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Published on: January 9, 2020
Supervised learning methods for the recognition of melanoma cell lines through the analysis of their Raman spectra
Enrico Baria1,2, Riccardo Cicchi2,3, Francesca Malentacchi4
1Department of Physics, University of Florence, Sesto Fiorentino, Italy.
Abstract:
Malignant melanoma is an aggressive form of skin cancer, which develops from the genetic mutations of melanocytes - the most frequent involving BRAF and NRAS genes. The choice and the effectiveness of the therapeutic approach depend on tumour mutation; therefore, its assessment is of paramount importance. Current methods for mutation analysis are destructive and take a long time; instead, Raman spectroscopy could provide a fast, label-free and non-destructive alternative. In this study, confocal Raman microscopy has been used for examining three in vitro melanoma cell lines, harbouring different molecular profiles and, in particular, specific BRAF and NRAS driver mutations. The molecular information obtained from Raman spectra has served for developing two alternative classification algorithms based on linear discriminant analysis and artificial neural network. Both methods provide high accuracy (≥90%) in discriminating all cell types, suggesting that Raman spectroscopy may be an effective tool for detecting molecular differences between melanoma mutations.
Insights
Raman spectroscopy offers a fast, non-destructive method to detect molecular differences in melanoma cell lines with BRAF and NRAS mutations. This technique accurately identifies genetic profiles, aiding in personalized cancer treatment strategies.
Area of Science:
- Oncology
- Biophysics
- Spectroscopy
Background:
- Malignant melanoma is an aggressive skin cancer driven by genetic mutations, frequently involving BRAF and NRAS genes.
- Accurate assessment of tumor mutations is crucial for selecting effective therapeutic approaches.
- Current mutation analysis methods are destructive and time-consuming, necessitating advanced diagnostic tools.
Purpose of the Study:
- To evaluate confocal Raman microscopy as a non-destructive, label-free alternative for analyzing molecular profiles in melanoma cell lines.
- To develop and assess classification algorithms for discriminating between melanoma cell types based on Raman spectral data.
- To investigate the potential of Raman spectroscopy in identifying specific BRAF and NRAS driver mutations in melanoma.
Main Methods:
- Confocal Raman microscopy was employed to analyze three distinct in vitro melanoma cell lines.
- Raman spectra were acquired to capture molecular information from the cell lines.
- Two classification algorithms, linear discriminant analysis (LDA) and artificial neural network (ANN), were developed and applied to the spectral data.
Main Results:
- Raman spectroscopy successfully obtained molecular information from melanoma cell lines with varying genetic profiles.
- Both LDA and ANN classification algorithms achieved high accuracy (≥90%) in discriminating between the analyzed cell types.
- The study demonstrated the capability of Raman spectroscopy to detect molecular differences linked to BRAF and NRAS mutations.
Conclusions:
- Raman spectroscopy is a promising non-destructive technique for the rapid, label-free molecular characterization of melanoma.
- The developed classification algorithms show high efficacy in distinguishing melanoma cell types based on spectral signatures.
- This approach holds potential for improving the diagnostic workflow and guiding personalized treatment strategies for melanoma patients.
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