Genomic and proteomic biomarker landscape in clinical trials

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  • dc.contributor.author Piñero González, Janet, 1977-
  • dc.contributor.author Rodriguez Fraga, Pablo S.
  • dc.contributor.author Valls-Margarit, Jordi
  • dc.contributor.author Ronzano, Francesco
  • dc.contributor.author Accuosto, Pablo
  • dc.contributor.author Lambea Jane, Ricard
  • dc.contributor.author Sanz, Ferran
  • dc.contributor.author Furlong, Laura I., 1971-
  • dc.date.accessioned 2023-05-23T10:34:22Z
  • dc.date.available 2023-05-23T10:34:22Z
  • dc.date.issued 2023
  • dc.description.abstract The use of molecular biomarkers to support disease diagnosis, monitor its progression, and guide drug treatment has gained traction in the last decades. While only a dozen biomarkers have been approved for their exploitation in the clinic by the FDA, many more are evaluated in the context of translational research and clinical trials. Furthermore, the information on which biomarkers are measured, for which purpose, and in relation to which conditions are not readily accessible: biomarkers used in clinical studies available through resources such as ClinicalTrials.gov are described as free text, posing significant challenges in finding, analyzing, and processing them by both humans and machines. We present a text mining strategy to identify proteomic and genomic biomarkers used in clinical trials and classify them according to the methodologies by which they are measured. We find more than 3000 biomarkers used in the context of 2600 diseases. By analyzing this dataset, we uncover patterns of use of biomarkers across therapeutic areas over time, including the biomarker type and their specificity. These data are made available at the Clinical Biomarker App at https://www.disgenet.org/biomarkers/, a new portal that enables the exploration of biomarkers extracted from the clinical studies available at ClinicalTrials.gov and enriched with information from the scientific literature. The App features several metrics that assess the specificity of the biomarkers, facilitating their selection and prioritization. Overall, the Clinical Biomarker App is a valuable and timely resource about clinical biomarkers, to accelerate biomarker discovery, development, and application.
  • dc.description.sponsorship This work was partially funded by IMI2-JU grants, resources which are composed of financial contributions from the European Union’s Horizon 2020 Research and Innovation Programme and EFPIA [GA: 116030 TransQST and GA: 777365 eTRANSAFE], and the EU H2020 Programme [GA: 871075 Elixir-CONVERGE and GA:964537 RISKHUNT3R, which is part of the ASPIS cluster]; Project 001-P-001647—Valorisation of EGA for Industry and Society funded by the European Regional Development Fund (ERDF) and Generalitat de Catalunya; Agència de Gestió d’Ajuts Universitaris i de Recerca Generalitat de Catalunya [2017SGR00519], and the Institute of Health Carlos III (project IMPaCT-Data, exp. IMP/00019), co-funded by the European Union, European Regional Development Fund (ERDF, “A way to make Europe”). The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), funded by ISCIII and ERDF (PRB2-ISCIII [PT13/0001/0023, of the PE I + D + i 2013-2016]). The MELIS is a ‘Unidad de Excelencia María de Maeztu’, funded by the MINECO [MDM-2014-0370]. This work reflects only the author’s view and that the IMI2-JU is not responsible for any use that may be made of the information it contains. The European Commission is not responsible for any use that may be made of the information it contains.
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Piñero J, Rodriguez PS, Valls-Margarit J, Ronzano F, Accuosto P, Lambea R, et al. Genomic and proteomic biomarker landscape in clinical trials. Computational and Structural Biotechnology Journal. 2023;21:2110-8.DOI: 10.1016/j.csbj.2023.03.014
  • dc.identifier.doi http://dx.doi.org/10.1016/j.csbj.2023.03.014
  • dc.identifier.issn 2001-0370
  • dc.identifier.uri http://hdl.handle.net/10230/56946
  • dc.language.iso eng
  • dc.publisher Elsevier
  • dc.relation.ispartof Computational and Structural Biotechnology Journal. 2023;21:2110-8
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/116030
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/777365
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/871075
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/964537
  • dc.rights © 2023 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
  • dc.subject.keyword Biomarker
  • dc.subject.keyword Genomic biomarker
  • dc.subject.keyword Proteomic biomarker
  • dc.subject.keyword Actionable biomarker
  • dc.subject.keyword Clinical trial
  • dc.subject.keyword Text mining
  • dc.title Genomic and proteomic biomarker landscape in clinical trials
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/publishedVersion

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