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Investigating In Vivo Tumor Biomolecular Changes Following Radiation Therapy Using Raman Spectroscopy
Varsha Karunakaran1, Sina Dadgar1, Santosh K Paidi2
1Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas 72701, United States.
Abstract:
Treatment resistance is a major bottleneck in the success of cancer therapy. Early identification of the treatment response or lack thereof in patients can enable an earlier switch to alternative treatment strategies that can enhance response rates. Here, Raman spectroscopy was applied to monitor early tumor biomolecular changes in sensitive (UM-SCC-22B) and resistant (UM-SCC-47) head and neck tumor xenografts for the first time in in vivo murine tumor models in response to radiation therapy. We used a validated multivariate curve resolution-alternating least-squares (MCR-ALS) model to resolve complex multicomponent Raman spectra into individual pure spectra and their respective contributions. We observed a significant radiation-induced increase in the contributions of lipid-like species (p = 0.0291) in the radiation-sensitive UM-SCC-22B tumors at 48 h following radiation compared to the nonradiated baseline (prior to commencing treatment). We also observed an increase in the contribution of collagen-like species in the radiation-resistant UM-SCC-47 tumors at 24 h following radiation compared to the nonradiated baseline (p = 0.0125). In addition to the in vivo analysis, we performed ex vivo confocal Raman microscopic imaging of frozen sections derived from the same tumors. A comparison of all control and treated tumors revealed similar trends in the contributions of lipid-like and collagen-like species in both in vivo and ex vivo measurements; however, when evaluated as a function of time, longitudinal trends in the scores of collagen-like and lipid-like components were not consistent between the two data sets, likely due to sample numbers and differences in sampling depth at which information is obtained. Nevertheless, this study demonstrates the potential of fiber-based Raman spectroscopy for identifying early tumor microenvironmental changes in response to clinical doses of radiation therapy.
Insights
Raman spectroscopy detected early biomolecular changes in head and neck tumors responding differently to radiation therapy. This technique shows promise for identifying treatment resistance and guiding therapy choices.
Area of Science:
- Biomedical Optics
- Cancer Research
- Spectroscopy
Background:
- Treatment resistance is a significant challenge in cancer therapy.
- Early identification of treatment response is crucial for optimizing patient outcomes.
- Raman spectroscopy offers a non-invasive method to probe tumor biomolecular composition.
Purpose of the Study:
- To apply fiber-based Raman spectroscopy for monitoring early tumor biomolecular changes in response to radiation therapy.
- To differentiate between radiation-sensitive and radiation-resistant head and neck tumor xenografts.
- To investigate the potential of Raman spectroscopy in predicting treatment response.
Main Methods:
- Utilized in vivo and ex vivo Raman spectroscopy on head and neck tumor xenografts (UM-SCC-22B and UM-SCC-47).
- Employed multivariate curve resolution-alternating least-squares (MCR-ALS) to analyze complex Raman spectra.
- Monitored changes in lipid-like and collagen-like species following radiation therapy.
Main Results:
- Observed a significant increase in lipid-like species in radiation-sensitive tumors (UM-SCC-22B) 48 hours post-radiation.
- Detected an increase in collagen-like species in radiation-resistant tumors (UM-SCC-47) 24 hours post-radiation.
- Showed consistent trends between in vivo and ex vivo measurements, though longitudinal data showed variability.
Conclusions:
- Fiber-based Raman spectroscopy can detect early, radiation-induced biomolecular changes in tumors.
- This technique has potential for identifying treatment response or resistance in head and neck cancers.
- Early detection via Raman spectroscopy could facilitate timely adjustments to cancer treatment strategies.
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