1
|
Costa M, García S A, Pastor O. The consequences of data dispersion in genomics: a comparative analysis of data sources for precision medicine. BMC Med Inform Decis Mak 2023; 23:256. [PMID: 37946154 PMCID: PMC10636939 DOI: 10.1186/s12911-023-02342-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 10/13/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Genomics-based clinical diagnosis has emerged as a novel medical approach to improve diagnosis and treatment. However, advances in sequencing techniques have increased the generation of genomics data dramatically. This has led to several data management problems, one of which is data dispersion (i.e., genomics data is scattered across hundreds of data repositories). In this context, geneticists try to remediate the above-mentioned problem by limiting the scope of their work to a single data source they know and trust. This work has studied the consequences of focusing on a single data source rather than considering the many different existing genomics data sources. METHODS The analysis is based on the data associated with two groups of disorders (i.e., oncology and cardiology) accessible from six well-known genomic data sources (i.e., ClinVar, Ensembl, GWAS Catalog, LOVD, CIViC, and CardioDB). Two dimensions have been considered in this analysis, namely, completeness and concordance. Completeness has been evaluated at two levels. First, by analyzing the information provided by each data source with regard to a conceptual schema data model (i.e., the schema level). Second, by analyzing the DNA variations provided by each data source as related to any of the disorders selected (i.e., the data level). Concordance has been evaluated by comparing the consensus among the data sources regarding the clinical relevance of each variation and disorder. RESULTS The data sources with the highest completeness at the schema level are ClinVar, Ensembl, and CIViC. ClinVar has the highest completeness at the data level data source for the oncology and cardiology disorders. However, there are clinically relevant variations that are exclusive to other data sources, and they must be considered in order to provide the best clinical diagnosis. Although the information available in the data sources is predominantly concordant, discordance among the analyzed data exist. This can lead to inaccurate diagnoses. CONCLUSION Precision medicine analyses using a single genomics data source leads to incomplete results. Also, there are concordance problems that threaten the correctness of the genomics-based diagnosis results.
Collapse
Affiliation(s)
- Mireia Costa
- PROS Research Center, VRAIN Research Institute, Universitat Politècnica de València, Camino de Vera, Valencia, Spain.
| | - Alberto García S
- PROS Research Center, VRAIN Research Institute, Universitat Politècnica de València, Camino de Vera, Valencia, Spain
| | - Oscar Pastor
- PROS Research Center, VRAIN Research Institute, Universitat Politècnica de València, Camino de Vera, Valencia, Spain
| |
Collapse
|
2
|
Schapranow MP, Borchert F, Bougatf N, Hund H, Eils R. Software-Tool Support for Collaborative, Virtual, Multi-Site Molecular Tumor Boards. SN COMPUTER SCIENCE 2023; 4:358. [PMID: 37131499 PMCID: PMC10136394 DOI: 10.1007/s42979-023-01771-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 03/03/2023] [Indexed: 05/04/2023]
Abstract
The availability of high-throughput molecular diagnostics builds the foundation for Molecular Tumor Boards (MTBs). Although more fine-grained data is expected to support decision making of oncologists, assessment of data is complex and time-consuming slowing down the implementation of MTBs, e.g., due to retrieval of the latest medical publications, assessment of clinical evidence, or linkage to the latest clinical guidelines. We share our findings from analysis of existing tumor board processes and defininion of clinical processes for the adoption of MTBs. Building on our findings, we have developed a real-world software prototype together with oncologists and medical professionals, which supports the preparation and conduct of MTBs and enables collaboration between medical experts by sharing medical knowledge even across the hospital locations. We worked in interdisciplinary teams of clinicians, oncologists, medical experts, medical informaticians, and software engineers using design thinking methodology. With their input, we identified challenges and limitations of the current MTB approaches, derived clinical process models using Business Process and Modeling Notation (BMPN), and defined personas, functional and non-functional requirements for software tool support. Based on it, we developed software prototypes and evaluated them with clinical experts from major university hospitals across Germany. We extended the Kanban methodology enabling holistic tracking of patient cases from "backlog" to "follow-up" in our app. The feedback from interviewed medical professionals showed that our clinical process models and software prototype provide suitable process support for the preparation and conduction of molecular tumor boards. The combination of oncology knowledge across hospitals and the documentation of treatment decision can be used to form a unique medical knowledge base by oncologists for oncologists. Due to the high heterogeneity of tumor diseases and the spread of the latest medical knowledge, a cooperative decision-making process including insights from similar patient cases was considered as a very valuable feature. The ability to transform prepared case data into a screen presentation was recognized as an essential feature speeding up the preparation process. Oncologists require special software tool support to incorporate and assess molecular data for the decision-making process. In particular, the need for linkage to the latest medical knowledge, clinical evidence, and collaborative tools to discuss individual cases were named to be of importance. With the experiences from the COVID-19 pandemic, the acceptance of online tools and collaborative working is expected to grow. Our virtual multi-site approach proved to allow a collaborative decision-making process for the first time, which we consider to have a positive impact on the overall treatment quality.
