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Akman T, Arendt LM, Geisler J, Kristensen VN, Frigessi A, Köhn-Luque A. Modeling of Mouse Experiments Suggests that Optimal Anti-Hormonal Treatment for Breast Cancer is Diet-Dependent. Bull Math Biol 2024; 86:42. [PMID: 38498130 DOI: 10.1007/s11538-023-01253-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 12/30/2023] [Indexed: 03/20/2024]
Abstract
Estrogen receptor positive breast cancer is frequently treated with anti-hormonal treatment such as aromatase inhibitors (AI). Interestingly, a high body mass index has been shown to have a negative impact on AI efficacy, most likely due to disturbances in steroid metabolism and adipokine production. Here, we propose a mathematical model based on a system of ordinary differential equations to investigate the effect of high-fat diet on tumor growth. We inform the model with data from mouse experiments, where the animals are fed with high-fat or control (normal) diet. By incorporating AI treatment with drug resistance into the model and by solving optimal control problems we found differential responses for control and high-fat diet. To the best of our knowledge, this is the first attempt to model optimal anti-hormonal treatment for breast cancer in the presence of drug resistance. Our results underline the importance of considering high-fat diet and obesity as factors influencing clinical outcomes during anti-hormonal therapies in breast cancer patients.
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Affiliation(s)
- Tuğba Akman
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0317, Oslo, Norway.
- Department of Computer Engineering, University of Turkish Aeronautical Association, 06790, Etimesgut, Ankara, Turkey.
| | - Lisa M Arendt
- Department of Comparative Biosciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Jürgen Geisler
- Department of Oncology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Campus AHUS, Oslo, Norway
| | - Vessela N Kristensen
- Department of Medical Genetics, Institute of Clinical Medicine, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0317, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Alvaro Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0317, Oslo, Norway.
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway.
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2
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Wu C, Gunnarsson EB, Myklebust EM, Köhn-Luque A, Tadele DS, Enserink JM, Frigessi A, Foo J, Leder K. Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data. PLoS Comput Biol 2024; 20:e1011888. [PMID: 38446830 PMCID: PMC10947663 DOI: 10.1371/journal.pcbi.1011888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 03/18/2024] [Accepted: 02/04/2024] [Indexed: 03/08/2024] Open
Abstract
Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages.
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Affiliation(s)
- Chenyu Wu
- Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Einar Bjarki Gunnarsson
- School of Mathematics, University of Minnesota, Minneapolis, Minnesota, United States of America
- Applied Mathematics Division, Science Institute, University of Iceland, Reykjavík, Iceland
| | - Even Moa Myklebust
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Alvaro Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Dagim Shiferaw Tadele
- Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Jorrit Martijn Enserink
- Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Jasmine Foo
- School of Mathematics, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Kevin Leder
- Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota, United States of America
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3
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Chan LYH, Rø G, Midtbø JE, Di Ruscio F, Watle SSV, Juvet LK, Littmann J, Aavitsland P, Nygård KM, Berg AS, Bukholm G, Kristoffersen AB, Engø-Monsen K, Engebretsen S, Swanson D, Palomares ADL, Lindstrøm JC, Frigessi A, de Blasio BF. Modeling geographic vaccination strategies for COVID-19 in Norway. PLoS Comput Biol 2024; 20:e1011426. [PMID: 38295111 PMCID: PMC10861074 DOI: 10.1371/journal.pcbi.1011426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 02/12/2024] [Accepted: 01/08/2024] [Indexed: 02/02/2024] Open
Abstract
Vaccination was a key intervention in controlling the COVID-19 pandemic globally. In early 2021, Norway faced significant regional variations in COVID-19 incidence and prevalence, with large differences in population density, necessitating efficient vaccine allocation to reduce infections and severe outcomes. This study explored alternative vaccination strategies to minimize health outcomes (infections, hospitalizations, ICU admissions, deaths) by varying regions prioritized, extra doses prioritized, and implementation start time. Using two models (individual-based and meta-population), we simulated COVID-19 transmission during the primary vaccination period in Norway, covering the first 7 months of 2021. We investigated alternative strategies to allocate more vaccine doses to regions with a higher force of infection. We also examined the robustness of our results and highlighted potential structural differences between the two models. Our findings suggest that early vaccine prioritization could reduce COVID-19 related health outcomes by 8% to 20% compared to a baseline strategy without geographic prioritization. For minimizing infections, hospitalizations, or ICU admissions, the best strategy was to initially allocate all available vaccine doses to fewer high-risk municipalities, comprising approximately one-fourth of the population. For minimizing deaths, a moderate level of geographic prioritization, with approximately one-third of the population receiving doubled doses, gave the best outcomes by balancing the trade-off between vaccinating younger people in high-risk areas and older people in low-risk areas. The actual strategy implemented in Norway was a two-step moderate level aimed at maintaining the balance and ensuring ethical considerations and public trust. However, it did not offer significant advantages over the baseline strategy without geographic prioritization. Earlier implementation of geographic prioritization could have more effectively addressed the main wave of infections, substantially reducing the national burden of the pandemic.
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Affiliation(s)
- Louis Yat Hin Chan
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | - Gunnar Rø
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | - Jørgen Eriksson Midtbø
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | - Francesco Di Ruscio
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | | | - Lene Kristine Juvet
- Department of Infection Control and Vaccines, Norwegian Institute of Public Health, Oslo, Norway
| | - Jasper Littmann
- Division of Infection Control, Norwegian Institute of Public Health, Oslo, Norway
- Bergen Centre for Ethics and Priority Setting (BCEPS), University of Bergen, Bergen, Norway
| | - Preben Aavitsland
- Division of Infection Control, Norwegian Institute of Public Health, Oslo, Norway
- Pandemic Centre, University of Bergen, Bergen, Norway
| | - Karin Maria Nygård
- Department of Infectious Diseases and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Are Stuwitz Berg
- Department of Infection Control and Vaccines, Norwegian Institute of Public Health, Oslo, Norway
| | - Geir Bukholm
- Division of Infection Control, Norwegian Institute of Public Health, Oslo, Norway
- Faculty of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway
| | | | | | | | - David Swanson
- Department of Biostatistics, MD Anderson Cancer Center, University of Texas, Houston, Texas, United States of America
| | | | | | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway
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4
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Wu C, Gunnarsson EB, Myklebust EM, Köhn-Luque A, Tadele DS, Enserink JM, Frigessi A, Foo J, Leder K. Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data. ArXiv 2023:arXiv:2303.08245v2. [PMID: 37396610 PMCID: PMC10312799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages.
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Affiliation(s)
- C Wu
- Department of Industrial and Systems Engineering, University of Minnesota, Twin Cities, MN 55455, USA
| | - E B Gunnarsson
- School of Mathematics, University of Minnesota, Twin Cities, MN 55455, USA
| | - E M Myklebust
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
| | - A Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - D S Tadele
- Department of Medical Genetics, Oslo University Hospital, 0424 Oslo, Norway
- Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH 44131, USA
| | - J M Enserink
- Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Norway
- Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - A Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - J Foo
- School of Mathematics, University of Minnesota, Twin Cities, MN 55455, USA
| | - K Leder
- Department of Industrial and Systems Engineering, University of Minnesota, Twin Cities, MN 55455, USA
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5
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Kamineni M, Engø-Monsen K, Midtbø JE, Forland F, de Blasio BF, Frigessi A, Engebretsen S. Effects of non-compulsory and mandatory COVID-19 interventions on travel distance and time away from home, Norway, 2021. Euro Surveill 2023; 28. [PMID: 37103789 DOI: 10.2807/1560-7917.es.2023.28.17.2200382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2023] Open
Abstract
BackgroundGiven the societal, economic and health costs of COVID-19 non-pharmaceutical interventions (NPI), it is important to assess their effects. Human mobility serves as a surrogate measure for human contacts and compliance with NPI. In Nordic countries, NPI have mostly been advised and sometimes made mandatory. It is unclear if making NPI mandatory further reduced mobility.AimWe investigated the effect of non-compulsory and follow-up mandatory measures in major cities and rural regions on human mobility in Norway. We identified NPI categories that most affected mobility.MethodsWe used mobile phone mobility data from the largest Norwegian operator. We analysed non-compulsory and mandatory measures with before-after and synthetic difference-in-differences approaches. By regression, we investigated the impact of different NPI on mobility.ResultsNationally and in less populated regions, time travelled, but not distance, decreased after follow-up mandatory measures. In urban areas, however, distance decreased after follow-up mandates, and the reduction exceeded the decrease after initial non-compulsory measures. Stricter metre rules, gyms reopening, and restaurants and shops reopening were significantly associated with changes in mobility.ConclusionOverall, distance travelled from home decreased after non-compulsory measures, and in urban areas, distance further decreased after follow-up mandates. Time travelled reduced more after mandates than after non-compulsory measures for all regions and interventions. Stricter distancing and reopening of gyms, restaurants and shops were associated with changes in mobility.
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Affiliation(s)
- Meghana Kamineni
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | | | - Jørgen E Midtbø
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | - Frode Forland
- Division of Infection Control, Norwegian Institute of Public Health, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway
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6
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Köhn-Luque A, Myklebust EM, Tadele DS, Giliberto M, Schmiester L, Noory J, Harivel E, Arsenteva P, Mumenthaler SM, Schjesvold F, Taskén K, Enserink JM, Leder K, Frigessi A, Foo J. Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data. Cell Rep Methods 2023; 3:100417. [PMID: 37056380 PMCID: PMC10088094 DOI: 10.1016/j.crmeth.2023.100417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 12/10/2022] [Accepted: 02/08/2023] [Indexed: 03/08/2023]
Abstract
Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug-screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses and estimates their drug sensitivities and frequencies within the bulk population. We apply PhenoPop to synthetically generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response.
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Affiliation(s)
- Alvaro Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
| | - Even Moa Myklebust
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
| | - Dagim Shiferaw Tadele
- Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, 0379 Oslo, Norway
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway
- Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH 44131, USA
| | - Mariaserena Giliberto
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, 0310 Oslo, Norway
- KG Jebsen Center for B-Cell Malignancies, Institute for Clinical Medicine, University of Oslo, 0450 Oslo, Norway
| | - Leonard Schmiester
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
| | - Jasmine Noory
- Institute for Mathematics and its Applications, School of Mathematics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Elise Harivel
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
- ENSTA, Institut Polytechnique de Paris, Palaiseau, 91120 Paris, France
| | - Polina Arsenteva
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
- Institut de Matématiques de Bourgogne, Universite de Bourgogne, Dijon Cedex, 21078 Dijon, France
| | - Shannon M. Mumenthaler
- Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA 90064, USA
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Department of Oncology, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Fredrik Schjesvold
- KG Jebsen Center for B-Cell Malignancies, Institute for Clinical Medicine, University of Oslo, 0450 Oslo, Norway
- Oslo Myeloma Center, Department of Hematology, Oslo University Hospital, 0450 Oslo, Norway
| | - Kjetil Taskén
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, 0310 Oslo, Norway
- KG Jebsen Center for B-Cell Malignancies, Institute for Clinical Medicine, University of Oslo, 0450 Oslo, Norway
| | - Jorrit M. Enserink
- Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, 0379 Oslo, Norway
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway
- Section for Biochemistry and Molecular Biology, Faculty of Mathematics and Natural Sciences, University of Oslo, 0037 Oslo, Norway
| | - Kevin Leder
- College of Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, 0372 Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, 0372 Oslo, Norway
| | - Jasmine Foo
- Institute for Mathematics and its Applications, School of Mathematics, University of Minnesota, Minneapolis, MN 55455, USA
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7
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Cavalieri S, Vener C, LeBlanc M, Lopez-Perez L, Fico G, Resteghini C, Monzani D, Marton G, Pravettoni G, Moreira-Soares M, Filippidou DE, Almeida A, Bilbao A, Mehanna H, Singer S, Thomas S, Lacerenza L, Manfuso A, Copelli C, Mercalli F, Frigessi A, Martinelli E, Licitra L. A multicenter randomized trial for quality of life evaluation by non-invasive intelligent tools during post-curative treatment follow-up for head and neck cancer: Clinical study protocol. Front Oncol 2023; 13:1048593. [PMID: 36798825 PMCID: PMC9927199 DOI: 10.3389/fonc.2023.1048593] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 01/10/2023] [Indexed: 02/02/2023] Open
Abstract
Patients surviving head and neck cancer (HNC) suffer from high physical, psychological, and socioeconomic burdens. Achieving cancer-free survival with an optimal quality of life (QoL) is the primary goal for HNC patient management. So, maintaining lifelong surveillance is critical. An ambitious goal would be to carry this out through the advanced analysis of environmental, emotional, and behavioral data unobtrusively collected from mobile devices. The aim of this clinical trial is to reduce, with non-invasive tools (i.e., patients' mobile devices), the proportion of HNC survivors (i.e., having completed their curative treatment from 3 months to 10 years) experiencing a clinically relevant reduction in QoL during follow-up. The Big Data for Quality of Life (BD4QoL) study is an international, multicenter, randomized (2:1), open-label trial. The primary endpoint is a clinically relevant global health-related EORTC QLQ-C30 QoL deterioration (decrease ≥10 points) at any point during 24 months post-treatment follow-up. The target sample size is 420 patients. Patients will be randomized to be followed up using the BD4QoL platform or per standard clinical practice. The BD4QoL platform includes a set of services to allow patients monitoring and empowerment through two main tools: a mobile application installed on participants' smartphones, that includes a chatbot for e-coaching, and the Point of Care dashboard, to let the investigators manage patients data. In both arms, participants will be asked to complete QoL questionnaires at study entry and once every 6 months, and will undergo post-treatment follow up as per clinical practice. Patients randomized to the intervention arm (n=280) will receive access to the BD4QoL platform, those in the control arm (n=140) will not. Eligibility criteria include completing curative treatments for non-metastatic HNC and the use of an Android-based smartphone. Patients undergoing active treatments or with synchronous cancers are excluded. Clinical Trial Registration: ClinicalTrials.gov, identifier (NCT05315570).
