Genomic Classification of Cutaneous Melanoma.

We describe the landscape of genomic alterations in cutaneous melanomas through DNA, RNA, and protein-based analysis of 333 primary and/or metastatic melanomas from 331 patients. We establish a framework for genomic classification into one of four subtypes based on the pattern of the most prevalent significantly mutated genes: mutant BRAF, mutant RAS, mutant NF1, and Triple-WT (wild-type). Integrative analysis reveals enrichment of KIT mutations and focal amplifications and complex structural rearrangements as a feature of the Triple-WT subtype. We found no significant outcome correlation with genomic classification, but samples assigned a transcriptomic subclass enriched for immune gene expression associated with lymphocyte infiltrate on pathology review and high LCK protein expression, a T cell marker, were associated with improved patient survival. This clinicopathological and multi-dimensional analysis suggests that the prognosis of melanoma patients with regional metastases is influenced by tumor stroma immunobiology, offering insights to further personalize therapeutic decision-making.

Focus on cutaneous and uveal melanoma specificities.

Cutaneous melanoma (CM) and uveal melanoma (UM) derive from cutaneous and uveal melanocytes that share the same embryonic origin and display the same cellular function. However, the etiopathogenesis and biological behaviors of these melanomas are very different. CM and UM display distinct landscapes of genetic alterations and show different metastatic routes and tropisms. Hence, therapeutic improvements achieved in the last few years for the treatment of CM have failed to ameliorate the clinical outcomes of patients with UM. The scope of this review is to discuss the differences in tumorigenic processes (etiologic factors and genetic alterations) and tumor biology (gene expression and signaling pathways) between CM and UM. We develop hypotheses to explain these differences, which might provide important clues for research avenues and the identification of actionable vulnerabilities suitable for the development of new therapeutic strategies for metastatic UM.

Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification.

Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA-SVM, SVM, UMAP-SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.

Restrospective reappraisal of the prognostic classification of spitzoid melanocytic neoplasms after BRAF and NRAS mutation characterisation: a single institution experience.

AIMS: The current WHO classification of melanocytic tumours excludes neoplasms showing BRAF or NRAS mutations from the Spitz category. This study aimed to review and reclassify atypical melanocytic tumours with spitzoid morphological features diagnosed between 2009 and 2021 in our hospital after expanding the molecular profile, including BRAF and NRAS mutations in all cases. METHODS AND RESULTS: A total of 71 neoplasms showing spitzoid features (Spitz-like) and atypia were included. The risk of progression of tumours was first studied by integrating the morphology, immunohistochemistry (p16, Ki67, HMB45 and PRAME) and fluorescence in-situ hybridisation (FISH) results (melanoma multiprobe and 9p21). In a second step, after expanding the molecular study, including BRAF and NRAS mutational status, the neoplasms were finally classified into four subgroups: atypical Spitz tumour (AST, n = 45); BRAF-mutated naevus/low-grade melanocytoma with spitzoid morphology (BAMS, n = 2); Spitz melanoma (SM, n = 14); and BRAF or NRAS mutated melanoma with spitzoid features (MSF, n = 10). Follow-up of patients revealed uneventful results for AST and BAMS. Only one SM presented lymph node metastasis after 134 months. Conversely, patients with MSF showed an unfavourable outcome: three developed lymph node metastases after a mean time of 22 months, with one patient presenting distant metastasis and dying of the disease 64 months from diagnosis. The progression-free survival showed significant differences between the four groups of spitzoid tumours (P < 0.001) and between both melanoma subtypes (P = 0.012). CONCLUSIONS: The classification and prognostication of atypical neoplasms with spitzoid features requires the integration of histomorphology with the molecular investigation of tumours, which should include BRAF and NRAS mutational status.

Melanoma in Pediatric and Young Adult Patients.

