Histogenesis of cutaneous malignant melanoma: The vast majority do not develop from melanocytic nevus but arise de novo as melanoma in situ

Nevus-associated Lentigo Maligna and Lentigo Maligna Melanoma, Clinicopathological Features

Nevus-associated lentigo maligna and lentigo maligna melanoma (NALMM) are rarely described in the literature and are considered an incidental finding. This study aimed to evaluate the frequency of NALMM and its clinicopathological features. A total of 201 histopathology reports were reviewed and among them 20% of the samples corresponded to NALMM, with females overrepresented in this group (p = 0.02). A significant association was also observed between NALMM with the presence of multiple nevi (p = 0.01), and dysplastic nevi (p = 0.04). Moreover, the risk of developing a second melanoma of nevus-associated type was 4.3 times higher in patients with NALMM. These results indicate that NALMM is more frequent than previously reported, suggesting that the associated nevus could interact or even act as a precursor for LM/LMM. Future studies with larger samples allied to techniques like confocal microscopy and molecular analysis are essential to determine this biological link between nevus and LM/LMM.

Deep Learning Approach to Classify Cutaneous Melanoma in a Whole Slide Image

Although the histopathological diagnosis of cutaneous melanocytic lesions is fairly accurate and reliable among experienced surgical pathologists, it is not perfect in every case (especially melanoma). Microscopic examination-clinicopathological correlation is the gold standard for the definitive diagnosis of melanoma. Pathologists may encounter diagnostic controversies when melanoma closely mimics Spitz's nevus or blue nevus, exhibits amelanotic histopathology, or is in situ. It would be beneficial if diagnosing cutaneous melanocytic lesions can be automated by using deep learning, particularly when assisting surgical pathologists with their workloads. In this preliminary study, we investigated the application of deep learning for classifying cutaneous melanoma in whole-slide images (WSIs). We trained models via weakly supervised learning using a dataset of 66 WSIs (33 melanomas and 33 non-melanomas). We evaluated the models on a test set of 90 WSIs (40 melanomas and 50 non-melanomas), achieving ROC-AUC at 0.821 for the WSI level and 0.936 for the tile level by the best model.

The Complex Interplay between Nevi and Melanoma: Risk Factors and Precursors

One effort to combat the rising incidence of malignant melanoma is focused on early detection by the clinical and dermoscopic screening of melanocytic nevi. However, the interaction between nevi, which are congenital or acquired benign melanocytic proliferations, and melanoma is still enigmatic. On the one hand, the majority of melanomas are thought to form de novo, as only a third of primary melanomas are associated with a histologically identifiable nevus precursor. On the other hand, an increased number of melanocytic nevi is a strong risk factor for developing melanoma, including melanomas that do not derive from nevi. The formation of nevi is modulated by diverse factors, including pigmentation, genetic risk factors, and environmental sun exposure. While the molecular alterations that occur during the progression of a nevus to melanoma have been well characterized, many unanswered questions remain surrounding the process of nevus to melanoma evolution. In this review, we discuss clinical, histological, molecular, and genetic factors that influence nevus formation and progression to melanoma.

Dysplastic nevi and melanoma: microRNAs tell a divergent story

BACKGROUND

dysplastic nevi (DN) share some clinical and histological features with melanoma and have been considered intermediate lesions toward malignant transformation. However, scientific evidence of DN representing melanoma precursors is still incomplete, and many observations pointed toward their being a distinct biological entity. The current definition of DN is also confusing and the practical consequence of this uncertainty is the excessive excision of DN with severe atypia. MicroRNAs (miRNAs) are small RNAs that regulate gene expression and whose global expression can classify normal and pathological tissues.

OBJECTIVES

given these considerations, we decided to perform a small RNA profiling study in a group of DN and invasive melanomas obtained from the same patient, to assess tumor evolution according to the global microRNA expression.

METHODS

we performed a small-RNA sequencing of 6 DN, 2 congenital nevi and 4 cutaneous melanomas obtained from 4 subjects and evaluated the global miRNA expression correlation between samples.

RESULTS AND CONCLUSIONS

the hierarchical clustering and principal component analyses of global miRNA expression, independently grouped together DN and their matching congenital nevi and showed a divergence of DN miRNA profile from melanoma. Our study suggests that DN have a peculiar and different miRNA expression profile compared to melanomas developed in the same patient, thus supporting the hypothesis that DN are distinct biological entities and not melanoma precursors.

Novel Non-Invasive Quantification and Imaging of Eumelanin and DHICA Subunit in Skin Lesions by Raman Spectroscopy and MCR Algorithm: Improving Dysplastic Nevi Diagnosis

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.