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Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review
OBJECTIVE: To review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for future biomarker projects. DESIGN: Scoping review...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8650485/ https://www.ncbi.nlm.nih.gov/pubmed/34873011 http://dx.doi.org/10.1136/bmjopen-2021-053674 |
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author | Glaab, Enrico Rauschenberger, Armin Banzi, Rita Gerardi, Chiara Garcia, Paula Demotes, Jacques |
author_facet | Glaab, Enrico Rauschenberger, Armin Banzi, Rita Gerardi, Chiara Garcia, Paula Demotes, Jacques |
author_sort | Glaab, Enrico |
collection | PubMed |
description | OBJECTIVE: To review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for future biomarker projects. DESIGN: Scoping review. METHODS: We searched PubMed, EMBASE and Web of Science to obtain a comprehensive list of articles from the biomedical literature published between January 2000 and July 2021, describing clinically validated biomarker signatures for patient stratification, derived using statistical learning approaches. All documents were screened to retain only peer-reviewed research articles, review articles or opinion articles, covering supervised and unsupervised machine learning applications for omics-based patient stratification. Two reviewers independently confirmed the eligibility. Disagreements were solved by consensus. We focused the final analysis on omics-based biomarkers which achieved the highest level of validation, that is, clinical approval of the developed molecular signature as a laboratory developed test or FDA approved tests. RESULTS: Overall, 352 articles fulfilled the eligibility criteria. The analysis of validated biomarker signatures identified multiple common methodological and practical features that may explain the successful test development and guide future biomarker projects. These include study design choices to ensure sufficient statistical power for model building and external testing, suitable combinations of non-targeted and targeted measurement technologies, the integration of prior biological knowledge, strict filtering and inclusion/exclusion criteria, and the adequacy of statistical and machine learning methods for discovery and validation. CONCLUSIONS: While most clinically validated biomarker models derived from omics data have been developed for personalised oncology, first applications for non-cancer diseases show the potential of multivariate omics biomarker design for other complex disorders. Distinctive characteristics of prior success stories, such as early filtering and robust discovery approaches, continuous improvements in assay design and experimental measurement technology, and rigorous multicohort validation approaches, enable the derivation of specific recommendations for future studies. |
format | Online Article Text |
id | pubmed-8650485 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-86504852021-12-22 Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review Glaab, Enrico Rauschenberger, Armin Banzi, Rita Gerardi, Chiara Garcia, Paula Demotes, Jacques BMJ Open Patient-Centred Medicine OBJECTIVE: To review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for future biomarker projects. DESIGN: Scoping review. METHODS: We searched PubMed, EMBASE and Web of Science to obtain a comprehensive list of articles from the biomedical literature published between January 2000 and July 2021, describing clinically validated biomarker signatures for patient stratification, derived using statistical learning approaches. All documents were screened to retain only peer-reviewed research articles, review articles or opinion articles, covering supervised and unsupervised machine learning applications for omics-based patient stratification. Two reviewers independently confirmed the eligibility. Disagreements were solved by consensus. We focused the final analysis on omics-based biomarkers which achieved the highest level of validation, that is, clinical approval of the developed molecular signature as a laboratory developed test or FDA approved tests. RESULTS: Overall, 352 articles fulfilled the eligibility criteria. The analysis of validated biomarker signatures identified multiple common methodological and practical features that may explain the successful test development and guide future biomarker projects. These include study design choices to ensure sufficient statistical power for model building and external testing, suitable combinations of non-targeted and targeted measurement technologies, the integration of prior biological knowledge, strict filtering and inclusion/exclusion criteria, and the adequacy of statistical and machine learning methods for discovery and validation. CONCLUSIONS: While most clinically validated biomarker models derived from omics data have been developed for personalised oncology, first applications for non-cancer diseases show the potential of multivariate omics biomarker design for other complex disorders. Distinctive characteristics of prior success stories, such as early filtering and robust discovery approaches, continuous improvements in assay design and experimental measurement technology, and rigorous multicohort validation approaches, enable the derivation of specific recommendations for future studies. BMJ Publishing Group 2021-12-05 /pmc/articles/PMC8650485/ /pubmed/34873011 http://dx.doi.org/10.1136/bmjopen-2021-053674 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Patient-Centred Medicine Glaab, Enrico Rauschenberger, Armin Banzi, Rita Gerardi, Chiara Garcia, Paula Demotes, Jacques Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review |
title | Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review |
title_full | Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review |
title_fullStr | Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review |
title_full_unstemmed | Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review |
title_short | Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review |
title_sort | biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review |
topic | Patient-Centred Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8650485/ https://www.ncbi.nlm.nih.gov/pubmed/34873011 http://dx.doi.org/10.1136/bmjopen-2021-053674 |
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