docA Burgun - High Throughput Phenotyping
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A Burgun - High Throughput Phenotyping
PheWAS studies and high throughput phenotyping Anita Burgun Paris Descartes School of Medicine, USPC Hôpital Européen Georges Pompidou (HEGP), APHP INSERM UMR 1138 eq22 Nov 4th, 2015 1 Personalized and Precision Medicine « We are not average » P Phenome § G E 2 Personalized and Precision Medicine P Omics sciences Phenome § G E Use genome sequencing to find the genetic basis of a rare undiagnosed disease Choose cancer treatments that target a genetic defect in the tumor 3 Personalized and Precision Medicine Public health and Environmental sciences P Phenome § G E Quantify your risk for specific diseases Establish goals and strategies for better health 4 Personalized and Precision Medicine Solve mysterious events or syndromes Connect to patient’s (family) history Adapt treatments (pharmacogenetics) Prevent adverse effects from drugs Monitor diseases Medicine and Biomedical informatics P Phenome § G E 5 Coverage of EHR at HEGP -‐ -‐ -‐ -‐ 700 beds (cardiology +++ and cancer +++, pharmacogene>cs dept) Paris Descartes University EHR since 2000 : DXC@re (Medasys) DXC@re Coverage : -‐ Clinical data -‐ Drug prescrip>on -‐ Lab test results, imaging, etc OPT-OUT i2b2 does not require any specific ontology Queries can be created using any system of local codes 8 Clinical Data Warehouse HEGP Type # instances Demographics (age, sex, Hospital vital status) Vital signs (temperature, blood pressure, weight, …) # pa-ents 742 487 15 million 141 725 Diagnoses (ICD 10 codes) 6 million 338 871 Medical Procedures (French CCAM codes) 4 million 285 097 80 million 468 057 4 million 359 547 103 million 379 483 4 million 121 441 Clinical data (EHR DxCare forms) Free Text reports (Hospitaliza>on, Surgery, Imaging, Pathology …) Lab test results Drug prescrip>ons Omics data 9 McCarthy et al, Nature Reviews Genetics, 2008 / Denny et al, Bioinformatics 2010 Activité TPMT Quantitative trait vs genotype ICD 10 codes Vs ICD9-CM Lab test results Neuraz et al. PLOS One 2013 N FDA & EMA recommendations Phenotype Low activity Intermediate activity Normal Activity Thiopurine dose 10 % dose 30 – 70 % dose 100 % dose ? Methods : population and data • « TPMT cohort » – – – – patients who underwent a TPMTa assay with at least one ICD10 code or one biological test result There were no exclusion criteria. 3 groups: • Very high activity> 15 mmol/h/mL RBC • Normal • Low activity (partial and completely deficient TPMTa patients) • Control group : no TPMT assessment • Phenotypes ICD 10 codes 11 lab tests : blood cells, glycemia, liver enzymes Methods : addressing time aspects why and how ? • We had to restrict ourselves to events that occured after the initiation of treatment treatment TPMT : any time • 2 methods – Extraction of the prescriptions from the CPOE system (n = 12) – Extraction of the prescriptions from text reports (n = 423) 1. Select the sentences with drug names 2. Extract temporal information : start/stop and dates 15 Lab test results : event free survival curves [Neuraz A et al. Phenome-wide association on a quantitative trait: Application to TPMT enzymatic activity and thiopurine therapy in pharmacogenomics. PLoS Comput Biology 2013, 9(12): e1003405] Discussion • • • • • • • • PheWAS using a quantitative trait PheWAS applied to pharmacogenetics Phenotypes with timeline 1st PheWAS using ICD10 ICD codes + lab tests à cross-validation LaboWAS : on-going work with Kyoto University (UGT1A1 variants) Information extraction from text : easy for medication Main issue : time (extraction from narrative reports) • TPMT: Phenotype Low activity Intermediate activity Normal Activity Very High Activity Thiopurine dose 10 % dose 30 – 70 % dose 100 % dose > 100 % Dose ? Population data bases and EHRs Wei WQ , Denny JC Extracting research-quality phenotypes from electronic health records to support precision medicine, Genome Medicine, 2015 18 IT suppor>ng large-‐scale studies 1. Text mining Wei WQ , Denny JC Extracting research-quality phenotypes from electronic health records to support precision medicine, 20 Genome Medicine, 2015 Medical Watson • In the future, I expect this technology to help us discover new and beber treatments for specific gene>c abnormali>es or associa>ons of gene>c changes. It will be able to help us evaluate rare or complex pa>ent condi>ons and iden>fy drugs that have already been approved for other uses that might also help in the situa>ons we’re dealing with. • Dr DiNardo, MD Anderson • hbp://venturebeat.com/2014/10/15/ibm-‐watson-‐ health/ RESEAUX SOCIAUX …. 