Stephan Meding

MALDI Imaging Identifies Prognostic Seven-Protein Signature of Novel Tissue Markers in Intestinal-Type Gastric Cancer

Proteomics-based approaches allow us to investigate the biology of cancer beyond genomic initiatives. We used histology-based matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry to identify proteins that predict disease outcome in gastric cancer after surgical resection. A total of 181 intestinal-type primary resected gastric cancer tissues from two independent patient cohorts were analyzed. Protein profiles of the discovery cohort (n = 63) were directly obtained from tumor tissue sections by MALDI imaging. A seven-protein signature was associated with an unfavorable overall survival independent of major clinical covariates. The prognostic significance of three individual proteins identified (CRIP1, HNP-1, and S100-A6) was validated immunohistochemically on tissue microarrays of an independent validation cohort (n = 118). Whereas HNP-1 and S100-A6 were found to further subdivide early-stage (Union Internationale Contre le Cancer [UICC]-I) and late-stage (UICC II and III) cancer patients into different prognostic groups, CRIP1, a protein previously unknown in gastric cancer, was confirmed as a novel and independent prognostic factor for all patients in the validation cohort. The protein pattern described here serves as a new independent indicator of patient survival complementing the previously known clinical parameters in terms of prognostic relevance. These results show that this tissue-based proteomic approach may provide clinically relevant information that might be beneficial in improving risk stratification for gastric cancer patients.

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 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.

MALDI imaging mass spectrometry reveals COX7A2, TAGLN2 and S100-A10 as novel prognostic markers in Barrett's adenocarcinoma.

To characterize proteomic changes found in Barretts 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 Barretts 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 Barretts adenocarcinoma and the precursor lesion: COX7A2 and S100-A10. Furthermore, among 22 m/z species that are differentially expressed in Barretts 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 Barretts 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.

Tissue-based proteomics reveals FXYD3, S100A11 and GSTM3 as novel markers for regional lymph node metastasis in colon cancer

Regional lymph node metastasis negatively affects prognosis in colon cancer patients. The molecular processes leading to regional lymph node metastasis are only partially understood and proteomic markers for metastasis are still scarce. Therefore, a tissue‐based proteomic approach was undertaken for identifying proteins associated with regional lymph node metastasis. Two complementary tissue‐based proteomic methods have been employed. MALDI imaging was used for identifying small proteins (≤25 kDa) in situ and label‐free quantitative proteomics was used for identifying larger proteins. A tissue cohort comprising primary colon tumours without metastasis (UICC II, pN0, n = 21) and with lymph node metastasis (UICC III, pN2, n = 33) was analysed. Subsequent validation of identified proteins was done by immunohistochemical staining on an independent tissue cohort consisting of primary colon tumour specimens (n = 168). MALDI imaging yielded ten discriminating m/z species, and label‐free quantitative proteomics 28 proteins. Two MALDI imaging‐derived candidate proteins (FXYD3 and S100A11) and one from the label‐free quantitative proteomics (GSTM3) were validated on the independent tissue cohort. All three markers correlated significantly with regional lymph node metastasis: FXYD3 (p = 0.0110), S100A11 (p = 0.0071), and GSTM3 (p = 0.0173). FXYD3 and S100A11 were more highly expressed in UICC II patient tumour tissues. GSTM3 was more highly expressed in UICC III patient tumour tissues. By our tissue‐based proteomic approach, we could identify a large panel of proteins which are associated with regional lymph node metastasis and which have not been described so far. Here we show that novel markers for regional lymph metastasis can be identified by MALDI imaging or label‐free quantitative proteomics and subsequently validated on an independent tissue cohort. Copyright

Classification of HER2/neu Status in Gastric Cancer Using a Breast-Cancer Derived Proteome Classifier.

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.