PURPOSE OF REVIEW: Melanoma in younger individuals has different clinical presentations, histologic characteristics and prognosis from older patients. This review summarizes key differences and important new insights into pediatric and young adult melanoma, as well as recent evolutions in treatment. RECENT FINDINGS: Molecular techniques have improved the classification of melanocytic neoplasms, and are especially useful in the workup of the diagnostically challenging lesions frequent in this age group. Molecular evaluation highlights differences between melanoma and atypical lesions with Spitz-like morphology, and should routinely be incorporated for diagnosing and classifying Spitzoid melanocytic to guide prognostication and treatment. Once diagnosed, the management of bona fide melanoma in children and young adults is largely similar to older patients, while the optimal management of lesions such as atypical Spitz tumors remains uncertain. Increased awareness of the presentation and diagnostic characteristics of melanoma in young individuals will allow earlier detection, and improved diagnostic techniques will allow optimum management without over- or under-treatment.

Comprehensive analysis of clinical images contributions for melanoma classification using convolutional neural networks.

BACKGROUND: Timely diagnosis plays a critical role in determining melanoma prognosis, prompting the development of deep learning models to aid clinicians. Questions persist regarding the efficacy of clinical images alone or in conjunction with dermoscopy images for model training. This study aims to compare the classification performance for melanoma of three types of CNN models: those trained on clinical images, dermoscopy images, and a combination of paired clinical and dermoscopy images from the same lesion. MATERIALS AND METHODS: We divided 914 image pairs into training, validation, and test sets. Models were built using pre-trained Inception-ResNetV2 convolutional layers for feature extraction, followed by binary classification. Training comprised 20 models per CNN type using sets of random hyperparameters. Best models were chosen based on validation AUC-ROC. RESULTS: Significant AUC-ROC differences were found between clinical versus dermoscopy models (0.661 vs. 0.869, p < 0.001) and clinical versus clinical + dermoscopy models (0.661 vs. 0.822, p = 0.001). Significant sensitivity differences were found between clinical and dermoscopy models (0.513 vs. 0.799, p = 0.01), dermoscopy versus clinical + dermoscopy models (0.799 vs. 1.000, p = 0.02), and clinical versus clinical + dermoscopy models (0.513 vs. 1.000, p < 0.001). Significant specificity differences were found between dermoscopy versus clinical + dermoscopy models (0.800 vs. 0.288, p < 0.001) and clinical versus clinical + dermoscopy models (0.650 vs. 0.288, p < 0.001). CONCLUSION: CNN models trained on dermoscopy images outperformed those relying solely on clinical images under our study conditions. The potential advantages of incorporating paired clinical and dermoscopy images for CNN-based melanoma classification appear less clear based on our findings.

Dermatologist-level classification of skin cancer with deep neural networks.

Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images-two orders of magnitude larger than previous datasets-consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.

Whole-genome landscapes of major melanoma subtypes.

Melanoma of the skin is a common cancer only in Europeans, whereas it arises in internal body surfaces (mucosal sites) and on the hands and feet (acral sites) in people throughout the world. Here we report analysis of whole-genome sequences from cutaneous, acral and mucosal subtypes of melanoma. The heavily mutated landscape of coding and non-coding mutations in cutaneous melanoma resolved novel signatures of mutagenesis attributable to ultraviolet radiation. However, acral and mucosal melanomas were dominated by structural changes and mutation signatures of unknown aetiology, not previously identified in melanoma. The number of genes affected by recurrent mutations disrupting non-coding sequences was similar to that affected by recurrent mutations to coding sequences. Significantly mutated genes included BRAF, CDKN2A, NRAS and TP53 in cutaneous melanoma, BRAF, NRAS and NF1 in acral melanoma and SF3B1 in mucosal melanoma. Mutations affecting the TERT promoter were the most frequent of all; however, neither they nor ATRX mutations, which correlate with alternative telomere lengthening, were associated with greater telomere length. Most melanomas had potentially actionable mutations, most in components of the mitogen-activated protein kinase and phosphoinositol kinase pathways. The whole-genome mutation landscape of melanoma reveals diverse carcinogenic processes across its subtypes, some unrelated to sun exposure, and extends potential involvement of the non-coding genome in its pathogenesis.

Refractory thrombotic thrombocytopenic purpura related to checkpoint inhibitor immunotherapy.

