Proportion of Thick versus Thin Melanomas as a Benchmarking Tool

Background: The ratio of benign moles excised for each malignant melanoma diagnosed (number-needed-to-excise (NNE)) is a metric used to express the efficiency of diagnostic accuracy of melanoma. The literature suggests a progressive effort to reduce the NNE, thus raising concerns about missing early melanoma because the NNE does not capture the most significant outcome for melanoma prognosis, which is linked to the Breslow thickness. A lower NNE could reduce health costs related to melanoma diagnosis only if doing so does not increase the proportion of thicker melanomas. Objectives: The diagnostic performance by two tertiary referral centres using the NNE and proportion of thick (Breslow thickness > 1 mm) versus thin (Breslow thickness ≤ 1 mm) excised melanoma (thick/thin ratio: TTR) was compared to determine if a lower NNE is associated with a greater proportion of thicker melanoma. Combining TTR with NNE allows a better estimate of the effectiveness in melanoma diagnosis, assessing both the overall cost for a given pool of excised melanomas and costs due to unnecessary nevi excision at a particular dermatology centre. Methods: Demographic data and Breslow thickness of excised melanoma were extracted from patient histologic records at two referral centres for melanoma (Parma Dermatology Unit and Ravenna and Meldola Skin Cancer Unit, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori. IRCCS (IRST)) on all skin tumours excised between 2002 and 2011 and diagnosed as melanoma or melanocytic nevus. NNE and TTR were calculated and compared among the considered variables. Logistic regression was used to assess the contribution of each variable in predicting a higher TTR. Results: Data from 16,738 excised lesions were analysed. The IRST Unit reported a mean NNE of 4.6, whereas the Parma Unit excised 10.6 nevi for each melanoma. No statistically significant differences existed in the mean (IRST Unit, 0.56 ± 0.89 mm; Parma Unit, 1.07 ± 2.2 mm) and median (range) Breslow thickness (IRST Unit, 0.4 (9) mm; Parma Unit 0.4 (30) mm). The TTR between centres was significantly different (Parma Unit, 24%; IRST Unit, 12%; p < 0.001). Based on logistic regression, the diagnosing centre was the most powerful factor in determining a thickness of >1 mm among diagnosed melanomas (OR = 1.8; 95% CI, 1.2–2.7; p < 0.01), with all other factors being equal. The NNE decreased at both centres from younger-to-older patients, whereas the TTR increased simultaneously; however, the increase in TTR was non-significantly related to NNE reduction after adjusting for confounders (age, gender, and localization). Conclusions: A better diagnostic performance is capable of reducing the NNE and TTR, i.e., unnecessary excisions of melanocytic nevi can be reduced without increasing the risk of overlooking melanomas. The TTR, in addition to the NNE, allows stakeholders to better estimate the effectiveness in melanoma diagnosis because both overall costs for a given pool of excised melanomas and costs due for unnecessary nevi excision at a particular dermatology centre can be compared.

Body Mass Index and Melanoma Prognosis

Introduction: Obesity has been suggested as a risk factor in the progression of malignancies, including melanoma. Most studies defined obesity using body mass index (BMI), although the index is considered an imperfect measure of body composition. Objective: The aim of this article is to examine whether BMI can impact the prognosis of cutaneous melanoma, regardless of anti-tumor therapy. The relationship between BMI and specific prognostic factors in melanoma patients has been reviewed. Methods: Literature search was conducted on PubMed using the terms “melanoma” and “body mass index” or “obesity”. We selected articles, published up to 30 November 2020, examining the prognostic aspects of melanoma. Articles evaluating the risk and incidence of melanoma were excluded as well as studies regarding morbidity and complications following surgical procedures, or those performed in metastatic melanoma patients treated with anti-tumor therapies. Results: Mixed results have emerged from studies assessing the clinical outcomes in melanoma patients in relation to BMI. More consistent data seem to support the relationship between BMI and Breslow thickness. Conclusions: Studies that focus specifically on the link between obesity and melanoma prognosis are limited; further research is needed to deepen our knowledge on this link.

Unraveling the Relevance of ARL GTPases in Cutaneous Melanoma Prognosis through Integrated Bioinformatics Analysis

Cutaneous melanoma (CM) is the deadliest skin cancer, whose molecular pathways underlying its malignancy remain unclear. Therefore, new information to guide evidence-based clinical decisions is required. Adenosine diphosphate (ADP)-ribosylation factor-like (ARL) proteins are membrane trafficking regulators whose biological relevance in CM is undetermined. Here, we investigated ARL expression and its impact on CM prognosis and immune microenvironment through integrated bioinformatics analysis. Our study found that all 22 ARLs are differentially expressed in CM. Specifically, ARL1 and ARL11 are upregulated and ARL15 is downregulated regardless of mutational frequency or copy number variations. According to TCGA data, ARL1 and ARL15 represent independent prognostic factors in CM as well as ARL11 based on GEPIA and OncoLnc. To investigate the mechanisms by which ARL1 and ARL11 increase patient survival while ARL15 reduces it, we evaluated their correlation with the immune microenvironment. CD4+ T cells and neutrophil infiltrates are significantly increased by ARL1 expression. Furthermore, ARL11 expression was correlated with 17 out of 21 immune infiltrates, including CD8+ T cells and M2 macrophages, described as having anti-tumoral activity. Likewise, ARL11 is interconnected with ZAP70, ADAM17, and P2RX7, which are implicated in immune cell activation. Collectively, this study provides the first evidence that ARL1, ARL11, and ARL15 may influence CM progression, prognosis, and immune microenvironment remodeling.

