MALDI-imaging reveals TYB4 to classify diploid from aneuploid colon cancer

Background: DNA aneuploidy has been identified as a prognostic factor for epithelial malignancies. In this study, we compared diploid and aneuploid colon cancer tissues against normal mucosa of the colon by means of matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS). Material and Methods: DNA image cytometry determined the ploidy status of tissue samples that were subsequently subjected to MALDI-IMS. After obtaining protein profiles through direct analysis of tissue sections, a discovery and a validation set were used to predict ploidy and disease status by applying proteomic classification algorithms [Supervised Neural Network (SNN) and Receiver Operating Characteristic (ROC)]. Clinical target validation was performed by immunohistochemistry using tissue microarrays (TMA) comprising healthy controls as well as diploid and aneuploid colorectal carcinomas. Results: SNN algorithm categorized 99% of normal mucosa and 90% of colon carcinoma as well as 99% of diploid and 94% of aneuploid colon cancers correctly. Validation of both comparisons showed a correct classification of normal mucosa in 92%, tumors in 96%, and diploid and aneuploid colon cancers in 92% and 78%, respectively. Five peaks (m/z 2,396 and 4,977 for the diploid vs. aneuploid comparison and m/z 3,375, 6,663, 8,581 for the normal mucosa vs. carcinoma comparison) reached significance in both SNN and ROC analysis. Among these, m/z 4,977 was identified as thymosin beta 4 (TYB4). TYB4 showed expression differences also in clinical samples using a tissue microarray of normal mucosa, diploid and aneuploid colorectal carcinomas and could serve to predict overall survival. Conclusion: Our data underscore the potential of MALDI-IMS proteomic algorithms to reveal significant molecular details from distinct tumor subtypes such as different ploidy types.