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      Requirements Analysis and Specification for a Molecular Tumor Board Platform Based on cBioPortal

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          Abstract

          Clinicians in molecular tumor boards (MTB) are confronted with a growing amount of genetic high-throughput sequencing data. Today, at German university hospitals, these data are usually handled in complex spreadsheets from which clinicians have to obtain the necessary information. The aim of this work was to gather a comprehensive list of requirements to be met by cBioPortal to support processes in MTBs according to clinical needs. Therefore, oncology experts at nine German university hospitals were surveyed in two rounds of interviews. To generate an interview guideline a scoping review was conducted. For visual support in the second round, screenshot mockups illustrating the requirements from the first round were created. Requirements that cBioPortal already meets were skipped during the second round. In the end, 24 requirements with sometimes several conceivable options were identified and 54 screenshot mockups were created. Some of the identified requirements have already been suggested to the community by other users or are currently being implemented in cBioPortal. This shows, that the results are in line with the needs expressed by various disciplines. According to our findings, cBioPortal has the potential to significantly improve the processes and analyses of an MTB after the implementation of the identified requirements.

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          Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

          The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
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            The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies

            Background Precision medicine in oncology relies on rapid associations between patient-specific variations and targeted therapeutic efficacy. Due to the advancement of genomic analysis, a vast literature characterizing cancer-associated molecular aberrations and relative therapeutic relevance has been published. However, data are not uniformly reported or readily available, and accessing relevant information in a clinically acceptable time-frame is a daunting proposition, hampering connections between patients and appropriate therapeutic options. One important therapeutic avenue for oncology patients is through clinical trials. Accordingly, a global view into the availability of targeted clinical trials would provide insight into strengths and weaknesses and potentially enable research focus. However, data regarding the landscape of clinical trials in oncology is not readily available, and as a result, a comprehensive understanding of clinical trial availability is difficult. Results To support clinical decision-making, we have developed a data loader and mapper that connects sequence information from oncology patients to data stored in an in-house database, the JAX Clinical Knowledgebase (JAX-CKB), which can be queried readily to access comprehensive data for clinical reporting via customized reporting queries. JAX-CKB functions as a repository to house expertly curated clinically relevant data surrounding our 358-gene panel, the JAX Cancer Treatment Profile (JAX CTP), and supports annotation of functional significance of molecular variants. Through queries of data housed in JAX-CKB, we have analyzed the landscape of clinical trials relevant to our 358-gene targeted sequencing panel to evaluate strengths and weaknesses in current molecular targeting in oncology. Through this analysis, we have identified patient indications, molecular aberrations, and targeted therapy classes that have strong or weak representation in clinical trials. Conclusions Here, we describe the development and disseminate system methods for associating patient genomic sequence data with clinically relevant information, facilitating interpretation and providing a mechanism for informing therapeutic decision-making. Additionally, through customized queries, we have the capability to rapidly analyze the landscape of targeted therapies in clinical trials, enabling a unique view into current therapeutic availability in oncology.
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              MIRACUM: Medical Informatics in Research and Care in University Medicine

              Summary Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Similar to other large international data sharing networks (e.g. OHDSI, PCORnet, eMerge, RD-Connect) MIRACUM is a consortium of academic and hospital partners as well as one industrial partner in eight German cities which have joined forces to create interoperable data integration centres (DIC) and make data within those DIC available for innovative new IT solutions in patient care and medical research. Objectives: Sharing data shall be supported by common interoperable tools and services, in order to leverage the power of such data for biomedical discovery and moving towards a learning health system. This paper aims at illustrating the major building blocks and concepts which MIRACUM will apply to achieve this goal. Governance and Policies: Besides establishing an efficient governance structure within the MIRACUM consortium (based on the steering board, a central administrative office, the general MIRACUM assembly, six working groups and the international scientific advisory board), defining DIC governance rules and data sharing policies, as well as establishing (at each MIRACUM DIC site, but also for MIRACUM in total) use and access committees are major building blocks for the success of such an endeavor. Architectural Framework and Methodology: The MIRACUM DIC architecture builds on a comprehensive ecosystem of reusable open source tools (MIRACOLIX), which are linkable and interoperable amongst each other, but also with the existing software environment of the MIRACUM hospitals. Efficient data protection measures, considering patient consent, data harmonization and a MIRACUM metadata repository as well as a common data model are major pillars of this framework. The methodological approach for shared data usage relies on a federated querying and analysis concept. Use Cases: MIRACUM aims at proving the value of their DIC with three use cases: IT support for patient recruitment into clinical trials, the development and routine care implementation of a clinico-molecular predictive knowledge tool, and molecular-guided therapy recommendations in molecular tumor boards. Results: Based on the MIRACUM DIC release in the nine months conceptual phase first large scale analysis for stroke and colorectal cancer cohorts have been pursued. Discussion: Beyond all technological challenges successfully applying the MIRACUM tools for the enrichment of our knowledge about diagnostic and therapeutic concepts, thus supporting the concept of a Learning Health System will be crucial for the acceptance and sustainability in the medical community and the MIRACUM university hospitals.
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                Author and article information

