Tutorial: Multivariate Statistical Treatment of Imaging Data for Clinical Biomarker Discovery
Tutorial: Multivariate Statistical Treatment of Imaging Data for Clinical Biomarker Discovery
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DOI：
10.1007/978-1-60761-746-4_22
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发表时间：
2010-01-01
期刊：
影响因子：
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通讯作者：
Suckau, Detlev
中科院分区：
文献类型：
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作者：
Deininger, Soeren-Oliver;Becker, Michael;Suckau, Detlev
肿瘤研究是MALDI组织成像新技术最有前途的应用领域之一。通过显著改变的代谢、生长和凋亡过程，可以很容易地将癌组织与健康组织区分开来。更令人感兴趣的是MALDI成像可以揭示经典组织学技术无法检测到的分子分化。因此，MALDI成像作为一种工具具有巨大的潜力，可以在活检中表征肿瘤的治疗易感性，并在终点研究中预测肿瘤进展。然而，对于成功的基于MALDI成像的癌症研究，有一些方面是重要的考虑因素。肿瘤切片通常是非常异质性的——不同的生化途径可能在个体肿瘤克隆、不同的发育阶段或不同的肿瘤微环境中活跃。只有在高分辨率光学图像上工作的经验丰富的组织病理学家才有可能在这个水平上理解组织。因此，如果分子图像与经典的高分辨率组织学图像以一种简单的方式相关联，而无需直接解释质谱，那么在组织病理学中使用MALDI成像结果的最大好处将会出现。每个MALDI成像数据集有效地提供了数百个分子的信息，并允许生成分子图像，显示这些分子在组织中的相对丰度。在组织学背景下解释这些是专家分析时间方面的主要挑战。这在临床工作中尤其适用，因为有数百个组织标本需要MALDI分析、解释和比较。因此，本文描述了一种MALDI成像工作流程，可以在组织学背景下快速、明确地解释MALDI成像数据。使用统计工具对图像数据进行预处理，可以让组织病理学家进行有效和直接的解释。在本章中，我们解释了使用主成分分析（PCA）和分层聚类（HC）来有效解释MALDI成像数据。我们还概述了如何使用这些方法在寻找生物标志物时比较患者之间的特定疾病状态。
Cancer research is one of the most promising application areas for the new technology of MALDI tissue imaging. Cancerous tissue can easily be distinguished from healthy tissue by dramatically changed metabolism, growth, and apoptotic processes. Of even higher interest is the fact that MALDI imaging allows to unveil molecular differentiation undetectable by classical histological techniques. Thus, MALDI imaging has tremendous potential as a tool to characterize the therapeutic susceptibility of tumors in biopsies as well as to predict tumor progression in endpoint studies. However, some aspects are important to consider for a successful MALDI imaging-based cancer research. Cancer sections are usually very heterogeneous - different biochemical pathways can be active in individual tumor clones, at different development stages or in various tumor microenvironments. Understanding tissue at this level is only possible for experienced histopathologists working on high-resolution optical images. Therefore, the largest benefit from the use of MALDI imaging results in histopathology will arise if molecular images are related to classical high-resolution histological images in a simple way without the need to interpret mass spectra directly. Each MALDI imaging data set effectively provides information on hundreds of molecules and permits the generation of molecular images displaying the relative abundance of these molecules across the tissue. The interpretation of these in the histological context is a major challenge in terms of expert analysis time. This is true especially for clinical work with hundreds of tissue specimens to be analyzed by MALDI, interpreted, and compared. Therefore, a MALDI imaging workflow is described here that enables fast and unambiguous interpretation of the MALDI imaging data in the histological context. Preprocessing of the image data using statistical tools allows efficient and straightforward interpretation by the histopathologist. In this chapter, we explain the use of principal component analysis (PCA) and hierarchical clustering (HC) for the efficient interpretation of MALDI imaging data. We also outline how these methods can be used to compare specific disease states between patients in the search for biomarkers.