Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging

Meding, Stephan; Nitsche, Ulrich; Balluff, Benjamin; Elsner, Mareike; Rauser, Sandra; Schöne, Cédrik; Nipp, Martin; Maak, Matthias; Feith, Marcus; Ebert, Matthias P; Friess, Helmut; Langer, Rupert; Höfler, Heinz; Zitzelsberger, Horst; Rosenberg, Robert; Walch, Axel (2012). Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging. Journal of proteome research, 11(3), pp. 1996-2003. Washington, D.C.: American Chemical Society 10.1021/pr200784p

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In clinical diagnostics, it is of outmost importance to correctly identify the source of a metastatic tumor, especially if no apparent primary tumor is present. Tissue-based proteomics might allow correct tumor classification. As a result, we performed MALDI imaging to generate proteomic signatures for different tumors. These signatures were used to classify common cancer types. At first, a cohort comprised of tissue samples from six adenocarcinoma entities located at different organ sites (esophagus, breast, colon, liver, stomach, thyroid gland, n = 171) was classified using two algorithms for a training and test set. For the test set, Support Vector Machine and Random Forest yielded overall accuracies of 82.74 and 81.18%, respectively. Then, colon cancer liver metastasis samples (n = 19) were introduced into the classification. The liver metastasis samples could be discriminated with high accuracy from primary tumors of colon cancer and hepatocellular carcinoma. Additionally, colon cancer liver metastasis samples could be successfully classified by using colon cancer primary tumor samples for the training of the classifier. These findings demonstrate that MALDI imaging-derived proteomic classifiers can discriminate between different tumor types at different organ sites and in the same site.

Item Type:	Journal Article (Original Article)
Division/Institute:	04 Faculty of Medicine > Service Sector > Institute of Pathology
UniBE Contributor:	Langer, Rupert
ISSN:	1535-3893
Publisher:	American Chemical Society
Language:	English
Submitter:	Factscience Import
Date Deposited:	04 Oct 2013 14:32
Last Modified:	05 Dec 2022 14:10
Publisher DOI:	10.1021/pr200784p
PubMed ID:	22224404
Web of Science ID:	000300916200052
URI:	https://boris.unibe.ch/id/eprint/12526 (FactScience: 218880)