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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.

HER2-testing in breast and gastric cancers is mandatory for the treatment with trastuzumab. We hypothesized that imaging mass spectrometry (IMS) of breast cancers may be useful for generating a classifier that may determine HER2-status in other cancer entities irrespective of primary tumor site. A total of 107 breast (n = 48) and gastric (n = 59) cryo tissue samples was analyzed by IMS (HER2 was present in 29 cases). The obtained proteomic profiles were used to create HER2 prediction models using different classification algorithms. A breast cancer proteome derived classifier, with HER2 present in 15 cases, correctly predicted HER2-status in gastric cancers with a sensitivity of 65% and a specificity of 92%. To create a universal classifier for HER2-status, breast and nonbreast cancer samples were combined, which increased sensitivity to 78%, and specificity was 88%. Our proof of principle study provides evidence that HER2-status can be identified on a proteomic level across different cancer types suggesting that HER2 overexpression may constitute a unique molecular event independent of the tumor site. Furthermore, these results indicate that IMS may be useful for the determination of potential drugable targets, as it offers a quicker, cheaper, and more objective analysis than the standard HER2-testing procedures immunohistochemistry and fluorescence in situ hybridization.

Clinical laboratory testing for HER2 status in breast cancer tissues is critically important for therapeutic decision making. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a powerful tool for investigating proteins through the direct and morphology-driven analysis of tissue sections. We hypothesized that MALDI-IMS may determine HER2 status directly from breast cancer tissues. Breast cancer tissues (n = 48) predefined for HER2 status were subjected to MALDI-IMS, and protein profiles were obtained through direct analysis of tissue sections. Protein identification was performed by tissue microextraction and fractionation followed by top-down tandem mass spectrometry. A discovery and an independent validation set were used to predict HER2 status by applying proteomic classification algorithms. We found that specific protein/peptide expression changes strongly correlated with the HER2 overexpression. Among these, we identified m/z 8404 as cysteine-rich intestinal protein 1. The proteomic signature was able to accurately define HER2-positive from HER2-negative tissues, achieving high values for sensitivity of 83%, for specificity of 92%, and an overall accuracy of 89%. Our results underscore the potential of MALDI-IMS proteomic algorithms for morphology-driven tissue diagnostics such as HER2 testing and show that MALDI-IMS can reveal biologically significant molecular details from tissues which are not limited to traditional high-abundance proteins.

Proteomics analyses have been exploited for the discovery of novel biomarkers for the early recognition and prognostic stratification of cancer patients. These analyses have now been extended to whole tissue sections by using a new tool, that is, MALDI imaging. This allows the spatial resolution of protein and peptides and their allocation to histoanatomical structures. Each MALDI imaging data set contains a large number of proteins and peptides, and their analysis can be quite tedious. We report here a new approach for the analysis of MALDI imaging results. Mass spectra are classified by hierarchical clustering by similarity and the resulting tissue classes are compared with the histology. The same approach is used to compare data sets of different patients. Tissue sections of gastric cancer and non-neoplastic mucosa obtained from 10 patients were forwarded to MALDI-Imaging. The in situ proteome expression was analyzed by hierarchical clustering and by principal component analysis (PCA). The reconstruction of images based on principal component scores allowed an unsupervised feature extraction of the data set. Generally, these images were in good agreement with the histology of the samples. The hierarchical clustering allowed a quick and intuitive access to the multidimensional information in the data set. It allowed a quick selection of spectra classes representative for different tissue features. The use of PCA for the comparison of MALDI spectra from different patients showed that the tumor and non-neoplastic mucosa are separated in the first three principal components. MALDI imaging in combination with hierarchical clustering allows the comprehensive analysis of the in situ cancer proteome in complex human cancers. On the basis of this cluster analysis, classification of complex human tissues is possible and opens the way for specific and cancer-related in situ biomarker analysis and identification.

Gastric cancer is the second most common malignancy and prognosis remains dismal. The reasons for the poor prognosis are the lack of sensitive serum markers for early detection and screening of high-risk individuals as well as the limited treatment options in advanced cancer stages. Using MALDI-TOF mass spectrometry after prefractionation of sera with magnet hydrophobic C8 coated beads sera from 14 patients with gastric cancer and 14 healthy controls mass spectra were generated. A peptide fragment was found to be highly elevated in cancer sera and was identified as fibrinopeptide A. To confirm proteome analysis of gastric cancer sera, we then screened a larger series of patients with gastric cancer (n = 99), high-risk individuals (n = 13) and normal controls (n = 111) for fibrinopeptide A serum levels. Interestingly, the mean logarithmic concentrations of serum fibrinopeptide A levels were significantly higher in cancer patients (mean 3.636 +/- 0.3738; p < 0.0001) and high-risk individuals (mean 3.569 +/- 0.4722; p < 0.05) compared to normal controls (mean 3.303 +/- 0.4012). In contrast, we observed no association of fibrinopeptide A levels with tumor stage, tumor location, presence of regional or distant metastasis, and Lauren type of gastric cancer. In conclusion, MALDI-TOF mass spectrometry of prefractionated gastric cancer sera allows the identification of potential biomarkers that may lead to the development of serum based tests for screening of high-risk individuals.