BACKGROUND: Checkpoint inhibitors enhance T-lymphocyte-mediated antitumor responses, resulting in increased survival for patients with neoplastic disease. However, a subset of patients receiving checkpoint inhibitor therapy may experience adverse complications that include the development of autoimmune conditions, such as thrombotic thrombocytopenic purpura (TTP). Given the potential etiologic differences of checkpoint inhibitor-related autoimmunity, TTP that develops in the presence of checkpoint inhibitors may be refractory to current treatment methods and therefore may require additional treatment and prognostic consideration. CASE REPORT: Herein, we describe the unique clinical course of a patient who was treated with the combined checkpoint inhibitors nivolumab and ipilimumab for Stage IV malignant melanoma, who subsequently developed TTP. Unlike many patients with TTP, this patient failed to develop a sustained response to therapeutic plasma exchange. Additional use of steroids, anti-CD20, and plasma cell-targeting therapy (bortezomib) also failed to substantially reverse thrombocytopenia in a sustainable fashion. During this time, her melanoma progressed, and she ultimately succumbed. CONCLUSION: This case illustrates not only that TTP may be a potential complication of checkpoint inhibitor therapy, but also that TTP developing in this setting may result in an unpredictable response to commonly employed TTP treatment modalities. Ultimately, checkpoint inhibitor-related TTP may require distinct management approaches and prognostic considerations.

Acral Lentiginous Melanoma: A United States Multi-Center Substage Survival Analysis.

BACKGROUND: Acral lentiginous melanoma is associated with worse survival than other subtypes of melanoma. Understanding prognostic factors for survival and recurrence can help better inform follow-up care. OBJECTIVES: To analyze the clinicopathologic features, melanoma-specific survival, and recurrence-free survival by substage in a large, multi-institutional cohort of primary acral lentiginous melanoma patients. METHODS: Retrospective review of the United States Melanoma Consortium database, a multi-center prospectively collected database of acral lentiginous melanoma patients treated between January 2000 and December 2017. RESULTS: Of the 433 primary acral lentiginous melanoma patients identified (median [range] age: 66 [8-97] years; 53% female, 83% white), 66% presented with stage 0-2 disease and the median time of follow-up for the 392 patients included in the survival analysis was 32.5 months (range: 0-259). The 5-year melanoma-specific survivals by stage were 0 = 100%, I = 93.8%, II = 76.2%, III = 63.4%, IIIA = 80.8%, and IV = 0%. Thicker Breslow depth ((HR) = 1.13; 95% CI = 1.05-1.21; P < .001)) and positive nodal status ((HR) = 1.79; 95% CI = 1.00-3.22; P = .050)) were independent prognostic factors for melanoma-specific survival. Breslow depth ((HR = 1.13; 95% CI = 1.07-1.20; P < .001), and positive nodal status (HR = 2.12; 95% CI = 1.38-3.80; P = .001) were also prognostic factors for recurrence-free survival. CONCLUSION: In this cohort of patients, acral lentiginous melanoma was associated with poor outcomes even in early stage disease, consistent with prior reports. Stage IIB and IIC disease were associated with particularly low melanoma-specific and recurrence-free survival. This suggests that studies investigating adjuvant therapies in stage II patients may be especially valuable in acral lentiginous melanoma patients.

Reflectance confocal microscopy terminology glossary for melanocytic skin lesions: A systematic review.

BACKGROUND: There is lack of uniformity in the reflectance confocal microscopy (RCM) terminology for melanocytic lesions. OBJECTIVE: To review published RCM terms for melanocytic lesions and identify redundant, synonymous terms. METHODS: A systematic review of original research articles adhering to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines was conducted until August 15, 2018. Two investigators gathered all published RCM terms used to describe melanoma and melanocytic nevi. Synonymous terms were grouped based on similarity in definition and in histopathologic correlation. RESULTS: Out of 156 full-text screened articles, 59 studies met the inclusion criteria. We identified 209 terms; 191 (91.4%) corresponding to high-magnification/cellular-level terms and 18 (8.6%) corresponding to low-magnification/architectural patterns terms. The overall average use frequency of RCM terms was 3.1 times (range, 1-31). By grouping of individual RCM terms based on likely synonymous definitions and by eliminating terms lacking clear definition, the total number of RCM terms could be potentially reduced from 209 to 40 terms (80.8% reduction). LIMITATIONS: Non-English and non-peer-reviewed articles were excluded. CONCLUSIONS: This systematic review of published RCM terms identified significant terminology redundancy. It provides the basis for subsequent terminology consensus on melanocytic neoplasms.

