S. Miranda, Tayana V. Barbosa, Monica S. Cardoso, Daniela M. M. Carneiro, Lilian C. Brito, Alexandre M. Ramos, Amanda F. P. L. Silva, Lucas L. L. Pontes, Jaqueline C. Tiengo, Tatiane Arantes, Paola E. Santana, Vilma Cordeiro, Milena Sant’Ana, Rosane O. Andrade, Hanna B. Anaissi, Ana K. M. Sampaio, Sara V. Abdallah, Emne A. Chinen, Ludmilla T. D. Braun, Alexcia C. Flores, Bianca C. T. Mello, Celso A. L. Claro, Laura C. L. Sztokfisz, Claudia Z. Altamirano, Carlos C. Carter, David R. F. Jesus, Victor H. F. Riechelmann, Rachel Medina, Tiago Gollob, Kenneth J. Martins, Vilma R. Setúbal, João C. Pelosof, Adriane G. Coimbra, Felipe J. Costa-Jr, Wilson L. Silva, Israel T. Nunes, Diana N. Curado, Maria P. Dias-Neto, Emmanuel Lab of Medical Genomics A.C.Camargo Cancer Center Sao Paulo Brazil International Research Center A.C.Camargo Cancer Center Sao Paulo Brazil Clinical Oncology Department A.C. Camargo Cancer Center São Paulo Brazil Department CIBIO University of Trento Trento Italy Biochemistry Department Chemistry Institute Universidade de São Paulo São Paulo Brazil Lab. of Bioinformatics and Computational Biology A.C.Camargo Cancer Center Sao Paulo Brazil Data Science group International Research Center A.C.Camargo Cancer Center Sao Paulo Brazil Department of Animal & Plant Sciences University of Sheffield Sheffield UK Instituto Alfa de Gastroenterologia Hospital das Clínicas Universidade Federal de Minas Gerais Belo Horizonte Brazil Instituto de Ciências Biológicas Universidade Federal do Pará Belém Brazil Núcleo de Pesquisas em Oncologia Universidade Federal do Pará Belém Brazil Lab. de Genômica e Biologia Molecular A.C.Camargo Cancer Center Sao Paulo Brazil Epidemiology and Statistics Group A.C.Camargo Cancer Center Sao Paulo Brazil Laboratório de Biologia Molecular Hospital Ophir Loyola Belém Brazil Universidade Federal de Goiás IPTESP – Instituto de Patologia Tropical e Saúde Pública Goiânia Brazil Department of Abdominal Surgery Hospital Araujo Jorge Goiânia Brazil Instituto de Saúde Coletiva da Universidade Federal da Bahia Salvador Brazil Instituto do Cancer do Ceará Fortaleza Brazil Department of Pathology A.C.Camargo Cancer Center São Paulo Brazil Endoscopy sector A.C.Camargo Cancer Center São Paulo Brazil Instituto Nacional de Enfermedades Neoplásicas Surquillo Peru Dept. of Biological and Medical Sciences Faculty of Health and Life Sciences Oxford Brookes University Oxford UK Translational Immuno-oncology Group A.C.Camargo Cancer Center Sao Paulo Brazil Tumor Biology and Biomarkers Group A.C.Camargo Cancer Center Sao Paulo Brazil Department of Abdominal Surgery A.C.Camargo Cancer Center São Paulo Brazil

Gastric cancer (GC) is the fifth most common type of cancer worldwide with high incidences in Asia, Central, and South American countries. This patchy distribution means that GC studies are neglected by large research centers from developed countries. The need for further understanding of this complex disease, including the local importance of epidemiological factors and the rich ancestral admixture found in Brazil, stimulated the implementation of the GE4GAC project. GE4GAC aims to embrace epidemiological, clinical, molecular and microbiological data from Brazilian controls and patients with malignant and pre-malignant gastric disease. In this letter, we summarize the main goals of the project, including subject and sample accrual and current findings.

