Proteogenomics for precision medicine: overcoming challenges in specificity, reproducibility, and data integration
dc.contributor.advisor
Wilson, Jim
dc.contributor.advisor
Navarro, Pau
dc.contributor.advisor
Klaric, Lucija
dc.contributor.author
Kuliesius, Simonas Jurgis
dc.contributor.sponsor
Medical Research Council (MRC)
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dc.contributor.sponsor
University of Edinburgh
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dc.contributor.sponsor
University of Edinburgh
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dc.date.accessioned
2025-08-19T10:46:19Z
dc.date.available
2025-08-19T10:46:19Z
dc.date.issued
2025-08-19
dc.description.abstract
The integration of proteomics with genomics—termed proteogenomics—offers an unprecedented opportunity to deepen our understanding of human biology, disease mechanisms, and biomarker discovery. Proteogenomics leverages protein abundance data to inform genomic analyses, refining our ability to detect and interpret variations in protein levels that underlie health and disease. However, despite its promise, proteogenomics faces several methodological and technological challenges, particularly in the precision, scalability, and reproducibility of proteomic measurements.
Multiplex proteomics platforms have evolved significantly over the last decade, providing extensive coverage of the human proteome. Traditional techniques such as mass spectrometry have been challenged by affinity-based proteomics, including SomaLogic's SomaScan and Olink’s proximity extension assay, which have vastly expanded the depth of protein quantification to thousands of proteins.
A fundamental challenge in proteogenomics today is target specificity and standardization. While genetic studies rely on the stability of DNA, proteins are highly dynamic, influenced by post-translational modifications, protein-protein interactions, and environmental conditions.
Nonetheless, proteogenomics holds transformative potential in clinical and translational research. Large proteomic datasets are already discovering new pathways for disease and health, in addition to demonstrating predictive power in disease onset modelling, outperforming traditional clinical biomarkers. The increasing integration of machine learning into proteogenomics workflows promises to enhance biomarker discovery and clinical translation.
This thesis explores the application of state-of-the-art proteomics technologies in proteogenomics, emphasizing their strengths, limitations, and implications for biomarker discovery and disease association studies. The work presented herein aims to address key challenges in proteomic specificity, cross-platform reproducibility, and the integration of proteomic data with genomic insights in small cohorts to advance precision medicine.
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dc.identifier.uri
https://hdl.handle.net/1842/43829
dc.identifier.uri
https://github.com/viking-genes/SomaScan-pQTL
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dc.identifier.uri
http://dx.doi.org/10.7488/era/6360
dc.language.iso
en
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dc.publisher
The University of Edinburgh
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dc.relation.hasversion
Efficient candidate drug target discovery through proteogenomics in a Scottish cohort Kuliesius, J., Timmers, P., Navarro, P., Klaric, L. & Wilson, J. F., 18 Aug 2025, (Accepted/In press) In: Nature
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dc.relation.hasversion
Epitope Effect Prevalence in Affinity-based pQTL studies Jurgis Kuliesius, Mihaly Badonyi, Pau Navarro, Joseph A. Marsh, Lucija Klaric, James F. Wilson bioRxiv 2025.06.20.660695; doi: https://doi.org/10.1101/2025.06.20.660695
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dc.rights.license
CC BY 4.0
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dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
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dc.subject
GWAS
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dc.subject
Proteogenomics
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dc.subject
Proteomics
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dc.subject
Precision Medicine
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dc.subject
pQTL
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dc.subject
SomaScan
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dc.subject
Biomarker discovery
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dc.subject
Mendelian Randomisation
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dc.subject
Olink
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dc.title
Proteogenomics for precision medicine: overcoming challenges in specificity, reproducibility, and data integration
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dc.type
Thesis or Dissertation
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dc.type.qualificationlevel
Doctoral
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dc.type.qualificationname
PhD Doctor of Philosophy
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