Abstract
Colorectal cancer (CRC) is a significant and growing global health concern, emphasising the importance of early detection to reduce mortality and healthcare expense. Non-invasive screening tests are the first step for the identification of high risk patients amongst an asymptomatic population. Current methods have room for improvement in terms of accuracy, particularly for early stage disease. This study aimed to discover novel biomarkers specific to CRC to improve the sensitivity and specificity of non-invasive screening tests.Preclinical mouse models were leveraged to mimic human CRC pathophysiology, enhance reproducibility, and reduce bias in the biomarker discovery process. A gut-specific mutation in Apc mimicked the first step of tumour initiation that occurs in the majority of CRC, and caused activation of Wnt signalling that is observed in 94% of CRC cases. Combined with the genetically engineered mouse model, advanced multiomics techniques (transcriptomics, proteomics, metabolomics, metagenomics) were used to comprehensively characterise disease progression. The multiomics approach provided global insights into various types of biomolecule, expanding the range of potential sources of biomarkers based on disease-specific alterations.
Firstly, the multiomics approach is described and the Apc-mutant mouse model is characterised at tissue-level. Secondly, the biomolecular changes are examined in non-invasive stool samples. Finally, multiomics data are integrated to evaluate biomarker candidates based on coordinated changes across multiple types of biomolecule.
This study presents new progress towards answering the challenge of CRC detection by identifying disease-specific biomarker candidates in a reproducible system using unbiased methods. This approach can be applied beyond CRC and broadens biomarker discovery possibilities. Future work involves validating biomarker candidates and creating assays to design non-invasive screening tests with enhanced sensitivity and specificity for CRC. Improved early detection promises better treatment outcomes, reduced burden from CRC, and improved patient outlook.
| Date of Award | 1 Mar 2024 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | affiliated academic (Supervisor) & Joana Neves (Supervisor) |