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.

中文翻译：

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使用组织微阵列的 MALDI-MSI 数据进行肿瘤分类：案例研究

通过组织微阵列 (TMA) 上的质谱成像 (MSI)，可以同时为许多患者研究大量生物分子，使其成为发现生物标志物的有吸引力的工具。在这里，我们研究是否可以从 MALDI-MSI 数据预测淋巴结转移。在 TMA 上进行测量，然后根据光谱强度和肿瘤细胞百分比进行过滤，然后对 122 名患者的所得数据进行进一步预处理。我们假设有和没有转移的患者之间的差异表现在有限数量的特征上。应用两种单变量特征选择方法来降低 MALDI-MSI 数据的维数。然后将选定的特征与三个分类器结合使用。最好的分类分数是用决策树分类器获得的，这对大约 72% 的患者进行了正确分类。几乎所有的预测能力都来自单个峰 (m/z 718.4)。我们的分类方法的敏感性一般可用于搜索生物标志物，使用人工修改的数据进行研究。