• Open Access

Noninvasive genomic detection of melanoma


  • Funding sources
    Support for this investigation was provided by DermTech International, Inc. (La Jolla, CA, U.S.A.), who also provided the tape strip packets used for sample collection.

  • Conflicts of interest
    W.W. was a recipient of research support from a UC Discovery award, co-sponsored by the University of California and DermTech International, and is an uncompensated member of the DermTech scientific advisory board; T.P. and S.C. are employees of DermTech and have DermTech stock options; J.Z. owns stock in DermTech; H.R. was an uncompensated member of DermTech’s melanoma advisory board, is a member of the scientific advisory board of Mela Sciences, and receives research support from Mela Sciences, Lucid, Spectral Image, Inc. and SciBase AB; D.P. is a member of DermTech’s melanoma advisory board; V.M. is a consultant to DermTech.

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William Wachsman.
E-mail: wwachsman@ucsd.edu


Background  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.

Objectives  To provide proof-of-principle that epidermal genetic information retrieval (EGIR™; DermTech International, La Jolla, CA, U.S.A.), a method that noninvasively samples cells from stratum corneum by means of adhesive tape stripping, can be used to discern melanomas from naevi.

Methods  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 histopathological evaluation.

Results  Supervised analysis of the microarray data identified 312 genes differentially expressed between melanomas, naevi and normal skin specimens (< 0·001, false discovery rate < 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 modelling of a training dataset, consisting of 37 melanomas and 37 naevi, discovered a 17-gene classifier that discriminates these skin lesions. Upon testing with an independent dataset, this classifier discerned in situ and invasive melanomas from naevi with 100% sensitivity and 88% specificity, with an area under the curve for the receiver operating characteristic of 0·955.

Conclusions  These results demonstrate that EGIR-harvested specimens can be used to detect melanoma accurately by means of a 17-gene genomic biomarker.