Novel molecular tumour classification using MALDI–mass spectrometry imaging of tissue micro-array
The development of tissue micro-array (TMA) technologies provides insights into high-throughput analysis of proteomics patterns from a large number of archived tumour samples. In the work reported here, matrix-assisted laser desorption/ionisation–ion mobility separation–mass spectrometry (MALDI–IMS–MS) profiling and imaging methodology has been used to visualise the distribution of several peptides and identify them directly from TMA sections after on-tissue tryptic digestion. A novel approach that combines MALDI–IMS–MSI and principal component analysis–discriminant analysis (PCA–DA) is described, which has the aim of generating tumour classification models based on protein profile patterns. The molecular classification models obtained by PCA–DA have been validated by applying the same statistical analysis to other tissue cores and patient samples. The ability to correlate proteomic information obtained from samples with known and/or unknown clinical outcome by statistical analysis is of great importance, since it may lead to a better understanding of tumour progression and aggressiveness and hence improve diagnosis, prognosis as well as therapeutic treatments. The selectivity, robustness and current limitations of the methodology are discussed.
KeywordsTumour classification Tissue micro-array Pancreatic cancer MALDI imaging Ion mobility separation
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