Imaging mass spectrometry to discriminate breast from pancreatic cancer metastasis in formalin‐fixed paraffin‐embedded tissues. Issue 7 (5th March 2014)

• Record Type: Journal Article
• Authors: Casadonte, Rita; Kriegsmann, Mark; Zweynert, Friederike; Friedrich, Katrin; Bretton, Gustavo; Otto, Mike; Deininger, Sören‐Oliver; Paape, Rainer; Belau, Eckhard; Suckau, Detlev; Aust, Daniela; Pilarsky, Christian; Kriegsmann, Jörg; Lodeiro, Carlos; Martínez, José Luis Capelo; Santos, Hugo M.
• Is Part Of: Proteomics. Volume 14:Issue 7/8(2014:Apr.)
• Page Start: 956
• Page End: 964
• Publication Date: 2014-03-05
• Subjects: Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201300430
• Languages: English
• ISSNs: 1615-9853

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Diagnosis of the origin of metastasis is mandatory for adequate therapy. In the past, classification of tumors was based on histology (morphological expression of a complex protein pattern), while supportive immunohistochemical investigation relied only on few "tumor specific" proteins. At present, histopathological diagnosis is based on clinical information, morphology, immunohistochemistry, and may include molecular methods. This process is complex, expensive, requires an experienced pathologist and may be time consuming. Currently, proteomic methods have been introduced in various clinical disciplines. MALDI imaging MS combines detection of numerous proteins with morphological features, and seems to be the ideal tool for objective and fast histopathological tumor classification. To study a special tumor type and to identify predictive patterns that could discriminate metastatic breast from pancreatic carcinoma MALDI imaging MS was applied to multitissue paraffin blocks. A statistical classification model was created using a training set of primary carcinoma biopsies. This model was validated on two testing sets of different breast and pancreatic carcinoma specimens. We could discern breast from pancreatic primary tumors with an overall accuracy of 83.38%, a sensitivity of 85.95% and a specificity of 76.96%. Furthermore, breast and pancreatic liver metastases were tested and classified correctly.</p> </abstract>