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Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis
Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophistica...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036914/ https://www.ncbi.nlm.nih.gov/pubmed/36968610 http://dx.doi.org/10.3389/fgene.2023.1100352 |
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author | Tsakiroglou, Maria Evans, Anthony Pirmohamed, Munir |
author_facet | Tsakiroglou, Maria Evans, Anthony Pirmohamed, Munir |
author_sort | Tsakiroglou, Maria |
collection | PubMed |
description | Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation. |
format | Online Article Text |
id | pubmed-10036914 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100369142023-03-25 Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis Tsakiroglou, Maria Evans, Anthony Pirmohamed, Munir Front Genet Genetics Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation. Frontiers Media S.A. 2023-03-10 /pmc/articles/PMC10036914/ /pubmed/36968610 http://dx.doi.org/10.3389/fgene.2023.1100352 Text en Copyright © 2023 Tsakiroglou, Evans and Pirmohamed. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Tsakiroglou, Maria Evans, Anthony Pirmohamed, Munir Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis |
title | Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis |
title_full | Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis |
title_fullStr | Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis |
title_full_unstemmed | Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis |
title_short | Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis |
title_sort | leveraging transcriptomics for precision diagnosis: lessons learned from cancer and sepsis |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036914/ https://www.ncbi.nlm.nih.gov/pubmed/36968610 http://dx.doi.org/10.3389/fgene.2023.1100352 |
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