Clinical and genetic characteristics of BAP1-mutated non-uveal and uveal melanoma

BackgroundScreening for gene mutations has become routine clinical practice across numerous tumor entities, including melanoma. BAP1 gene mutations have been identified in various tumor types and acknowledged as a critical event in metastatic uveal melanoma, but their role in non-uveal melanoma remains inadequately characterized.MethodsA retrospective analysis of all melanomas sequenced in our department from 2014–2022 (n=2650) was conducted to identify BAP1 mutated samples. Assessment of clinical and genetic characteristics was performed as well as correlations with treatment outcome.ResultsBAP1 mutations were identified in 129 cases and distributed across the entire gene without any apparent hot spots. Inactivating BAP1 mutations were more prevalent in uveal (55%) compared to non-uveal (17%) melanomas. Non-uveal BAP1 mutated melanomas frequently exhibited UV-signature mutations and had a significantly higher mutation load than uveal melanomas. GNAQ and GNA11 mutations were common in uveal melanomas, while MAP-Kinase mutations were frequent in non-uveal melanomas with NF1, BRAF V600 and NRAS Q61 mutations occurring in decreasing frequency, consistent with a strong UV association. Survival outcomes did not differ among non-uveal melanoma patients based on whether they received targeted or immune checkpoint therapy, or if their tumors harbored inactivating BAP1 mutations.ConclusionIn contrast to uveal melanomas, where BAP1 mutations serve as a significant prognostic indicator of an unfavorable outcome, BAP1 mutations in non-uveal melanomas are primarily considered passenger mutations and do not appear to be relevant from a prognostic or therapeutic perspective

Rare TERT Promoter Mutations Present in Benign and Malignant Cutaneous Vascular Tumors

Mutations in the promoter of the telomerase reverse transcriptase (TERT) gene have been described as the most common hot-spot mutations in different solid tumors. High frequencies of TERT promoter mutations have been reported to occur in tumors arising in tissues with low rates of self-renewal. For cutaneous vascular tumors, the prevalence of TERT promoter mutations has not yet been investigated in larger mixed cohorts. With targeted next-generation sequencing (NGS), we screened for different known recurrent TERT promoter mutations in various cutaneous vascular proliferations. In our cohort of 104 representative cutaneous vascular proliferations, we identified 7 TERT promoter mutations. We could show that 4 of 64 (6.3%) hemangiomas and vascular malformations harbored TERT promoter mutations (1 Chr.5:1295228 C > T mutations, 1 Chr.5:1295228_9 CC > TT mutation, and 2 Chr.5:1295250 C > T mutations), 1 of 19 (5.3%) angiosarcomas harbored a Chr.5:1295250 C > T TERT promoter mutation, and 2 of 21 (9.5%) Kaposi’s sarcomas harbored TERT promoter mutations (2 Chr.5:1295250 C > T mutations). To our knowledge, this is the first general description of the distribution of TERT promoter mutations in a mixed cohort of cutaneous vascular tumors, revealing that TERT promoter mutations seem to occur with low prevalence in both benign and malignant cutaneous vascular proliferations

Genetic and Clinical Characteristics of ARID1A Mutated Melanoma Reveal High Tumor Mutational Load without Implications on Patient Survival

(1) Background: Melanoma has the highest mortality of all cutaneous tumors, despite recent treatment advances. Many relevant genetic events have been identified in the last decade, including recurrent ARID1A mutations, which in various tumors have been associated with improved outcomes to immunotherapy. (2) Methods: Retrospective analysis of 116 melanoma samples harboring ARID1A mutations. Assessment of clinical and genetic characteristics was performed as well as correlations with treatment outcome applying Kaplan–Meier (log-rank test), Fisher’s exact and Chi-squared tests. (3) Results: The majority of ARID1A mutations were in cutaneous and occult melanoma. ARID1A mutated samples had a higher number of mutations than ARID1A wild-type samples and harbored UV-mutations. A male predominance was observed. Many samples also harbored NF1 mutations. No apparent differences were noted between samples harboring genetically inactivating (frame-shift or nonsense) mutations and samples with other mutations. No differences in survival or response to immunotherapy of patients with ARID1A mutant melanoma were observed. (4) Conclusions: ARID1A mutations primarily occur in cutaneous melanomas with a higher mutation burden. In contrast to findings in other tumors, our data does not support ARID1A mutations being a biomarker of favorable response to immunotherapies in melanoma. Larger prospective studies would still be warranted

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods: We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation: For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. Interpretation: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity. (C) 2019 The Author(s). Published by Elsevier Ltd