Tumor classification with MALDI-MSI data of tissue microarrays: A case study.

With mass spectrometry imaging (MSI) on tissue microarrays (TMAs) a large number of biomolecules can be studied for many patients at the same time, making it an attractive tool for biomarker discovery. Here we investigate whether lymph node metastasis can be predicted from MALDI-MSI data. Measurements are performed on TMAs and then filtered based on spectral intensity and the percentage of tumor cells, after which the resulting data for 122 patients is further preprocessed. We assume differences between patients with and without metastasis are expressed in a limited number of features. Two univariate feature selection methods are applied to reduce the dimensionality of the MALDI-MSI data. The selected features are then used in combination with three classifiers. The best classification scores are obtained with a decision tree classifier, which classifies about 72% of patients correctly. Almost all the predictive power comes from a single peak (m/z 718.4). The sensitivity of our classification approach, which can be generically used to search for biomarkers, is investigated using artificially modified data.

Mass Spectrometry Imaging of the Hypoxia Marker Pimonidazole in a Breast Tumor Model.

Although tumor hypoxia is associated with tumor aggressiveness and resistance to cancer treatment, many details of hypoxia-induced changes in tumors remain to be elucidated. Mass spectrometry imaging (MSI) is a technique that is well suited to study the biomolecular composition of specific tissue regions, such as hypoxic tumor regions. Here, we investigate the use of pimonidazole as an exogenous hypoxia marker for matrix-assisted laser desorption/ionization (MALDI) MSI. In hypoxic cells, pimonidazole is reduced and forms reactive products that bind to thiol groups in proteins, peptides, and amino acids. We show that a reductively activated pimonidazole metabolite can be imaged by MALDI-MSI in a breast tumor xenograft model. Immunohistochemical detection of pimonidazole adducts on adjacent tissue sections confirmed that this metabolite is localized to hypoxic tissue regions. We used this metabolite to image hypoxic tissue regions and their associated lipid and small molecule distributions with MALDI-MSI. We identified a heterogeneous distribution of 1-methylnicotinamide and acetylcarnitine, which mostly colocalized with hypoxic tumor regions. As pimonidazole is a widely used immunohistochemical marker of tissue hypoxia, it is likely that the presented direct MALDI-MSI approach is also applicable to other tissues from pimonidazole-injected animals or humans.

The use of mass spectrometry imaging to predict treatment response of patient-derived xenograft models of triple-negative breast cancer.

In recent years, mass spectrometry imaging (MSI) has been shown to be a promising technique in oncology. The effective application of MSI, however, is hampered by the complexity of the generated data. Bioinformatic approaches that reduce the complexity of these data are needed for the effective use in a (bio)medical setting. This holds especially for the analysis of tissue microarrays (TMA), which consist of hundreds of small tissue cores. Here we present an approach that combines MSI on tissue microarrays with principal component linear discriminant analysis (PCA-LDA) to predict treatment response. The feasibility of such an approach was evaluated on a set of patient-derived xenograft models of triple-negative breast cancer (TNBC). PCA-LDA was used to classify TNBC tumor tissues based on the proteomic information obtained with matrix-assisted laser desorption ionization (MALDI) MSI from the TMA surface. Classifiers based on two different tissue microarrays from the same tumor models showed overall classification accuracies between 59 and 77%, as determined by cross-validation. Reproducibility tests revealed that the two models were similar. A clear effect of intratumor heterogeneity of the classification scores was observed. These results demonstrate that the analysis of MALDI-MSI data by PCA-LDA is a valuable approach for the classification of treatment response and tumor heterogeneity in breast cancer.