Genomic detection of melanoma, W. Wachsman et al. 801 Table 2 Top biological functions of 312 differentially expressed genes among melanoma, naevi and normal skin controls Ratio P-value No. of molecules 5 12 67 66 63 20 30 13 37 35 83 33 74 33 17 Molecular and cellular functions Amino acid metabolism Small molecule biochemistry Cellular development Cellular growth and proliferation Cell death Physiological system development and function Hair and skin development and function Embryonic development Renal and urological system development and function Organ development Cardiovascular system development and function Diseases and disorders Cancer Gastrointestinal disease Skeletal and muscular disorders Infectious disease Respiratory disease Canonical pathways Melanocyte development and pigmentation signalling Factors producing cardiogenesis in vertebrates Axonal guidance signalling Human embryonic stem cell pluripotency Wnt ⁄ b-catenin signalling multigene class predictor (i.e. classiﬁer) to distinguish a mela- noma from a naevus using the expression proﬁles of known samples – a training set consisting of melanoma and naevi – and then use the resultant multigene classiﬁer to assess an independent group of samples – a test set containing melano- mas and naevi (details of the analytical schema are provided in Data S1; see Supporting Information). Accordingly, we employed EGIR to obtain RNA from 202 tape-stripped melanomas and naevi (Table 1), each of which was proﬁled on the Affymetrix human U133 plus 2.0 Gene- Chip. The resulting microarray data were randomly divided into training and test datasets. The training set consisted of data from 37 melanomas, including in situ (14) and invasive superﬁcial spreading melanomas (19), lentigo maligna (3) and lentigo maligna melanoma (1), and 37 naevi (both dys- plastic and nondysplastic), while the test set contained data from 39 melanomas including in situ (11) and invasive super- ﬁcial spreading melanomas (21), lentigo maligna (2), lentigo maligna melanoma (4) and nodular melanoma (1), and 89 naevi (both dysplastic and nondysplastic). The training data- set yielded some 422 genes differentially expressed between melanomas and naevi, as determined by Student’s t-test with multiple testing correction (P < 0Æ05, FDR q < 0Æ05) (the annotated list of genes is provided in Table S2; see Support- ing Information). The group of 422 differentially expressed genes was then subjected to class prediction modelling to identify a multigene classiﬁer capable of discerning a mela- noma from a naevus after which its performance was assessed by the independent, test set data. This was accom- 2011 The Authors BJD 2011 British Association of Dermatologists 2011 164, pp797–806 2Æ73 · 10)8–1Æ40 · 10)2 2Æ73 · 10)8–1Æ40 · 10)2 5Æ78 · 10)8–1Æ40 · 10)2 1Æ25 · 10)6–1Æ40 · 10)2 1Æ34 · 10)6–1Æ40 · 10)2 2Æ73 · 10)8–1Æ40 · 10)2 4Æ99 · 10)6–1Æ40 · 10)2 6Æ37 · 10)5–1Æ40 · 10)2 7Æ43 · 10)5–1Æ40 · 10)2 1Æ53 · 10)4–1Æ40 · 10)2 2Æ32 · 10)9–1Æ40 · 10)2 1Æ37 · 10)6–1Æ40 · 10)2 3Æ74 · 10)6–1Æ40 · 10)2 5Æ42 · 10)6–9Æ34 · 10)3 5Æ42 · 10)6–2Æ44 · 10)3 1Æ35 · 10)4 9Æ33 · 10)4 4Æ39 · 10)3 6Æ46 · 10)3 6Æ97 · 10)3 7 ⁄ 88 (0Æ08) 6 ⁄ 89 (0Æ067) 12 ⁄ 403 (0Æ03) 6 ⁄ 148 (0Æ041) 7 ⁄ 168 (0Æ042) plished by stochastic gradient boosting analysis, using the TreeNet data mining algorithm, which permits selection and performance testing of a multigene classiﬁer. Unlike classical modelling techniques, the stochastic gradient boosting algo- rithm utilizes very ﬂexible structural assumptions and is capa- ble of accommodating multiple sets of genes associated with different classiﬁcation outcomes in a true multivariate fash- ion. We found that a classiﬁer from the training set contain- ing 168 of the 422 genes was sufﬁcient to correctly identify all 37 melanomas and 35 of 37 naevi (the annotated list of the 168 genes is provided in Table S3; see Supporting Infor- mation). Thus, the sensitivity and speciﬁcity of detecting melanoma in the training dataset was 100% and 95%, respectively. The performance of this class prediction model was evaluated with an independent test dataset of 39 mela- nomas and 89 naevi. All 39 of the melanomas in the test dataset were accurately identiﬁed and 78 of the 89 naevi were called correctly by the 168-gene classiﬁer, indicative of 100% sensitivity and 88% speciﬁcity. The pathology of the false-positive specimens was variable and included dysplastic naevus (n = 7), compound naevus (n = 5) and junctional naevus (n = 1). Further TreeNet modelling revealed that the classiﬁer could be reduced to a set of 17 of 168 genes that remained 100% sensitive and 88% speciﬁc upon testing (Tables 3 and 4; more complete annotation on these 17 genes is provided in Table S4; see Supporting Information). Furthermore, receiver operating characteristic (ROC) curve analysis of the application of the 17-gene classiﬁer to the test dataset resulted in an area