Serum Selenium Level and 10-Year Survival after Melanoma

Melanoma is one of the most aggressive human malignancies. The determination of prognostic biomarkers is important for the early detection of recurrence and for the enrollment of the patients into different treatment regimens. Herein, we report the 10-year survival of 375 melanoma patients depending on their serum selenium levels. The study group was followed up from the date of melanoma diagnosis until death or 2020. Patients were assigned to one of four categories, in accordance with the increasing selenium level (I–IV quartiles). The subgroup with low selenium levels had a significant lower survival rate in relation to patients with high selenium levels, HR = 8.42; p = 0.005 and HR = 5.83; p = 0.02, for uni- and multivariable models, respectively. In the univariable analysis, we also confirmed the association between Breslow thickness, Clark classification and age at melanoma prognosis. In conclusion, a low serum selenium level was associated with an increased mortality rate in the 10 years following melanoma diagnosis. Future studies in other geographic regions with low soil selenium levels should be conducted to confirm our findings.

Hierarchical Clustering of Cutaneous Melanoma Based on Immunogenomic Profiling

Cutaneous melanoma is an aggressive malignancy with high heterogeneity. Several studies have been performed to identify cutaneous melanoma subtypes based on genomic profiling. However, few classifications based on assessments of immune-associated genes have limited clinical implications for cutaneous melanoma. Using 470 cutaneous melanoma samples from The Cancer Genome Atlas (TCGA), we calculated the enrichment levels of 29 immune-associated gene sets in each sample and hierarchically clustered them into Immunity High (Immunity_H, n=323, 68.7%), Immunity Medium (Immunity_M, n=135, 28.7%), and Immunity Low (Immunity_L, n=12, 2.6%) based on the ssGSEA score. The ESTIMATE algorithm was used to calculate stromal scores (range: -1,800.51–1,901.99), immune scores (range: -1,476.28–3,780.33), estimate scores (range: -2,618.28–5,098.14) and tumor purity (range: 0.216–0.976) and they were significantly correlated with immune subtypes (Kruskal–Wallis test, P &lt; 0.001). The Immunity_H group tended to have higher expression levels of HLA and immune checkpoint genes (Kruskal–Wallis test, P &lt; 0.05). The Immunity_H group had the highest level of naïve B cells, resting dendritic cells, M1 macrophages, resting NK cells, plasma cells, CD4 memory activated T cells, CD8 T cells, follicular helper T cells and regulatory T cells, and the Immunity_L group had better overall survival. The GO terms identified in the Immunity_H group were mainly immune related. In conclusion, immune signature-associated cutaneous melanoma subtypes play a role in cutaneous melanoma prognosis stratification. The construction of immune signature-associated cutaneous melanoma subtypes predicted possible patient outcomes and provided possible immunotherapy candidates.

Dermatological Disorder Detection Using Machine Learning

Skin diseases are considered one of the biggest scientific troubles in 21st century because of its especially complex and luxurious prognosis with problems and subjectivity of human interpretation. In cases of deadly illnesses like Melanoma prognosis in early tiers play a critical part in determining the possibility of getting cured. The software of automated strategies will assist in early diagnosis specifically with photographs with variety of analysis. Hence, in this system we present a completely automated machine of skin sickness recognition via lesion images, a device intervention in evaluation to traditional clinical personnel based detection. This system is designed into 3 levels compromising of statistics series and augmentation, designing version and subsequently prediction of disease. This proposed system uses more than one AI algorithms like Convolutional Neural Network and naive Bayes classifier and amalgamated it with image processing tools to shape a higher shape, leading to better accuracy.

Human melanocyte development and melanoma dedifferentiation at single cell resolution

SUMMARYIn humans, epidermal melanocytes are responsible for skin pigmentation, defense against ultraviolet radiation, and the deadliest common skin cancer, melanoma. While there is substantial overlap in melanocyte development pathways between different model organisms, species dependent differences are frequent and the conservation of these processes in human skin remains unresolved1–3. Thus, the biology of developing and adult human melanocytes remains largely uncharacterized. Here, we used a single-cell enrichment and RNA-sequencing pipeline to study human epidermal melanocytes derived directly from skin, capturing transcriptomes across different anatomic sites, developmental age, sexes, and multiple skin tones. Using donor-matched skin from distinct volar and non-volar anatomic locations, we uncovered subpopulations of melanocytes exhibiting site-specific enrichment that occurs during gestation and persists through adulthood. In addition, we identified human melanocyte differentiation transcriptional programs that are distinct from gene signatures generated from model systems. Finally, we use these programs to define patterns of dedifferentiation that are predictive of melanoma prognosis. Overall, the characterization of human melanocytes fresh from skin revealed new subpopulations, human-specific transcriptional programs, and valuable insights into melanoma dedifferentiation.