Digital dewaxing of Raman signals: discrimination between nevi and melanoma spectra obtained from paraffin-embedded skin biopsies

Abstract

Malignant melanoma (MM) is the most severe tumor affecting the skin and accounts for three quarters of all skin cancer deaths. Raman spectroscopy is a promising nondestructive tool that has been increasingly used for characterization of the molecular features of cancerous tissues. Different multivariate statistical analysis techniques are used in order to extract relevant information that can be considered as functional spectroscopic descriptors of a particular pathology. Paraffin embedding (waxing) is a highly efficient process used to conserve biopsies in tumor banks for several years. However, the use of non-dewaxed formalin-fixed paraffin-embedded tissues for Raman spectroscopic investigations remains very restricted, limiting the development of the technique as a routine analytical tool for biomedical purposes. This is due to the highly intense signal of paraffin, which masks important vibrations of the biological tissues. In addition to being time consuming and chemical intensive, chemical dewaxing methods are not efficient and they leave traces of the paraffin in tissues, which affects the Raman signal. In the present study, we use independent component analysis (ICA) on Raman spectral images collected on melanoma and nevus samples. The sources obtained from these images are then used to eliminate, using non-negativity constrained least squares (NCLS), the paraffin contribution from each individual spectrum of the spectral images of nevi and melanomas. Corrected spectra of both types of lesion are then compared and classified into dendrograms using hierarchical cluster analysis (HCA).