Collapse
Affiliation(s)
- Matthieu-P. Schapranow
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germany
| | - Florian Borchert
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germany
| | - Nina Bougatf
- Department of Radiation Oncology, Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg University Hospital, Im Neuenheimer Feld 450, 69120 Heidelberg, Germany
| | - Hauke Hund
- GECKO Institute, Heilbronn University of Applied Sciences, Max-Planck-Straße 39, 74081 Heilbronn, Germany
| | - Roland Eils
- Center for Digital Health, Berlin Institute of Health and Charité Universitätsmedizin Berlin, Kapelle-Ufer 2, 10117 Berlin, Germany
| |
Collapse
|
3
|
Rodríguez Ruiz N, Abd Own S, Ekström Smedby K, Eloranta S, Koch S, Wästerlid T, Krstic A, Boman M. Data-driven support to decision-making in molecular tumour boards for lymphoma: A design science approach. Front Oncol 2022; 12:984021. [PMID: 36457495 PMCID: PMC9705761 DOI: 10.3389/fonc.2022.984021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/03/2022] [Indexed: 09/10/2024] Open
Abstract
Background The increasing amount of molecular data and knowledge about genomic alterations from next-generation sequencing processes together allow for a greater understanding of individual patients, thereby advancing precision medicine. Molecular tumour boards feature multidisciplinary teams of clinical experts who meet to discuss complex individual cancer cases. Preparing the meetings is a manual and time-consuming process. Purpose To design a clinical decision support system to improve the multimodal data interpretation in molecular tumour board meetings for lymphoma patients at Karolinska University Hospital, Stockholm, Sweden. We investigated user needs and system requirements, explored the employment of artificial intelligence, and evaluated the proposed design with primary stakeholders. Methods Design science methodology was used to form and evaluate the proposed artefact. Requirements elicitation was done through a scoping review followed by five semi-structured interviews. We used UML Use Case diagrams to model user interaction and UML Activity diagrams to inform the proposed flow of control in the system. Additionally, we modelled the current and future workflow for MTB meetings and its proposed machine learning pipeline. Interactive sessions with end-users validated the initial requirements based on a fictive patient scenario which helped further refine the system. Results The analysis showed that an interactive secure Web-based information system supporting the preparation of the meeting, multidisciplinary discussions, and clinical decision-making could address the identified requirements. Integrating artificial intelligence via continual learning and multimodal data fusion were identified as crucial elements that could provide accurate diagnosis and treatment recommendations. Impact Our work is of methodological importance in that using artificial intelligence for molecular tumour boards is novel. We provide a consolidated proof-of-concept system that could support the end-to-end clinical decision-making process and positively and immediately impact patients. Conclusion Augmenting a digital decision support system for molecular tumour boards with retrospective patient material is promising. This generates realistic and constructive material for human learning, and also digital data for continual learning by data-driven artificial intelligence approaches. The latter makes the future system adaptable to human bias, improving adequacy and decision quality over time and over tasks, while building and maintaining a digital log.
