De novo discovery of phenotypic intratumour heterogeneity using imaging mass spectrometry.

An essential and so far unresolved factor influencing the evolution of cancer and the clinical management of patients is intratumour clonal and phenotypic heterogeneity. However, the de novo identification of tumour subpopulations is so far both a challenging and an unresolved task. Here we present the first systematic approach for the de novo discovery of clinically detrimental molecular tumour subpopulations. In this proof-of-principle study, spatially resolved, tumour-specific mass spectra were acquired, using matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry, from tissues of 63 gastric carcinoma and 32 breast carcinoma patients. The mass spectra, representing the proteomic heterogeneity within tumour areas, were grouped by a corroborated statistical clustering algorithm in order to obtain segmentation maps of molecularly distinct regions. These regions were presumed to represent different phenotypic tumour subpopulations. This was confirmed by linking the presence of these tumour subpopulations to the patients' clinical data. This revealed several of the detected tumour subpopulations to be associated with a different overall survival of the gastric cancer patients (p = 0.025) and the presence of locoregional metastases in patients with breast cancer (p = 0.036). The procedure presented is generic and opens novel options in cancer research, as it reveals microscopically indistinct tumour subpopulations that have an adverse impact on clinical outcome. This enables their further molecular characterization for deeper insights into the biological processes of cancer, which may finally lead to new targeted therapies.

High-resolution MALDI mass spectrometric imaging of lipids in the mammalian retina.

Matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI-MSI) is emerging as a powerful tool for the analysis of molecular distributions in biological samples in situ. When compared to classical histology, the major benefit of this method is the ability to identify and localize many molecules in a single tissue sample. MALDI-MSI spatial resolution currently falls short of traditional microscopic methods as it is limited by instrumentation and sample preparation. Tissue preparation steps, such as matrix deposition, are critical when considering strategies to further enhance the spatial resolution. The mammalian retina was selected as the tissue of choice for method development; its stratified anatomy renders it an ideal tissue to test high-resolution MALDI-MSI as the different layers correspond to specific neuronal classes and cellular structures. We compared alcohol-fixed, paraffin-embedded retina to fresh-frozen samples and matrix that had been deposited by spray or by sublimation. We present a lipid imaging method based on MALDI-MSI of frozen retinal sections with sublimated 2,5-dihydroxybenzoic acid matrix, which results in a highly advanced resolution compared to previous established methods. Hierarchical clustering of the primary data allows robust detection and differentiation of molecular distributions at a spatial resolution between 10 and 20 µm, thus approaching single-cell resolution.

Multicenter matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) identifies proteomic differences in breast-cancer-associated stroma.

MALDI mass spectrometry imaging (MSI) has rapidly established itself as a powerful biomarker discovery tool. To date, no formal investigation has assessed the center-to-center comparability of MALDI MSI experiments, an essential step for it to develop into a new diagnostic method. To test such capabilities, we have performed a multicenter study focused on biomarkers of stromal activation in breast cancer. MALDI MSI experiments were performed in two centers using independent tissue banks, infrastructure, methods, and practitioners. One of the data sets was used for discovery and the other for validation. Areas of intra- and extratumoral stroma were selected, and their protein signals were compared. Four protein signals were found to be significantly associated with tumor-associated stroma in the discovery data set measured in Munich. Three of these peaks were also detected in the independent validation data set measured in Leiden, all of which were also significantly associated with intratumoral stroma. Hierarchical clustering displayed 100% accuracy in the Munich MSI data set and 80.9% accuracy in the Leiden MSI data set. The association of one of the identified mass signals (PA28) with stromal activation was confirmed with immunohistochemistry performed on 20 breast tumors. Independent and international MALDI MSI investigations could identify validated biomarkers of stromal activation.

Clinical response to chemotherapy in oesophageal adenocarcinoma patients is linked to defects in mitochondria.

Chemotherapeutic drugs kill cancer cells, but it is unclear why this happens in responding patients but not in non-responders. Proteomic profiles of patients with oesophageal adenocarcinoma may be helpful in predicting response and selecting more effective treatment strategies. In this study, pretherapeutic oesophageal adenocarcinoma biopsies were analysed for proteomic changes associated with response to chemotherapy by MALDI imaging mass spectrometry. Resulting candidate proteins were identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and investigated for functional relevance in vitro. Clinical impact was validated in pretherapeutic biopsies from an independent patient cohort. Studies on the incidence of these defects in other solid tumours were included. We discovered that clinical response to cisplatin correlated with pre-existing defects in the mitochondrial respiratory chain complexes of cancer cells, caused by loss of specific cytochrome c oxidase (COX) subunits. Knockdown of a COX protein altered chemosensitivity in vitro, increasing the propensity of cancer cells to undergo cell death following cisplatin treatment. In an independent validation, patients with reduced COX protein expression prior to treatment exhibited favourable clinical outcomes to chemotherapy, whereas tumours with unchanged COX expression were chemoresistant. In conclusion, previously undiscovered pre-existing defects in mitochondrial respiratory complexes cause cancer cells to become chemosensitive: mitochondrial defects lower the cells' threshold for undergoing cell death in response to cisplatin. By contrast, cancer cells with intact mitochondrial respiratory complexes are chemoresistant and have a high threshold for cisplatin-induced cell death. This connection between mitochondrial respiration and chemosensitivity is relevant to anticancer therapeutics that target the mitochondrial electron transport chain.

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.

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 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.

MALDI imaging mass spectrometry for direct tissue analysis: technological advancements and recent applications.

Matrix assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a method that allows the investigation of the molecular content of tissues within its morphological context. Since it is able to measure the distribution of hundreds of analytes at once, while being label free, this method has great potential which has been increasingly recognized in the field of tissue-based research. In the last few years, MALDI-IMS has been successfully used for the molecular assessment of tissue samples mainly in biomedical research and also in other scientific fields. The present article will give an update on the application of MALDI-IMS in clinical and preclinical research. It will also give an overview of the multitude of technical advancements of this method in recent years. This includes developments in instrumentation, sample preparation, computational data analysis and protein identification. It will also highlight a number of emerging fields for application of MALDI-IMS like drug imaging where MALDI-IMS is used for studying the spatial distribution of drugs in tissues.

MALDI imaging mass spectrometry in cancer research: combining proteomic profiling and histological evaluation.

Despite the tendency to regard tumors as a simple mass of cancer cells, tumors have a high degree of complexity that is difficult to access with most analytical methods. Because the cancer tissue itself directly contains all information concerning proteomic and genetic changes, it represents the best possible sample material for any molecular research. However, an analytical method should also take advantage of morphological information contained within the cancer tissues, a feat that is not easily possible with methods based on sample homogenization such as conventional mass spectrometry. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry represents a method that allows the combination of mass spectrometric analyses with simultaneous histological evaluation to analyze various analytes such as proteins, peptides, lipids, or exogenous and endogenous small molecules. Spatially resolved mass spectrometric measurements are directly taken from a tissue section without destroying it. This combination allows for direct analysis of tumor samples while retaining the morphological information contained within the tissues, making it a very valuable tool in cancer research by complementing other currently used approaches. In this review, we discuss the position that MALDI imaging mass spectrometry currently occupies in the field of cancer research by showing its fields of application as well as the results and new discoveries that could be obtained using this method.

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

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.