Five-year survival in patients with nodular and superficial spreading melanomas in the US population.

BACKGROUND: Although superficial spreading melanomas (SSM) are diagnosed as thinner lesions, nodular melanomas (NM) have a more rapid growth rate and are biologically more aggressive compared with other histologic subtypes. OBJECTIVE: To determine the difference in 5-year relative survival in patients with NM and SSM at the same Breslow depth and TNM stage. METHODS: A population-based cross-sectional analysis compared the 5-year relative survival of patients with NM and SSM using data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER)∗Stat software (version 8.2.1-8.3.5). Chi-square tests compared the proportions, and Kaplan-Meier method with Z-score compared 5-year relative survival. RESULTS: For patients receiving a diagnosis between 2004 and 2009, 5-year relative survival was lower in NM compared with SSM (53.7% vs 87.3%; Z score, -41.35; P < .001). Similarly, for patients receiving a diagnosis between 2010 and 2015, 5-year relative survival was lower in NM compared with SSM (61.5% vs 89.7%; Z score, -2.7078; P < .01). Subgroup analyses showed inferior survival in NM in T1b, and survival differences remained significant after excluding patients with nodal or distant metastases. CONCLUSIONS: Five-year relative survival is worse in NM compared with SSM especially in T1b, T2a, and T2b melanomas. Melanoma subtype should be taken into consideration when making treatment recommendations.

A standardized approach to classifying melanocytic neoplasms? Comments on the MPATH-Dx system.

The potential benefits and limitations of the MPATH-Dx classification system for melanocytic neoplasms are presented and discussed.

Terminology for melanocytic skin lesions and the MPATH-Dx classification schema: A survey of dermatopathologists.

BACKGROUND: Diagnostic terms used in histopathology reports of cutaneous melanocytic lesions are not standardized. We describe dermatopathologists' views regarding diverse diagnostic terminology and the utility of the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx) for categorizing melanocytic lesions. METHODS: July 2018-2019 survey of board-certified and/or fellowship-trained dermatopathologists with experience interpreting melanocytic lesions. RESULTS: Among 160 participants, 99% reported witnessing different terminology being used for the same melanocytic lesion. Most viewed diverse terminology as confusing to primary care physicians (98%), frustrating to pathologists (83%), requiring more of their time as a consultant (64%), and providing necessary clinical information (52%). Most perceived that adoption of the MPATH-Dx would: improve communication with other pathologists and treating physicians (87%), generally be a change for the better (80%), improve patient care (79%), be acceptable to clinical colleagues (68%), save time in pathology report documentation (53%), and protect from malpractice (51%). CONCLUSIONS: Most dermatopathologists view diverse terminology as contributing to miscommunication with clinicians and patients, adversely impacting patient care. They view the MPATH-Dx as a promising tool to standardize terminology and improve communication. The MPATH-Dx may be a useful supplement to conventional pathology reports. Further revision and refinement are necessary for widespread clinical use.

The utility of PRAME immunohistochemistry in the evaluation of challenging melanocytic tumors.

BACKGROUND: PRAME (PReferentially expressed Antigen in Melanoma) immunohistochemistry has demonstrated high specificity for unequivocal melanomas; however, its utility in ambiguous melanocytic neoplasms has yet to be fully elucidated. METHODS: Cases of challenging melanocytic neoplasms were subclassified into one of three categories: challenging, favor benign (FB), challenging, cannot be subclassified (CCS), or challenging, favor malignant (FM). Using a previously published system, whereby cases with diffuse staining (>75%) were considered positive, scoring of PRAME was performed. Additionally, tumors with hotspot staining were also considered positive. RESULTS: Sixteen out of 85 tumors showed positive staining representing 5% of FB tumors, 24% of CCS tumors, and 47% of FM. In FB and CCS tumors, positive staining was mainly encountered in atypical intraepidermal melanocytic proliferations and spitzoid neoplasms. The specificity of positive PRAME staining was 95% and its concordance with the final diagnostic interpretation was 75%. CONCLUSIONS: PRAME positivity is more common in neoplasms favored to be malignant by histopathologic evaluation. Its clinical utility may include early diagnosis of incipient melanoma in situ. Rarely, benign melanocytic neoplasms could show diffuse expression of PRAME, and additional studies are needed to determine optimal utilization. Lastly, hotspot staining may increase its sensitivity without much compromise in specificity.