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Modeling the causal regulatory network by integrating chromatin accessibility and transcriptome data

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National Science Review 2016年第2期3卷 240-251页

作者： Yong Wang Rui Jiang Wing Hung Wong Department of Statistics Department of Biomedical Data ScienceBio-X ProgramStanford University Academy of Mathematics and Systems Science National Center for Mathematics and Interdisciplinary SciencesChinese Academy of Sciences MOE Key Laboratory of Bioinformatics Bioinformatics Division and Center for Synthetic and Systems BiologyTNLISTDepartment of AutomationTsinghua University

Cell packs a lot of genetic and regulatory information through a structure known as chromatin,*** is wrapped around histone proteins and is tightly packed in a remarkable *** express a gene in a specific coding region,the chromatin would open up and DNA loop may be formed by interacting enhancers and ***,the mediator and cohesion complexes,sequence-specific transcription factors,and RNA polymerase Ⅱ are recruited and work together to elaborately regulate the expression *** is in pressing need to understand how the information,about when,where,and to what degree genes should be expressed,is embedded into chromatin structure and gene regulatory *** to large consortia such as Encyclopedia of DNA Elements(ENCODE) and Roadmap Epigenomic projects,extensive data on chromatin accessibility and transcript abundance are available across many tissues and cell *** rich data offer an exciting opportunity to model the causal regulatory ***,we will review the current experimental approaches,foundational data,computational problems,interpretive frameworks,and integrative models that will enable the accurate interpretation of regulatory ***,we will discuss the efforts to organize,analyze,model,and integrate the DNA accessibility data,transcriptional data,and functional genomic regions *** believe that these efforts will eventually help us understand the information flow within the cell and will influence research directions across many fields.

关键词： gene regulatory network open chromatin DNA accessibility transcription factor colocalization statistical model data integration

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Author Correction: The IPDGC/GP2 Hackathon - an open science event for training in data science, genomics, and collaboration using Parkinson’s disease data

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npj Parkinson's Disease 2023年第1期9卷 1页

作者： Hampton L. Leonard Anni Moore Dan Vitale Francis P. Grenn Hirotaka Iwaki Jonggeol Jeff Kim Julie Lake Mary B. Makarious Michael Ta Pilar Alvarez Jerez Tarek Antar Yeajin Song Andrew Singleton Mike A. Nalls Sara Bandres-Ciga Cornelis Blauwendraat Ruqaya Murtadha Anastasia Illarionova Alejandro Martinez-Carrasco Catherine Storm Clodagh Towns Lesley Wu Manuela M. X. Tan Raquel Real Alina Jama Devina Chetty Zuné Jansen van Rensburg Shameemah Abrahams Amica Corda Müller-Nedebock Regina H. Reynolds William J. Scotton Ana-Luisa Gil-Martinez Bernabe I. Bustos Bharati Jadhav Brook Huxford Isabelle Francesca Foote Sumit Dey Alastair J. Noyce Eric Yu Konstantin Senkevich Prabhjyot Saini Gabriela Salazar Geoffrey Rateau Inas Elsayed Jie Yuan Ruifneg Hu Kajsa Brolin María Teresa Periñán Nikita Simone Pillay Oswaldo Lorenzo Betancor Paula R. Reyes-Pérez Rami al-Ouran Ramiya Sivakumar Shilpa C. Rao Thiago Peixoto Leal Vassilena Iankova Laboratory of Neurogenetics National Institute on Aging National Institutes of Health Bethesda MD USA Center for Alzheimer’s and Related Dementias (CARD) National Institute on Aging and National Institute of Neurological Disorders and Stroke National Institutes of Health Bethesda MD USA Data Tecnica International LLC Washington DC USA German Center for Neurodegenerative Diseases (DZNE) Tübingen Germany Preventive Neurology Unit Wolfson Institute of Population Health Queen Mary University of London London UK Department of Clinical and Movement Neurosciences UCL Queen Square Institute of Neurology University College London London UK Department of Neurology Oslo University Hospital Oslo Norway Department of Neuromuscular Diseases UCL Queen Square Institute of Neurology University College London London UK Division of Molecular Biology and Human Genetics Department of Biomedical Sciences Faculty of Medicine and Health Sciences Stellenbosch University Cape Town South Africa South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders Research Unit Stellenbosch University Cape Town South Africa Department of Neurodegenerative Disease University College London London UK Great Ormond Street Institute of Child Health Genetics and Genomic Medicine University College London London UK The Ken & Ruth Davee Department of Neurology and Simpson Querrey Center of Neurogenetics Feinberg Feinberg School of Medicine Northwestern University Chicago IL 60611 USA Department of Genetics and Genomic Sciences and Mindich Child Health and Development Institute Icahn School of Medicine at Mount Hess Center for Science and Medicine New York NY 10029 USA Unit for Psychological Medicine Wolfson Institute of Population Health Queen Mary University of London London UK Department of Human Genetics McGill University Montreal QC Canada The Neuro (Montreal Neurological Institute-Hospital) McGill University Montreal QC Canada Department of Neurology and

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Author Correction: Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition

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Nature genetics 2023年第6期55卷 1080页