Collapse
Affiliation(s)
- Núria Rodríguez Ruiz
- Department of Learning, Informatics, Management and Ethics (LIME), Health Informatics Centre, Karolinska Institutet, Stockholm, Sweden
| | - Sulaf Abd Own
- Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden
- Department of Laboratory Medicine, Division of Pathology, Karolinska University Hospital Huddinge, Stockholm, Sweden
| | - Karin Ekström Smedby
- Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden
- Department of Hematology, Karolinska University Hospital, Stockholm, Sweden
| | - Sandra Eloranta
- Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden
| | - Sabine Koch
- Department of Learning, Informatics, Management and Ethics (LIME), Health Informatics Centre, Karolinska Institutet, Stockholm, Sweden
| | - Tove Wästerlid
- Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden
- Department of Hematology, Karolinska University Hospital, Stockholm, Sweden
| | - Aleksandra Krstic
- Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Magnus Boman
- Department of Learning, Informatics, Management and Ethics (LIME), Health Informatics Centre, Karolinska Institutet, Stockholm, Sweden
- School of Electrical Engineering and Computer Science (EECS)/Software and Computer Systems, KTH Royal Institute of Technology, Stockholm, Sweden
| |
Collapse
|
4
|
Jiménez‐Santos MJ, García‐Martín S, Fustero‐Torre C, Di Domenico T, Gómez‐López G, Al‐Shahrour F. Bioinformatics roadmap for therapy selection in cancer genomics. Mol Oncol 2022; 16:3881-3908. [PMID: 35811332 PMCID: PMC9627786 DOI: 10.1002/1878-0261.13286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/22/2022] [Accepted: 07/08/2022] [Indexed: 12/24/2022] Open
Abstract
Tumour heterogeneity is one of the main characteristics of cancer and can be categorised into inter- or intratumour heterogeneity. This heterogeneity has been revealed as one of the key causes of treatment failure and relapse. Precision oncology is an emerging field that seeks to design tailored treatments for each cancer patient according to epidemiological, clinical and omics data. This discipline relies on bioinformatics tools designed to compute scores to prioritise available drugs, with the aim of helping clinicians in treatment selection. In this review, we describe the current approaches for therapy selection depending on which type of tumour heterogeneity is being targeted and the available next-generation sequencing data. We cover intertumour heterogeneity studies and individual treatment selection using genomics variants, expression data or multi-omics strategies. We also describe intratumour dissection through clonal inference and single-cell transcriptomics, in each case providing bioinformatics tools for tailored treatment selection. Finally, we discuss how these therapy selection workflows could be integrated into the clinical practice.
Collapse
Affiliation(s)
| | | | - Coral Fustero‐Torre
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Tomás Di Domenico
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Gonzalo Gómez‐López
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Fátima Al‐Shahrour
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| |
Collapse
|
5
|
Li PH, Chen TF, Yu JY, Shih SH, Su CH, Lin YH, Tsai HK, Juan HF, Chen CY, Huang JH. pubmedKB: an interactive web server for exploring biomedical entity relations in the biomedical literature. Nucleic Acids Res 2022; 50:W616-W622. [PMID: 35536289 PMCID: PMC9252824 DOI: 10.1093/nar/gkac310] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 04/06/2022] [Accepted: 04/18/2022] [Indexed: 11/15/2022] Open
Abstract
With the proliferation of genomic sequence data for biomedical research, the exploration of human genetic information by domain experts requires a comprehensive interrogation of large numbers of scientific publications in PubMed. However, a query in PubMed essentially provides search results sorted only by the date of publication. A search engine for retrieving and interpreting complex relations between biomedical concepts in scientific publications remains lacking. Here, we present pubmedKB, a web server designed to extract and visualize semantic relationships between four biomedical entity types: variants, genes, diseases, and chemicals. pubmedKB uses state-of-the-art natural language processing techniques to extract semantic relations from the large number of PubMed abstracts. Currently, over 2 million semantic relations between biomedical entity pairs are extracted from over 33 million PubMed abstracts in pubmedKB. pubmedKB has a user-friendly interface with an interactive semantic graph, enabling the user to easily query entities and explore entity relations. Supporting sentences with the highlighted snippets allow to easily navigate the publications. Combined with a new explorative approach to literature mining and an interactive interface for researchers, pubmedKB thus enables rapid, intelligent searching of the large biomedical literature to provide useful knowledge and insights. pubmedKB is available at https://www.pubmedkb.cc/.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Huai-Kuang Tsai
- Taiwan AI Labs, Taipei 10351, Taiwan.,Institute of Information Science, Academia Sinica, Taipei, 11529, Taiwan
| | - Hsueh-Fen Juan
- Taiwan AI Labs, Taipei 10351, Taiwan.,Department of Life Science, National Taiwan University, Taipei 10617, Taiwan.,Center for Computational and Systems Biology, National Taiwan University, Taipei 10617, Taiwan
| | - Chien-Yu Chen
- Taiwan AI Labs, Taipei 10351, Taiwan.,Center for Computational and Systems Biology, National Taiwan University, Taipei 10617, Taiwan.,Department of Biomechatronics Engineering, National Taiwan University, Taipei, 10617, Taiwan
| | | |
Collapse
|
6
|
Borchert F, Meister L, Langer T, Follmann M, Arnrich B, Schapranow MP. Controversial Trials First: Identifying Disagreement Between Clinical Guidelines and New Evidence. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2021:237-246. [PMID: 35308948 PMCID: PMC8861732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Clinical guidelines integrate latest evidence to support clinical decision-making. As new research findings are published at an increasing rate, it would be helpful to detect when such results disagree with current guideline recommendations. In this work, we describe a software system for the automatic identification of disagreement between clinical guidelines and published research. A critical feature of the system is the extraction and cross-lingual normalization of information through natural language processing. The initial version focuses on the detection of cancer treatments in clinical trial reports that are not addressed in oncology guidelines. We evaluate the relevance of trials retrieved by our system retrospectively by comparison with historic guideline updates and also prospectively through manual evaluation by guideline experts. The system improves precision over state-of-the-art literature research strategies while maintaining near-total recall. Detailed error analysis highlights challenges for fine-grained clinical information extraction, in particular when extracting population definitions for tumor-agnostic therapies.
