Klein Karoline, Klamminger Gilbert Georg, Mombaerts Laurent, Jelke Finn, Arroteia Isabel Fernandes, Slimani Rédouane, Mirizzi Giulia, Husch Andreas, Frauenknecht Katrin B M, Mittelbronn Michel, Hertel Frank, Kleine Borgmann Felix B
Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany.
National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg.
Molecules. 2024 Feb 23;29(5):979. doi: 10.3390/molecules29050979.
Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim at rapid and non-destructive tissue analysis, where each spectrum generated reflects the individual molecular composition of an examined spot within a (heterogenous) tissue sample. Using a combination of supervised and unsupervised machine learning methods as well as a solid database of Raman spectra of native glioblastoma samples, we succeed not only in distinguishing explicit tumor areas-vital tumor tissue and necrotic tumor tissue can correctly be predicted with an accuracy of 76%-but also in determining and classifying different spectral entities within the histomorphologically distinct class of vital tumor tissue. Measurements of non-pathological, autoptic brain tissue hereby serve as a healthy control since their respective spectroscopic properties form an individual and reproducible cluster within the spectral heterogeneity of a vital tumor sample. The demonstrated decipherment of a spectral glioblastoma heterogeneity will be valuable, especially in the field of spectroscopically guided surgery to delineate tumor margins and to assist resection control.
理解和分类肿瘤内在异质性是一种多模态方法，可在基因、生化或形态学等层面进行。诸如拉曼光谱之类的光学光谱方法旨在实现快速且无损的组织分析，其中所生成的每个光谱都反映了（异质性）组织样本中被检查点的个体分子组成。通过结合监督式和非监督式机器学习方法以及天然胶质母细胞瘤样本的拉曼光谱坚实数据库，我们不仅成功区分了明确的肿瘤区域——活性肿瘤组织和坏死肿瘤组织能够以76%的准确率被正确预测——还成功确定并分类了活性肿瘤组织这一组织形态学上不同类别中的不同光谱实体。非病理性尸检脑组织的测量在此用作健康对照，因为它们各自的光谱特性在活性肿瘤样本的光谱异质性中形成了一个独特且可重复的聚类。所展示的胶质母细胞瘤光谱异质性的破译将具有重要价值，尤其是在光谱引导手术领域，用于勾勒肿瘤边界并辅助切除控制。