Recent advances in medicine have dramatically reduced the incidence and mortality of many cardiovascular, infectious, and certain neoplastic diseases; the overall mortality for most malignant solid tumors remains high. The poor prognosis in these cancers is due, in part, to the absence of adequate early screening tests, leading to delays in diagnosis. Three strategies have been applied to fight cancer: analysis of the molecular mechanisms involved in its pathogenesis and progression, improvement of early diagnosis, and the development of novel treatment strategies. There have been major advances in our understanding of cancer biology and pathogenesis and in the development of new (targeted) treatment modalities. However, insufficient progress has been made with respect to improving the methods for the early diagnosis and screening of many cancers. Therefore, cancer is often diagnosed at advanced stages, delaying timely treatment and leading to poor prognosis. Proteome analysis has recently been used for the identification of biomarkers or biomarker patterns that may allow for the early diagnosis of cancer. This tool is of special interest, since it allows for the identification of tumor-derived secretory products in serum or other body fluids. In addition, it may be used to detect reduced levels or loss of proteins in the serum of cancer patients that are present in noncancer individuals. These changes in the serum proteome may result from cancer-specific metabolic or immunological alterations, which are, at least partly, independent of tumor size or mass, thereby facilitating early discovery.

Gastric cancer mortality is second only to lung cancer, and its prognosis is dismal. Using surface-enhanced laser desorption/ionization-time-of-flight mass spectrometry, we previously identified a single best mass, which could separate gastric cancer from patients without cancer, with a sensitivity of 89.9% and a specificity of 90%. Using protein liquid chromatography systems with various chromatography media and MS/MS analysis, we were able to identify thrombin light chain A, a proteolytic fragment of prothrombin, as the single best mass for early detection of gastric cancer patients. These findings indicate that disturbances in the coagulation-system are early events in gastric cancer biology and that a decrease or loss of thrombin light chain A, which we termed negative serum protein profiling, may contribute to the diagnosis of cancer patients.

Using surface-enhanced laser desorption ionization mass spectrometry (SELDI/TOF-MS) and ProteinChip technology, coupled with a pattern-matching algorithm and serum samples, we screened for protein patterns to differentiate gastric cancer patients from noncancer patients. A classifier ensemble, consisting of 50 decision trees, correctly classified all gastric cancers and all controls of a training set (100% sensitivity and 100% specificity). Eight of 9 stage I gastric cancers (88.9% sensitivity for stage I) were correctly classified. In addition, 28 sera from gastric cancer patients taken in different hospitals were correctly classified (100% sensitivity). Furthermore, all 11 control sera obtained from patients without gastric cancer (100% specificity) were classified correctly and 29 of 30 healthy blood-donors were classified as noncancerous. ProteinChip technology in conjunction with bioinformatics allows the highly sensitive and specific recognition of gastric cancer patients.

A well-described animal model was used to understand the molecular mechanisms of carcinogenesis and metastasis of gastric cancer at the protein level. Gastric cancer was induced in 12 Wistar rats by oral administration of N-methyl-N'-Nitro-N-Nitrosoguanidine (MNNG). The protein expression patterns of normal gastric tissue, gastric cancer, and corresponding metastases were analyzed by proteomics in matched tissues of 3 rats. Proteins in the region of molecular masses of 15-75 kDa and an isoelectric point of 3-7 were separated by two-dimensional electrophoresis (2-DE) and identified by peptide fingerprinting with matrix-assisted laser desorption/ionization-time-of-flight-mass spectrometry (MALDI-TOF-MS). Twenty-seven spots corresponding to 25 different proteins served as landmarks for comparison between tissues. The identified proteins included cytoskeletal proteins, stress associated proteins, proteins involved in signal transduction, cell proliferation and differentiation, and metabolism. Eleven proteins were up-regulated and 2 proteins were down-regulated in tumor tissue when compared with normal tissue. Twelve proteins were up-regulated and 8 proteins were down-regulated in the metastases when compared with the primary tumor. The overexpression of HSP27 in gastric cancer was confirmed by immunohistochemical analysis of human gastric cancer specimens. Combining well-defined animal models with proteome analysis will improve our understanding of the fundamental changes that contribute to the process of carcinogenesis and the formation of metastases in gastric cancer.