BRAF and epithelial-mesenchymal transition in primary cutaneous melanoma: a role for Snail and E-cadherin?

Mitchell, Brendon C.; Leone, Dominick; Feller, John Kyle; Yang, Shi; Mahalingam, Meera

doi:10.1016/j.humpath.2015.12.030

Cited by 17 publications

(29 citation statements)

References 35 publications

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“…As several of the morphologic features described to be specific for mutated BRAF predominantly affect the epidermis, it may be the case that it is easier for the network to detect these features in thinner tumors. In studies correlating BRAF mutations and tumor thickness, some have found BRAF mutations to be associated with thinner tumors 22 while others have reported either an inverse 23, 24 or no relationship 25, 26 with Breslow depth.…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Approach for Rapid Mutational Screening in Melanoma

Kim

Nomikou

Coudray³

et al. 2019

Preprint

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36DNA-based molecular assays for determining mutational status in melanomas are time-37 consuming and costly. As an alternative, we applied a deep convolutional neural network 38 (CNN) to histopathology images of tumors from 257 melanoma patients and developed a 39 fully automated model that first selects for tumor-rich areas (Area under the curve 40 AUC=0.98), and second, predicts for the presence of mutated BRAF or NRAS. Network 41 performance was enhanced on BRAF-mutated melanomas 1.0 mm (AUC=0.83) and on 42 non-ulcerated NRAS-mutated melanomas (AUC=0.92). Applying our models to 43 histological images of primary melanomas from The Cancer Genome Atlas database also 44 demonstrated improved performances on thinner BRAF-mutated melanomas and non-45 ulcerated NRAS-mutated melanomas. We propose that deep learning-based analysis of 46 histological images has the potential to become integrated into clinical decision making 47 for the rapid detection of mutations of interest in melanoma. 48 49 50 Mutations in the BRAF oncogene are found in 50-60% of all melanomas 1 , while NRAS 51 mutations comprise an additional 15-20%. With the development of targeted therapies 2, 52 3 , determining the mutational status of BRAF and NRAS has become an integral 53 component for the management of Stage III/IV melanomas. DNA molecular assays such 54 as Sanger sequencing, pyrosequencing, and next generation sequencing (NGS) are the 55 current gold standard to determine mutational status 4 . However, these methods are costly 56 and time-consuming. Immunohistochemistry, real-time polymerase chain reaction (PCR), 57 and automated platforms 5, 6, 7 are rapid and less expensive alternatives, but are limited to 58 screening for specific mutations, such as BRAF-V600E/K or NRAS-Q61R/L, and may 59 potentially fail to identify rare mutational variants in patients that might have otherwise 60 benefited from adjuvant targeted therapy.61 62 Deep Convolutional Neural Network (CNN) methods to predict mutational status have 63 been demonstrated in other solid tumors. CNNs utilize multiple layers of convolution 64 operations, pooling layers, and fully connected layers to perform classification of images 65 to classes of interest through identification of various image features often not directly 66 detectable by the human eye. Deep CNNs, which utilize non-linear learning algorithms, 67have been successful in manipulating and processing large data sets, particularly for 68 image analysis 8 . Using images from The Cancer Genome Atlas (TCGA), a collaborative 69 cancer genomics database 9 , our group has previously developed a machine learning 70 algorithm that can predict for 6 different genes, including EGFR and STK11, in lung 71 carcinoma 10 . In breast cancer, deep learning applied to tumor microarray images has 72 been shown to predict for ER status with an 84% accuracy 11 . 73 74 In this study, we adapt our previous deep learning algorithm to a different dataset 75 comprised of histopathology images of primary melanomas resected from patients 76 pros...