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Affiliation(s)
- Stefano Cavalieri
- Head and Neck Medical Oncology Department, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Nazionale dei Tumori, Milan, Italy,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy,*Correspondence: Stefano Cavalieri,
| | - Claudia Vener
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Marissa LeBlanc
- Oslo Center for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway,Oslo Center for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Laura Lopez-Perez
- Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, Madrid, Spain
| | - Giuseppe Fico
- Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, Madrid, Spain
| | - Carlo Resteghini
- Head and Neck Medical Oncology Department, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Nazionale dei Tumori, Milan, Italy
| | - Dario Monzani
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy,Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy,Department of Psychology, Educational Science and Human Movement (SPPEFF), University of Palermo, Palermo, Italy
| | - Giulia Marton
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy,Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Gabriella Pravettoni
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy,Applied Research Division for Cognitive and Psychological Science, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | | | | | - Aitor Almeida
- Information Technology Programme Management Office, DOTSOFT, Thessaloniki, Greece
| | - Aritz Bilbao
- DeustoTech, Faculty of Engineering, Universidad de Deusto, Bilbao, Spain
| | - Hisham Mehanna
- Institute of head and neck studies and Education, University of Birmingham, Birmingham, United Kingdom
| | - Susanne Singer
- Division of Epidemiology and Health Care Research, JGU - Johannes Gutenberg University, Mainz, Germany
| | - Steve Thomas
- Division of Oral and Maxillofacial Surgery - Bristol Dental Hospital, University of Bristol - Bristol Medical School, Bristol, United Kingdom
| | - Luca Lacerenza
- Maxillo-Facial Surgery, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Alfonso Manfuso
- Maxillo-Facial Surgery, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Chiara Copelli
- Maxillo-Facial Surgery, Interdisciplinary Department of Medicine, University of Bari “Aldo Moro”, Bari, Italy
| | | | - Arnoldo Frigessi
- Oslo Center for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway,Oslo Center for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Elena Martinelli
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Lisa Licitra
- Head and Neck Medical Oncology Department, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Nazionale dei Tumori, Milan, Italy,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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8
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Engebretsen S, Diz-Lois Palomares A, Rø G, Kristoffersen AB, Lindstrøm JC, Engø-Monsen K, Kamineni M, Hin Chan LY, Dale Ø, Midtbø JE, Stenerud KL, Di Ruscio F, White R, Frigessi A, de Blasio BF. A real-time regional model for COVID-19: Probabilistic situational awareness and forecasting. PLoS Comput Biol 2023; 19:e1010860. [PMID: 36689468 PMCID: PMC9894546 DOI: 10.1371/journal.pcbi.1010860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 02/02/2023] [Accepted: 01/08/2023] [Indexed: 01/24/2023] Open
Abstract
The COVID-19 pandemic is challenging nations with devastating health and economic consequences. The spread of the disease has revealed major geographical heterogeneity because of regionally varying individual behaviour and mobility patterns, unequal meteorological conditions, diverse viral variants, and locally implemented non-pharmaceutical interventions and vaccination roll-out. To support national and regional authorities in surveilling and controlling the pandemic in real-time as it unfolds, we here develop a new regional mathematical and statistical model. The model, which has been in use in Norway during the first two years of the pandemic, is informed by real-time mobility estimates from mobile phone data and laboratory-confirmed case and hospitalisation incidence. To estimate regional and time-varying transmissibility, case detection probabilities, and missed imported cases, we developed a novel sequential Approximate Bayesian Computation method allowing inference in useful time, despite the high parametric dimension. We test our approach on Norway and find that three-week-ahead predictions are precise and well-calibrated, enabling policy-relevant situational awareness at a local scale. By comparing the reproduction numbers before and after lockdowns, we identify spatially heterogeneous patterns in their effect on the transmissibility, with a stronger effect in the most populated regions compared to the national reduction estimated to be 85% (95% CI 78%-89%). Our approach is the first regional changepoint stochastic metapopulation model capable of real time spatially refined surveillance and forecasting during emergencies.
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Affiliation(s)
| | | | - Gunnar Rø
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
| | | | | | | | - Meghana Kamineni
- Oslo Centre for Biostatistics and Epidemiology. University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Louis Yat Hin Chan
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
| | | | - Jørgen Eriksson Midtbø
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
- Telenor Norge AS Fornebu, Norway
| | | | - Francesco Di Ruscio
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
| | - Richard White
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology. University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology. University of Oslo and Oslo University Hospital, Oslo, Norway
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9
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Vitelli V, Fleischer T, Ankill J, Arjas E, Frigessi A, Kristensen VN, Zucknick M. Transcriptomic pan-cancer analysis using rank-based Bayesian inference. Mol Oncol 2022; 17:548-563. [PMID: 36562628 PMCID: PMC10061294 DOI: 10.1002/1878-0261.13354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 09/30/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022] Open
Abstract
The analysis of whole genomes of pan-cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation. Specifically, we tackle this unsupervised problem via a novel rank-based Bayesian clustering method. The advantages of our method are the integration and quantification of all uncertainties related to both the input data and the model, the probabilistic interpretation of final results to allow straightforward assessment of the stability of clusters leading to reliable conclusions, and the transparent biological interpretation of the identified clusters since each cluster is characterized by its top-ranked genomic features. We applied our method to RNA-seq data from cancer samples from 12 tumor types from the Cancer Genome Atlas. We identified a robust clustering that mostly reflects tissue of origin but also includes pan-cancer clusters. Importantly, we identified three pan-squamous clusters composed of a mix of lung squamous cell carcinoma, head and neck squamous carcinoma, and bladder cancer, with different biological functions over-represented in the top genes that characterize the three clusters. We also found two novel subtypes of kidney cancer that show different prognosis, and we reproduced known subtypes of breast cancer. Taken together, our method allows the identification of robust and biologically meaningful clusters of pan-cancer samples.
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Affiliation(s)
- Valeria Vitelli
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Norway
| | - Thomas Fleischer
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Norway
| | - Jørgen Ankill
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Norway
| | - Elja Arjas
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Norway.,Department of Mathematics and Statistics, University of Helsinki, Finland
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Norway
| | - Vessela N Kristensen
- Department of Medical Genetics, Clinic for Laboratory Medicine, Oslo University Hospital, Norway.,Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Norway
| | - Manuela Zucknick
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Norway
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10
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Ravindran V, Wagoner J, Athanasiadis P, Den Hartigh AB, Sidorova JM, Ianevski A, Fink SL, Frigessi A, White J, Polyak SJ, Aittokallio T. Discovery of host-directed modulators of virus infection by probing the SARS-CoV-2-host protein-protein interaction network. Brief Bioinform 2022; 23:6775548. [PMID: 36305426 PMCID: PMC9677461 DOI: 10.1093/bib/bbac456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 09/05/2022] [Accepted: 09/23/2022] [Indexed: 12/14/2022] Open
Abstract
The ongoing coronavirus disease 2019 (COVID-19) pandemic has highlighted the need to better understand virus-host interactions. We developed a network-based method that expands the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-host protein interaction network and identifies host targets that modulate viral infection. To disrupt the SARS-CoV-2 interactome, we systematically probed for potent compounds that selectively target the identified host proteins with high expression in cells relevant to COVID-19. We experimentally tested seven chemical inhibitors of the identified host proteins for modulation of SARS-CoV-2 infection in human cells that express ACE2 and TMPRSS2. Inhibition of the epigenetic regulators bromodomain-containing protein 4 (BRD4) and histone deacetylase 2 (HDAC2), along with ubiquitin-specific peptidase (USP10), enhanced SARS-CoV-2 infection. Such proviral effect was observed upon treatment with compounds JQ1, vorinostat, romidepsin and spautin-1, when measured by cytopathic effect and validated by viral RNA assays, suggesting that the host proteins HDAC2, BRD4 and USP10 have antiviral functions. We observed marked differences in antiviral effects across cell lines, which may have consequences for identification of selective modulators of viral infection or potential antiviral therapeutics. While network-based approaches enable systematic identification of host targets and selective compounds that may modulate the SARS-CoV-2 interactome, further developments are warranted to increase their accuracy and cell-context specificity.
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Affiliation(s)
- Vandana Ravindran
- Oslo Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, Norway
- Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, Oslo, Norway
| | - Jessica Wagoner
- Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
| | - Paschalis Athanasiadis
- Oslo Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, Norway
- Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, Oslo, Norway
| | - Andreas B Den Hartigh
- Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
| | - Julia M Sidorova
- Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
| | - Aleksandr Ianevski
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Susan L Fink
- Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Judith White
- Department of Cell Biology and Department of Microbiology, University of Virginia, Charlottesville, VA, USA
| | - Stephen J Polyak
- Corresponding authors. Stephen J. Polyak, Department of Laboratory Medicine & Pathology, University of Washington, 325 9th Ave Box 359690 Seattle, WA, 98104, USA. Tel: +1 206-897-5224; E-mail: ; Tero Aittokallio, Institute for Molecular Medicine Finland (FIMM), HiLIFE, Tukholmankatu 8, Biomedicum Helsinki 2U, FI-0014 University of Helsinki, Finland. Tel: +358 50 3182426; E-mail:
| | - Tero Aittokallio
- Corresponding authors. Stephen J. Polyak, Department of Laboratory Medicine & Pathology, University of Washington, 325 9th Ave Box 359690 Seattle, WA, 98104, USA. Tel: +1 206-897-5224; E-mail: ; Tero Aittokallio, Institute for Molecular Medicine Finland (FIMM), HiLIFE, Tukholmankatu 8, Biomedicum Helsinki 2U, FI-0014 University of Helsinki, Finland. Tel: +358 50 3182426; E-mail:
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11
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Pesonen H, Simola U, Köhn‐Luque A, Vuollekoski H, Lai X, Frigessi A, Kaski S, Frazier DT, Maneesoonthorn W, Martin GM, Corander J. ABC of the future. Int Stat Rev 2022. [DOI: 10.1111/insr.12522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Henri Pesonen
- Oslo Centre for Biostatistics and Epidemiology University of Oslo Oslo Norway
| | - Umberto Simola
- Helsinki Institute of Information Technology, Department of Mathematics and Statistics University of Helsinki Helsinki Finland
| | - Alvaro Köhn‐Luque
- Oslo Centre for Biostatistics and Epidemiology University of Oslo Oslo Norway
| | - Henri Vuollekoski
- Helsinki Institute of Information Technology, Department of Computer Science Aalto University Helsinki Finland
| | - Xiaoran Lai
- Oslo Centre for Biostatistics and Epidemiology University of Oslo Oslo Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology University of Oslo Oslo Norway
- Oslo Centre for Biostatistics and Epidemiology Oslo University Hospital Oslo Norway
| | - Samuel Kaski
- Helsinki Institute of Information Technology, Department of Computer Science Aalto University Helsinki Finland
- Department of Computer Science University of Manchester Manchester UK
| | - David T. Frazier
- Department of Econometrics & Business Statistics Monash University Clayton Victoria Australia
| | | | - Gael M. Martin
- Department of Econometrics & Business Statistics Monash University Clayton Victoria Australia
| | - Jukka Corander
- Oslo Centre for Biostatistics and Epidemiology University of Oslo Oslo Norway
- Helsinki Institute of Information Technology, Department of Mathematics and Statistics University of Helsinki Helsinki Finland
- Parasites and Microbes Wellcome Sanger Institute Hinxton UK
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12
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Jalali N, Brustad HK, Frigessi A, MacDonald EA, Meijerink H, Feruglio SL, Nygård KM, Rø G, Madslien EH, de Blasio BF. Increased household transmission and immune escape of the SARS-CoV-2 Omicron compared to Delta variants. Nat Commun 2022; 13:5706. [PMID: 36175424 DOI: 10.1101/2022.02.07.22270437] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 09/08/2022] [Indexed: 05/22/2023] Open
Abstract
Understanding the epidemic growth of the novel SARS-CoV-2 Omicron variant is critical for public health. We compared the ten-day secondary attack rate (SAR) of the Omicron and Delta variants in households using Norwegian contact tracing data, December 2021 - January 2022. Omicron SAR was higher than Delta, with a relative risk (RR) of 1.41 (95% CI 1.27-1.56). We observed increased susceptibility to Omicron infection in household contacts compared to Delta, independent of contacts' vaccination status. Among three-dose vaccinated contacts, the mean SAR was lower for both variants. We found increased Omicron transmissibility from primary cases to contacts in all vaccination groups, except 1-dose vaccinated, compared to Delta. Omicron SAR of three-dose vaccinated primary cases was high, 46% vs 11 % for Delta. In conclusion, three-dose vaccinated primary cases with Omicron infection can efficiently spread in households, while three-dose vaccinated contacts have a lower risk of being infected by Delta and Omicron.
