Label-free Raman spectroscopy for cancer therapy response: from methodological framework to clinical translation.
7 affiliations
- 1CNR-Institute for Photonics and Nanotechnologies (CNR-IFN), P.zza Leonardo Da Vinci 32, 20133 Milan, Italy. [email protected].
- 2Department of Physics, Politecnico di Milano, P.zza Leonardo da Vinci 32, 20133 Milan, Italy.
- 3Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
- 4Clinical Biophotonics, Department of Otorhinolaryngology, Jena University Hospital, 07747 Jena, Germany.
- 5Comprehensive Cancer Center Central Germany, 07747 Jena, Germany.
- 6Laboratory of Nanomedicine, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.
- 7Department of Otorhinolaryngology, Jena University Hospital, 07747 Jena, Germany.
Abstract
The early detection of therapy-induced biomolecular changes and the prediction of therapeutic response remain major challenges in oncology. Current clinical methods often assess treatment response through macroscopic or morphological endpoints, whereas molecular alterations associated with sensitivity, resistance, or recurrence may arise earlier and remain undetected. Raman spectroscopy offers a label-free and non-destructive approach to probe the intrinsic biochemical composition of cells, tissues, and biofluids, providing spectral and spatial information that can support therapy response prediction and longitudinal monitoring. This review discusses Raman-based technologies for cancer therapy assessment, from preclinical models to emerging clinical applications. We first outline the clinical need for rapid, non-destructive, and molecularly informative tools, considering tumor heterogeneity, therapy resistance, the tumor microenvironment, and current standards for response evaluation. We then provide a methodological framework for selecting Raman modalities, including spontaneous Raman (SR) spectroscopy, surface-enhanced Raman spectroscopy (SERS), stimulated Raman spectroscopy (SRS), and coherent anti-Stokes Raman spectroscopy (CARS), together with key considerations on acquisition strategies, multimodal integration, experimental design, preprocessing, feature analysis, and machine learning. We summarize Raman spectral biomarkers associated with therapy-induced changes in nucleic acids, proteins, lipids, metabolites, extracellular matrix components, and anticancer drugs, and critically examine studies using Raman spectroscopy to predict or monitor responses to major therapeutic approaches. Finally, we discuss barriers to clinical translation, including acquisition speed, inter-instrument reproducibility, preclinical model limitations, regulatory requirements, workflow integration, clinical trials, and prospective validation. Overall, Raman spectroscopy emerges as a promising platform for molecular phenotyping in precision oncology, provided that future studies progress toward standardized, clinically validated, and actionable workflows.
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BibTeX
@article{alcolearodriguez2026labelfree,
title = {{Label-free Raman spectroscopy for cancer therapy response: from methodological framework to clinical translation.}},
author = {Victor Alcolea-Rodriguez and Arianna Bresci and Anna Mühlig and Chiara Agrimi and Carlo Morasso and Dario Polli and Orlando Guntinas-Lichius and Renzo Vanna},
journal = {Chemical Society reviews},
year = {2026},
month = oct,
doi = {10.1039/d6cs00966b},
url = {https://europepmc.org/article/MED/42949705},
note = {CC BY},
}
RIS
TY - JOUR
TI - Label-free Raman spectroscopy for cancer therapy response: from methodological framework to clinical translation.
AU - Victor Alcolea-Rodriguez
AU - Arianna Bresci
AU - Anna Mühlig
AU - Chiara Agrimi
AU - Carlo Morasso
AU - Dario Polli
AU - Orlando Guntinas-Lichius
AU - Renzo Vanna
PY - 2026
DA - 2026/10/28
JO - Chemical Society reviews
DO - 10.1039/d6cs00966b
UR - https://europepmc.org/article/MED/42949705
AB - The early detection of therapy-induced biomolecular changes and the prediction of therapeutic response remain major challenges in oncology. Current clinical methods often assess treatment response through macroscopic or morphological endpoints, whereas molecular alterations associated with sensitivity, resistance, or recurrence may arise earlier and remain undetected. Raman spectroscopy offers a label-free and non-destructive approach to probe the intrinsic biochemical composition of cells, tissues, and biofluids, providing spectral and spatial information that can support therapy response prediction and longitudinal monitoring. This review discusses Raman-based technologies for cancer therapy assessment, from preclinical models to emerging clinical applications. We first outline the clinical need for rapid, non-destructive, and molecularly informative tools, considering tumor heterogeneity, therapy resistance, the tumor microenvironment, and current standards for response evaluation. We then provide a methodological framework for selecting Raman modalities, including spontaneous Raman (SR) spectroscopy, surface-enhanced Raman spectroscopy (SERS), stimulated Raman spectroscopy (SRS), and coherent anti-Stokes Raman spectroscopy (CARS), together with key considerations on acquisition strategies, multimodal integration, experimental design, preprocessing, feature analysis, and machine learning. We summarize Raman spectral biomarkers associated with therapy-induced changes in nucleic acids, proteins, lipids, metabolites, extracellular matrix components, and anticancer drugs, and critically examine studies using Raman spectroscopy to predict or monitor responses to major therapeutic approaches. Finally, we discuss barriers to clinical translation, including acquisition speed, inter-instrument reproducibility, preclinical model limitations, regulatory requirements, workflow integration, clinical trials, and prospective validation. Overall, Raman spectroscopy emerges as a promising platform for molecular phenotyping in precision oncology, provided that future studies progress toward standardized, clinically validated, and actionable workflows.
SN - 0306-0012
SN - 1460-4744
ER -