Dataset Information

Identification of a Robust Methylation Classifier for Cutaneous Melanoma Diagnosis.

ABSTRACT: Early diagnosis improves melanoma survival, yet the histopathological diagnosis of cutaneous primary melanoma can be challenging, even for expert dermatopathologists. Analysis of epigenetic alterations, such as DNA methylation, that occur in melanoma can aid in its early diagnosis. Using a genome-wide methylation screening, we assessed CpG methylation in a diverse set of 89 primary invasive melanomas, 73 nevi, and 41 melanocytic proliferations of uncertain malignant potential, classified based on interobserver review by dermatopathologists. Melanomas and nevi were split into training and validation sets. Predictive modeling in the training set using ElasticNet identified a 40-CpG classifier distinguishing 60 melanomas from 48 nevi. High diagnostic accuracy (area under the receiver operator characteristic curve = 0.996, sensitivity = 96.6%, and specificity = 100.0%) was independently confirmed in the validation set (29 melanomas, 25 nevi) and other published sample sets. The 40-CpG melanoma classifier included homeobox transcription factors and genes with roles in stem cell pluripotency or the nervous system. Application of the 40-CpG melanoma classifier to the diagnostically uncertain samples assigned melanoma or nevus status, potentially offering a diagnostic tool to assist dermatopathologists. In summary, the robust, accurate 40-CpG melanoma classifier offers a promising assay for improving primary melanoma diagnosis.

SUBMITTER: Conway K

PROVIDER: S-EPMC6535139 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Identification of a Robust Methylation Classifier for Cutaneous Melanoma Diagnosis.

Conway Kathleen K Edmiston Sharon N SN Parker Joel S JS Kuan Pei Fen PF Tsai Yi-Hsuan YH Groben Pamela A PA Zedek Daniel C DC Scott Glynis A GA Parrish Eloise A EA Hao Honglin H Pearlstein Michelle V MV Frank Jill S JS Carson Craig C CC Wilkerson Matthew D MD Zhao Xiaobei X Slater Nathaniel A NA Moschos Stergios J SJ Ollila David W DW Thomas Nancy E NE

The Journal of investigative dermatology 20181206 6

Early diagnosis improves melanoma survival, yet the histopathological diagnosis of cutaneous primary melanoma can be challenging, even for expert dermatopathologists. Analysis of epigenetic alterations, such as DNA methylation, that occur in melanoma can aid in its early diagnosis. Using a genome-wide methylation screening, we assessed CpG methylation in a diverse set of 89 primary invasive melanomas, 73 nevi, and 41 melanocytic proliferations of uncertain malignant potential, classified based o ...[more]

PMID: 30529013

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Project description:BackgroundCancer onset and progression are driven by genetic and epigenetic alterations leading to oncogene activation and the silencing of tumor suppressor genes. Among epigenetic mechanisms, DNA methylation (methDNA) is gaining growing interest in cancer. Promoter hypomethylation is associated with oncogene activation while intragenic methDNA can be involved in transcriptional elongation, alternative spicing, and the activation of cryptic start sites. Several genes involved in the modulation of the tumor microenvironment are regulated by methDNA, including the Solute Carrier Family 22 Member 17 (SLC22A17), which is involved in iron trafficking and extracellular matrix remodeling cooperating with the Gelatinase-Associated Lipocalin (NGAL) ligand. However, the exact role of intragenic methDNA in cancer has not been fully investigated. Therefore, the aim of the present study is to explore the role of methDNA in the regulation of SLC22A17 in cutaneous melanoma (CM), used as a tumor model.MethodsCorrelation and differential analyses between SLC22A17 expression and methDNA were performed using the data contained in The Cancer Genome Atlas and Gene Expression Omnibus databases. Functional studies on melanoma cell lines treated with 5-Azacytidine (5-Aza) were conducted to assess the correlation between methDNA and SLC22A17 expression. A validation study on the diagnostic potential of the in silico-identified SLC22A17 methDNA hotspot was finally performed by analyzing tissue samples obtained from CM patients and healthy controls.ResultsThe computational analyses revealed that SLC22A17 was significantly downregulated in CM, and its expression was related to promoter hypomethylation and intragenic hypermethylation. Moreover, SLC22A17 overexpression and hypermethylation of two intragenic methDNA hotspots were associated with a better clinical outcome in CM patients. The correlation between SLC22A17 methDNA and expression was confirmed in 5-Aza-treated cells. In agreement with in silico analyses, the SLC22A17 promoter methylation hotspot showed higher methDNA levels in CM samples compared to nevi. In addition, the methDNA levels of this hotspot were positively correlated with advanced CM.ConclusionsThe SLC22A17 methDNA hotspot could represent a promising biomarker for CM, highlighting the regulatory role of methDNA on SLC22A17 expression. These results pave the way for the identification of novel epigenetic biomarkers and therapeutic targets for the management of CM patients.

Project description:Pediatric sarcomas provide a unique diagnostic challenge. There is considerable morphologic overlap between entities, increasing the importance of molecular studies in the diagnosis, treatment, and identification of therapeutic targets. We developed and validated a genome-wide DNA methylation based classifier to differentiate between osteosarcoma, Ewing's sarcoma, and synovial sarcoma. DNA methylation status of 482,421 CpG sites in 10 Ewing's sarcoma, 11 synovial sarcoma, and 15 osteosarcoma samples were determined using the Illumina Infinium HumanMethylation450 array. We developed a random forest classifier trained from the 400 most differentially methylated CpG sites within the training set of 36 sarcoma samples. This classifier was validated on data drawn from The Cancer Genome Atlas (TCGA) synovial sarcoma, TARGET Osteosarcoma, and a recently published series of Ewing's sarcoma. Methylation profiling revealed three distinct patterns, each enriched with a single sarcoma subtype. Within the validation cohorts, all samples from TCGA were accurately classified as synovial sarcoma (10/10, sensitivity and specificity 100%), all but one sample from TARGET-OS were classified as osteosarcoma (85/86, sensitivity 98%, specificity 100%) and 14/15 Ewing's sarcoma samples classified correctly (sensitivity 93%, specificity 100%). The single misclassified osteosarcoma sample demonstrated high EWSR1 and ETV1 expression on RNA-seq although no fusion was found on manual curation of the transcript sequence. Two additional clinical samples, that were difficult to classify by morphology and molecular methods, were classified as osteosarcoma when previously suspected to be a synovial sarcoma and Ewing's sarcoma on initial diagnosis, respectively. Osteosarcoma, synovial sarcoma, and Ewing's sarcoma have distinct epigenetic profiles. Our validated methylation-based classifier can be used to provide diagnostic assistance when histological and standard techniques are inconclusive.

Dataset Information

Identification of a Robust Methylation Classifier for Cutaneous Melanoma Diagnosis.

Publications

Identification of a Robust Methylation Classifier for Cutaneous Melanoma Diagnosis.

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