无标记拉曼光谱和机器学习能够灵敏地评估对免疫治疗的差异反应
Label-free Raman spectroscopy and machine learning enables sensitive evaluation of differential response to immunotherapy
摘要：
癌症免疫疗法仅在一小部分患者中提供持久的临床益处，特别是由于缺乏可靠的生物标志物来准确预测治疗结果和评估反应。在这里，我们展示了无标记拉曼光谱的第一个应用，用于阐明肿瘤微环境中免疫治疗引起的生化变化。我们使用CT 26鼠结直肠癌细胞来生长肿瘤异种移植物，并使它们经受抗CTLA-4和抗PD-L1抗体的治疗。从治疗和对照肿瘤获得的拉曼光谱数据集的多变量曲线分辨率-交替最小二乘（MCR-ALS）分解显示了由于治疗导致的脂质、核酸和胶原蛋白含量的细微差异。我们使用支持向量机和随机森林进行的监督分类分析为免疫检查点抑制剂提供了出色的预测准确性，并描绘了每种疗法特有的重要光谱标记物，与其不同的作用机制一致。我们的研究结果为使用无标记拉曼光谱和机器学习在临床患者中进行免疫治疗反应的体内研究铺平了道路。
英文摘要：
Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, particularly due to a lack of reliable biomarkers for accurate prediction of treatment outcomes and evaluation of response. Here, we demonstrate the first application of label-free Raman spectroscopy for elucidating biochemical changes induced by immunotherapy in the tumor microenvironment. We used CT26 murine colorectal cancer cells to grow tumor xenografts and subjected them to treatment with anti-CTLA-4 and anti-PD-L1 antibodies. Multivariate curve resolution - alternating least squares (MCR-ALS) decomposition of Raman spectral dataset obtained from the treated and control tumors revealed subtle differences in lipid, nucleic acid, and collagen content due to therapy. Our supervised classification analysis using support vector machines and random forests provided excellent prediction accuracies for both immune checkpoint inhibitors and delineated important spectral markers specific to each therapy, consistent with their differential mechanisms of action. Our findings pave the way for in vivo studies of response to immunotherapy in clinical patients using label-free Raman spectroscopy and machine learning.
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