作者： Bernardo Rodriguez-Martin Eva G Alvarez Adrian Baez-Ortega Jorge Zamora Fran Supek Jonas Demeulemeester Martin Santamarina Young Seok Ju Javier Temes Daniel Garcia-Souto Harald Detering Yilong Li Jorge Rodriguez-Castro Ana Dueso-Barroso Alicia L Bruzos Stefan C Dentro Miguel G Blanco Gianmarco Contino Daniel Ardeljan Marta Tojo Nicola D Roberts Sonia Zumalave Paul A Edwards Joachim Weischenfeldt Montserrat Puiggròs Zechen Chong Ken Chen Eunjung Alice Lee Jeremiah A Wala Keiran M Raine Adam Butler Sebastian M Waszak Fabio C P Navarro Steven E Schumacher Jean Monlong Francesco Maura Niccolo Bolli Guillaume Bourque Mark Gerstein Peter J Park David C Wedge Rameen Beroukhim David Torrents Jan O Korbel Iñigo Martincorena Rebecca C Fitzgerald Peter Van Loo Haig H Kazazian Kathleen H Burns Peter J Campbell Jose M C Tubio Department of Zoology Genetics and Physical Anthropology Universidade de Santiago de Compostela Santiago de Compostela Spain. Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain. Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Transmissible Cancer Group Department of Veterinary Medicine University of Cambridge Cambridge UK. The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Genome Data Science Institute for Research in Biomedicine (IRB Barcelona) The Barcelona Institute of Science and Technology (BIST) Barcelona Spain. Institucio Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain. The Francis Crick Institute London UK. Department of Human Genetics University of Leuven Leuven Belgium. Genomes and Disease Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea. Cancer Ageing and Somatic Mutation Programme Wellcome Sanger Institute Cambridge UK. Department of Biochemistry Genetics and Immunology University of Vigo Vigo Spain. Galicia Sur Health Research Institute Vigo Spain. Faculty of Science and Technology University of Vic-Central University of Catalonia (UVic-UCC) Vic Spain. Barcelona Supercomputing Center (BSC) Barcelona Spain. Experimental Cancer Genetics Wellcome Sanger Institute Cambridge UK. Oxford Big Data Institute University of Oxford Oxford UK. DNA Repair and Genome Integrity Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Department of Biochemistry and Molecular Biology Universidade de Santiago de Compostela Santiago de Compostela Spain. Medical R

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Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals (vol 52, pg 1314, 2020)

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NATURE GENETICS 2021年第5期53卷 762-762页

