Fluorescence In Situ Hybridization for Melanoma Diagnosis

Ferrara, Gerardo; Vanna, Anna Chiara De

doi:10.1097/dad.0000000000000380

Cited by 43 publications

(32 citation statements)

References 65 publications

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“…Furthermore, differences between previous studies and the present study may be due to the absence of investigation of LVI and necrosis in many of the above-mentioned studies. However, various molecular alterations accompanying the BRAF V600 mutation may also be features of an ordinary nevus, such as promoter mutations of telomerase reverse transcriptase (TERT) (27,28); mutations in NRAS, PTEN, CDK2NA, STK19, KIT, GNAQ, GNA11 and NF 1 genes (29)(30)(31) or undetected interactions between the BRAF V600 mutation and other signaling pathways (26). Further studies on genotypic and phenotypic alterations in specimens of primary tumours obtained from both metastatic and nonmetastatic patients may provide more information about the impact of the BRAF mutation on prognostic features of melanoma.…”

Section: Discussionmentioning

confidence: 99%

Braf v600 mutation profile of metastatic melanoma in the thrace region of turkey

Can¹,

Taştekin²,

Yalta³

et al. 2018

TJPATH

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Objective: BRAF is the most common mutation in melanoma. The most common subtype is BRAF V600E, followed by V600K. Initially, the authors aimed to investigate whether clinicopathological features of melanoma are associated with BRAF mutations. We then aimed to present the relationships between the clinicopathological features and the mutated subtype (V600E vs V600K). Material and Method:61 patients with metastatic malignant melanoma (affecting the lymph node or other distant sites) were selected. Patient data regarding age at the time of diagnosis, sex, metastatic site (lymph node, distant metastasis or both) and primary tumour site were obtained from the hospital's database. Tissue samples containing at least 30% tumour cells were isolated from the specimens of 61 patients (24 samples from primary tumours and 37 from metastatic foci) for BRAF analysis. Comparisons between the BRAF V600 mutation and clinicopathological and histopathological features were performed.Results: BRAF V600 mutation was detected in 34 (55.7%) patients. The subtype was BRAF V600E in 22 (64.7%) patients, BRAF V600K in 11(32.4%) patients and BRAF V600R in 1(2.9%) patient. The crucial results of the present study may be summarized as follows: i) BRAF V600 mutation was more common in older patients and tumors with BRAF V600 mutation revealed necrosis and LVI more commonly than wild-type tumors, ii) BRAF V600K mutation was more common in older patients and BRAF V600K mutated tumors exhibited ulceration more commonly than tumors with BRAF V600E mutation (close to significant). Conclusion:The BRAF V600 mutation may have interactions with prognostic clinicoptahological features of melanoma including necrosis and lymphovascular invasion. V600K mutation may be more common than expected and may have different associations with properties of the tumor such as tumor ulceration and patient age. Investigation of the mutated subtype of the BRAF gene may therefore reveal more detailed data about the management of melanoma and may also prevent missing of candidates for BRAF inhibitor therapies.

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Section: Discussionmentioning

confidence: 99%

Braf v600 mutation profile of metastatic melanoma in the thrace region of turkey

Can¹,

Taştekin²,

Yalta³

et al. 2018

TJPATH

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“…Heterogeneous changes in CM occur at the molecular level, and the changes are different among CM subtypes and between CM and melanocytic nevi . Fluorescence in situ hybridization (FISH) may improve diagnosis and classification of melanocytic proliferations, using different probes, which target different chromosomes . Several genetic abnormalities in critical signalling pathways have been proposed to influence CM development and progression, including mutations in mitogen‐activated protein kinase (MAPK) proteins and aberrations in MYC and CDKN2A genes.…”

Section: Introductionmentioning

confidence: 99%

Melanoma types by in vivo reflectance confocal microscopy correlated with protein and molecular genetic alterations: A pilot study

