Balluff B, Hanselmann M, Heeren R M A
Maastricht University, Maastricht MultiModal Molecular Imaging institute (M4I), Maastricht, The Netherlands.
Heidelberg Collaboratory for Image Processing (HCI), Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany.
Adv Cancer Res. 2017;134:201-230. doi: 10.1016/bs.acr.2016.11.008. Epub 2016 Dec 20.
One of the big clinical challenges in the treatment of cancer is the different behavior of cancer patients under guideline therapy. An important determinant for this phenomenon has been identified as inter- and intratumor heterogeneity. While intertumor heterogeneity refers to the differences in cancer characteristics between patients, intratumor heterogeneity refers to the clonal and nongenetic molecular diversity within a patient. The deciphering of intratumor heterogeneity is recognized as key to the development of novel therapeutics or treatment regimens. The investigation of intratumor heterogeneity is challenging since it requires an untargeted molecular analysis technique that accounts for the spatial and temporal dynamics of the tumor. So far, next-generation sequencing has contributed most to the understanding of clonal evolution within a cancer patient. However, it falls short in accounting for the spatial dimension. Mass spectrometry imaging (MSI) is a powerful tool for the untargeted but spatially resolved molecular analysis of biological tissues such as solid tumors. As it provides multidimensional datasets by the parallel acquisition of hundreds of mass channels, multivariate data analysis methods can be applied for the automated annotation of tissues. Moreover, it integrates the histology of the sample, which enables studying the molecular information in a histopathological context. This chapter will illustrate how MSI in combination with statistical methods and histology has been used for the description and discovery of intratumor heterogeneity in different cancers. This will give evidence that MSI constitutes a unique tool for the investigation of intratumor heterogeneity, and could hence become a key technology in cancer research.
癌症治疗中的一大临床挑战是癌症患者在指南治疗下的不同表现。这一现象的一个重要决定因素已被确定为肿瘤间和肿瘤内的异质性。肿瘤间异质性是指患者之间癌症特征的差异，而肿瘤内异质性是指患者体内的克隆和非基因分子多样性。肿瘤内异质性的破译被认为是开发新型疗法或治疗方案的关键。肿瘤内异质性的研究具有挑战性，因为它需要一种非靶向分子分析技术，该技术要考虑到肿瘤的空间和时间动态。到目前为止，下一代测序对理解癌症患者体内的克隆进化贡献最大。然而，它在考虑空间维度方面存在不足。质谱成像（MSI）是一种强大的工具，可用于对实体瘤等生物组织进行非靶向但空间分辨的分子分析。由于它通过并行采集数百个质量通道提供多维数据集，因此可以应用多变量数据分析方法对组织进行自动注释。此外，它整合了样本的组织学信息，这使得能够在组织病理学背景下研究分子信息。本章将说明质谱成像如何与统计方法和组织学相结合，用于描述和发现不同癌症中的肿瘤内异质性。这将证明质谱成像构成了研究肿瘤内异质性的独特工具，因此可能成为癌症研究中的一项关键技术。
