Non-melanoma skin cancers (NMSCs) include basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and Merkel cell carcinoma (MCC). These neoplasms are highly diverse in their clinical presentation, as well as in their biological evolution. While the deregulation of the Hedgehog pathway is commonly observed in BCC, SCC and MCC are characterized by a strikingly elevated mutational and neoantigen burden. As result of our improved understanding of the biology of non-melanoma skin cancers, innovative treatment options including inhibitors of the Hedgehog pathway and immunotherapeutic agents have been recently investigated against these malignancies, leading to their approval by regulatory authorities. Herein, we review the most relevant biological and clinical features of NMSC, focusing on innovative treatment approaches.

In recent years, the preferentially expressed antigen in melanoma (PRAME) has also been used in the histopathological diagnosis of melanocytic lesions, in order to understand if it could constitute a valid, inexpensive, and useful resource in dermatopathological fields. We performed a double-center study to evaluate whether the data on the usefulness and possible limitations of PRAME could also be confirmed by our group. From 1 December 2021 to 29 March 2022, we collected 275 cases of melanocytic lesions that were immunostained with PRAME (Ab219650) and rabbit monoclonal antibody (Abcam). To better correlate the PRAME expression with its nature (benign, uncertain potential for malignancy, or malignant), we categorized PRAME tumor cells’ percentage positivity and intensity of immunostaining in a cumulative score obtained by adding the quartile of positive tumor cells (0, 1+, 2+, 3+, 4+) to the PRAME expression intensity in tumor cells (0, 1+, 2+, 3+). Of these 275 lesions, 136 were benign, 12 were of uncertain potential for malignancy (MELTUMP or SAMPUS or SPARK nevus), and 127 were malignant. The immunoexpression of PRAME was completely negative in 125/136 benign lesions (91.9%), with only a few positive melanocytes (1+) and intensity 1+ in the remaining 11 cases (8.1%). Of the 127 cases of melanoma (superficial spreading, lentigo maligna, and pagetoid histotypes), PRAME was strongly positive in 104/127 cases (81.8%) with intensity 4+ and 3+. In 17 cases (13.3%; melanoma spindle and nevoid cell histotypes), PRAME was positive in percentage 2+ and with intensity ranging from 2+ to 3+. In 7 cases (5.5%) of desmoplastic melanoma, PRAME was 1+ positive and/or completely negative. Of the 12 cases of lesions with uncertain potential for malignancy, the immunoexpression of PRAME was much more heterogeneous and irregularly distributed throughout the lesion. These data are perfectly in agreement with the current literature, and they demonstrate that the reliability of PRAME is quite high, but its use cannot cause physicians to disregard the morphological information and the execution of other ancillary immunohistochemical stains such as Melan-A, HMB-45, MiTF, and SOX-10.

The application of artificial intelligence (AI) algorithms in medicine could support diagnostic and prognostic analyses and decision making. In the field of dermatopathology, there have been various papers that have trained algorithms for the recognition of different types of skin lesions, such as basal cell carcinoma (BCC), seborrheic keratosis (SK) and dermal nevus. Furthermore, the difficulty in diagnosing particular melanocytic lesions, such as Spitz nevi and melanoma, considering the grade of interobserver variability among dermatopathologists, has led to an objective difficulty in training machine learning (ML) algorithms to a totally reliable, reportable and repeatable level. In this work we tried to train a fast random forest (FRF) algorithm, typically used for the classification of clusters of pixels in images, to highlight anomalous areas classified as melanoma “defects” following the Allen–Spitz criteria. The adopted image vision diagnostic protocol was structured in the following steps: image acquisition by selecting the best zoom level of the microscope; preliminary selection of an image with a good resolution; preliminary identification of macro-areas of defect in each preselected image; identification of a class of a defect in the selected macro-area; training of the supervised machine learning FRF algorithm by selecting the micro-defect in the macro-area; execution of the FRF algorithm to find an image vision performance indicator; and analysis of the output images by enhancing lesion defects. The precision achieved by the FRF algorithm proved to be appropriate with a discordance of 17% with respect to the dermatopathologist, allowing this type of supervised algorithm to be nominated as a help to the dermatopathologist in the challenging diagnosis of malignant melanoma.

In recent years, an increasing enthusiasm has been observed towards artificial intelligence and machine learning, involving different areas of medicine. Among these, although still in the embryonic stage, the dermatopathological field has also been partially involved, with the attempt to develop and train algorithms that could assist the pathologist in the differential diagnosis of complex melanocytic lesions. In this article, we face this new challenge of the modern era, carry out a review of the literature regarding the state of the art and try to determine promising future perspectives.

Primary Malignant Melanoma of the Esophagus (PMME) is an extremely rare cancer of the esophagus, accounting for 0.1–0.8% of all oro-esophageal cancers and <0.05% of all melanoma subtypes, with an estimated incidence of 0.0036 cases per million/year. We conduct a careful analysis of the literature starting from 1906 to the beginning of 2022, searching the PubMed, Science.gov, Scopus and Web of Science (WoS) databases. A total of 457 records were initially identified in the literature search, of which 17 were duplicates. After screening for eligibility and inclusion criteria, 303 publications were ultimately included, related to 347 patients with PMME. PMME represents a very rare entity whose very existence has been the subject of debate for a long time. Over time, an increasing number of cases have been reported in the literature, leading to an increase in knowledge and laying the foundations for a discussion on the treatment of this pathology, which still remains largely represented by surgery. In recent times, the possibility of discovering greater mutations in gene hotspots has made it possible to develop new therapeutic strategies of which nivolumab is an example. Future studies with large case series, with clinicopathological and molecular data, will be necessary to improve the outcome of patients with PMME.

Background: Malignant melanoma is the most aggressive of skin cancers and the 19th most common cancer worldwide, with an estimated age-standardized incidence rate of 2.8–3.1 per 100,000; although there have been clear advances in therapeutic treatment, the prognosis of MM patients with Breslow thickness greater than 1 mm is still quite poor today. The study of how melanoma cells manage to survive and proliferate by consuming glucose has been partially addressed in the literature, but some rather interesting results are starting to be present. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and a search of PubMed and Web of Sciences (WoS) databases was performed until 27 September 2021 using the terms: glucose transporter 1 and 3 and GLUT1/3 in combination with each of the following: melanoma, neoplasm and immunohistochemistry. Results: In total, 46 records were initially identified in the literature search, of which six were duplicates. After screening for eligibility and inclusion criteria, 16 publications were ultimately included. Conclusions: the results discussed regarding the role and expression of GLUT are still far from definitive, but further steps toward understanding and stopping this mechanism have, at least in part, been taken. New studies and new discoveries should lead to further clarification of some aspects since the various mechanisms of glucose uptake by neoplastic cells are not limited to the transporters of the GLUT family alone.