Melanoma (n = 76) 60Æ5 (26–95) 27 ⁄ 49 Naevus (n = 126) 45Æ3 (19–79) 58 ⁄ 68 2 3 2 26 74 10 9 51 75 Table 1 Summary of patient information Age (years), mean (range) Female ⁄ male Anatomical location Scalp Face Neck Shoulder Trunk Upper limb Lower limb and hip Superﬁcial spreading melanoma In situ Invasive Lentigo maligna melanoma In situ Invasive Nodular melanoma Benign naevi Atypical naevi Tumour stage Tis T1 T2 T3 T4 8 13 3 14 12 15 11 25 40 5 5 1 31 35 8 1 1 remainder coming from the upper back, and all were at a minimum distance of 15 cm from the suspicious lesion. Materials and reagents The EGIR tape kit contains four small circular adhesive discs, each 17 mm in diameter, with a polyurethane backing. The tape was purchased from Adhesives Research (Glen Rock, PA, U.S.A.) and fabricated into discs with a polyurethane backing by Diagnostic Laminations Engineering (Oceanside, CA, U.S.A.). Universal human reference RNA was purchased from Stratagene (San Diego, CA, U.S.A.). Reverse transcriptase, Taq- man Universal Master Mix, which included all buffers and enzymes necessary for the ampliﬁcation and ﬂuorescent detec- tion of b-actin cDNA, was purchased from Applied Biosystems (Foster City, CA, U.S.A.). MELT total nucleic acid isolation sys- tem was purchased from Ambion (Austin, TX, U.S.A.). Gene- Chip human genome U133 plus 2.0 arrays were purchased from Affymetrix (Santa Clara, CA, U.S.A.). RNA isolation and quantiﬁcation All tape strips were processed in the laboratory at DermTech International. The RNA was extracted from tapes by means of MELT and quantiﬁed by TaqMan qRT-PCR for b-actin mRNA expression level, as per Wong et al.22 RNA quality was assessed by microﬂuidic electrophoretic analysis using an Experion 2011 The Authors BJD 2011 British Association of Dermatologists 2011 164, pp797–806 Genomic detection of melanoma, W. Wachsman et al. 799 Automated Electrophoresis Station (BioRad, Inc., Hercules, CA, U.S.A.). RNA ampliﬁcation and array hybridization RNA harvested from the EGIR tape strips was ampliﬁed using the Ovation Pico RNA Ampliﬁcation System (NuGEN Technol- ogies, Inc., San Carlos, CA, U.S.A.) and hybridized with Affymetrix human genome U133 plus 2.0 GeneChip, accord- ing to standard manufacturer protocols. Gene expression analysis The image ﬁles from scanning the Affymetrix GeneChips with the Affymetrix series 3000 scanner were converted to CEL-for- mat ﬁles using the Affymetrix GeneChip Operating Software version 1.4. Normalization of GeneChip CEL ﬁles was carried out using the GCRMA software from Bioconductor (http:// www.bioconductor.org). After ﬁltering out for background and low expressed genes (level < 100 for a gene target across all samples), data were imported into GeneSpring (Agilent, Santa Clara, CA, U.S.A.). A supervised analysis was performed to identify genes differentially expressed between pigmented lesions (both melanoma and naevi) (biopsy proven) and control, nonlesional skin specimens. This was performed by ANOVA with multiple testing correction using the Westfall and Young permutation method24 [P < 0Æ001, false discovery rate (FDR) q < 0Æ05]. Cluster analysis was performed according to Eisen et al.25 Data were ﬁrst log2 transformed and then median centred for genes and samples. The resulting normalized data were further analysed by the self-organizing map algorithm26 and then clustered with Spearman rank correlation similarity metrics. To develop a melanoma detection classiﬁer, only the Gene- Chip data from melanomas and naevi were used. These were randomly divided into a training set used for multigene classi- ﬁer selection and a test set for evaluating the performance of the resultant classiﬁer. Differentially expressed genes were identiﬁed by unpaired t-test and multiple testing correction24 (P < 0Æ05, q < 0Æ05) from a training set of 37 melanomas and 37 naevi. This group of genes was then mined by stochas- tic gradient boosting, as developed by Friedman27,28 (available for use as TreeNet ; Salford Systems, San Diego, CA, U.S.A.) to develop a class prediction model. The resultant multigene classiﬁer was validated with an independent test dataset composed of 39 melanomas and 89 naevi. Gene ontology and pathway analysis was performed with Ingenuity Pathway Analysis (IPA) system software version 8.5 (Ingenuity Systems, Inc., Redwood City, CA, U.S.A.). Genes with their corresponding identiﬁers and fold change values were uploaded for interrogation. After analysis, signiﬁcance of the biological functions and the canonical pathways was tested by the Fisher’s exact test P-value to determine the probability that each biological ⁄ canonical pathway assigned to the dataset is due to chance alone.