作者： Surendran, Praveen Feofanova, Elena V. Lahrouchi, Najim Ntalla, Ioanna Karthikeyan, Savita Cook, James Chen, Lingyan Mifsud, Borbala Yao, Chen Kraja, Aldi T. Cartwright, James H. Hellwege, Jacklyn N. Giri, Ayush Tragante, Vinicius Thorleifsson, Gudmar Liu, Dajiang J. Prins, Bram P. Stewart, Isobel D. Cabrera, Claudia P. Eales, James M. Akbarov, Artur Auer, Paul L. Bielak, Lawrence F. Bis, Joshua C. Braithwaite, Vickie S. Brody, Jennifer A. Daw, E. Warwick Warren, Helen R. Drenos, Fotios Nielsen, Sune Fallgaard Faul, Jessica D. Fauman, Eric B. Fava, Cristiano Ferreira, Teresa Foley, Christopher N. Franceschini, Nora Gao, He Giannakopoulou, Olga Giulianini, Franco Gudbjartsson, Daniel F. Guo, Xiuqing Harris, Sarah E. Havulinna, Aki S. Helgadottir, Anna Huffman, Jennifer E. Hwang, Shih-Jen Kanoni, Stavroula Kontto, Jukka Larson, Martin G. Li-Gao, Ruifang Lindstrom, Jaana Lotta, Luca A. Lu, Yingchang Luan, Jian'an Mahajan, Anubha Malerba, Giovanni Masca, Nicholas G. D. Mei, Hao Menni, Cristina Mook-Kanamori, Dennis O. Mosen-Ansorena, David Muller-Nurasyid, Martina Pare, Guillaume Paul, Dirk S. Perola, Markus Poveda, Alaitz Rauramaa, Rainer Richard, Melissa Richardson, Tom G. Sepulveda, Nuno Sim, Xueling Smith, Albert V. Smith, Jennifer A. Staley, James R. Stanakova, Alena Sulem, Patrick Theriault, Sebastien Thorsteinsdottir, Unnur Trompet, Stella Varga, Tibor V. Velez Edwards, Digna R. Veronesi, Giovanni Weiss, Stefan Willems, Sara M. Yao, Jie Young, Robin Yu, Bing Zhang, Weihua Zhao, Jing-Hua Zhao, Wei Zhao, Wei Evangelou, Evangelos Aeschbacher, Stefanie Asllanaj, Eralda Blankenberg, Stefan Bonnycastle, Lori L. Bork-Jensen, Jette Brandslund, Ivan Braund, Peter S. Burgess, Stephen Cho, Kelly Christensen, Cramer Connell, John de Mutsert, Renee Dominiczak, Anna F. Dorr, Marcus Eiriksdottir, Gudny Farmaki, Aliki-Eleni Gaziano, J. Michael Grarup, Niels Grove, Megan L. Hallmans, Goran Hansen, Torben Have, Christian T. Heiss, Gerardo Jorgensen, Marit E. Jousilahti, Pekka Kajantie, Eero Kamat, Mihir Karajamaki, AnneMari Kar British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK British Heart Foundation Centre of Research Excellence University of Cambridge Cambridge UK Health Data Research UK Cambridge Wellcome Genome Campus and University of Cambridge Cambridge UK Rutherford Fund Fellow Department of Public Health and Primary Care University of Cambridge Cambridge UK MRC Biostatistics Unit University of Cambridge Cambridge UK National Institute for Health Research Cambridge Biomedical Research Centre University of Cambridge and Cambridge University Hospitals Cambridge UK MRC Integrative Epidemiology Unit (IEU) Population Health Sciences Bristol Medical School University of Bristol Bristol UK Robertson Centre for Biostatistics University of Glasgow Glasgow UK MRC Epidemiology Unit University of Cambridge School of Clinical Medicine Cambridge UK Department of Immunology and Inflammation Imperial College London London UK Centre for Non-communicable Disease Research (CNCR) Dhaka Bangladesh National Institute for Health Research (NIHR) Blood and Transplant Research Unit (BTRU) in Donor Health and Genomics at the University of Cambridge Cambridge UK Department of Human Genetics Wellcome Sanger Institute Wellcome Genome Campus Hinxton Cambridge UK Department of Biostatistics and Epidemiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Non-Communicable Diseases Karachi Pakistan Department of Genetics Novo Nordisk Research Centre Oxford Oxford UK Human Genetics Center Department of Epidemiology Human Genetics and Environmental Sciences School of Public Health University of Texas Health Science Center at Houston Houston TX USA Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Cardiovascular Research Center Center for Genomic Medicine

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Essential guidelines for computational method benchmarking

arXiv

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arXiv 2018年

作者： Weber, Lukas M. Saelens, Wouter Cannoodt, Robrecht Soneson, Charlotte Hapfelmeier, Alexander Gardner, Paul P. Boulesteix, Anne-Laure Saeys, Yvan Robinson, Mark D. Institute of Molecular Life Sciences University of Zurich Zurich Switzerland SIB Swiss Institute of Bioinformatics University of Zurich Zurich Switzerland Data Mining and Modelling for Biomedicine VIB Center for Inflammation Research Ghent Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Institute of Medical Informatics Statistics and Epidemiology Technical University of Munich Munich Germany Department of Biochemistry University of Otago Dunedin New Zealand Institute for Medical Information Processing Biometry and Epidemiology Ludwig-Maximilians-University Munich Germany Friedrich Miescher Institute for Biomedical Research SIB Swiss Institute of Bioinformatics Basel Switzerland

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology. Copyright © 2018, The Authors. All rights reserved.

关键词： Benchmarking

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Multiple testing with persistent homology

arXiv

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arXiv 2018年

作者： Vejdemo-Johansson, Mikael Mukherjee, Sayan CUNY College of Staten Island CUNY Graduate Center 2800 Victory Boulevard 1S-215 Staten Island NY10314 United States Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Humboldtstraße 25 Leipzig04105 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 Leipzig04103 Germany Departments of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics Duke University DurhamNC27708 United States

In this paper we propose a computationally efficient multiple hypothesis testing procedure for persistent homology. The computational efficiency of our procedure is based on the observation that one can empirically simulate a null distribution that is universal across many hypothesis testing applications involving persistence homology. Our observation suggests that one can simulate the null distribution efficiently based on a small number of summaries of the collected data and use this null in the same way that p-value tables were used in classical statistics. To illustrate the efficiency and utility of the null distribution we provide procedures for rejecting acyclicity with both control of the Family-Wise Error Rate (FWER) and the False Discovery Rate (FDR). We will argue that the empirical null we propose is very general conditional on a few summaries of the data based on simulations and limit theorems for persistent homology for point *** Codes 55N31 Copyright © 2018, The Authors. All rights reserved.