25 55 à 105 millions de personnes aux USA Coût estimé de 2 à 4 milliards $ annuellement Sous repérage par les moyens standard car pas de traitement Tweets + traitement des données textuelles + algorithmes d’apprentissage Réponse par tweet pour repérage du restaurant si nécessaire Towards Using Literature-‐based Discovery to Explain Drug Adverse Effects Dimitar HRISTOVSKIa, , Anita BURGUN-PARENTHOINEb, Paul AVILLACHb and Thomas C RINDFLESCHc a Institute for Biostatistics and Medical Informatics, Medical faculty, University of Ljubljana, Slovenia b University of Rennes, France c National Library of Medicine, NIH, Bethesda, USA e-mail: [email protected] 26 Introduc>on / Mo>va>on • Pharmacovigilance: – detec>on, – understanding and – preven>on of drug adverse effects • Goal of our research: – Find pharmacogenomic explana>on of – Known (drug, adverse effect) pairs 27 Example: Providing Explana>on through an Table 1. Providing an explanation for the reported drug adverse effect (Lithium, Brugada syndrome) through Intermediate Concept the linking concept Sodium Channel. Aligned relations for Sodium Channel: X-Relation-Y Y-Relation-Z Sodium Channel Lithium INHIBITS Sodium Channel Lithium can unmask Brugada syndrome through its ability to block sodium channels , even at subtherapeutic concentrations. (PMID: 20016437) CONCLUSIONS: The widely used drug lithium is a potent blocker of cardiac sodium channels and may unmask patients with the Brugada syndrome. (PMID: 16144991) Because lithium is a potent blocker of cardiac sodium channels , and given the critical importance of sodium channels in pacemaker activity, lithium-induced sodium channel blockade is likely an important mechanism in sinus node dysfunction. (PMID: 17347696) ASSOCIATED_WITH Brugada syndrome SCN5A, the gene encoding the alpha subunit of the sodium channel , is the only gene thus far linked to Brugada syndrome …(PMID: 16415541) Mutations in SCN5A, a cardiac sodium channel gene, have been recently associated with Brugada syndrome . (PMID: 11960580) Sodium Channel CAUSES Brugada syndrome Loss of function mutations in SCN5A, encoding the cardiac sodium channel , are one cause of the Brugada syndrome ... (PMID: 16415376) Changes in the sodium channel are responsible for long QT syndrome, Brugada syndrome and conduction defects. (PMID: 17497250) Sodium Brugada PREDISPOSES Channel syndrome A mutation in the human cardiac sodium channel (E161K) contributes to sick sinus syndrome, conduction disease and Brugada syndrome in two families. (PMID: 15910881) 28 IT suppor>ng large-‐scale studies 2. Data Integra>on 8 SIRIC research programs in France : Curie, LYRIC, BRIO, OncoLille, OncoPACA, Montpellier, IGR, CARPEM Cancer Research for Personalized Medicine (Pr Pierre Laurent-Puig) Multidisciplinary and translational research Oncology departments Research groups : basic research, oncogenomics, social sciences, IT Keyword is integration Existing platforms?? Canuel V, Rance B, Avillach P, Degoulet P, Burgun A. Translational research platforms integrating clinical and omics data: a review of publicly available solutions. Brief. Bioinformatics. 2015;16(2):280‑90 30 OMICS data User database Intranet APHP CARPEM servers HEGP Constructeur de projet tranSMART Fédération d’identités & de-identification Control qualité i2b2 CARPEM data Apps tranSMART Conversion vers standards data-‐ extracteur REDCap eCRF DB pii Pre-‐ tranSMART Données rétrospec>ves Saisie manuelle REDCap eCohorts Fichiers de données cliniques Etudes cliniques industrielles Intranet APHP Internet APHP Headquarter pii: Personally identifiable information p = 0,04 Badoual Cancer Res. 2013 06/05/2013 Study Clinical DWH IT suppor>ng large-‐scale studies 3. Data Sharing Share the algorithms (case identification + data analysis) Base na-onale des causes de déces CepiDC Clinique Maladie du système immunitaire Biologie Sang: leucocytes Coloscopie : polype Anapath Médicaments CANCER Aspirine Caisse Nationale d’Assurance Maladie SNIIR-AM Médicaments Visites PMSI hospitalisa>ons Chirurgie Adeno Chimio Toxicité. . . Tumeur Immunoscore Gene expression T cells NGS 37 IT suppor>ng large-‐scale studies 4. Infrastructures Anita Burgun, MD, PhD Aviesan, ITMO Santé Publique Hôpital Européen Georges Pompidou, AP-HP, Faculté de Médecine Paris 5, INSERM Centre de Recherche des Cordeliers eq22 Worflows Infrastructures ANAEE: Ecosystems BBMRI: Biomolecular Resources EATRIS: Translational Research ECRIN: Clinical Research ELIXIR: Biological Information EMBCR: Marine Biological Resource ERINHA: Highly Pathogenic Agents EU-OPENSCREEN: Chemical Biology EURO BIOIMAGING INFRAFRONTIER: Model Mammalian Gen. INSTRUCT: Structural Biology ISBE: Systems Biology-Europe MIRRI: Microbial An opportunity Standards Public Health Research Infrastructure for Sharing of health and Medical Administrative data Data volume : genomic data (source : E. Barillot ITMO