Melanoma diagnosis using deep learning techniques on dermatoscopic images.

BACKGROUND: Melanoma has become more widespread over the past 30 years and early detection is a major factor in reducing mortality rates associated with this type of skin cancer. Therefore, having access to an automatic, reliable system that is able to detect the presence of melanoma via a dermatoscopic image of lesions and/or skin pigmentation can be a very useful tool in the area of medical diagnosis. METHODS: Among state-of-the-art methods used for automated or computer assisted medical diagnosis, attention should be drawn to Deep Learning based on Convolutional Neural Networks, wherewith segmentation, classification and detection systems for several diseases have been implemented. The method proposed in this paper involves an initial stage that automatically crops the region of interest within a dermatoscopic image using the Mask and Region-based Convolutional Neural Network technique, and a second stage based on a ResNet152 structure, which classifies lesions as either "benign" or "malignant". RESULTS: Training, validation and testing of the proposed model was carried out using the database associated to the challenge set out at the 2017 International Symposium on Biomedical Imaging. On the test data set, the proposed model achieves an increase in accuracy and balanced accuracy of 3.66% and 9.96%, respectively, with respect to the best accuracy and the best sensitivity/specificity ratio reported to date for melanoma detection in this challenge. Additionally, unlike previous models, the specificity and sensitivity achieve a high score (greater than 0.8) simultaneously, which indicates that the model is good for accurate discrimination between benign and malignant lesion, not biased towards any of those classes. CONCLUSIONS: The results achieved with the proposed model suggest a significant improvement over the results obtained in the state of the art as far as performance of skin lesion classifiers (malignant/benign) is concerned.

Defining the Histopathological Term Atypical Intraepidermal Melanocytic Proliferation: A Retrospective Cross-Sectional Study.

BACKGROUND: Atypical intraepidermal melanocytic proliferation (AIMP) is a general term assigned to melanocytic proliferations of uncertain biological potential when a definitive histopathological diagnosis cannot be achieved. There are few data available describing the possibility of malignancy of AIMP, or ways to further define diagnosis. OBJECTIVE: To determine the rate of diagnostic change of AIMP to melanoma or melanoma in situ (MIS) after conventional excision. In addition, to determine the role of immunohistochemistry (IHC) in defining AIMP biopsies. METHODS: Retrospective cross-sectional, single-center review of biopsies with a diagnosis of AIMP with a follow-up conventional excision from 2012-2016 was performed. In a separate analysis, a search was performed for AIMP biopsied lesions in which IHC was subsequently performed. RESULTS: The rate of diagnostic change of AIMP to MIS was 4.8% (8/167) after excision. Punch biopsy was a risk factor for diagnostic change to MIS (odds ratio 12.94, confidence interval 2.56-65.38, P = 0.008). The rate of diagnostic change of AIMP biopsies after examining with IHC was 21.3% (34/160) to MIS and 4.4% (7/160) to melanoma. CONCLUSION: The possibility of malignancy of AIMP lesions must be taken into consideration when counseling patients and when planning treatment options. IHC is a useful tool and should be used in the evaluation of AIMP specimens.

From Melanocytes to Melanoma Cells: Characterization of the Malignant Transformation by Four Distinctly Different Melanin Fluorescence Spectra (Review).

