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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1100352/full |
| _version_ | 1811155351165206528 |
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| author | Maria Tsakiroglou Anthony Evans Munir Pirmohamed |
| author_facet | Maria Tsakiroglou Anthony Evans Munir Pirmohamed |
| author_sort | Maria Tsakiroglou |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-10T04:32:42Z |
| format | Article |
| id | doaj.art-45274395e84c47f5ad78ae3db2604bf1 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-10T04:32:42Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-45274395e84c47f5ad78ae3db2604bf12023-03-10T05:43:40ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-03-011410.3389/fgene.2023.11003521100352Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsisMaria Tsakiroglou0Anthony Evans1Munir Pirmohamed2Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United KingdomComputational Biology Facility, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United KingdomDepartment of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United KingdomDiagnostics 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.https://www.frontiersin.org/articles/10.3389/fgene.2023.1100352/fullbiomarkercancerdiagnosissepsistranscriptomics |
| spellingShingle | Maria Tsakiroglou Anthony Evans Munir Pirmohamed Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis Frontiers in Genetics biomarker cancer diagnosis sepsis transcriptomics |
| 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 | biomarker cancer diagnosis sepsis transcriptomics |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1100352/full |
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