关键词： Computational efficiency

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Unsupervised extraction of phenotypes from cancer clinical notes for association studies

arXiv

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arXiv 2019年

作者： Stark, Stefan G. Hyland, Stephanie L. Pradier, Melanie F. Lehmann, Kjong-Van Wicki, Andreas Perez-Cruz, Fernando Vogt, Julia E. Rätsch, Gunnar Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional Ph.D. Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Department of Computer Science ETH Zürich Zürich Switzerland Medical Informatics Group University Hospital Zürich Zürich Switzerland Swiss Institute for Bioinformatics Zurich Switzerland Department of Biology ETH Zürich Zürich Switzerland Department of Signal Processing and Information Theory University Carlos III in Madrid Leganés Spain School of Engineering and Applied Sciences Harvard University CambridgeMA United States Department of Biomedicine University of Basel Basel Switzerland Tumorzentrum University Hospital Basel Basel Switzerland Swiss Data Science Center ETH Zürich and EPFL Lausanne Switzerland Department of Mathematics and Computer Science University of Basel Basel Switzerland

The recent adoption of Electronic Health Records (EHRs) by healthcare providers has introduced an important source of data that provides detailed and highly specific insights into patient phenotypes over large cohorts. These datasets, in combination with machine learning and statistical approaches, generate new opportunities for research and clinical care. However, many methods require the patient representations to be in structured formats, while the information in the EHR is often locked in unstructured text designed for human readability. In this work, we develop the methodology to automatically extract clinical features from clinical narratives from large EHR corpora without the need for prior knowledge. We consider medical terms and sentences appearing in clinical narratives as atomic information units. We propose an efficient clustering strategy suitable for the analysis of large text corpora and utilize the clusters to represent information about the patient compactly. Additionally, we define the sentences on ontologic and natural language vocabularies to automatically detect pertinent combinations of concepts present in the corpus, even when an ontology is not available. To demonstrate the utility of our approach, we perform an association study of clinical features with somatic mutation profiles from 4,007 cancer patients and their tumors. We apply the proposed algorithm to a dataset consisting of .65 thousand documents with a total of .3.2 million sentences. After correcting for cancer type and other confounding factors, we identify a total of 340 significant statistical associations between the presence of somatic mutations and clinical features. We annotated these associations according to their novelty and we report several known associations. We also propose 37 plausible, testable hypothesis for associations where the underlying biological mechanism does not appear to be known. These results illustrate that the automated discovery of clinical features

关键词： Diseases

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Corrigendum to "Identification of Differential Tumor Subtypes of T1 Bladder Cancer" [Eur. Urol. 78 (2020) 533-537]

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European urology 2022年第2期81卷 e53页

作者： A Gordon Robertson Clarice S Groeneveld Brian Jordan Xiaoqi Lin Kimberly A McLaughlin Arighno Das Leigh Ann Fall Damiano Fantini Timothy J Taxter Lauren S Mogil Sia Viborg Lindskrog Lars Dyrskjøt David J McConkey Robert S Svatek Aurélien de Reyniès Mauro A A Castro Joshua J Meeks Dxige Research Inc. Courtenay BC Canada. Cartes d'Identité des Tumeurs Program Ligue Nationale Contre le Cancer Paris France Oncologie Moleculaire Institut Curie Equipe Labellisée Ligue Contre le Cancer Paris France. Department of Urology University of Washington Seattle WA USA. Department of Pathology Northwestern University Feinberg School of Medicine Chicago IL USA. Departments of Urology Biochemistry and Molecular Genetics Feinberg School of Medicine Northwestern University Chicago IL USA. Tempus Labs Inc. Chicago IL USA. Center for Translational Data Science University of Chicago Chicago IL USA. Department of Molecular Medicine Aarhus University Hospital Aarhus Denmark. Johns Hopkins Greenberg Bladder Cancer Institute Brady Urological Institute Johns Hopkins University Baltimore MD USA. Experimental Developmental Therapeutics Program UT Health MD Anderson San Antonio TX USA Department of Urology UT Health San Antonio San Antonio TX USA. Cartes d'Identité des Tumeurs Program Ligue Nationale Contre le Cancer Paris France. Bioinformatics and Systems Biology Laboratory Federal University of Paraná Curitiba Brazil. Departments of Urology Biochemistry and Molecular Genetics Feinberg School of Medicine Northwestern University Chicago IL USA Jesse Brown VA Medical Center Chicago IL USA. Electronic address: joshua.meeks@northwestern.edu.

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