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Recent research revealed the superiority of artificial intelligence over dermatologists to diagnose melanoma from images. However, 30–50% of all melanomas and more than half of those in young patients evolve from initially benign lesions. Despite its high relevance for melanoma screening, neither clinicians nor computers are yet able to reliably predict a nevus' oncologic transformation. The cause of this lies in the static nature of lesion presentation in the current standard of care, both for clinicians and algorithms. The status quo makes it difficult to train algorithms (and clinicians) to precisely assess the likelihood of a benign skin lesion to transform into melanoma. In addition, it inhibits the precision of current algorithms since 'evolution' image features may not be part of their decision. The current literature reveals certain types of melanocytic nevi (i.e. 'spitzoid' or 'dysplastic' nevi) and criteria (i.e. visible vasculature) that, in general, appear to have a higher chance to transform into melanoma. However, owing to the cumulative nature of oncogenic mutations in melanoma, a more fine-grained early morphologic footprint is likely to be detectable by an algorithm. In this perspective article, the concept of melanoma prediction is further explored by the discussion of the evolution of melanoma, the concept for training of such a nevi classifier and the implications of early melanoma prediction for clinical practice. In conclusion, the authors believe that artificial intelligence trained on prospective image data could be transformative for skin cancer diagnostics by (a) predicting melanoma before it occurs (i.e. pre-in situ) and (b) further enhancing the accuracy of current melanoma classifiers. Necessary prospective images for this research are obtained via free mole-monitoring mobile apps. • Thirty percent to 50% of all melanomas and more than half of those in young patients evolve from initially benign lesions. • Neither clinicians nor artificial intelligence (AI) algorithms are yet able to reliably predict a nevus' oncologic transformation. • AI trained on prospective image data could predict melanoma before it occurs and enhance the accuracy of diagnoses. [ABSTRACT FROM AUTHOR]

The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports 25–26% of discordance for classifying a benign nevus versus malignant melanoma. Deep learning was successfully implemented to enhance the precision of lung and breast cancer diagnoses. The aim of this study is to illustrate the potential of deep learning to assist human assessment for a histopathologic melanoma diagnosis. Six hundred ninety-five lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi and 345 melanomas). Only the haematoxylin and eosin stained (H&E) slides of these lesions were digitalised using a slide scanner and then randomly cropped. Five hundred ninety-five of the resulting images were used for the training of a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison with the original class labels. The total discordance with the histopathologist was 18% for melanoma (95% confidence interval [CI]: 7.4–28.6%), 20% for nevi (95% CI: 8.9–31.1%) and 19% for the full set of images (95% CI: 11.3–26.7%). Even in the worst case, the discordance of the CNN was about the same compared with the discordance between human pathologists as reported in the literature. Despite the vastly reduced amount of data, time necessary for diagnosis and cost compared with the pathologist, our CNN archived on-par performance. Conclusively, CNNs indicate to be a valuable tool to assist human melanoma diagnoses. • A convolutional neural network (CNN) was trained with 595 histopathologic images of melanomas and nevi that were classified by an expert dermatohistopathologist. • The CNN was then tested with 100 additional images (melanoma/nevi = 1:1) and revealed a discordance of only 19% to the histopathologist. • Thus, even in the worst case, the discordance of the CNN is about the same compared with the discordance between human pathologists as reported in the literature (25–26%). [ABSTRACT FROM AUTHOR]

Most patients with advanced basal cell carcinomas (BCCs) may not benefit sufficiently from standard treatment comprising surgery and radiation. Vismodegib, an oral selective hedgehog pathway inhibitor, is approved for treatment of patients with locally advanced BCC inappropriate for surgery or radiotherapy, or for patients with symptomatic metastatic BCC. In order to enhance understanding of the effectiveness, safety and utilization of vismodegib in clinical practice in Germany, a non-interventional study, JONAS, was conducted. A total of 53 patients with locally advanced BCC who initiated treatment with vismodegib between 2016 and 2018 were included in the study, which was embedded in the German ADOReg skin cancer registry. Duration of response, the primary endpoint, was 12.4 months, progression-free survival 32.2 months and overall response rate 77.4%. Most adverse events were mild to moderate. Overall, results confirmed previous findings, demonstrating favourable responses and manageable safety of vismodegib in patients with locally advanced BCC in clinical practice. [ABSTRACT FROM AUTHOR]

