Over the years, increasing information has been asked of the pathologist: we have moved from a purely morphological diagnosis to biomolecular and genetic studies, which have made it possible to implement the use of molecular targeted therapies, such as anti-epidermal growth factor receptor (EGFR) molecules in EGFR-mutated lung cancer, for example. Today, next generation sequencing (NGS) has changed the approach to neoplasms, to the extent that, in a short time, it has gained a place of absolute importance and diagnostic, prognostic and therapeutic utility. In this scenario, formaldehyde-fixed and paraffin-embedded (FFPE) biological tissue samples are a source of clinical and molecular information. However, problems can arise in the genetic material (DNA and RNA) for use in NGS due to fixation, and work is being devoted to possible strategies to reduce its effects. In this paper, we discuss the applications of FFPE tissue samples in the execution of NGS, we focus on the problems arising with the use of this type of material for nucleic acid extraction and, finally, we consider the most useful strategies to prevent and reduce single nucleotide polymorphisms (SNV) and other fixation artifacts.

The study of SARS-CoV-2 positive pregnant women is of some importance for gynecologists, obstetricians, neonatologists and women themselves. In recent months, new works have tried to clarify what happens at the fetal–placental level in women positive for the virus, and different pathogenesis mechanisms have been proposed. Here, we present the results of a large series of placentas of Coronavirus disease (COVID) positive women, in a reference center for COVID-positive pregnancies, on which we conducted histological, immunohistochemical and electron microscopy investigations. A case–control study was conducted in order to highlight any histopathological alterations attributable to SARS-CoV-2. The prevalence of maternal vascular malperfusion was not significantly different between cases and controls (54.3% vs. 43.7% p = 0.19), whereas the differences with regard to fetal vascular malperfusion (21.1% vs. 4.2% p < 0.001) were significant. More frequent in cases with respect to controls were decidual arteriopathy (40.9% vs. 1.4% p < 0.0001), decidual inflammation (32.4% vs. 0.7% p < 0.0001), perivillous fibrin deposition (36.6% vs. 3.5% p < 0.0001) and fetal vessel thrombi (22.5% vs. 0.7% p < 0.0001). No significant differences in the percentage of terminal villous hyperplasia and chorioamnionitis were observed between the two groups. As the pandemic continues, these studies will become more urgent in order to clarify the possible mechanism of maternal–fetal transmission of the virus.

Epidermolysis bullosa (EB) is a group of rare congenital diseases caused by mutations in structural proteins of the dermal/epidermal junction that are characterized by extreme epithelial fragility, which determines the formation of bullae and erosions either spontaneously or after local mechanical traumas. In EB patients, skin fragility leads to many possible complications and comorbidities. One of the most feared complications is the development of cutaneous squamous cell carcinomas (SCCs) that particularly in the dystrophic recessive EB subtype can be extremely aggressive and often metastatic. SCCs in EB patients generally arise more often in the extremities, where chronic blisters and scars are generally located. SCCs represent a big therapeutic challenge in the EB population. No standard of care exists for the treatment of SCC in these patients, and therapy is based on small case studies. Moreover, the pathogenesis of cSCC in EB patients is still unclear. Many theories have been indeed postulated in order to explain why cSCC behaves so much more aggressively in EB patients compared to the general population. cSCC in EB seems to be the result of many complex interactions among cancer cells, skin microenvironment, susceptibility to DNA mutations and host immune response. In this review, we analyze the different pathogenetic mechanisms of cSCC in EB patients, as well as new therapies for this condition.

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.

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.

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.