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Affiliation(s)
- Neda Jalali
- Norwegian Institute of Public Health, Oslo, Norway
| | - Hilde K Brustad
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | | | | | | | | | - Gunnar Rø
- Norwegian Institute of Public Health, Oslo, Norway
| | | | - Birgitte Freiesleben de Blasio
- Norwegian Institute of Public Health, Oslo, Norway.
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.
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13
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Eide S, Leslie DS, Frigessi A. Dynamic slate recommendation with gated recurrent units and Thompson sampling. Data Min Knowl Discov 2022. [DOI: 10.1007/s10618-022-00849-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
AbstractWe consider the problem of recommending relevant content to users of an internet platform in the form of lists of items, called slates. We introduce a variational Bayesian Recurrent Neural Net recommender system that acts on time series of interactions between the internet platform and the user, and which scales to real world industrial situations. The recommender system is tested both online on real users, and on an offline dataset collected from a Norwegian web-based marketplace, FINN.no, that is made public for research. This is one of the first publicly available datasets which includes all the slates that are presented to users as well as which items (if any) in the slates were clicked on. Such a data set allows us to move beyond the common assumption that implicitly assumes that users are considering all possible items at each interaction. Instead we build our likelihood using the items that are actually in the slate, and evaluate the strengths and weaknesses of both approaches theoretically and in experiments. We also introduce a hierarchical prior for the item parameters based on group memberships. Both item parameters and user preferences are learned probabilistically. Furthermore, we combine our model with bandit strategies to ensure learning, and introduce ‘in-slate Thompson sampling’ which makes use of the slates to maximise explorative opportunities. We show experimentally that explorative recommender strategies perform on par or above their greedy counterparts. Even without making use of exploration to learn more effectively, click rates increase simply because of improved diversity in the recommended slates.
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14
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Page CM, Nøst TH, Djordjilović V, Thoresen M, Frigessi A, Sandanger TM, Veierød MB. Pre-diagnostic DNA methylation in blood leucocytes in cutaneous melanoma; a nested case–control study within the Norwegian Women and Cancer cohort. Sci Rep 2022; 12:14200. [PMID: 35987900 PMCID: PMC9392730 DOI: 10.1038/s41598-022-18585-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 08/16/2022] [Indexed: 12/03/2022] Open
Abstract
The prognosis of cutaneous melanoma depends on early detection, and good biomarkers for melanoma risk may provide a valuable tool to detect melanoma development at a pre-clinical stage. By studying the epigenetic profile in pre-diagnostic blood samples of melanoma cases and cancer free controls, we aimed to identify DNA methylation sites conferring melanoma risk. DNA methylation was measured at 775,528 CpG sites using the Illumina EPIC array in whole blood in incident melanoma cases (n = 183) and matched cancer-free controls (n = 183) in the Norwegian Women and Cancer cohort. Phenotypic information and ultraviolet radiation exposure were obtained from questionnaires. Epigenome wide association (EWAS) was analyzed in future melanoma cases and controls with conditional logistic regression, with correction for multiple testing using the false discovery rate (FDR). We extended the analysis by including a public data set on melanoma (GSE120878), and combining these different data sets using a version of covariate modulated FDR (AdaPT). The analysis on future melanoma cases and controls did not identify any genome wide significant CpG sites (0.85 ≤ padj ≤ 0.99). In the restricted AdaPT analysis, 7 CpG sites were suggestive at the FDR level of 0.15. These CpG sites may potentially be used as pre-diagnostic biomarkers of melanoma risk.
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15
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Azulay N, Olsen RB, Nielsen CS, Stubhaug A, Jenssen TG, Schirmer H, Frigessi A, Rosseland LA, Tronstad C. Reduced heart rate variability is related to the number of metabolic syndrome components and manifest diabetes in the sixth Tromsø study 2007-2008. Sci Rep 2022; 12:11998. [PMID: 35835836 PMCID: PMC9283528 DOI: 10.1038/s41598-022-15824-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 06/29/2022] [Indexed: 11/15/2022] Open
Abstract
Both diabetes mellitus (DM) and the metabolic syndrome (MetS) are associated with autonomic neuropathy, which predisposes to cardiac events and death. Measures of heart rate variability (HRV) can be used to monitor the activity of the autonomic nervous system (ANS), and there are strong indications that HRV can be used to study the progression of ANS-related diabetes complications. This study aims to investigate differences in HRV in healthy, MetS and diabetic populations. Based on 7880 participants from the sixth health survey in Tromsø (Tromsø 6, 2007–2008), we found a significant negative association between the number of MetS components and HRV as estimated from short-term pulse wave signals (PRV). This decrease in PRV did not appear to be linear, instead it leveled off after the third component, with no significant difference in PRV between the MetS and DM populations. There was a significant negative association between HbA1c and PRV, showing a decrease in PRV occurring already within the normal HbA1c range. The MetS and DM populations are different from healthy controls with respect to PRV, indicating impaired ANS in both conditions. In the future, a study with assessment of PRV measurements in relation to prospective cardiovascular events seems justified.
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Affiliation(s)
- Naomi Azulay
- Department of Research and Development, Division of Emergencies and Critical Care, Oslo University Hospital, Oslo, Norway. .,Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Roy Bjørkholt Olsen
- Department of Anesthesiology and Intensive Care, Sørlandet Hospital, Arendal, Norway
| | - Christopher Sivert Nielsen
- Department of Chronic Diseases, Norwegian Institute of Public Health, Oslo, Norway.,Department of Pain Management and Research, Oslo University Hospital, Oslo, Norway
| | - Audun Stubhaug
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Pain Management and Research, Oslo University Hospital, Oslo, Norway
| | - Trond Geir Jenssen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Transplantation Medicine, Section of Nephrology, Oslo University Hospital, Rikshospitalet, Oslo, Norway.,Metabolic and Renal Research Group, Faculty of Health Sciences, UiT- The Arctic University of Norway, Tromsø, Norway
| | - Henrik Schirmer
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Cardiology, Akershus University Hospital, Lørenskog, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Leiv Arne Rosseland
- Department of Research and Development, Division of Emergencies and Critical Care, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Christian Tronstad
- Department of Clinical and Biomedical Engineering, Oslo University Hospital, Oslo, Norway
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16
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Lai X, Taskén HA, Mo T, Funke SW, Frigessi A, Rognes ME, Köhn-Luque A. A scalable solver for a stochastic, hybrid cellular automaton model of personalized breast cancer therapy. Int J Numer Method Biomed Eng 2022; 38:e3542. [PMID: 34716985 DOI: 10.1002/cnm.3542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 10/24/2021] [Indexed: 06/13/2023]
Abstract
Mathematical modeling and simulation is a promising approach to personalized cancer medicine. Yet, the complexity, heterogeneity and multi-scale nature of cancer pose significant computational challenges. Coupling discrete cell-based models with continuous models using hybrid cellular automata (CA) is a powerful approach for mimicking biological complexity and describing the dynamical exchange of information across different scales. However, when clinically relevant cancer portions are taken into account, such models become computationally very expensive. While efficient parallelization techniques for continuous models exist, their coupling with discrete models, particularly CA, necessitates more elaborate solutions. Building upon FEniCS, a popular and powerful scientific computing platform for solving partial differential equations, we developed parallel algorithms to link stochastic CA with differential equations (https://bitbucket.org/HTasken/cansim). The algorithms minimize the communication between processes that share CA neighborhood values while also allowing for reproducibility during stochastic updates. We demonstrated the potential of our solution on a complex hybrid cellular automaton model of breast cancer treated with combination chemotherapy. On a single-core processor, we obtained nearly linear scaling with an increasing problem size, whereas weak parallel scaling showed moderate growth in solving time relative to increase in problem size. Finally, we applied the algorithm to a problem that is 500 times larger than previous work, allowing us to run personalized therapy simulations based on heterogeneous cell density and tumor perfusion conditions estimated from magnetic resonance imaging data on an unprecedented scale.
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Affiliation(s)
- Xiaoran Lai
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Håkon A Taskén
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Torgeir Mo
- Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | | | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | | | - Alvaro Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
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17
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Lindstrøm JC, Engebretsen S, Kristoffersen AB, Rø GØI, Palomares ADL, Engø-Monsen K, Madslien EH, Forland F, Nygård KM, Hagen F, Gantzel G, Wiklund O, Frigessi A, de Blasio BF. Increased transmissibility of the alpha SARS-CoV-2 variant: evidence from contact tracing data in Oslo, January to February 2021. Infect Dis (Lond) 2021; 54:72-77. [PMID: 34618665 DOI: 10.1080/23744235.2021.1977382] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
BACKGROUND Information about the contagiousness of new SARS-CoV-2 variants, including the alpha lineage, and how they spread in various locations is essential. Country-specific estimates are needed because local interventions influence transmissibility. METHODS We analysed contact tracing data from Oslo municipality, reported from January through February 2021, when the alpha lineage became predominant in Norway and estimated the relative transmissibility of the alpha lineage with the use of Poisson regression. RESULTS Within households, we found an increase in the secondary attack rate by 60% (95% CI 20-114%) among cases infected with the alpha lineage compared to other variants; including all close contacts, the relative increase in the secondary attack rate was 24% (95% CI -6%-43%). There was a significantly higher risk of infecting household members in index cases aged 40-59 years who were infected with the alpha lineage; we found no association between transmission and household size. Overall, including all close contacts, we found that the reproduction number among cases with the alpha lineage was increased by 24% (95% CI 0%-52%), corresponding to an absolute increase of 0.19, compared to the group of index cases infected with other variants. CONCLUSION Our study suggests that households are the primary locations for rapid transmission of the new lineage alpha.