Historically, cancer diagnoses have been made by pathologists using two-dimensional histological slides. However, with the advent of digital pathology and artificial intelligence, slides are being digitised, providing new opportunities to integrate their information. Since nature is 3-dimensional (3D), it seems intuitive to digitally reassemble the 3D structure for diagnosis. To develop the first human-3D-melanoma-histology-model with full data and code availability. Further, to evaluate the 3D-simulation together with experienced pathologists in the field and discuss the implications of digital 3D-models for the future of digital pathology. A malignant melanoma of the skin was digitised via 3 µm cuts by a slide scanner; an open-source software was then leveraged to construct the 3D model. A total of nine pathologists from four different countries with at least 10 years of experience in the histologic diagnosis of melanoma tested the model and discussed their experiences as well as implications for future pathology. We successfully constructed and tested the first 3D-model of human melanoma. Based on testing, 88.9% of pathologists believe that the technology is likely to enter routine pathology within the next 10 years; advantages include a better reflectance of anatomy, 3D assessment of symmetry and the opportunity to simultaneously evaluate different tissue levels at the same time; limitations include the high consumption of tissue and a yet inferior resolution due to computational limitations. 3D-histology-models are promising for digital pathology of cancer and melanoma specifically, however, there are yet limitations which need to be carefully addressed. • Presentation of the first human 3D-melanoma-histology-model. • Nine pathologists from four different countries evaluated the model. • Implications, limitations and the likelihood of a 2D to 3D transition are discussed. • Further development is ensured by complete data and code availability. [ABSTRACT FROM AUTHOR]

Targeted therapies for cutaneous T-cell lymphoma (CTCL) are limited and curative approaches are still lacking. Relapses and drug-induced side effects are major challenges in the therapeutic management of CTCL patients, resulting in an urgent need for new, effective therapies. Expression of CD30 receptor is usually restricted to a small population of activated B- and T-cells, but is also found in hematologic malignancies, such as CTCL. In mycosis fungoides (MF) its expression seems to be associated with advanced disease stage and large-cell transformation. However, the functional role of CD30 in CTCL remains unknown. Consequently, this study aims to determine 1) CD30 and CD30 ligand (CD30L) expression in CTCL cell lines and primary CD4-positive cells from CTCL patients, 2) the dynamics of CD30 expression over time and after treatment, 3) CD30 function in vitro , ex vivo and in vivo , 4) mechanisms involved in CD30 signaling. The CTCL cell lines HH, SeAx and HUT-78 were used for in vitro experiments. For ex vivo analysis, primary CD4-positive T-cells isolated from fresh blood samples of patients with Sézary syndrome were used. Cells were challenged with dimethyl fumarate for 24 hours. CD30 was stimulated via its natural ligand. Methods used for expression and pathway analysis were FACS, western blot, PCR and luciferase assay. For in vivo experiments, CTCL cells were suspended in PBS/Matrigel (1:1, vol/vol) and injected intradermally into the right flank of female NOD/SCID/Il2Rg null (NSG) mice. After development of tumors, CD30L was injected into the lesions. We found that CD30 activation can influence viability of CTCL cells in vitro , which is currently under investigation in an in vivo setting. Involved mechanisms include the MEK/ERK and NF-κB pathway. The highest CD30 expression was detected in the HH cell line. The primary CD4-positive cells analyzed so far were negative for CD30 surface expression. However, intracellular CD30 protein was detectable via western blot. CD30 levels were influenced by cell culture conditions in HH cells and significantly reduced upon therapeutic NFκB inhibition in HH, SeAx and HUT-78 cells. Our results indicate a therapeutic potential of CD30 receptor stimulation as a new targeted therapy in CD30-positive MF in vitro , which has to be proven in vivo. Furthermore, we found a dynamic nature of CD30 expression in CTCL, which might account for the described effectiveness of the antibody-drug conjugate brentuximab vedotin directed to CD30 also in patients diagnosed as CD30 low-expressing. [ABSTRACT FROM AUTHOR]

