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Raman Spectroscopy and Machine Learning Reveals Early Tumor Microenvironmental Changes Induced by Immunotherapy
Cancer Research ( IF 12.5 ) Pub Date : 2021-11-15 , DOI: 10.1158/0008-5472.can-21-1438
Santosh Kumar Paidi 1 , Joel Rodriguez Troncoso 2 , Piyush Raj 1 , Paola Monterroso Diaz 2 , Jesse D Ivers 2 , David E Lee 3 , Nathan L Avaritt 4, 5, 6 , Allen J Gies 4, 5 , Charles M Quick 7 , Stephanie D Byrum 4, 5, 6 , Alan J Tackett 4, 5, 6 , Narasimhan Rajaram 2, 5 , Ishan Barman 1, 8, 9
Affiliation  

Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, and identifying these patients is difficult due to a lack of reliable biomarkers for prediction and evaluation of treatment response. Here, we demonstrate the first application of label-free Raman spectroscopy for elucidating biomolecular changes induced by anti–CTLA4 and anti–PD-L1 immune checkpoint inhibitors (ICI) in the tumor microenvironment (TME) of colorectal tumor xenografts. Multivariate curve resolution–alternating least squares (MCR-ALS) decomposition of Raman spectral datasets revealed early changes in lipid, nucleic acid, and collagen content following therapy. Support vector machine classifiers and random forests analysis provided excellent prediction accuracies for response to both ICIs and delineated spectral markers specific to each therapy, consistent with their differential mechanisms of action. Corroborated by proteomics analysis, our observation of biomolecular changes in the TME should catalyze detailed investigations for translating such markers and label-free Raman spectroscopy for clinical monitoring of immunotherapy response in cancer patients. Significance: This study provides first-in-class evidence that optical spectroscopy allows sensitive detection of early changes in the biomolecular composition of tumors that predict response to immunotherapy with immune checkpoint inhibitors.

中文翻译:

拉曼光谱和机器学习揭示了免疫疗法引起的早期肿瘤微环境变化

癌症免疫疗法仅在一小部分患者中提供持久的临床益处,并且由于缺乏可靠的生物标志物来预测和评估治疗反应,因此很难识别这些患者。在这里,我们展示了无标记拉曼光谱的首次应用,用于阐明抗 CTLA4 和抗 PD-L1 免疫检查点抑制剂 (ICI) 在结直肠肿瘤异种移植物的肿瘤微环境 (TME) 中诱导的生物分子变化。拉曼光谱数据集的多变量曲线分辨率交替最小二乘 (MCR-ALS) 分解揭示了治疗后脂质、核酸和胶原蛋白含量的早期变化。支持向量机分类器和随机森林分析提供了出色的预测准确度,可用于响应 ICI 和针对每种疗法所描绘的光谱标记,与其不同的作用机制一致。经蛋白质组学分析证实,我们对 TME 中生物分子变化的观察应促进对此类标记物和无标记拉曼光谱的详细研究,以用于临床监测癌症患者的免疫治疗反应。意义:这项研究提供了一流的证据,证明光学光谱可以敏感地检测肿瘤生物分子组成的早期变化,从而预测对免疫检查点抑制剂免疫治疗的反应。我们对 TME 中生物分子变化的观察应促进对此类标记物和无标记拉曼光谱的详细研究,以用于临床监测癌症患者的免疫治疗反应。意义:这项研究提供了一流的证据,证明光学光谱可以敏感地检测肿瘤生物分子组成的早期变化,从而预测对免疫检查点抑制剂免疫治疗的反应。我们对 TME 中生物分子变化的观察应促进对此类标记物和无标记拉曼光谱的详细研究,以用于临床监测癌症患者的免疫治疗反应。意义:这项研究提供了一流的证据,证明光学光谱可以敏感地检测肿瘤生物分子组成的早期变化,从而预测对免疫检查点抑制剂免疫治疗的反应。
更新日期:2021-11-15
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