[Show abstract][Hide abstract] ABSTRACT: 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.
Journal of Proteome Research 03/2012; 11(3):1996-2003. · 5.06 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: To characterize proteomic changes found in Barrett's adenocarcinoma and its premalignant stages, the proteomic profiles of histologically defined precursor and invasive carcinoma lesions were analyzed by MALDI imaging MS. For a primary proteomic screening, a discovery cohort of 38 fresh frozen Barrett's adenocarcinoma patient tissue samples was used. The goal was to find proteins that might be used as markers for monitoring cancer development as well as for predicting regional lymph node metastasis and disease outcome. Using mass spectrometry for protein identification and validating the results by immunohistochemistry on an independent validation set, we could identify two of 60 differentially expressed m/z species between Barrett's adenocarcinoma and the precursor lesion: COX7A2 and S100-A10. Furthermore, among 22 m/z species that are differentially expressed in Barrett's adenocarcinoma cases with and without regional lymph node metastasis, one was identified as TAGLN2. In the validation set, we found a correlation of the expression levels of COX7A2 and TAGLN2 with a poor prognosis while S100-A10 was confirmed by multivariate analysis as a novel independent prognostic factor in Barrett's adenocarcinoma. Our results underscore the high potential of MALDI imaging for revealing new biologically significant molecular details from cancer tissues which might have potential for clinical application. This article is part of a Special Issue entitled: Translational Proteomics.
Journal of proteomics 02/2012; 75(15):4693-704. · 5.07 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In papillary thyroid carcinoma (PTC), metastasis is a feature of an aggressive tumor phenotype. To identify protein biomarkers that distinguish patients with an aggressive tumor behavior, proteomic signatures in metastatic and non-metastatic tumors were investigated comparatively. In particular, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) was used to analyze primary tumor samples. We investigated a tumor cohort of PTC (n = 118) that were matched for age, tumor stage, and gender. Proteomic screening by MALDI-IMS was performed for a discovery set (n = 29). Proteins related to the discriminating mass peaks were identified by 1D-gel electrophoresis followed by mass spectrometry. The candidate proteins were subsequently validated by immunohistochemistry (IHC) using a tissue microarray for an independent PTC validation set (n = 89). In this study, we found 36 mass-to-charge-ratio (m/z) species that specifically distinguished metastatic from non-metastatic tumors, among which m/z 11,608 was identified as thioredoxin, m/z 11,184 as S100-A10, and m/z 10,094 as S100-A6. Furthermore, using IHC on the validation set, we showed that the overexpression of these three proteins was highly associated with lymph node metastasis in PTC (p < 0.005). For functional analysis of the metastasis-specific proteins, we performed an Ingenuity Pathway Analysis and discovered a strong relationship of all candidates with the TGF-β-dependent EMT pathway. Our results demonstrated the potential application of the MALDI-IMS proteomic approach in identifying protein markers of metastasis in PTC. The novel protein markers identified in this study may be used for risk stratification regarding metastatic potential in PTC.
Journal of Molecular Medicine 09/2011; 90(2):163-74. · 4.77 Impact Factor