Cancer Working Group ) The first V is for Volume • • • • • • • 1 Bit = Binary Digit 8 Bits = 1 Byte 1024 Bytes = 1 Kilobyte (10^3) 1024 Kilobytes = 1 Megabyte (6) 1024 Megabytes = 1 Gigabyte (9) 1024 Gigabytes = 1 Terabyte (12) 1024 Terabytes = 1 Petabyte (10^15) • • • • • 1024 Petabytes = 1 Exabyte (10^18) 1024 Exabytes = 1 ZeZabyte (10^21) (sex-llion) 1024 ZeZabytes = 1 YoZabyte (24) 1024 Yobabytes = 1 Brontobyte 1024 Brontobytes = 1 Geopbyte 41 Big Data • Volume – « Big data isn’t much more than a sexier version of sta>s>cs »?? • Vélocité – Nécessite de nouveaux programmes informa>que, de nouveaux modèles de bases de données, de nouveaux algorithmes • Variété – Intégra>on de données cliniques, génomiques, comportement, etc sur des plateformes sécurisées – Données « tradi>onnelles » structurées et « big data » non structurées (texte, image, etc) – Traitement de données hétérogènes • Véracité – U>lise/ ré-‐u>lise les données existantes telles qu’elles sont Purpose of DNALand • DNA.Land is a place where you can learn more about your genome while enabling scien>sts like us to make new discoveries for the benefit of humanity. The website is not-‐ for-‐profit and run by the Erlich and Pickrell labs affiliated with Columbia University and the New York Genome Center. The purpose of DNA.Land is to enable you to learn more about your DNA and allow you the autonomy to share your data to facilitate important scien>fic research at the forefront of genome sciences and medicine. Our goal is to help members interpret their data and connect poten>al par>cipants with research studies. 44 Risks (DNA Land) • There are no physical risks in par>cipa>ng in this study but there may be informa>on risks. • We are going to provide you with informa>on about your ancestry, traits, and relatedness with other individuals. You might learn unexpected findings about you or your family. These can include finding a certain ethnic heritage, predisposi>on for a trait, or a non-‐paternity event in your family (all examples are from people that we know and have worked with). For some, such informa>on is empowering; for others, these findings may cause anxiety and discomfort. 45 Already exists Project Biobanking + Robot Robots (biology) Electronic Health records Care Research Biological samples ADN, Serum, urine… New biomarkers Data warehouse IT suppor>ng large-‐scale studies 5. IoT Electronic Medical Record T E X T S T R U C T U R E D CHAD2VASC2 DRUG PRESCRIPTION Your patient, Mrs firstname/lastname …….. Her cardiovascular risk factors include hypertension, dyslipemia, and diabetes ………………….. She presents dyspnea and signs of right heart failure, including edema, and hepatomegaly,. The transthoracic ultrasound exam perfomed by Dr XXX shows aortic valve calcifications……. LVEF 30%.............. I10 hypertension E785 hyperlipidemia E119 diabetes …. Metformin 500 Rosuvastatin Celiprolol INR = no information (date) Device-transmitted data AF duration 0:00:38 AF alert CHAD2VASC2 (calculated score) 7 Prevention of TE (inferred) NONE AF duration 0:00:38 AF alert Rosier A et al Europace 2015, in press AF alert AF alert AF alert Phenotyping strategies Algorithm for the identification of subjects with Diabetes from EHRs 1. ICD code for diabetes in billing reports OR 2. Several instances of high blood glucose OR 3. Elevated HbA1C OR 4. Prescription of anti diabetic drug (insulin or T2D medication) OR 5. Diabetes and/or anti diabetic drug identified from text reports using information extraction techniques Electronic Medical Record CHAD2VASC2 DRUG PRESCRIPTION Your patient, Mrs firstname/lastname …….. Her cardiovascular risk factors include hypertension, dyslipemia, and diabetes ………………….. She presents dyspnea and signs of right heart failure, including edema, and hepatomegaly,. The transthoracic ultrasound exam perfomed by Dr XXX shows aortic valve calcifications……. LVEF 30%.............. I10 hypertension E785 hyperlipidemia E119 diabetes …. Metformin 500 Rosuvastatin Celiprolol INR = no information (date) Rosier A et al Europace 2015, in press 50 FORMAL ONTOLOGY (description logics) Integrate and unify data Model domain knowledge Reasoning Rosier et al, IEEE Biomed Health Inform 2015 Rosier et al, Europace 2015 51 Objets connectés 52 IT suppor>ng large-‐scale studies 6 . New designs for clinical trials New designs in the era of precision medicine 56 57 Redhouanne Abdellali Philippe Beaune Guy Bensoussan Gilles Chatellier Vincent Canuel Hector Contournis Patrice Degoulet Jean-‐Bap>ste Escudié Jean-‐Francois Ethier Aurélie Névéol Nicolas Garcelon Anne Dominique Pham Dimitar Hristovski Bas>en Rance Sandrine Katsahian Grégoire Rey Anne-‐Sophie Jannot Eric Zapletal Marie-‐Anne Loriot Sarah Zohar Pierre Laurent-‐Puig Antoine Neuraz [email protected]
