Research Service, VA San Diego Healthcare System, San Diego, CA Department of Medicine, Division of Hematology-Oncology and Moores Cancer Center, University of California San Diego, La Jolla, CA Department of Medicine, Division of Dermatology, University of California San Diego, La Jolla, CA Therapeutics Clinical Research, San Diego, CA DermTech International, Inc., La Jolla, CA Dermatology Associates, Florence, KY Dermatologist Medical Group of North County, Oceanside, CA Dermatology Research Associates, Los Angeles, CA Solano Dermatology Associates, Vallejo, CA Skin Surgery Medical Group, San Diego, CA Skin and Cancer Associates, Plantation, FL Departments of Dermatology and Pathology, New York University School of Medicine, New York, NY.
Br J Dermatol. 2011 Feb 5. doi: 10.1111/j.1365-2133.2011.10239.x.
Early detection and treatment of melanoma is important for optimal clinical outcome, leading to biopsy of pigmented lesions deemed suspicious for the disease. The vast majority of such lesions are benign. Thus, a more objective and accurate means for detection of melanoma is needed to identify lesions for excision.
The goal of this study is to provide proof-of-principal that epidermal genetic information retrieval (EGIR(TM)), a method that non-invasively samples cells from stratum corneum by means of adhesive tape stripping, can be used to discern melanomas from nevi.
Skin overlying pigmented lesions clinically suspicious for melanoma was harvested using EGIR. RNA isolated from the tapes was amplified and gene expression profiled. All lesions were removed for histopathologic evaluation.
Supervised analysis of the microarray data identified 312 genes differentially expressed between melanomas, nevi, and normal skin specimens (p< 0.001, FDR <0.05). Surprisingly, many of these genes are known to have a role in melanocyte development and physiology, melanoma, cancer, and cell growth control. Subsequent class prediction modeling of a training dataset, consisting of 37 melanomas and 37 nevi, discovered a 17-gene classifier that discriminates these skin lesions. Upon testing with an independent dataset, this classifier discerns in situ and invasive melanomas from nevi with 100% sensitivity and 88% specificity, with an area under the curve for the receiver operating characteristic of >0.955.
These results demonstrate that EGIR-harvested specimens can be used to accurately detect melanoma by means of a 17-gene genomic biomarker.
Copyright © 2011 British Association of Dermatologists.
PMID: 21294715 [PubMed - as supplied by publisher] Source: National Library of Medicine.
