Chadokiya Jay, Chang Kai, Sharma Saurabh, Hu Jack, Lill Jennie R, Dionne Jennifer, Kirane Amanda
Department of Surgery, Stanford School of Medicine, Stanford University Medical Center, Stanford, CA, United States.
Department of Electrical Engineering, Stanford University, Stanford, CA, United States.
Front Immunol. 2025 Jan 9;15:1520860. doi: 10.3389/fimmu.2024.1520860. eCollection 2024.
Molecular characterization of tumors is essential to identify predictive biomarkers that inform treatment decisions and improve precision immunotherapy development and administration. However, challenges such as the heterogeneity of tumors and patient responses, limited efficacy of current biomarkers, and the predominant reliance on single-omics data, have hindered advances in accurately predicting treatment outcomes. Standard therapy generally applies a "one size fits all" approach, which not only provides ineffective or limited responses, but also an increased risk of off-target toxicities and acceleration of resistance mechanisms or adverse effects. As the development of emerging multi- and spatial-omics platforms continues to evolve, an effective tumor assessment platform providing utility in a clinical setting should i) enable high-throughput and robust screening in a variety of biological matrices, ii) provide in-depth information resolved with single to subcellular precision, and iii) improve accessibility in economical point-of-care settings. In this perspective, we explore the application of label-free Raman spectroscopy as a tumor profiling tool for precision immunotherapy. We examine how Raman spectroscopy's non-invasive, label-free approach can deepen our understanding of intricate inter- and intra-cellular interactions within the tumor-immune microenvironment. Furthermore, we discuss the analytical advances in Raman spectroscopy, highlighting its evolution to be utilized as a single "Raman-omics" approach. Lastly, we highlight the translational potential of Raman for its integration in clinical practice for safe and precise patient-centric immunotherapy.
肿瘤的分子特征对于识别预测性生物标志物至关重要，这些标志物可为治疗决策提供依据，并改善精准免疫治疗的开发与应用。然而，诸如肿瘤和患者反应的异质性、当前生物标志物疗效有限以及主要依赖单组学数据等挑战，阻碍了在准确预测治疗结果方面取得进展。标准疗法通常采用“一刀切”的方法，这不仅提供无效或有限的反应，还会增加脱靶毒性风险以及加速耐药机制或产生不良反应。随着新兴的多组学和空间组学平台不断发展，一个在临床环境中具有实用性的有效肿瘤评估平台应具备以下特点：i）能够在多种生物基质中进行高通量且稳健的筛选；ii）提供从单细胞到亚细胞分辨率的深入信息；iii）在经济的即时护理环境中提高可及性。从这个角度出发，我们探讨无标记拉曼光谱作为精准免疫治疗的肿瘤分析工具的应用。我们研究拉曼光谱的非侵入性、无标记方法如何能加深我们对肿瘤免疫微环境中复杂的细胞间和细胞内相互作用的理解。此外，我们讨论拉曼光谱的分析进展，强调其向单一“拉曼组学”方法发展的过程。最后，我们强调拉曼光谱在临床实践中整合以实现以患者为中心的安全精准免疫治疗的转化潜力。
