Abstract
Simple SummaryThe diagnosis of dysplastic nevi is a dermatological challenge since it is an intermediate lesion between benign and malignant tumors. Currently, clinical diagnosis relies on biopsies and subsequent histopathologic examinations, which are invasive, expensive, time-consuming and subjective. Accordingly, in this work, we evaluate the potential of Raman spectroscopy, coupled with multivariate analytical methods, to non-invasively diagnose skin tumor biopsies by characterizing their pigment composition. We show an innovative methodology for a non-invasive quantification and localization of the eumelanin pigment and its DHICA subunit in skin lesions, which represents a further step to analyze the pigment content compared to the established invasive technique HPLC. Furthermore, we report for the first time that the DHICA content in dysplastic lesions is lower than in benign and malignant ones. This leads to the accurate classification of dysplastic lesions with 94.1% sensitivity and 100% specificity in an objective, cost-effective, non-invasive and rapid way.Malignant melanoma (MM) is the most aggressive form of skin cancer, and around 30% of them may develop from pre-existing dysplastic nevi (DN). Diagnosis of DN is a relevant clinical challenge, as these are intermediate lesions between benign and malignant tumors, and, up to date, few studies have focused on their diagnosis. In this study, the accuracy of Raman spectroscopy (RS) is assessed, together with multivariate analysis (MA), to classify 44 biopsies of MM, DN and compound nevus (CN) tumors. For this, we implement a novel methodology to non-invasively quantify and localize the eumelanin pigment, considered as a tumoral biomarker, by means of RS imaging coupled with the Multivariate Curve Resolution-Alternative Least Squares (MCR-ALS) algorithm. This represents a step forward with respect to the currently established technique for melanin analysis, High-Performance Liquid Chromatography (HPLC), which is invasive and cannot provide information about the spatial distribution of molecules. For the first time, we show that the 5, 6-dihydroxyindole (DHI) to 5,6-dihydroxyindole-2-carboxylic acid (DHICA) ratio is higher in DN than in MM and CN lesions. These differences in chemical composition are used by the Partial Least Squares-Discriminant Analysis (PLS-DA) algorithm to identify DN lesions in an efficient, non-invasive, fast, objective and cost-effective method, with sensitivity and specificity of 100% and 94.1%, respectively.
Highlights
Malignant melanoma (MM) is the most aggressive form of skin cancer, with an increasing incidence rate worldwide
These differences in band intensity mean that each tumor group contains different concentrations of the molecular components present in tissues and, these appear as subtly differentiated traits by visual inspection, in this study, we show that multivariate methods such as Partial Least Squares-Discriminant Analysis (PLS-DA) or Multivariate Curve Resolution-Alternative Least Squares (MCR-ALS) are able to identify them and use them to classify skin tumors efficiently or extract meaningful molecular information, respectively
We show, for the first time, a novel methodology based on Raman spectroscopy (RS) and coupled with MCR-ALS able to localize and quantify the distribution of the total eumelanin and the dihydroxyindole-2-carboxylic acid (DHICA) subunit content within skin lesions non-destructively
Summary
Malignant melanoma (MM) is the most aggressive form of skin cancer, with an increasing incidence rate worldwide. DN is recognized as a histologic entity whose biological relevance is that their appearance can be considered as a marker of melanoma risk [3,6–8]; for instance, the risk of suffering from melanoma increases 10-fold in people with ≥5 DN [9], and the risk is higher with the presence of nevi with a high grade of atypia [8] It is still debated if DN are precursors of melanomas [3], as only a small number of them are reported to evolve to malignancy [9,10]. Considering all this, a high necessity currently exists for developing tools able to provide an accurate clinical identification of DN lesions and to avoid the performance of biopsies This would allow us to study their evolution after their in vivo diagnosis and answer the question of whether they really evolve to melanomas
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