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Section: Discussionmentioning

confidence: 99%

A Deep Learning Approach for Rapid Mutational Screening in Melanoma

Kim

Nomikou

Coudray³

et al. 2019

Preprint

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“…found that the expression of SNAIL correlated with the loss of CYLD expression and activation of the N-cadherin promoter, and was linked to tumor invasiveness and thickness in melanoma tissue (21). Loss of E-cadherin was found to be associated with proto-oncogene B-raf ( BRAF ) mutation and Breslow thickness greater than 1 mm in primary cutaneous melanoma samples from 68 patients (22). …”

Section: Discussionmentioning

confidence: 99%

Epithelial–Mesenchymal Expression Phenotype of Primary Melanoma and Matched Metastases and Relationship with Overall Survival

Yan

Holderness

et al. 2016

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E-Cadherin and N-cadherin are important components of epithelial–mesenchymal transition (EMT). The majority of studies on EMT in melanoma have been performed with cultured cell lines or pooled melanoma samples. The goal of our study was to evaluate the expression of E-cadherin and N-cadherin in matched tissue samples from primary and metastatic sites of melanoma and to determine the correlation with survival outcome. We analyzed tissues from 42 melanoma primary lesions and their corresponding metastases, as well as 53 benign nevi, for expression levels of E-cadherin and N-cadherin using immunohistochemical methods. There were heterogenous expression patterns of E- and N-cadherin in both primary and metastatic melanomas. Overall, metastatic tumor showed a decrease in E-cadherin expression and an increase in N-cadherin expression compared to the primary tumor, although the difference did not reach statistical significance (p=0.24 and 0.28 respectively). A switch of membranous expression from E-cadherin to N-cadherin from primary to metastatic melanoma was seen in eight patients (19%). Aberrant E-cadherin expression (defined as negative to weak membranous E-cadherin or positive nuclear E-cadherin expression) was more frequently observed in metastatic than in primary melanomas (p=0.03). Multivariate analysis showed that absence of N-cadherin expression in primary melanomas and the presence of aberrant E-cadherin expression in primary melanomas and metastatic melanomas was associated with a significantly worse overall survival. Our data support the importance of E-cadherin and N-cadherin proteins in melanoma progression and patient survival.

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“…Both stimuli are linked to PI3K or MAPK pathways. Mutant BRAF correlates with loss of E‐cadherin expression in melanoma . Cadherin switch causes melanocytes to form new cell‐cell connections with fibroblasts, vascular endothelial cells and other melanocytes expressing N‐cadherin.…”

Section: Changes In Cell‐cell Communication Facilitate Tumour Progresmentioning

confidence: 99%

Cutaneous pH landscape as a facilitator of melanoma initiation and progression

Koch

Schwab

2018

Acta Physiologica

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Melanoma incidence is on the rise and currently causes the majority of skin cancer-related deaths. Yet, therapies for metastatic melanoma are still insufficient so that new concepts are essential. Malignant transformation of melanocytes and melanoma progression are intimately linked to the cutaneous pH landscape and its dysregulation in tumour lesions. The pH landscape of normal skin is characterized by a large pH gradient of up to 3 pH units between surface and dermis. The Na /H exchanger NHE1 is one of the major contributors of acidity in superficial skin layers. It is also activated by the most frequent mutation in melanoma, BRAF , thereby causing pH dysregulation during melanoma initiation. Melanoma progression is supported by an extracellular acidification and/or NHE1 activity which promote the escape of single melanoma cells from the primary tumour, migration and metastatic spreading. We propose that viewing melanoma against the background of the acid-base physiology of the skin provides a better understanding of the pathophysiology of this disease and allows the development of novel therapeutic concepts.

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BRAF and epithelial-mesenchymal transition in primary cutaneous melanoma: a role for Snail and E-cadherin?

Cited by 17 publications

References 35 publications

A Deep Learning Approach for Rapid Mutational Screening in Melanoma

A Deep Learning Approach for Rapid Mutational Screening in Melanoma

Epithelial–Mesenchymal Expression Phenotype of Primary Melanoma and Matched Metastases and Relationship with Overall Survival

Cutaneous pH landscape as a facilitator of melanoma initiation and progression

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