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Affiliation(s)
| | | | - Anja Bråthen Kristoffersen
- Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Gunnar Øyvind Isaksson Rø
- Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Alfonso Diz-Lois Palomares
- Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | | | - Elisabeth Henie Madslien
- Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Frode Forland
- Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Karin Maria Nygård
- Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Frode Hagen
- Oslo Municipality Health Service, Oslo, Norway
| | | | | | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
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18
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Swanson DM, Lien T, Bergholtz H, Sørlie T, Frigessi A. A Bayesian two-way latent structure model for genomic data integration reveals few pan-genomic cluster subtypes in a breast cancer cohort. Bioinformatics 2020; 35:4886-4897. [PMID: 31077301 DOI: 10.1093/bioinformatics/btz381] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 04/05/2019] [Accepted: 05/01/2019] [Indexed: 01/09/2023] Open
Abstract
MOTIVATION Unsupervised clustering is important in disease subtyping, among having other genomic applications. As genomic data has become more multifaceted, how to cluster across data sources for more precise subtyping is an ever more important area of research. Many of the methods proposed so far, including iCluster and Cluster of Cluster Assignments (COCAs), make an unreasonable assumption of a common clustering across all data sources, and those that do not are fewer and tend to be computationally intensive. RESULTS We propose a Bayesian parametric model for integrative, unsupervised clustering across data sources. In our two-way latent structure model, samples are clustered in relation to each specific data source, distinguishing it from methods like COCAs and iCluster, but cluster labels have across-dataset meaning, allowing cluster information to be shared between data sources. A common scaling across data sources is not required, and inference is obtained by a Gibbs Sampler, which we improve with a warm start strategy and modified density functions to robustify and speed convergence. Posterior interpretation allows for inference on common clusterings occurring among subsets of data sources. An interesting statistical formulation of the model results in sampling from closed-form posteriors despite incorporation of a complex latent structure. We fit the model with Gaussian and more general densities, which influences the degree of across-dataset cluster label sharing. Uniquely among integrative clustering models, our formulation makes no nestedness assumptions of samples across data sources so that a sample missing data from one genomic source can be clustered according to its existing data sources. We apply our model to a Norwegian breast cancer cohort of ductal carcinoma in situ and invasive tumors, comprised of somatic copy-number alteration, methylation and expression datasets. We find enrichment in the Her2 subtype and ductal carcinoma among those observations exhibiting greater cluster correspondence across expression and CNA data. In general, there are few pan-genomic clusterings, suggesting that models assuming a common clustering across genomic data sources might yield misleading results. AVAILABILITY AND IMPLEMENTATION The model is implemented in an R package called twl ('two-way latent'), available on CRAN. Data for analysis are available within the R package. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David M Swanson
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Tonje Lien
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Helga Bergholtz
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Therese Sørlie
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
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19
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Bergholtz H, Lien TG, Swanson DM, Frigessi A, Daidone MG, Tost J, Wärnberg F, Sørlie T. Contrasting DCIS and invasive breast cancer by subtype suggests basal-like DCIS as distinct lesions. NPJ Breast Cancer 2020; 6:26. [PMID: 32577501 PMCID: PMC7299965 DOI: 10.1038/s41523-020-0167-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 05/20/2020] [Indexed: 12/19/2022] Open
Abstract
Ductal carcinoma in situ (DCIS) is a non-invasive type of breast cancer with highly variable potential of becoming invasive and affecting mortality. Currently, many patients with DCIS are overtreated due to the lack of specific biomarkers that distinguish low risk lesions from those with a higher risk of progression. In this study, we analyzed 57 pure DCIS and 313 invasive breast cancers (IBC) from different patients. Three levels of genomic data were obtained; gene expression, DNA methylation, and DNA copy number. We performed subtype stratified analyses and identified key differences between DCIS and IBC that suggest subtype specific progression. Prominent differences were found in tumors of the basal-like subtype: Basal-like DCIS were less proliferative and showed a higher degree of differentiation than basal-like IBC. Also, core basal tumors (characterized by high correlation to the basal-like centroid) were not identified amongst DCIS as opposed to IBC. At the copy number level, basal-like DCIS exhibited fewer copy number aberrations compared with basal-like IBC. An intriguing finding through analysis of the methylome was hypermethylation of multiple protocadherin genes in basal-like IBC compared with basal-like DCIS and normal tissue, possibly caused by long range epigenetic silencing. This points to silencing of cell adhesion-related genes specifically in IBC of the basal-like subtype. Our work confirms that subtype stratification is essential when studying progression from DCIS to IBC, and we provide evidence that basal-like DCIS show less aggressive characteristics and question the assumption that basal-like DCIS is a direct precursor of basal-like invasive breast cancer.
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Affiliation(s)
- Helga Bergholtz
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Tonje G Lien
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - David M Swanson
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway.,Department of Biostatistics, University of Oslo, Oslo, Norway
| | | | - Maria Grazia Daidone
- Department of Applied Research and Technical development, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Jörg Tost
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA-Institut de Biologie Francois Jacob, Evry, France
| | - Fredrik Wärnberg
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.,Department of Surgery, Uppsala Academic Hospital, Uppsala, Sweden
| | - Therese Sørlie
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
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20
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Engebretsen S, Engø-Monsen K, Aleem MA, Gurley ES, Frigessi A, de Blasio BF. Time-aggregated mobile phone mobility data are sufficient for modelling influenza spread: the case of Bangladesh. J R Soc Interface 2020; 17:20190809. [PMID: 32546112 PMCID: PMC7328378 DOI: 10.1098/rsif.2019.0809] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Human mobility plays a major role in the spatial dissemination of infectious diseases. We develop a spatio-temporal stochastic model for influenza-like disease spread based on estimates of human mobility. The model is informed by mobile phone mobility data collected in Bangladesh. We compare predictions of models informed by daily mobility data (reference) with that of models informed by time-averaged mobility data, and mobility model approximations. We find that the gravity model overestimates the spatial synchrony, while the radiation model underestimates the spatial synchrony. Using time-averaged mobility resulted in spatial spreading patterns comparable to the daily mobility model. We fit the model to 2014–2017 influenza data from sentinel hospitals in Bangladesh, using a sequential version of approximate Bayesian computation. We find a good agreement between our estimated model and the case data. We estimate transmissibility and regional spread of influenza in Bangladesh, which are useful for policy planning. Time-averaged mobility appears to be a good proxy for human mobility when modelling infectious diseases. This motivates a more general use of the time-averaged mobility, with important implications for future studies and outbreak control. Moreover, time-averaged mobility is subject to less privacy concerns than daily mobility, containing less temporal information on individual movements.
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Affiliation(s)
- Solveig Engebretsen
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.,Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway.,Norwegian Computing Center, Oslo, Norway
| | | | - Mohammad Abdul Aleem
- International Centre for Diarrhoeal Disease Research, Bangladesh, ICDDR,B, Dhaka, Bangladesh
| | - Emily Suzanne Gurley
- International Centre for Diarrhoeal Disease Research, Bangladesh, ICDDR,B, Dhaka, Bangladesh.,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.,Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
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21
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Pladsen AV, Nilsen G, Rueda OM, Aure MR, Borgan Ø, Liestøl K, Vitelli V, Frigessi A, Langerød A, Mathelier A, Engebråten O, Kristensen V, Wedge DC, Van Loo P, Caldas C, Børresen-Dale AL, Russnes HG, Lingjærde OC. DNA copy number motifs are strong and independent predictors of survival in breast cancer. Commun Biol 2020; 3:153. [PMID: 32242091 PMCID: PMC7118095 DOI: 10.1038/s42003-020-0884-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 03/05/2020] [Indexed: 11/15/2022] Open
Abstract
Somatic copy number alterations are a frequent sign of genome instability in cancer. A precise characterization of the genome architecture would reveal underlying instability mechanisms and provide an instrument for outcome prediction and treatment guidance. Here we show that the local spatial behavior of copy number profiles conveys important information about this architecture. Six filters were defined to characterize regional traits in copy number profiles, and the resulting Copy Aberration Regional Mapping Analysis (CARMA) algorithm was applied to tumors in four breast cancer cohorts (n = 2919). The derived motifs represent a layer of information that complements established molecular classifications of breast cancer. A score reflecting presence or absence of motifs provided a highly significant independent prognostic predictor. Results were consistent between cohorts. The nonsite-specific occurrence of the detected patterns suggests that CARMA captures underlying replication and repair defects and could have a future potential in treatment stratification.
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Affiliation(s)
- Arne V Pladsen
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Ullernchausseen 70 N-0310, Oslo, Norway
| | - Gro Nilsen
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Gaustadalléen 23 B N-0373, Oslo, Norway
| | - Oscar M Rueda
- Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Miriam R Aure
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Ullernchausseen 70 N-0310, Oslo, Norway
| | - Ørnulf Borgan
- Department of Mathematics, University of Oslo, Moltke Moes vei 35 N-0851, Oslo, Norway
| | - Knut Liestøl
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Gaustadalléen 23 B N-0373, Oslo, Norway
| | - Valeria Vitelli
- Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Domus Medica, Sognsvannsveien 9 N-0372, Oslo, Norway
| | - Arnoldo Frigessi
- Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Domus Medica, Sognsvannsveien 9 N-0372, Oslo, Norway
| | - Anita Langerød
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Ullernchausseen 70 N-0310, Oslo, Norway
| | - Anthony Mathelier
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Ullernchausseen 70 N-0310, Oslo, Norway
- Centre for Molecular Medicine Norway, University of Oslo, Forskningsparken, Gaustadalléen 21 N-0349, Oslo, Norway
| | - Olav Engebråten
- Institute for Clinical Medicine, University of Oslo, Kirkeveien 166 N-0450, Oslo, Norway
- Department of Oncology, Oslo University Hospital, POB 4953 Nydalen, N-0424, Oslo, Norway
| | - Vessela Kristensen
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Ullernchausseen 70 N-0310, Oslo, Norway
| | - David C Wedge
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7FZ, UK
- NIHR Biomedical Research Centre, Warneford Ln, Headington, Oxford, OX3 7JX, UK
| | - Peter Van Loo
- The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Carlos Caldas
- Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
| | - Anne-Lise Børresen-Dale
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Ullernchausseen 70 N-0310, Oslo, Norway
- Institute for Clinical Medicine, University of Oslo, Kirkeveien 166 N-0450, Oslo, Norway
| | - Hege G Russnes
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Ullernchausseen 70 N-0310, Oslo, Norway
- Department of Pathology, Oslo University Hospital, POB 4953 Nydalen N-0424, Oslo, Norway
| | - Ole Christian Lingjærde
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Ullernchausseen 70 N-0310, Oslo, Norway.
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Gaustadalléen 23 B N-0373, Oslo, Norway.
- KG Jebsen Centre for B-cell malignancies, Institute for Clinical Medicine, University of Oslo, Ullernchausseen 70 N-0372, Oslo, Norway.
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22
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Page CM, Djordjilović V, Nøst TH, Ghiasvand R, Sandanger TM, Frigessi A, Thoresen M, Veierød MB. Lifetime Ultraviolet Radiation Exposure and DNA Methylation in Blood Leukocytes: The Norwegian Women and Cancer Study. Sci Rep 2020; 10:4521. [PMID: 32161338 PMCID: PMC7066249 DOI: 10.1038/s41598-020-61430-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 02/26/2020] [Indexed: 12/04/2022] Open
Abstract
Ultraviolet radiation (UVR) exposure is a leading cause of skin cancers and an ubiquitous environmental exposure. However, the molecular mechanisms relating UVR exposure to melanoma is not fully understood. We aimed to investigate if lifetime UVR exposure could be robustly associated to DNA methylation (DNAm). We assessed DNAm in whole blood in three data sets (n = 183, 191, and 125) from the Norwegian Woman and Cancer cohort, using Illumina platforms. We studied genome-wide DNAm, targeted analyses of CpG sites indicated in the literature, global methylation, and accelerated aging. Lifetime history of UVR exposure (residential ambient UVR, sunburns, sunbathing vacations and indoor tanning) was collected by questionnaires. We used one data set for discovery and the other two for replication. One CpG site showed a genome-wide significant association to cumulative UVR exposure (cg01884057) (pnominal = 3.96e-08), but was not replicated in any of the two replication sets (pnominal ≥ 0.42). Two CpG sites (cg05860019, cg00033666) showed suggestive associations with the other UVR exposures. We performed extensive analyses of the association between long-term UVR exposure and DNAm. There was no indication of a robust effect of past UVR exposure on DNAm.
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Affiliation(s)
- Christian M Page
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
- Centre for Fertility and Health, Norwegian Institute of Public health, Oslo, Norway
| | - Vera Djordjilović
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Therese H Nøst
- Department of Community Medicine, UiT - the Arctic University of Norway, Tromsø, Norway
| | - Reza Ghiasvand
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Research, Cancer Registry of Norway, Oslo, Norway
| | - Torkjel M Sandanger
- Department of Community Medicine, UiT - the Arctic University of Norway, Tromsø, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Magne Thoresen
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Marit B Veierød
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
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23
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Tekpli X, Lien T, Røssevold AH, Nebdal D, Borgen E, Ohnstad HO, Kyte JA, Vallon-Christersson J, Fongaard M, Due EU, Svartdal LG, Sveli MAT, Garred Ø, Frigessi A, Sahlberg KK, Sørlie T, Russnes HG, Naume B, Kristensen VN. An independent poor-prognosis subtype of breast cancer defined by a distinct tumor immune microenvironment. Nat Commun 2019; 10:5499. [PMID: 31796750 PMCID: PMC6890706 DOI: 10.1038/s41467-019-13329-5] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 10/30/2019] [Indexed: 12/14/2022] Open
Abstract
How mixtures of immune cells associate with cancer cell phenotype and affect pathogenesis is still unclear. In 15 breast cancer gene expression datasets, we invariably identify three clusters of patients with gradual levels of immune infiltration. The intermediate immune infiltration cluster (Cluster B) is associated with a worse prognosis independently of known clinicopathological features. Furthermore, immune clusters are associated with response to neoadjuvant chemotherapy. In silico dissection of the immune contexture of the clusters identified Cluster A as immune cold, Cluster C as immune hot while Cluster B has a pro-tumorigenic immune infiltration. Through phenotypical analysis, we find epithelial mesenchymal transition and proliferation associated with the immune clusters and mutually exclusive in breast cancers. Here, we describe immune clusters which improve the prognostic accuracy of immune contexture in breast cancer. Our discovery of a novel independent prognostic factor in breast cancer highlights a correlation between tumor phenotype and immune contexture. In breast cancer, the immune infiltration of the tumour associates with clinical outcome. Here, the authors infer immune context based on gene expression data and identify a new independent subtype linked to pro-tumorigenic immune infiltration.
