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Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis
Maria Tsakiroglou1, Anthony Evans2, Munir Pirmohamed1
1Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom.
Frontiers in Genetics
|March 27, 2023
Summary
Integrating multi-omic and clinical data, particularly RNA gene expression, offers powerful diagnostic and prognostic insights for diseases like cancer and sepsis. Overcoming analytical challenges can unlock transcriptomics
Area of Science:
- Transcriptomics and Computational Biology
- Clinical Diagnostics
- Disease Pathogenesis
Background:
- Clinical utility of diagnostics hinges on precision and predictive power.
- Multi-omic data integration with clinical information is vital for understanding disease.
- Sophisticated computational tools are needed to interpret complex omics data for clinical application.
Purpose of the Study:
- To review the clinical applications of transcriptomics in cancer and infection.
- To highlight challenges in identifying diagnostic and prognostic biomarkers from gene expression data.
- To propose strategies for overcoming implementation hurdles in clinical settings.
Main Methods:
- Review of current literature on transcriptomics in clinical diagnostics.
- Analysis of gene expression-based tests in cancer (e.g., Oncotype DX).
- Examination of transcriptomics for sepsis endotyping and infection discrimination.
Main Results:
- Gene expression profiling aids in early breast cancer treatment decisions.
- Transcriptomics can identify sepsis endotypes with prognostic value.
- RNA's dynamic regulatory information is underutilized in clinical diagnostics.
Conclusions:
- Transcriptomics holds significant promise for patient stratification in clinical practice and trials.
- Standardization of technical and analytical methods is crucial for widespread implementation.
- Addressing current impediments will facilitate the clinical translation of transcriptomic biomarkers.
