Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging
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...
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| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
January 8, 2012
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| In: |
Journal of proteome research
Year: 2012, Jahrgang: 11, Heft: 3, Pages: 1996-2003 |
| ISSN: | 1535-3907 |
| DOI: | 10.1021/pr200784p |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1021/pr200784p Verlag, Volltext: https://pubs.acs.org/doi/pdf/10.1021/pr200784p 
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| Verfasserangaben: | Stephan Meding, Ulrich Nitsche, Benjamin Balluff, Mareike Elsner, Sandra Rauser, Cédrik Schöne, Martin Nipp, Matthias Maak, Marcus Feith, Matthias P. Ebert, Helmut Friess, Rupert Langer, Heinz Höfler, Horst Zitzelsberger, Robert Rosenberg, and Axel Walch |
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| 245 | 1 | 0 | |a Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging |c Stephan Meding, Ulrich Nitsche, Benjamin Balluff, Mareike Elsner, Sandra Rauser, Cédrik Schöne, Martin Nipp, Matthias Maak, Marcus Feith, Matthias P. Ebert, Helmut Friess, Rupert Langer, Heinz Höfler, Horst Zitzelsberger, Robert Rosenberg, and Axel Walch |
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| 520 | |a 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. | ||
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