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Affiliation(s)
- Xavier Tekpli
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Tonje Lien
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Andreas Hagen Røssevold
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,Department of Oncology, Division of Cancer Medicine, Oslo University Hospital, Oslo, Norway
| | - Daniel Nebdal
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Elin Borgen
- Department of Pathology, Division of Laboratory Medicine, Oslo University Hospital, Oslo, Norway
| | - Hege Oma Ohnstad
- Department of Oncology, Division of Cancer Medicine, Oslo University Hospital, Oslo, Norway
| | - Jon Amund Kyte
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,Department of Oncology, Division of Cancer Medicine, Oslo University Hospital, Oslo, Norway
| | - Johan Vallon-Christersson
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Scheelegatan 2, Medicon Village, 22185, Lund, Sweden
| | - Marie Fongaard
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Eldri Undlien Due
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Lisa Gregusson Svartdal
- Department of Pathology, Division of Laboratory Medicine, Oslo University Hospital, Oslo, Norway
| | - My Anh Tu Sveli
- Department of Pathology, Division of Laboratory Medicine, Oslo University Hospital, Oslo, Norway
| | - Øystein Garred
- Department of Pathology, Division of Laboratory Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Arnoldo Frigessi
- Department of Biostatistics, Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Research Support Services, Oslo University Hospital, Oslo, Norway
| | - Kristine Kleivi Sahlberg
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,Department of Research, Vestre Viken Hospital Trust, Drammen, Norway
| | - Therese Sørlie
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,Centre for Cancer Biomarkers CCBIO, Bergen, Norway
| | - Hege G Russnes
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,Department of Pathology, Division of Laboratory Medicine, Oslo University Hospital, Oslo, Norway
| | - Bjørn Naume
- Department of Oncology, Division of Cancer Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Vessela N Kristensen
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway. .,Centre for Cancer Biomarkers CCBIO, Bergen, Norway. .,Department of Clinical Molecular Biology, Division of Medicine, Akershus University Hospital, Lørenskog, Norway.
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24
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Liu Q, Reiner AH, Frigessi A, Scheel I. Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows model. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2019.104960] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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25
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Engebretsen S, Frigessi A, Engø-Monsen K, Furberg AS, Stubhaug A, de Blasio BF, Nielsen CS. The peer effect on pain tolerance. Scand J Pain 2019; 18:467-477. [PMID: 29794275 DOI: 10.1515/sjpain-2018-0060] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 04/04/2018] [Indexed: 11/15/2022]
Abstract
Background and aims Twin studies have found that approximately half of the variance in pain tolerance can be explained by genetic factors, while shared family environment has a negligible effect. Hence, a large proportion of the variance in pain tolerance is explained by the (non-shared) unique environment. The social environment beyond the family is a potential candidate for explaining some of the variance in pain tolerance. Numerous individual traits have previously shown to be associated with friendship ties. In this study, we investigate whether pain tolerance is associated with friendship ties. Methods We study the friendship effect on pain tolerance by considering data from the Tromsø Study: Fit Futures I, which contains pain tolerance measurements and social network information for adolescents attending first year of upper secondary school in the Tromsø area in Northern Norway. Pain tolerance was measured with the cold-pressor test (primary outcome), contact heat and pressure algometry. We analyse the data by using statistical methods from social network analysis. Specifically, we compute pairwise correlations in pain tolerance among friends. We also fit network autocorrelation models to the data, where the pain tolerance of an individual is explained by (among other factors) the average pain tolerance of the individual's friends. Results We find a significant and positive relationship between the pain tolerance of an individual and the pain tolerance of their friends. The estimated effect is that for every 1 s increase in friends' average cold-pressor tolerance time, the expected cold-pressor pain tolerance of the individual increases by 0.21 s (p-value: 0.0049, sample size n=997). This estimated effect is controlled for sex. The friendship effect remains significant when controlling for potential confounders such as lifestyle factors and test sequence among the students. Further investigating the role of sex on this friendship effect, we only find a significant peer effect of male friends on males, while there is no significant effect of friends' average pain tolerance on females in stratified analyses. Similar, but somewhat lower estimates were obtained for the other pain modalities. Conclusions We find a positive and significant peer effect in pain tolerance. Hence, there is a significant tendency for students to be friends with others with similar pain tolerance. Sex-stratified analyses show that the only significant effect is the effect of male friends on males. Implications Two different processes can explain the friendship effect in pain tolerance, selection and social transmission. Individuals might select friends directly due to similarity in pain tolerance, or indirectly through similarity in other confounding variables that affect pain tolerance. Alternatively, there is an influence effect among friends either directly in pain tolerance, or indirectly through other variables that affect pain tolerance. If there is indeed a social influence effect in pain tolerance, then the social environment can account for some of the unique environmental variance in pain tolerance. If so, it is possible to therapeutically affect pain tolerance through alteration of the social environment.
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Affiliation(s)
- Solveig Engebretsen
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Post box 1122 Blindern, 0316 Oslo, Norway, Phone: +47 470 83 876.,Department of Infectious Disease Epidemiology and Modelling, Norwegian Institute of Public Health, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | | | - Anne-Sofie Furberg
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.,Department of Microbiology and Infection Control, University Hospital of North Norway, Tromsø, Norway
| | - Audun Stubhaug
- Department of Pain Management and Research, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Department of Infectious Disease Epidemiology and Modelling, Norwegian Institute of Public Health, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
| | - Christopher Sivert Nielsen
- Department of Pain Management and Research, Oslo University Hospital, Oslo, Norway.,Department of Ageing, Norwegian Institute of Public Health, Oslo, Norway
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26
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Lai X, Geier OM, Fleischer T, Garred Ø, Borgen E, Funke SW, Kumar S, Rognes ME, Seierstad T, Børresen-Dale AL, Kristensen VN, Engebraaten O, Köhn-Luque A, Frigessi A. Toward Personalized Computer Simulation of Breast Cancer Treatment: A Multiscale Pharmacokinetic and Pharmacodynamic Model Informed by Multitype Patient Data. Cancer Res 2019; 79:4293-4304. [PMID: 31118201 DOI: 10.1158/0008-5472.can-18-1804] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 02/13/2019] [Accepted: 05/17/2019] [Indexed: 11/16/2022]
Abstract
The usefulness of mechanistic models to disentangle complex multiscale cancer processes, such as treatment response, has been widely acknowledged. However, a major barrier for multiscale models to predict treatment outcomes in individual patients lies in their initialization and parametrization, which needs to reflect individual cancer characteristics accurately. In this study, we use multitype measurements acquired routinely on a single breast tumor, including histopathology, MRI, and molecular profiling, to personalize parts of a complex multiscale model of breast cancer treated with chemotherapeutic and antiangiogenic agents. The model accounts for drug pharmacokinetics and pharmacodynamics. We developed an open-source computer program that simulates cross-sections of tumors under 12-week therapy regimens and used it to individually reproduce and elucidate treatment outcomes of 4 patients. Two of the tumors did not respond to therapy, and model simulations were used to suggest alternative regimens with improved outcomes dependent on the tumor's individual characteristics. It was determined that more frequent and lower doses of chemotherapy reduce tumor burden in a low proliferative tumor while lower doses of antiangiogenic agents improve drug penetration in a poorly perfused tumor. Furthermore, using this model, we were able to correctly predict the outcome in another patient after 12 weeks of treatment. In summary, our model bridges multitype clinical data to shed light on individual treatment outcomes. SIGNIFICANCE: Mathematical modeling is used to validate possible mechanisms of tumor growth, resistance, and treatment outcome.
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Affiliation(s)
- Xiaoran Lai
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Oliver M Geier
- Department of Diagnostic Physics, Clinic of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Thomas Fleischer
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Øystein Garred
- Department of Pathology, Oslo University Hospital, Oslo, Norway
| | - Elin Borgen
- Department of Pathology, Oslo University Hospital, Oslo, Norway
| | - Simon W Funke
- Center for Biomedical Computing, Simula Research Laboratory, Lysaker, Norway
| | - Surendra Kumar
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Marie E Rognes
- Center for Biomedical Computing, Simula Research Laboratory, Lysaker, Norway
| | - Therese Seierstad
- Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Anne-Lise Børresen-Dale
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Vessela N Kristensen
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,Department of Clinical Molecular Biology and Laboratory Science (EpiGen), Division of Medicine, Akershus University Hospital, Lørenskog, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Olav Engebraaten
- Department of Oncology, Oslo University Hospital, Oslo, Norway.,Department of Tumor Biology, Institute for Cancer Research, University of Oslo, Oslo, Norway
| | - Alvaro Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway. .,Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
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27
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Engebretsen S, Engø-Monsen K, Frigessi A, Freiesleben de Blasio B. A theoretical single-parameter model for urbanisation to study infectious disease spread and interventions. PLoS Comput Biol 2019; 15:e1006879. [PMID: 30845153 PMCID: PMC6424465 DOI: 10.1371/journal.pcbi.1006879] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 03/19/2019] [Accepted: 02/18/2019] [Indexed: 11/27/2022] Open
Abstract
The world is continuously urbanising, resulting in clusters of densely populated urban areas and more sparsely populated rural areas. We propose a method for generating spatial fields with controllable levels of clustering of the population. We build a synthetic country, and use this method to generate versions of the country with different clustering levels. Combined with a metapopulation model for infectious disease spread, this allows us to in silico explore how urbanisation affects infectious disease spread. In a baseline scenario with no interventions, the underlying population clustering seems to have little effect on the final size and timing of the epidemic. Under within-country restrictions on non-commuting travel, the final size decreases with increased population clustering. The effect of travel restrictions on reducing the final size is larger with higher clustering. The reduction is larger in the more rural areas. Within-country travel restrictions delay the epidemic, and the delay is largest for lower clustering levels. We implemented three different vaccination strategies-uniform vaccination (in space), preferentially vaccinating urban locations and preferentially vaccinating rural locations. The urban and uniform vaccination strategies were most effective in reducing the final size, while the rural vaccination strategy was clearly inferior. Visual inspection of some European countries shows that many countries already have high population clustering. In the future, they will likely become even more clustered. Hence, according to our model, within-country travel restrictions are likely to be less and less effective in delaying epidemics, while they will be more effective in decreasing final sizes. In addition, to minimise final sizes, it is important not to neglect urban locations when distributing vaccines. To our knowledge, this is the first study to systematically investigate the effect of urbanisation on infectious disease spread and in particular, to examine effectiveness of prevention measures as a function of urbanisation.
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Affiliation(s)
- Solveig Engebretsen
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Infectious Disease Epidemiology and Modelling, Division for Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | | | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Infectious Disease Epidemiology and Modelling, Division for Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
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28
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Crispino M, Arjas E, Vitelli V, Barrett N, Frigessi A. A Bayesian Mallows approach to nontransitive pair comparison data: How human are sounds? Ann Appl Stat 2019. [DOI: 10.1214/18-aoas1203] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Erango M, Frigessi A, Rosseland LA. A three minutes supine position test reveals higher risk of spinal anesthesia induced hypotension during cesarean delivery. An observational study. F1000Res 2018; 7:1028. [PMID: 30135733 PMCID: PMC6085602 DOI: 10.12688/f1000research.15142.1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/28/2018] [Indexed: 11/22/2022] Open
Abstract
Background: Cesarean delivery is performed under spinal anesthesia, and vasodilation is the main cause for a drop in blood pressure. The compression of the aorta and inferior vena cava by the gravid uterus is of additional clinical importance. Hypotension may occur during cesarean delivery even if prophylactic infusion of phenylephrine is practiced. We have tested if a 3 minute supine observation, can identify a subset of women with decreasing systolic arterial pressure (SAP) under spinal anesthesia. Methods: We performed a prospective observational study at Oslo University Hospital on healthy pregnant women for planned cesarean delivery. Continuous measurements of calibrated invasive SAP and estimated cardiac output were recorded for 76 women in a 3 minutes measurement with the woman in the left lateral position, followed by supine position for 3 minutes. Using functional data clustering, principal component analysis and curve smoothing, to filter way noise and reduce the dimensionality of the signal, we clustered the women into separate SAP groups. Results: We identified two significantly different groups of women during supine position; one characterized by initial drop in SAP, the other showed initial increase. After spinal anesthesia, the mean SAP curve of the women in the first group showed a drop in blood pressure, which was more rapid than for the other women. A minor difference in cardiac output was observed between the two groups of women with the mean cardiac output curve for the first group being higher. Conclusions: This work indicates that supine position affect clinically relevant cardiovascular measurements in pregnant women. A simple test may identify patients with increased risk of spinal anesthesia induced hypotension.