                Journal
                Diagnostics (Basel)
                Diagnostics (Basel)
                diagnostics
                Diagnostics
                MDPI
                2075-4418
                10 February 2020
                February 2020
                : 10
                : 2
                : 93
                Affiliations
                [1 ]Department of Medical Informatics, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), 91058 Erlangen-Tennenlohe, Germany; philipp.buechner@ 123456fau.de (P.B.); marc.hinderer@ 123456fau.de (M.H.); philipp.unberath@ 123456fau.de (P.U.)
                [2 ]Institute of Medical Bioinformatics and Systems Medicine, Faculty of Medicine and Medical Center-University of Freiburg, 79110 Freiburg, Germany; patrick.metzger@ 123456mol-med.uni-freiburg.de
                [3 ]Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
                [4 ]Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, 79104 Freiburg, Germany; martin.boeker@ 123456imbi.uni-freiburg.de
                [5 ]Institute of Neuropathology, Justus-Liebig-University Giessen, 35392 Giessen, Germany; till.acker@ 123456patho.med.uni-giessen.de
                [6 ]Institute of Pathology, University Hospital Erlangen, 91054 Erlangen, Germany; florian.haller@ 123456uk-erlangen.de
                [7 ]Department of Hematology, Oncology and Immunology, Philipps-University Marburg, and University Hospital Gießen and Marburg, Marburg, Germany Baldingerstraße, 35043 Marburg, Germany; elisabeth.mack@ 123456staff.uni-marburg.de
                [8 ]Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany; daniel.nowak@ 123456medma.uni-heidelberg.de
                [9 ]Heinrich-Lanz-Center for Digital Health, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
                [10 ]Pediatric Hematology/Oncology, Children’s Hospital, University Medical Center of the Johannes Gutenberg-University Mainz, 55131 Mainz, Germany; paretc@ 123456uni-mainz.de
                [11 ]University Cancer Center (UCT) of the University Medical Center of the Johannes Gutenberg-University Mainz, 55131 Mainz, Germany
                [12 ]Institute of Human Genetics, University Hospital Magdeburg, Faculty of Medicine, Otto-von-Guericke University, 39120 Magdeburg, Germany; denny.schanze@ 123456med.ovgu.de
                [13 ]Department of Hematology and Oncology, Medical Center, University of Schleswig-Holstein, Campus Lübeck, 23538 Lübeck, Germany; nikolas.bubnoff@ 123456uniklinik-freiburg.de
                [14 ]German Cancer Consortium (DKTK), partner site Freiburg, 79106 Freiburg, Germany
                [15 ]Department of Hematology, Oncology and Stem Cell Transplantation, Medical Center, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
                [16 ]Department of Medicine 2, Hematology/Oncology, Goethe University Hospital, 60590 Frankfurt am Main, Germany; swagner@ 123456med.uni-frankfurt.de
                [17 ]Institute of Experimental Dermatology and Institute of Cardiogenetics, University of Lübeck, 23562 Lübeck, Germany; hauke.busch@ 123456uni-luebeck.de
                [18 ]Comprehensive Cancer Center Freiburg (CCCF), Faculty of Medicine and Medical Center – University of Freiburg, 79106 Freiburg, Germany
                [19 ]German Cancer Consortium (DKTK), partner site Freiburg; and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-2451-1943
                https://orcid.org/0000-0003-4061-494X
                https://orcid.org/0000-0002-9094-1634
                https://orcid.org/0000-0003-4763-4521
                https://orcid.org/0000-0003-4369-3591
                Article
                diagnostics-10-00093
                10.3390/diagnostics10020093
                7167859
                32050609
                fc4265cf-48e5-4ec9-a9b8-784eec02245d
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 15 December 2019
                : 07 February 2020
                Categories
                Article

                decision making, computer-assisted,decision support systems, clinical,precision medicine,computational biology,molecular tumor board,cbioportal,requirements analysis,neoplasms

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