The melanin fluorescence emitted by pigment cells of the human skin has been a central research topic for decades, because melanin, on the one hand, protects against (solar) radiation in the near-UV range, whereas on the other hand, melanocytes are the starting point for the malignant transformation into melanoma. Until recently, however, melanin fluorescence was not accessible in the context of conventional spectroscopy, because it is ultraweak and is overshadowed by the more intense so-called autofluorescence of endogenous fluorophores. The advent of a new method of laser spectroscopy has made this melanin fluorescence measurable in vivo. A stepwise two-photon absorption with 800 nm photons is used, which more selectively excites melanin (dermatofluoroscopy). Our review summarizes the experimental results on melanin fluorescence of the four types of cutaneous pigment cells from healthy and malignant tissues. Outstanding is the finding that different types of melanocytes (i.e., melanocytes of common nevi, versus dysplastic nevi or versus melanoma cells) show characteristically different fluorescence spectra. The possibilities of using this melanin fluorescence for melanoma diagnosis are shown. Moreover, the uniform fluorescence spectra emitted by different melanoma subtypes are essential. Conclusions are drawn about the molecular processes in the melanosomes that determine fluorescence. Finally, experimental suggestions for further investigations are given.

A Transcriptome-Wide Isoform Landscape of Melanocytic Nevi and Primary Melanomas Identifies Gene Isoforms Associated with Malignancy.

Genetic splice variants have become of central interest in recent years, as they play an important role in different cancers. Little is known about splice variants in melanoma. Here, we analyzed a genome-wide transcriptomic dataset of benign melanocytic nevi and primary melanomas (n = 80) for the expression of specific splice variants. Using kallisto, a map for differentially expressed splice variants in melanoma vs. benign melanocytic nevi was generated. Among the top genes with differentially expressed splice variants were Ras-related in brain 6B (RAB6B), a member of the RAS family of GTPases, Macrophage Scavenger Receptor 1 (MSR1), Collagen Type XI Alpha 2 Chain (COLL11A2), and LY6/PLAUR Domain Containing 1 (LYPD1). The Gene Ontology terms of differentially expressed splice variants showed no enrichment for functional gene sets of melanoma vs. nevus lesions, but between type 1 (pigmentation type) and type 2 (immune response type) melanocytic lesions. A number of genes such as Checkpoint Kinase 1 (CHEK1) showed an association of mutational patterns and occurrence of splice variants in melanoma. Moreover, mutations in genes of the splicing machinery were common in both benign nevi and melanomas, suggesting a common mechanism starting early in melanoma development. Mutations in some of these genes of the splicing machinery, such as Serine and Arginine Rich Splicing Factor A3 and B3 (SF3A3, SF3B3), were significantly enriched in melanomas as compared to benign nevi. Taken together, a map of splice variants in melanoma is presented that shows a multitude of differentially expressed splice genes between benign nevi and primary melanomas. The underlying mechanisms may involve mutations in genes of the splicing machinery.

Expression Profiles of ASIC1/2 and TRPV1/4 in Common Skin Tumors.

The acid-sensing ion channels ASIC1 and ASIC2, as well as the transient receptor potential vanilloid channels TRPV1 and TRPV4, are proton-gated cation channels that can be activated by low extracellular pH (pHe), which is a hallmark of the tumor microenvironment in solid tumors. However, the role of these channels in the development of skin tumors is still unclear. In this study, we investigated the expression profiles of ASIC1, ASIC2, TRPV1 and TRPV4 in malignant melanoma (MM), squamous cell carcinoma (SCC), basal cell carcinoma (BCC) and in nevus cell nevi (NCN). We conducted immunohistochemistry using paraffin-embedded tissue samples from patients and found that most skin tumors express ASIC1/2 and TRPV1/4. Striking results were that BCCs are often negative for ASIC2, while nearly all SCCs express this marker. Epidermal MM sometimes seem to lack ASIC1 in contrast to NCN. Dermal portions of MM show strong expression of TRPV1 more frequently than dermal NCN portions. Some NCN show a decreasing ASIC1/2 expression in deeper dermal tumor tissue, while MM seem to not lose ASIC1/2 in deeper dermal portions. ASIC1, ASIC2, TRPV1 and TRPV4 in skin tumors might be involved in tumor progression, thus being potential diagnostic and therapeutic targets.