Beretti

Bertoni

Farnetani

et al. 2019

Experimental Dermatology

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Cutaneous melanoma (CM) is one of the most prevalent skin cancers, which lacks both a prognostic marker and a specific and lasting treatment, due to the complexity of the disease and heterogeneity of patients. Reflectance confocal microscopy (RCM) in vivo analysis is a versatile approach offering immediate morphological information, enabling the identification of four primary cutaneous RCM CM types. Whether RCM CM types are associated with a specific protein and molecular genetic profiles at the tissue level remains unclear. The current pilot study was designed to identify potential correlations between RCM CM types and specific biological characteristics, combining immunohistochemistry (IHC) and molecular analyses. Eighty primary CMs evaluated at patient bedside with RCM (type 1 [19, 24%], type 2 [12, 15%], type 3 [7, 9%] and type 4 [42, 52%]) were retrospectively evaluated by IHC stains (CD271, CD20, CD31, cyclin D1), fluorescence in situ hybridization FISH for MYC gain and CDKN2A loss and molecular analysis for somatic mutations (BRAF, NRAS and KIT). RCM CM types correlated with markers of stemness property, density of intra‐tumoral lymphocytic B infiltrate and cyclin D1 expression, while no significant association was found with blood vessel density nor molecular findings. RCM CM types show a different marker profile expression, suggestive of a progression and an increase in aggressiveness, according to RCM morphologies.

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“…Despite the limited size of the training set, the sensitivity and specificity of the MiRTM for validation sets thresholded on the discovery cohort was 0.83 and 0.71-0.83. This performance of the MiRTM is comparable to other molecular tests for distinguishing benign melanocytic nevi from melanoma, including chromosomal analysis by fluorescence in situ hybridization (sensitivity 0.72-1.00, specificity 0.90-1.00) (Gerami et al 2010;Ferrara and De Vanna 2016) and myPath Melanoma gene expression profiling (sensitivity 0.63-0.90, specificity 0.88-0.93) (Clarke et al 2017;Minca et al 2016). The MiRTM does not perform as well as chromosomal analysis by array comparative genomic hybridization (aCGH, sensitivity 0.92-0.96, specificity 0.87-1.00) (Bastian et al 2003;Wang et al 2013).…”

Section: Mirtm Correlation Matrix (Nevus )mentioning

confidence: 64%

A machine-learning classifier trained with microRNA ratios to distinguish melanomas from nevi

Torres

Lang

Hejna

et al. 2018

Preprint

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The use of microRNAs as biomarkers has been proposed for many diseases including the diagnosis of melanoma. Although hundreds of microRNAs have been identified as differentially expressed in melanomas as compared to benign melanocytic lesions, limited consensus has been achieved across studies, constraining the effective use of these potentially useful markers. In this study we quantified microRNAs by next-generation sequencing from melanomas and their adjacent benign precursor nevi.We applied a machine learning-based pipeline to identify a microRNA signature that separated melanomas from nevi and was unaffected by confounding variables, such as patient age and tumor cell content. By employing the ratios of microRNAs that were either enriched or depleted in melanoma compared to nevi as a normalization strategy, the classifier performed similarly across multiple published microRNA datasets, obtained by microarray, small RNA sequencing, or RT-qPCR. Validation on separate cohorts of melanomas and nevi correctly classified lesions with 83% sensitivity and 71-83% specificity, independent of variation in tumor cell content of the sample or patient age.

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Fluorescence In Situ Hybridization for Melanoma Diagnosis

Cited by 43 publications

References 65 publications

Braf v600 mutation profile of metastatic melanoma in the thrace region of turkey

Braf v600 mutation profile of metastatic melanoma in the thrace region of turkey

Melanoma types by in vivo reflectance confocal microscopy correlated with protein and molecular genetic alterations: A pilot study

A machine-learning classifier trained with microRNA ratios to distinguish melanomas from nevi

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