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REVIEW article
Front. Immunol.
Sec. Systems Immunology
Volume 15 - 2024 |
doi: 10.3389/fimmu.2024.1520860
This article is part of the Research Topic Single Cell Technologies for the Interrogation of Immunological Disease Mechanisms View all 9 articles
Advancing Precision Cancer Immunotherapy Drug Development, Administration, and Response Prediction with AI-enabled Raman Spectroscopy
Provisionally accepted- 1 Stanford University, Stanford, United States
- 2 Pumpkin Seed, Palo Alto, United States
- 3 Genentech Inc., San Francisco, California, United States
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 (1), 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 noninvasive, 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 "Ramanomics" approach. Lastly, we highlight the translational potential of Raman for its integration in clinical practice for safe and precise patient-centric immunotherapy.
Keywords: Raman spectroscopy, label-free analysis, Immunotherapy, Time analysis, multiomics
Received: 31 Oct 2024; Accepted: 25 Nov 2024.
Copyright: © 2024 Chadokiya, Chang, Sharma, Hu, Lill, Dionne and Kirane. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jennifer Dionne, Stanford University, Stanford, United States
Amanda Kirane, Stanford University, Stanford, United States
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