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Affiliation(s)
- Markos Erango
- School of Mathematical and Statistical Sciences, Hawassa University, Hawassa, Ethiopia
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Leiv Arne Rosseland
- Department of Research and Development, Division of Emergencies and Critical Care, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
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LeBlanc M, Zuber V, Thompson WK, Andreassen OA, Frigessi A, Andreassen BK. A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework. BMC Genomics 2018; 19:494. [PMID: 29940862 PMCID: PMC6019513 DOI: 10.1186/s12864-018-4859-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Accepted: 06/06/2018] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND There is considerable evidence that many complex traits have a partially shared genetic basis, termed pleiotropy. It is therefore useful to consider integrating genome-wide association study (GWAS) data across several traits, usually at the summary statistic level. A major practical challenge arises when these GWAS have overlapping subjects. This is particularly an issue when estimating pleiotropy using methods that condition the significance of one trait on the signficance of a second, such as the covariate-modulated false discovery rate (cmfdr). RESULTS We propose a method for correcting for sample overlap at the summary statistic level. We quantify the expected amount of spurious correlation between the summary statistics from two GWAS due to sample overlap, and use this estimated correlation in a simple linear correction that adjusts the joint distribution of test statistics from the two GWAS. The correction is appropriate for GWAS with case-control or quantitative outcomes. Our simulations and data example show that without correcting for sample overlap, the cmfdr is not properly controlled, leading to an excessive number of false discoveries and an excessive false discovery proportion. Our correction for sample overlap is effective in that it restores proper control of the false discovery rate, at very little loss in power. CONCLUSIONS With our proposed correction, it is possible to integrate GWAS summary statistics with overlapping samples in a statistical framework that is dependent on the joint distribution of the two GWAS.
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Affiliation(s)
- Marissa LeBlanc
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo universitetssykehus HF, Sogn Arena, PB 4950 Nydalen, Oslo, 0424 Norway
| | - Verena Zuber
- MRC Biostatistics Unit, University of Cambridge, MRC Biostatistics Unit, Cambridge Institute of Public Health, Robinson Way, Cambridge, CB2 0SR United Kingdom
| | - Wesley K. Thompson
- Department of Psychiatry, University of California, San Diego, 9500 Gilman Drive, MC 0603, La Jolla, CA, 92093-0603 USA
| | - Ole A. Andreassen
- NORMENT-KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, P.O. Box 1039 Blindern, Oslo, N-0315 Norway
- Division of Mental Health and Addiction, Oslo University Hospital HF, Ullevaal Hospital, building 49,P.O. Box 4956 Nydalen, Oslo, N-0424 Norway
| | - Schizophrenia and Bipolar Disorder Working Groups of the Psychiatric Genomics Consortium
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo universitetssykehus HF, Sogn Arena, PB 4950 Nydalen, Oslo, 0424 Norway
- MRC Biostatistics Unit, University of Cambridge, MRC Biostatistics Unit, Cambridge Institute of Public Health, Robinson Way, Cambridge, CB2 0SR United Kingdom
- Department of Psychiatry, University of California, San Diego, 9500 Gilman Drive, MC 0603, La Jolla, CA, 92093-0603 USA
- NORMENT-KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, P.O. Box 1039 Blindern, Oslo, N-0315 Norway
- Division of Mental Health and Addiction, Oslo University Hospital HF, Ullevaal Hospital, building 49,P.O. Box 4956 Nydalen, Oslo, N-0424 Norway
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo universitetssykehus HF, Sogn Arena, PB 4950 Nydalen, Oslo, 0424 Norway
- Department of Research, Cancer Registry of Norway, P.O. box 5313 Majorstuen, Oslo, N-0304 Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo universitetssykehus HF, Sogn Arena, PB 4950 Nydalen, Oslo, 0424 Norway
| | - Bettina Kulle Andreassen
- Department of Research, Cancer Registry of Norway, P.O. box 5313 Majorstuen, Oslo, N-0304 Norway
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Affiliation(s)
- Øystein Sørensen
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway
| | | | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway
| | - Magne Thoresen
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway
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Rohrbeck C, Costain DA, Frigessi A. Bayesian spatial monotonic multiple regression. Biometrika 2018. [DOI: 10.1093/biomet/asy019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- C Rohrbeck
- Department of Mathematics and Statistics, Lancaster University, Bailrigg, Lancaster, U.K
| | - D A Costain
- Department of Mathematics and Statistics, Lancaster University, Bailrigg, Lancaster, U.K
| | - A Frigessi
- Department of Biostatistics, University of Oslo, PB 1122 Blindern, 0317 Oslo, Norway
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Linder MD, Frigessi A, Piaserico S, Keilman N. Simulating the life course of psoriasis patients: the interplay between therapy intervention and marital status. J Eur Acad Dermatol Venereol 2017; 32:62-67. [PMID: 28850745 DOI: 10.1111/jdv.14567] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2017] [Accepted: 08/01/2017] [Indexed: 02/04/2023]
Abstract
BACKGROUND Psoriasis, a chronic relapsing inflammatory disease affecting primarily the skin, shows multiple comorbidities including depression, cardiovascular diseases and other relevant conditions. Psoriasis patients experience social isolation, job loss, financial difficulties and partnership problems. Inversely, psychosocial impairments may negatively influence the disease course. OBJECTIVE To explore the feasibility of a model describing the interaction of psychosocial and clinical factors over the life course of patients. METHODS We considered only seven states for members of a hypothetical population: single and healthy, single and having a psoriasis flare, single and 'cured', coupled and healthy, coupled and having a psoriasis flare, coupled and 'cured', and dead. Transition probabilities between states were taken from the Norwegian Population Register for the healthy population and from epidemiological research articles. Clinical experience allowed adjustments on the assumed parameters. RESULTS Our macromodel, which simulates the effect of therapy intervention on patients' partnership status, yields a description of the transitions between the seven states. Treatment efficacy shows only a negligible effect on the chances of living with a partner. CONCLUSIONS Mathematical modelling of interactions between social and health variables is in principle feasible. However, complex models, comprising more variables (for instance: employment status, depression level, obesity etc.), are needed for more realistic simulations for the interactions studied. As increasing the number of variables leads to an exponential increase of the model's state space, switching to micromodelling (representing each individual separately) may be necessary.
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Affiliation(s)
- M D Linder
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
| | - A Frigessi
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
| | - S Piaserico
- Dermatology Unit, Department of Medicine, Padua University, Padua, Italy
| | - N Keilman
- Department of Economics, University of Oslo, Oslo, Norway
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Fleischer T, Tekpli X, Mathelier A, Wang S, Nebdal D, Dhakal HP, Sandberg KK, Schliting E, Børresen-Dale AL, Borgen E, Naume B, Eskeland R, Frigessi A, Tost J, Hurtado A, Kristensen VN. Abstract LB-097: DNA methylation at enhancers distinguishes distinct breast cancer lineages. Cancer Res 2017. [DOI: 10.1158/1538-7445.am2017-lb-097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Breast cancers exhibit genome-wide aberrant DNA methylation patterns. To investigate how these affect the transcriptome and which changes are linked to transformation or progression we applied genome-wide expression-methylation quantitative trait loci (emQTL) analysis between 5meCpG and gene expression. We find that on a whole genome scale, DNA methylation and gene expression have remarkably and reproducibly conserved patterns of association, both in cis and in trans, which lead to identify distinct transcriptional networks and specific disease cell lineages deviating from the normal tissue. In three breast cancer cohorts (n=104, n=253 and n=330), we invariably identify enhancers whose DNA methylation tethers the binding of three key transcription factors in breast cancer in a lineage specific manner. Our emQTL analysis also identifies tumor infiltrating immune cell signatures. Using ChromHMM segmentation and ChIP-seq associated TF binding regions, we identify enhancers and TF binding sites around emQTLs-CpGs. We functionally validate the genes associated with these CpGs using knock-down by siRNA and applying GRO-seq analysis after transcriptional stimulation with estrogen. DNA methylation at TF binding regions is considered an early event during normal breast cell transformation into estrogen dependent breast cancer. Two distinct key gene regulatory networks reported here are prominently altered by DNA methylation.
Citation Format: Thomas Fleischer, Xavier Tekpli, Anthony Mathelier, Shixiong Wang, Daniel Nebdal, Hari P. Dhakal, Kristine Kleivi Sandberg, Ellen Schliting, Oslo Breast Cancer Research Consortium (OSBREAC), Anne-Lise Børresen-Dale, Elin Borgen, Bjørn Naume, Ragnhild Eskeland, Arnoldo Frigessi, Jörg Tost, Antoni Hurtado, Vessela N. Kristensen. DNA methylation at enhancers distinguishes distinct breast cancer lineages [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-097. doi:10.1158/1538-7445.AM2017-LB-097
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Affiliation(s)
- Thomas Fleischer
- 1Department of Cancer Genetics, Istitute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway
| | - Xavier Tekpli
- 2Dept. of Clinical Molecular Biology (EpiGen), Division of Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Anthony Mathelier
- 3Centre for Molecular Medicine Norway (NCMM), University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Shixiong Wang
- 4Breast Cancer Research Group, Centre for Molecular Medicine Norway (NCMM), University of Oslo, Oslo, Norway
| | - Daniel Nebdal
- 1Department of Cancer Genetics, Istitute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway
| | - Hari P. Dhakal
- 5Department of Pathology, Oslo University Hospital, Oslo, Norway
| | | | - Ellen Schliting
- 7Section for Breast and Endocrine Surgery, Oslo University Hospital Ullevål, Oslo, Norway
| | - Anne-Lise Børresen-Dale
- 1Department of Cancer Genetics, Istitute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway
| | - Elin Borgen
- 5Department of Pathology, Oslo University Hospital, Oslo, Norway
| | - Bjørn Naume
- 8Department of Oncology, Oslo University Hospital, Oslo, Norway
| | | | - Arnoldo Frigessi
- 10Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway
| | - Jörg Tost
- 11Laboratory for Epigenetics and Environment, Centre National de Génotypage, CEA – Institut de Génomique, Evry, France
| | - Antoni Hurtado
- 4Breast Cancer Research Group, Centre for Molecular Medicine Norway (NCMM), University of Oslo, Oslo, Norway
| | - Vessela N. Kristensen
- 12Dept. of Clinical Molecular Biology (EpiGen), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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Belay DB, Kifle YG, Goshu AT, Gran JM, Yewhalaw D, Duchateau L, Frigessi A. Joint Bayesian modeling of time to malaria and mosquito abundance in Ethiopia. BMC Infect Dis 2017; 17:415. [PMID: 28606100 PMCID: PMC5467264 DOI: 10.1186/s12879-017-2496-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 05/28/2017] [Indexed: 11/26/2022] Open
Abstract
Background This paper studies the effect of mosquito abundance and malaria incidence in the last 3 weeks, and their interaction, on the hazard of time to malaria in a previously studied cohort of children in Ethiopia. Methods We model the mosquito abundance and time to malaria data jointly in a Bayesian framework. Results We found that the interaction of mosquito abundance and incidence plays a prominent role on malaria risk. We quantify and compare relative risks of various factors, and determine the predominant role of the interaction between incidence and mosquito abundance in describing malaria risk. Seasonal rain patterns, distance to a water source of the households, temperature and relative humidity are all significant in explaining mosquito abundance, and through this affect malaria risk. Conclusion Analyzing jointly the time to malaria data and the mosquito abundance allows a precise comparison of factors affecting the spread of malaria. The effect of the interaction between mosquito abundances and local presence of malaria parasites has an important effect on the hazard of time to malaria, beyond abundance alone. Each additional one km away from the dam gives an average reduction of malaria relative risk of 5.7%. The importance of the interaction between abundance and incidence leads to the hypothesis that preventive intervention could advantageously target the infectious population, in addition to mosquito control, which is the typical intervention today.