Collapse
Affiliation(s)
- Florian Borchert
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Germany
| | - Laura Meister
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Germany
| | - Thomas Langer
- German Guideline Program in Oncology, German Cancer Society, Berlin, Germany
| | - Markus Follmann
- German Guideline Program in Oncology, German Cancer Society, Berlin, Germany
| | - Bert Arnrich
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Germany
| | | |
Collapse
|
7
|
Horak P, Leichsenring J, Goldschmid H, Kreutzfeldt S, Kazdal D, Teleanu V, Endris V, Gieldon L, Allgäuer M, Volckmar AL, Dikow N, Renner M, Kirchner M, Penzel R, Ploeger C, Brandt R, Seker-Cin H, Budczies J, Heilig CE, Neumann O, Schaaf CP, Schirmacher P, Fröhling S, Stenzinger A. Assigning evidence to actionability: An introduction to variant interpretation in precision cancer medicine. Genes Chromosomes Cancer 2021; 61:303-313. [PMID: 34331337 DOI: 10.1002/gcc.22987] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 07/25/2021] [Indexed: 12/15/2022] Open
Abstract
Modern concepts in precision cancer medicine are based on increasingly complex genomic analyses and require standardized criteria for the functional evaluation and reporting of detected genomic alterations in order to assess their clinical relevance. In this article, we propose and address the necessary steps in systematic variant evaluation consisting of bioinformatic analysis, functional annotation and clinical interpretation, focusing on the latter two aspects. We discuss the role and clinical application of current variant classification systems and point out their scope and limitations. Finally, we highlight the significance of the molecular tumor board as a platform for clinical decision-making based on genomic analyses.
Collapse
Affiliation(s)
- Peter Horak
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany.,Center for Personalized Medicine (ZPM), Heidelberg, Germany
| | - Jonas Leichsenring
- Institut für Pathologie, Zytologie und molekulare Diagnostik, Regiomed Klinikum Coburg, Coburg, Germany.,Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Hannah Goldschmid
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Simon Kreutzfeldt
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
| | - Daniel Kazdal
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.,German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany
| | - Veronica Teleanu
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany.,Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
| | - Volker Endris
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Laura Gieldon
- Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
| | - Michael Allgäuer
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Anna-Lena Volckmar
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Nicola Dikow
- Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
| | - Marcus Renner
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
| | - Martina Kirchner
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Roland Penzel
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Carolin Ploeger
- Center for Personalized Medicine (ZPM), Heidelberg, Germany.,Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Regine Brandt
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Huriye Seker-Cin
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Jan Budczies
- German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany.,Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.,German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany
| | - Christoph E Heilig
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
| | - Olaf Neumann
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | | | - Peter Schirmacher
- German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany.,Center for Personalized Medicine (ZPM), Heidelberg, Germany.,Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Stefan Fröhling
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany.,Center for Personalized Medicine (ZPM), Heidelberg, Germany
| | - Albrecht Stenzinger
- German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany.,Center for Personalized Medicine (ZPM), Heidelberg, Germany.,Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.,German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany
| |
Collapse
|