Cancer diagnosis and treatment is often a race against time to ensure the best possible outcomes for the patient. A new study, published on the bioRxiv* preprint server, gives us one new tool for the rapid diagnosis and analysis of tumors by making use of Raman spectroscopy.1
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What is Raman Spectroscopy?
Raman spectroscopy is a workhorse technique in chemical identification and quantification. It is sensitive to the frequencies at which the chemical bonds in a molecule or material oscillate. As these frequencies are dependent on the masses of the atoms and the strengths of the bonds between them, these frequencies provide a unique fingerprint of the molecule that can be used for identification.
The Research
The US-based team, led by researchers at John Hopkins University, has developed a characterization method using Raman spectroscopy that allows them to identify the metastatic phenotype of a tumor – important information that can help identify how a tumor will continue to grow and progress. While Raman spectroscopy has already been used to investigate a range of biomedical problems, including tumor compositions, this was the first time it had been used to profile tumor phenotypes specifically.
Using a portable Raman instrument, the team made use of the unique fingerprints of the molecular species contributing to each phenotype to distinguish subtle changes in different tumor phenotypes in breast cancer cells from mice. Such cells are an excellent and widely used model for human cancers. These small differences that arise because of different gene expressions in tumors are key to the metastatic potential of a tumor, or how easily it will grow and spread.
Key to the success of this analysis strategy was the use of machine learning that meant the information recorded by the Raman instrument could be analyzed in an automated way. The team grouped potential tumor types into several categories and tried to classify them depending on their growth potential and current stage of development. They used five of the most unique spectral features in each Raman spectrum to classify the tumors to analyze the biomolecular content of each tissue sample.
After training the models, the team found that their approach was not just capable of differentiating between different tumor phenotypes, but also of differentiating between tumors that had undergone phenotype switching from natural metastasis or gene silencing experiments.
Given that metastasis to other parts of the body is one of the major causes of death from breast cancer, having a rapid diagnostic tool that can provide information on the metastasis stage and risk of tumors is a powerful asset for improving patient outcomes. Being able to identify what happens in tumors at a molecular level and how this changes during their growth processes is also invaluable information for understanding the fundamental science behind tumor development and for paving the way for new treatment approaches.
One of the advantages of using Raman spectroscopy for such diagnosis, as well as its excellent spectral sensitivity, is that this is a label-free technique that minimizes the need for sample preparation. Once the tumors had been excised, they were snap-frozen and then thawed in a saline buffer solution prior to measurements. For other diagnostic techniques, such as fluorescence microscopy, sample labeling is often an essential part of the process and there are some concerns that the use of labels can distort local environments during measurements.
The portability of the spectrometer and the automation of the spectral analysis with this approach may mean that this analysis method could potentially be used for rapid metastatic risk profiling of tumors in ‘live’ settings, such as surgery, where additional sample preparation and time-consuming measurements are unfeasible.
*Important notice
bioRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.
References and Further Reading
- Paidi, S. K., Troncoso, J. R., Harper, M. G., Liu, Z., Khue, G., Ravindranathan, S., … Barman, I. (2021). Raman spectroscopy reveals phenotype switches in breast cancer metastasis. BioRxiv.doi:10.1101/2021.06.02.446487v1
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