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Affiliation(s)
- Denekew Bitew Belay
- School of Mathematical and Statistical Sciences, College of Natural and Computational Science, Hawassa University, Hawassa, Ethiopia.
| | - Yehenew Getachew Kifle
- Department of Statistics and Operations Research, University of Limpopo, Limpopo, South Africa
| | - Ayele Taye Goshu
- School of Mathematical and Statistical Sciences, College of Natural and Computational Science, Hawassa University, Hawassa, Ethiopia
| | - Jon Michael Gran
- Oslo Center for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Delenasaw Yewhalaw
- Department of Medical Laboratory Sciences and Pathology, College of Health Sciences, Jimma University, Jimma, Ethiopia
| | - Luc Duchateau
- Department of Comparative Physiology and Biometrics, Ghent University, Ghent, Belgium
| | - Arnoldo Frigessi
- Oslo Center for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway
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Aure MR, Vitelli V, Jernström S, Kumar S, Krohn M, Due EU, Haukaas TH, Leivonen SK, Vollan HKM, Lüders T, Rødland E, Vaske CJ, Zhao W, Møller EK, Nord S, Giskeødegård GF, Bathen TF, Caldas C, Tramm T, Alsner J, Overgaard J, Geisler J, Bukholm IRK, Naume B, Schlichting E, Sauer T, Mills GB, Kåresen R, Mælandsmo GM, Lingjærde OC, Frigessi A, Kristensen VN, Børresen-Dale AL, Sahlberg KK. Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome. Breast Cancer Res 2017; 19:44. [PMID: 28356166 PMCID: PMC5372339 DOI: 10.1186/s13058-017-0812-y] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 02/05/2017] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. METHODS Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering. RESULTS Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. CONCLUSIONS The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.
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Affiliation(s)
- Miriam Ragle Aure
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Valeria Vitelli
- Oslo Center for Biostatistics and Epidemiology, Institute of Basic Medical Science, University of Oslo, Oslo, Norway
| | - Sandra Jernström
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Surendra Kumar
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Molecular Biology (EpiGen), Division of Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Marit Krohn
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Eldri U. Due
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tonje Husby Haukaas
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Suvi-Katri Leivonen
- Genome-Scale Biology Research Program, University of Helsinki, Helsinki, Finland
| | - Hans Kristian Moen Vollan
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Torben Lüders
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Molecular Biology (EpiGen), Division of Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Einar Rødland
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
| | | | - Wei Zhao
- Department of Systems Biology, University of Texas M.D. Anderson Cancer Center, Houston, TX USA
| | - Elen K. Møller
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Silje Nord
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guro F. Giskeødegård
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Tone Frost Bathen
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Carlos Caldas
- Cambridge University Hospitals Trust, Addenbrookes Hospital, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Trine Tramm
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Jan Alsner
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Jens Overgaard
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Jürgen Geisler
- Department of Oncology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Ida R. K. Bukholm
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Surgery, Akershus University Hospital, Lørenskog, Norway
| | - Bjørn Naume
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Oncology, Division of Cancer Medicine, Oslo University Hospital, Oslo, Norway
| | - Ellen Schlichting
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Breast and Endocrine Surgery, Oslo University Hospital, Oslo, Norway
| | - Torill Sauer
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Pathology, Akershus University Hospital, Lørenskog, Norway
| | - Gordon B. Mills
- Department of Systems Biology, University of Texas M.D. Anderson Cancer Center, Houston, TX USA
| | - Rolf Kåresen
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Breast and Endocrine Surgery, Oslo University Hospital, Oslo, Norway
| | - Gunhild M. Mælandsmo
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
| | - Ole Christian Lingjærde
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Cancer Biomedicine, University of Oslo, Oslo, Norway
- Department of Computer Science, University of Oslo, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Center for Biostatistics and Epidemiology, Institute of Basic Medical Science, University of Oslo, Oslo, Norway
- Oslo Center for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Vessela N. Kristensen
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Molecular Biology (EpiGen), Division of Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Anne-Lise Børresen-Dale
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kristine K. Sahlberg
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
- K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Research, Vestre Viken Hospital Trust, Drammen, Norway
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Affiliation(s)
- Derbachew Asfaw
- Hawassa University School of Mathematical and Statistical Sciences; P. O. Box 05 Hawassa Ethiopia
| | - Valeria Vitelli
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics; University of Oslo; P. O. Box 1122 Blindern Oslo N-0317 Norway
| | - Øystein Sørensen
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics; University of Oslo; P. O. Box 1122 Blindern Oslo N-0317 Norway
| | - Elja Arjas
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics; University of Oslo; P. O. Box 1122 Blindern Oslo N-0317 Norway
- Department of Mathematics and Statistics; P. O. Box 68, FI-00014 University of Helsinki; Helsinki Finland
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics; University of Oslo; P. O. Box 1122 Blindern Oslo N-0317 Norway
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Frigessi A, Engel J. Editorial: a special ENBIS issue of SMIJ. STAT MODEL 2016. [DOI: 10.1191/1471082x04st077ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Abstract
We compare two different modelling strategies for continuous space discrete time data. The first strategy is in the spirit of Gaussian kriging. The model is a general stationary space-time Gaussian field where the key point is the choice of a parametric form for the covariance function. In the main, covariance functions that are used are separable in space and time. Nonseparable covariance functions are useful in many applications, but construction of these is not easy. The second strategy is to model the time evolution of the process more directly. We consider models of the autoregressive type where the process at time t is obtained by convolving the process at time t − 1 and adding spatially correlated noise. Under specific conditions, the two strategies describe two different formulations of the same stochastic process. We show how the two representations look in different cases. Furthermore, by transforming time-dynamic convolution models to Gaussian fields we can obtain new covariance functions and by writing a Gaussian field as a time-dynamic convolution model, interesting properties are discovered. The computational aspects of the two strategies are discussed through experiments on a dataset of daily UK temperatures. Although algorithms for performing estimation, simulation, and so on are easy to do for the first strategy, more computer-efficient algorithms based on the second strategy can be constructed.
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Affiliation(s)
- Geir Storvik
- Department of Mathematics, University of Oslo, Norway, Norwegian
Computing Center, Oslo, Norway,
| | - Arnoldo Frigessi
- Department of Mathematics, University of Oslo, Norway, Norwegian
Computing Center, Oslo, Norway
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Solvang HK, Frigessi A, Kaveh F, Riis MLH, Lüders T, Bukholm IRK, Kristensen VN, Andreassen BK. Gene expression analysis supports tumor threshold over 2.0 cm for T-category breast cancer. EURASIP J Bioinform Syst Biol 2016; 2016:6. [PMID: 26900390 PMCID: PMC4746218 DOI: 10.1186/s13637-015-0034-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2014] [Accepted: 12/23/2015] [Indexed: 11/17/2022]
Abstract
Tumor size, as indicated by the T-category, is known as a strong prognostic indicator for breast cancer. It is common practice to distinguish the T1 and T2 groups at a tumor size of 2.0 cm. We investigated the 2.0-cm rule from a new point of view. Here, we try to find the optimal threshold based on the differences between the gene expression profiles of the T1 and T2 groups (as defined by the threshold). We developed a numerical algorithm to measure the overall differential gene expression between patients with smaller tumors and those with larger tumors among multiple expression datasets from different studies. We confirmed the performance of the proposed algorithm by a simulation study and then applied it to three different studies conducted at two Norwegian hospitals. We found that the maximum difference in gene expression is obtained at a threshold of 2.2–2.4 cm, and we confirmed that the optimum threshold was over 2.0 cm, as indicated by a validation study using five publicly available expression datasets. Furthermore, we observed a significant differentiation between the two threshold groups in terms of time to local recurrence for the Norwegian datasets. In addition, we performed an associated network and canonical pathway analyses for the genes differentially expressed between tumors below and above the given thresholds, 2.0 and 2.4 cm, using the Norwegian datasets. The associated network function illustrated a cellular assembly of the genes for the 2.0-cm threshold: an energy production for the 2.4-cm threshold and an enrichment in lipid metabolism based on the genes in the intersection for the 2.0- and 2.4-cm thresholds.
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Affiliation(s)
- Hiroko K Solvang
- Department of Marine Mammals, Institute of Marine Research, C. Sundts Gate 64, Bergen, 5004 Norway
| | - Arnoldo Frigessi
- Department of Biostatistics, Institute of Basic Medical Science, University of Oslo, Norway and Statistics for Innovation-(sfi)2, Oslo, Norway
| | - Fateme Kaveh
- Medical Genetics Department, Oslo University Hospital (Ullevål), Oslo, Norway
| | - Margit L H Riis
- Department of Surgery, Akershus University Hospital, Lørenskog, Norway ; Department of Molecular Biology and Laboratory Sciences (EpiGen), Institute of Clinical Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Torben Lüders
- Department of Surgery, Akershus University Hospital, Lørenskog, Norway ; Department of Molecular Biology and Laboratory Sciences (EpiGen), Institute of Clinical Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Ida R K Bukholm
- Department of Surgery, Akershus University Hospital, Lørenskog, Norway ; Institute of Clinical Medicine, University of Oslo, Norwegian Center of HPH Network, Oslo, Norway
| | - Vessela N Kristensen
- Department of Molecular Biology and Laboratory Sciences (EpiGen), Institute of Clinical Medicine, Akershus University Hospital, Lørenskog, Norway ; Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway
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LeBlanc M, Zuber V, Andreassen BK, Witoelar A, Zeng L, Bettella F, Wang Y, McEvoy LK, Thompson WK, Schork AJ, Reppe S, Barrett-Connor E, Ligthart S, Dehghan A, Gautvik KM, Nelson CP, Schunkert H, Samani NJ, Ridker PM, Chasman DI, Aukrust P, Djurovic S, Frigessi A, Desikan RS, Dale AM, Andreassen OA. Identifying Novel Gene Variants in Coronary Artery Disease and Shared Genes With Several Cardiovascular Risk Factors. Circ Res 2015; 118:83-94. [PMID: 26487741 DOI: 10.1161/circresaha.115.306629] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2014] [Accepted: 10/20/2015] [Indexed: 01/02/2023]
Abstract
RATIONALE Coronary artery disease (CAD) is a critical determinant of morbidity and mortality. Previous studies have identified several cardiovascular disease risk factors, which may partly arise from a shared genetic basis with CAD, and thus be useful for discovery of CAD genes. OBJECTIVE We aimed to improve discovery of CAD genes and inform the pathogenic relationship between CAD and several cardiovascular disease risk factors using a shared polygenic signal-informed statistical framework. METHODS AND RESULTS Using genome-wide association studies summary statistics and shared polygenic pleiotropy-informed conditional and conjunctional false discovery rate methodology, we systematically investigated genetic overlap between CAD and 8 traits related to cardiovascular disease risk factors: low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, type 2 diabetes mellitus, C-reactive protein, body mass index, systolic blood pressure, and type 1 diabetes mellitus. We found significant enrichment of single-nucleotide polymorphisms associated with CAD as a function of their association with low-density lipoprotein, high-density lipoprotein, triglycerides, type 2 diabetes mellitus, C-reactive protein, body mass index, systolic blood pressure, and type 1 diabetes mellitus. Applying the conditional false discovery rate method to the enriched phenotypes, we identified 67 novel loci associated with CAD (overall conditional false discovery rate <0.01). Furthermore, we identified 53 loci with significant effects in both CAD and at least 1 of low-density lipoprotein, high-density lipoprotein, triglycerides, type 2 diabetes mellitus, C-reactive protein, systolic blood pressure, and type 1 diabetes mellitus. CONCLUSIONS The observed polygenic overlap between CAD and cardiometabolic risk factors indicates a pathogenic relation that warrants further investigation. The new gene loci identified implicate novel genetic mechanisms related to CAD.