Sézary syndrome (SS), the leukemic variant of cutaneous T cell lymphoma (CTCL), shows high relapse rates and poor prognosis, which complicates the clinical course as well as the treatment. The circulating atypical T cells known as Sézary cells (SC) display a CD4+/CD8–T helper cell phenotype. A diagnostic marker profile of SC is defined by loss of CD7 and/or CD26 and increased CD158k expressions. Due to frequent relapses and treatment-related side effects of current therapies as well as progressive therapy resistance of SS, there is an urgent need for the development of effective and well-tolerated therapeutic options. Dimethyl fumarate (DMF) is a potent NFκB inhibitor and is being used in a clinical phase 2 study for CTCL patients. Our group has shown that DMF induces apoptosis, specifically in malignant CTCL cells while sparing the healthy T cells. Extracorporeal photopheresis (ECP) is an established standard treatment for erythrodermic CTCL such as SS. We found in 3 SS patients that the new targeted combination therapy of DMF and ECP (in part with interferon-alpha) decreased the SC count drastically. All of these patients had received ECP and DMF separately as monotherapies before receiving the combination therapy, but had developed a resistance. However, with the combination therapy approach patients had long-term complete remissions in the blood and the interval between therapies, a parameter for the efficacy and tolerability of the treatment, was at least 3 times higher than for all other CTCL therapies. In light of these interesting clinical findings, our aim is to investigate ECP and DMF as a novel combined therapy in CTCL cell lines and patient derived CD4+ T cells and understand their synergistic effects based on immunomodulation and cell death. Hence, the underlying cellular and molecular mechanisms of this new therapeutic approach will be identified. CD4+ T cells isolated from peripheral blood monocytes of SS patients as well as the CTCL cell lines SeAx, HH and Hut-78 were treated ex vivo with UVA to mimic ECP therapy in combination with DMF treatment. SS patient cells show different cell death responses to the combination treatment depending on the previous therapies that the patients received. Additionally, in CTCL cell lines combination treatment led to distinctive cell death patterns as consequence of different signaling pathways and alterations that they represent. We will further study the synergistic mechanism of the combination treatment and unveil involved pathways by performing single cell RNA sequencing analysis of CTCL patient cells and RNA sequencing analysis in cell lines. Furthermore, activity of NFKB subunits and the cytokine profile present in the serum of patients will be investigated before and after the combination treatment. In addition, we aim to study this combinatory effect also in vivo in a CTCL mouse model in order to eventually translate it into a clinical study. [ABSTRACT FROM AUTHOR]

Simple Summary: Treatment for metastatic melanoma patients has significantly improved in the last decade. Literature suggests a better prognosis for patients with minimal disease burden. No consensus exists on the surveillance of melanoma patients at high risk of recurrence. In this trial we evaluated the use of whole-body MRI for the surveillance of melanoma patients, more specifically in patients with stage IIIb/c disease following surgical resection and in patients obtaining a durable response on systemic therapy. Overall, we can conclude that whole- body MRI is a safe and sensitive technique for the discovery of distant melanoma metastasis. Introduction: No standard protocol for surveillance for melanoma patients is established. Whole-body magnetic resonance imaging (whole-body MRI) is a safe and sensitive technique that avoids exposure to X-rays and contrast agents. This prospective study explores the use of whole-body MRI for the early detection of recurrences. Material and Methods: Patients with American Joint Committee on Cancer Staging Manual (seventh edition; AJCC-7) stages IIIb/c or -IV melanoma who were disease-free following resection of macrometastases (cohort A), or obtained a durable complete response (CR) or partial response (PR) following systemic therapy (cohort B), were included. All patients underwent whole-body MRI, including T1, Short Tau Inversion Recovery, and diffusion-weighted imaging, every 4 months the first 3 years of follow-up and every 6 months in the following 2 years. A total body skin examination was performed every 6 months. Results: From November 2014 to November 2019, 111 patients were included (four screen failures, cohort A: 68 patients; cohort B: 39 patients). The median follow-up was 32 months. Twenty-six patients were diagnosed with suspected lesions. Of these, 15 patients were diagnosed with a recurrence on MRI. Eleven suspected lesions were considered to be of non-neoplastic origin. In addition, nine patients detected a solitary subcutaneous metastasis during self-examination, and two patients presented in between MRIs with recurrences. The overall sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were, respectively, 58%, 98%, 58%, 98%, and 98%. Sensitivity and specificity for the detection of distant metastases was respectively 88% and 98%. No patient experienced a clinically meaningful (>grade 1) adverse event. Conclusions: Whole-body MRI for the surveillance of melanoma patients is a safe and sensitive technique sparing patients′ cumulative exposure to X-rays and contrast media. [ABSTRACT FROM AUTHOR]