Raman spectroscopy is a non-invasive optical technique that can be used to investigate biochemical information embedded in cells and tissues exposed to ionizing radiation used in cancer therapy. Raman spectroscopy could potentially be incorporated in personalized radiation treatment design as a tool to monitor radiation response in at the metabolic level. However, tracking biochemical dynamics remains challenging for Raman spectroscopy. Here we developed a novel analytical framework by combining group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF). This framework can monitor radiation response profiles in different molecular histotypes and biochemical dynamics in irradiated breast cancer cells. Five subtypes of; human breast cancer (MCF-7, BT-474, MDA-MB-230, and SK-BR-3) and normal cells derived from human breast tissue (MCF10A) which had been exposed to ionizing radiation were tested in this framework. Reference Raman spectra of 20 biochemicals were collected and used as the constrained Raman biomarkers in the GBR-NMF-RF framework. We obtained scores for individual biochemicals corresponding to the contribution of each Raman reference spectrum to each spectrum obtained from the five cell types. A random forest classifier was then fitted to the chemical scores for performing molecular histotype classifications (HER2, PR, ER, Ki67, and cancer versus non-cancer) and assessing the importance of the Raman biochemical basis spectra for each classification test. Overall, the GBR-NMF-RF framework yields classification results with high accuracy (>97%), high sensitivity (>97%), and high specificity (>97%). Variable importance calculated in the random forest model indicated high contributions from glycogen and lipids (cholesterol, phosphatidylserine, and stearic acid) in molecular histotype classifications.

拉曼光谱是一种非侵入性光学技术，可用于研究暴露于癌症治疗中使用的电离辐射的细胞和组织中嵌入的生化信息。拉曼光谱有可能被纳入个性化放射治疗设计中，作为监测代谢水平放射反应的工具。然而，追踪生化动力学对于拉曼光谱仍然具有挑战性。在这里，我们通过结合群和基限制非负矩阵分解和随机森林（GBR-NMF-RF）开发了一种新颖的分析框架。该框架可以监测不同分子组织型的辐射反应曲线和受辐射乳腺癌细胞的生化动力学。五个亚型；在此框架中测试了人类乳腺癌（MCF-7、BT-474、MDA-MB-230 和 SK-BR-3）以及来自暴露于电离辐射的人类乳腺组织（MCF10A）的正常细胞。收集了 20 种生化物质的参考拉曼光谱，并将其用作 GBR-NMF-RF 框架中的受限拉曼生物标志物。我们获得了各个生化物质的分数，对应于每个拉曼参考光谱对从五种细胞类型获得的每个光谱的贡献。然后将随机森林分类器与化学评分相匹配，以执行分子组织型分类（HER2、PR、ER、Ki67 以及癌症与非癌症），并评估拉曼生化基础光谱对于每个分类测试的重要性。总体而言，GBR-NMF-RF 框架产生的分类结果具有高精度 (>97%)、高灵敏度 (>97%) 和高特异性 (>97%)。 随机森林模型中计算的变量重要性表明糖原和脂质（胆固醇、磷脂酰丝氨酸和硬脂酸）在分子组织型分类中的贡献很大。