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Affiliation(s)
- Marissa LeBlanc
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Verena Zuber
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Bettina Kulle Andreassen
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Aree Witoelar
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Lingyao Zeng
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Francesco Bettella
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Yunpeng Wang
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Linda K McEvoy
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Wesley K Thompson
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Andrew J Schork
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Sjur Reppe
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Elizabeth Barrett-Connor
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Symen Ligthart
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Abbas Dehghan
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Kaare M Gautvik
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Christopher P Nelson
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Heribert Schunkert
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Nilesh J Samani
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | | | - Paul M Ridker
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Daniel I Chasman
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Pål Aukrust
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Srdjan Djurovic
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Arnoldo Frigessi
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Rahul S Desikan
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Anders M Dale
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
| | - Ole A Andreassen
- From the Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.L., B.K.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital, Oslo, Norway (M.L., A.F.); NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway (V.Z., A.W., F.B., Y.W., S.D., O.A.A.); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (V.Z., A.W., F.B., S.D., O.A.A.); Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway (B.K.A.); Deutsches Herzzentrum München, Technische Universität München, Munich, Germany (L.Z., H.S.); Deutsches Zentrum für Herz-Kreislauf-Forschung, partner site Munich Heart Alliance, Munich, Germany (L.Z., H.S.); Multimodal Imaging Laboratory, University of California at San Diego, La Jolla (Y.W., L.K.M., A.J.S., R.S.D., A.M.D., O.A.A.); Department of Neurosciences, University of California, San Diego, La Jolla, (Y.W., A.M.D.); Department of Radiology, University of California, San Diego, La Jolla (L.K.M., R.S.D., A.M.D.); Department of Psychiatry, University of California, San Diego, La Jolla (W.K.T., A.M.D.); Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, (A.J.S.); Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway (S.R.); Department of Medical Biochemistry, Lovisenberg Diakonale Hospital, Oslo, Norway (S.R., K.M.G.); Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway (S.R., K.M.G.); Family and Preventive Medicine, Division of Epidemiology, University of California, San Diego, La Jolla (E.B.-C.); Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands (S.L., A.D.); Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom (C.P.N
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Fairbairn CE, Sayette MA, Aalen OO, Frigessi A. Alcohol and Emotional Contagion: An Examination of the Spreading of Smiles in Male and Female Drinking Groups. Clin Psychol Sci 2014; 3:686-701. [PMID: 26504673 DOI: 10.1177/2167702614548892] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Researchers have hypothesized that men gain greater reward from alcohol than women. However, alcohol-administration studies testing participants drinking alone have offered weak support for this hypothesis. Research suggests that social processes may be implicated in gender differences in drinking patterns. We examined the impact of gender and alcohol on "emotional contagion"-a social mechanism central to bonding and cohesion. Social drinkers (360 male, 360 female) consumed alcohol, placebo, or control beverages in groups of three. Social interactions were video recorded, and both Duchenne and non-Duchenne smiling were continuously coded using the Facial Action Coding System. Results revealed that Duchenne smiling (but not non-Duchenne smiling) contagion correlated with self-reported reward and typical drinking patterns. Importantly, Duchenne smiles were significantly less "infectious" among sober male versus female groups, and alcohol eliminated these gender differences in smiling contagion. Findings identify new directions for research exploring social-reward processes in the etiology of alcohol problems.
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Affiliation(s)
| | | | - Odd O Aalen
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital
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Fleischer T, Frigessi A, Johnson KC, Edvardsen H, Touleimat N, Klajic J, Riis ML, Haakensen VD, Wärnberg F, Naume B, Helland A, Børresen-Dale AL, Tost J, Christensen BC, Kristensen VN. Genome-wide DNA methylation profiles in progression to in situ and invasive carcinoma of the breast with impact on gene transcription and prognosis. Genome Biol 2014. [PMID: 25146004 PMCID: PMC4165906 DOI: 10.1186/s13059-014-0435-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Ductal carcinoma in situ (DCIS) of the breast is a precursor of invasive breast carcinoma. DNA methylation alterations are thought to be an early event in progression of cancer, and may prove valuable as a tool in clinical decision making and for understanding neoplastic development. RESULTS We generate genome-wide DNA methylation profiles of 285 breast tissue samples representing progression of cancer, and validate methylation changes between normal and DCIS in an independent dataset of 15 normal and 40 DCIS samples. We also validate a prognostic signature on 583 breast cancer samples from The Cancer Genome Atlas. Our analysis reveals that DNA methylation profiles of DCIS are radically altered compared to normal breast tissue, involving more than 5,000 genes. Changes between DCIS and invasive breast carcinoma involve around 1,000 genes. In tumors, DNA methylation is associated with gene expression of almost 3,000 genes, including both negative and positive correlations. A prognostic signature based on methylation level of 18 CpGs is associated with survival of breast cancer patients with invasive tumors, as well as with survival of patients with DCIS and mixed lesions of DCIS and invasive breast carcinoma. CONCLUSIONS This work demonstrates that changes in the epigenome occur early in the neoplastic progression, provides evidence for the possible utilization of DNA methylation-based markers of progression in the clinic, and highlights the importance of epigenetic changes in carcinogenesis.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Vessela N Kristensen
- Department of Genetics, Institute for Cancer Research, OUS Radiumhospitalet, Montebello, Oslo, 0310, Norway.
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Fleischer T, Frigessi A, Johnson KC, Edvardsen H, Touleimat N, Klajic J, Riis ML, Haakensen VD, Wärnberg F, Naume B, Helland A, Børresen-Dale AL, Tost J, Christensen BC, Kristensen VN. Genome-wide DNA methylation profiles in progression to in situ and invasive carcinoma of the breast with impact on gene transcription and prognosis. Genome Biol 2014; 15:435. [PMID: 25146004 DOI: 10.1186/preaccept-2333349012841587] [Citation(s) in RCA: 88] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2014] [Accepted: 08/08/2014] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Ductal carcinoma in situ (DCIS) of the breast is a precursor of invasive breast carcinoma. DNA methylation alterations are thought to be an early event in progression of cancer, and may prove valuable as a tool in clinical decision making and for understanding neoplastic development. RESULTS We generate genome-wide DNA methylation profiles of 285 breast tissue samples representing progression of cancer, and validate methylation changes between normal and DCIS in an independent dataset of 15 normal and 40 DCIS samples. We also validate a prognostic signature on 583 breast cancer samples from The Cancer Genome Atlas. Our analysis reveals that DNA methylation profiles of DCIS are radically altered compared to normal breast tissue, involving more than 5,000 genes. Changes between DCIS and invasive breast carcinoma involve around 1,000 genes. In tumors, DNA methylation is associated with gene expression of almost 3,000 genes, including both negative and positive correlations. A prognostic signature based on methylation level of 18 CpGs is associated with survival of breast cancer patients with invasive tumors, as well as with survival of patients with DCIS and mixed lesions of DCIS and invasive breast carcinoma. CONCLUSIONS This work demonstrates that changes in the epigenome occur early in the neoplastic progression, provides evidence for the possible utilization of DNA methylation-based markers of progression in the clinic, and highlights the importance of epigenetic changes in carcinogenesis.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Vessela N Kristensen
- Department of Genetics, Institute for Cancer Research, OUS Radiumhospitalet, Montebello, Oslo, 0310, Norway.
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Tramm T, Mohammed H, Myhre S, Kyndi M, Alsner J, Børresen-Dale AL, Sørlie T, Frigessi A, Overgaard J. Development and validation of a gene profile predicting benefit of postmastectomy radiotherapy in patients with high-risk breast cancer: a study of gene expression in the DBCG82bc cohort. Clin Cancer Res 2014; 20:5272-80. [PMID: 25149560 DOI: 10.1158/1078-0432.ccr-14-0458] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
PURPOSE To identify genes predicting benefit of radiotherapy in patients with high-risk breast cancer treated with systemic therapy and randomized to receive or not receive postmastectomy radiotherapy (PMRT). EXPERIMENTAL DESIGN The study was based on the Danish Breast Cancer Cooperative Group (DBCG82bc) cohort. Gene-expression analysis was performed in a training set of frozen tumor tissue from 191 patients. Genes were identified through the Lasso method with the endpoint being locoregional recurrence (LRR). A weighted gene-expression index (DBCG-RT profile) was calculated and transferred to quantitative real-time PCR (qRT-PCR) in corresponding formalin-fixed, paraffin-embedded (FFPE) samples, before validation in FFPE from 112 additional patients. RESULTS Seven genes were identified, and the derived DBCG-RT profile divided the 191 patients into "high LRR risk" and "low LRR risk" groups. PMRT significantly reduced risk of LRR in "high LRR risk" patients, whereas "low LRR risk" patients showed no additional reduction in LRR rate. Technical transfer of the DBCG-RT profile to FFPE/qRT-PCR was successful, and the predictive impact was successfully validated in another 112 patients. CONCLUSIONS A DBCG-RT gene profile was identified and validated, identifying patients with very low risk of LRR and no benefit from PMRT. The profile may provide a method to individualize treatment with PMRT.
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Affiliation(s)
- Trine Tramm
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark.
| | - Hayat Mohammed
- Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Simen Myhre
- Department of Genetics, Institute of Cancer Research, Oslo University Hospital, Radiumhospitalet, Norway. K-G. Jebsen Center for Breast Cancer Research, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. Atlantis Medical University College, Oslo, Norway
| | - Marianne Kyndi
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Jan Alsner
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Anne-Lise Børresen-Dale
- Department of Genetics, Institute of Cancer Research, Oslo University Hospital, Radiumhospitalet, Norway. K-G. Jebsen Center for Breast Cancer Research, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Therese Sørlie
- Department of Genetics, Institute of Cancer Research, Oslo University Hospital, Radiumhospitalet, Norway. K-G. Jebsen Center for Breast Cancer Research, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Arnoldo Frigessi
- Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Jens Overgaard
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
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Abstract
Combined analyses of molecular data, such as DNA copy-number alteration, mRNA and protein expression, point to biological functions and molecular pathways being deregulated in multiple cancers. Genomic, metabolomic and clinical data from various solid cancers and model systems are emerging and can be used to identify novel patient subgroups for tailored therapy and monitoring. The integrative genomics methodologies that are used to interpret these data require expertise in different disciplines, such as biology, medicine, mathematics, statistics and bioinformatics, and they can seem daunting. The objectives, methods and computational tools of integrative genomics that are available to date are reviewed here, as is their implementation in cancer research.
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Affiliation(s)
- Vessela N Kristensen
- 1] Department of Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Montebello, 0310 Oslo, Norway. [2] K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, 0313 Oslo, Norway. [3] Department of Clinical Molecular Oncology, Division of Medicine, Akershus University Hospital, 1478 Ahus, Norway
| | - Ole Christian Lingjærde
- 1] K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, 0313 Oslo, Norway. [2] Division for Biomedical Informatics, Department of Computer Science, University of Oslo, 0316 Oslo, Norway
| | - Hege G Russnes
- 1] Department of Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Montebello, 0310 Oslo, Norway. [2] K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, 0313 Oslo, Norway. [3] Department of Pathology, Oslo University Hospital, 0450 Oslo, Norway
| | - Hans Kristian M Vollan
- 1] Department of Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Montebello, 0310 Oslo, Norway. [2] K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, 0313 Oslo, Norway. [3] Department of Oncology, Division of Cancer, Surgery and Transplantation, Oslo University Hospital, 0450 Oslo, Norway
| | - Arnoldo Frigessi
- 1] Statistics for Innovation, Norwegian Computing Center, 0314 Oslo, Norway. [2] Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Anne-Lise Børresen-Dale
- 1] Department of Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Montebello, 0310 Oslo, Norway. [2] K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, 0313 Oslo, Norway
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Affiliation(s)
| | | | | | - Arnoldo Frigessi
- Norwegian Computing Center
- Institute of Basic Medical Sciences, Department of Biostatistics; University of Oslo
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Sandve GK, Gundersen S, Johansen M, Glad IK, Gunathasan K, Holden L, Holden M, Liestøl K, Nygård S, Nygaard V, Paulsen J, Rydbeck H, Trengereid K, Clancy T, Drabløs F, Ferkingstad E, Kalaš M, Lien T, Rye MB, Frigessi A, Hovig E. The Genomic HyperBrowser: an analysis web server for genome-scale data. Nucleic Acids Res 2013; 41:W133-41. [PMID: 23632163 PMCID: PMC3692097 DOI: 10.1093/nar/gkt342] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2013] [Revised: 03/27/2013] [Accepted: 04/10/2013] [Indexed: 11/14/2022] Open
Abstract
The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome.
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Affiliation(s)
- Geir K. Sandve
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Sveinung Gundersen
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Morten Johansen
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Ingrid K. Glad
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Krishanthi Gunathasan
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Lars Holden
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Marit Holden
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Knut Liestøl
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Ståle Nygård
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Vegard Nygaard
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Jonas Paulsen
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Halfdan Rydbeck
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Kai Trengereid
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Trevor Clancy
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Finn Drabløs
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Egil Ferkingstad
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Matúš Kalaš
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Tonje Lien
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Morten B. Rye
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Arnoldo Frigessi
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
| | - Eivind Hovig
- Department of Informatics, University of Oslo, PO Box 1080, Blindern, 0316 Oslo, Norway, Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, PO Box 4950, Nydalen, 0424 Oslo, Norway, Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway, Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4950, Nydalen, N-0424 Oslo, Norway, Department of Mathematics, University of Oslo, PO Box 1053, Blindern, 0316 Oslo, Norway, Department of Medical Biology, Faculty of Health Science, University of Tromsø, 9037 Tromsø, Norway, Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway, Bioinformatics Core Facility, Oslo University Hospital and University of Oslo, PO Box 4950 Nydalen, N-0424 Oslo, Norway, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway, Computational Biology Unit, Uni Computing, Uni Research AS, 5020 Bergen, Norway and Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway
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