Proteomic Analysis of PAXgene-Fixed Tissues

Formalin fixation and paraffin embedding is the standard technique for preserving biological material for both storage and histological analysis. Although recent progress has been made in the molecular analysis of formalin-fixed, paraffin-embedded (FFPE) tissues, proteomic applications are a special challenge due to the cross-linking property of formalin. Here we present the results of a new formalin-free tissue fixative, PAXgene, and demonstrate successful extraction of nondegraded and immunoreactive protein for subsequent standard protein assays, such as Western blot analysis and reverse-phase protein arrays. High amounts of protein can be obtained from PAXgene-fixed, paraffin-embedded (PFPE) mouse liver and human spleen, breast, duodenum, and stomach tissues, similar to frozen material. By Western blot analysis, we found that the detection of membrane, cytoplasmic, nuclear, and phosphorylated protein from PAXgene-fixed human tissue samples was comparable to cryopreserved samples. Furthermore, the distribution of protein in PAXgene-fixed human tissue specimens is adequate for matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry for in situ proteomic analysis. Taken together, we demonstrate here that PAXgene has great potential to serve as a novel multimodal fixative for modern pathology, enabling extensive protein biomarker studies on clinical tissue samples.

Comprehensive identification of proteins from MALDI imaging

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a powerful tool for the visualization of proteins in tissues and has demonstrated considerable diagnostic and prognostic value. One main challenge is that the molecular identity of such potential biomarkers mostly remains unknown. We introduce a generic method that removes this issue by systematically identifying the proteins embedded in the MALDI matrix using a combination of bottom-up and top-down proteomics. The analyses of ten human tissues lead to the identification of 1400 abundant and soluble proteins constituting the set of proteins detectable by MALDI IMS including >90% of all IMS biomarkers reported in the literature. Top-down analysis of the matrix proteome identified 124 mostly N- and C-terminally fragmented proteins indicating considerable protein processing activity in tissues. All protein identification data from this study as well as the IMS literature has been deposited into MaTisse, a new publically available database, which we anticipate will become a valuable resource for the IMS community.

Matrix-assisted laser desorption/ionization imaging protocol for in situ characterization of tryptic peptide identity and distribution in formalin-fixed tissue

RATIONALE Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry provides the means to map the in situ distribution of tryptic peptides in formalin-fixed clinical tissue samples. The ability to analyze clinical samples is of great importance to further developments in the imaging field. However, there is a requirement in this field of research for additional methods describing the characterization of tryptic peptides by MALDI imaging. METHODS AND RESULTS This protocol gives highly detailed instructions, with examples, for (1) successfully performing tryptic peptide MALDI imaging on formalin-fixed cancer tissue using a MALDI-TOF/TOF MS instrument, (2) tentatively generating identifications through nLC/MS/MS, and (3) validating these identifications by in situ MS/MS of peptides of interest. CONCLUSIONS This protocol provides a detailed and straightforward description of the methods required for groups new to MALDI imaging to begin analysis of formalin-fixed clinical samples.

Internal calibrants allow high accuracy peptide matching between MALDI imaging MS and LC-MS/MS ☆

One of the important challenges for MALDI imaging mass spectrometry (MALDI-IMS) is the unambiguous identification of measured analytes. One way to do this is to match tryptic peptide MALDI-IMS m/z values with LC-MS/MS identified m/z values. Matching using current MALDI-TOF/TOF MS instruments is difficult due to the variability of in situ time-of-flight (TOF) m/z measurements. This variability is currently addressed using external calibration, which limits achievable mass accuracy for MALDI-IMS and makes it difficult to match these data to downstream LC-MS/MS results. To overcome this challenge, the work presented here details a method for internally calibrating data sets generated from tryptic peptide MALDI-IMS on formalin-fixed paraffin-embedded sections of ovarian cancer. By calibrating all spectra to internal peak features the m/z error for matches made between MALDI-IMS m/z values and LC-MS/MS identified peptide m/z values was significantly reduced. This improvement was confirmed by follow up matching of LC-MS/MS spectra to in situ MS/MS spectra from the same m/z peak features. The sum of the data presented here indicates that internal calibrants should be a standard component of tryptic peptide MALDI-IMS experiments.

S100-A10, thioredoxin, and S100-A6 as biomarkers of papillary thyroid carcinoma with lymph node metastasis identified by MALDI Imaging

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.