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.