Colorectal cancer (CRC) is a leading cause of global cancer-related morbidity and mortality. A deeper understanding of the biological processes that drive CRC development and progression is essential in order to improve patient outcomes along the CRC clinical pathway (diagnosis; treatment; prognostication/surveillance).
Metabonomics (metabolic profiling/metabolomics) is a rapidly advancing field in systems biology that generates disease-relevant micro-molecular information downstream of the genome and proteome. Metabolic profiling studies utilising nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) have demonstrated early promise in cancer research.
For this body of work I have designed and implemented a multi-axis metabolic profiling strategy for evaluation of fresh frozen CRC tissue; the study framework has been specifically designed to assess the translational utility of different NMR and MS –based technologies at key phases of the CRC clinical spectrum and to develop next-generation bioinformatics solutions to facilitate translational deployment of these approaches. My findings have demonstrated that:
(i) High-resolution magic angle spinning NMR (HR-MAS NMR) spectroscopy allows rapid and accurate diagnosis and local staging of CRC, making it well suited to translational deployment at the diagnostic end of the CRC clinical pathway. In addition this approach can potentially be used to develop an alternative to frozen section for intra-operative tissue evaluation.
(ii) Matrix-assisted laser desorption ionisation imaging MS (MALDI-MSI) has potential to supplement conventional histopathological methods of tissue assessment and reveals novel tumour-associated ‘field effects’ which may have translational utility in CRC prognostication.
(iii) Desorption electrospray ionisation imaging MS (DESI-MSI) represents a more sophisticated method for histology-driven imaging mass spectrometry and permits ‘chemical mapping’ of the CRC tumour microenvironment in a way not possible until now. This technique can be used to derive an entirely new pool of biomolecular data with which to develop next-generation cancer biomarkers for prognostic and therapeutic personalisation.
Currently, the complexity and volume of data generated by MSI experimentation represents a major rate-limiting step, hampering translational application of this approach. My work in CRC MSI profiling has concurrently led to the development of a novel bio-informatics pipeline designed to address current challenges, and this platform is also introduced in the following thesis.