Kashani-Sabet Mohammed, Rangel Javier, Torabian Sima, Nosrati Mehdi, Simko Jeff, Jablons David M, Moore Dan H, Haqq Chris, Miller James R, Sagebiel Richard W
Auerback Melanoma Research Laboratory, Comprehensive Cancer Center and Department of Dermatology, University of California, San Francisco, CA 94115, USA.
Proc Natl Acad Sci U S A. 2009 Apr 14;106(15):6268-72. doi: 10.1073/pnas.0901185106. Epub 2009 Mar 30.
The histopathological diagnosis of melanoma can be challenging. No currently used molecular markers accurately distinguish between nevus and melanoma. Recent transcriptome analyses have shown the differential expression of several genes in melanoma progression. Here, we describe a multi-marker diagnostic assay using 5 markers (ARPC2, FN1, RGS1, SPP1, and WNT2) overexpressed in melanomas. Immunohistochemical marker expression was analyzed in 693 melanocytic neoplasms comprising a training set (tissue microarray of 534 melanomas and nevi), and 4 independent validation sets: tissue sections of melanoma arising in a nevus; dysplastic nevi; Spitz nevi; and misdiagnosed melanocytic neoplasms. Both intensity and pattern of expression were scored for each marker. Based on the differential expression of these 5 markers between nevi and melanomas in the training set, a diagnostic algorithm was obtained. Using this algorithm, the lesions in the validation sets were diagnosed as nevus or melanoma, and the results were compared with the known histological diagnoses. Both the intensity and pattern of expression of each marker were significantly different in melanomas compared to nevi. The diagnostic algorithm exploiting these differences achieved a specificity of 95% and a sensitivity of 91% in the training set. In the validation sets, the multi-marker assay correctly diagnosed a high percentage of melanomas arising in a nevus, Spitz nevi, dysplastic nevi, and misdiagnosed lesions. The multi-marker assay described here can aid in the diagnosis of melanoma.
黑色素瘤的组织病理学诊断可能具有挑战性。目前使用的分子标志物均无法准确区分痣和黑色素瘤。最近的转录组分析显示了几种基因在黑色素瘤进展过程中的差异表达。在此，我们描述了一种使用在黑色素瘤中过表达的5种标志物（ARPC2、FN1、RGS1、SPP1和WNT2）的多标志物诊断检测方法。对693例黑素细胞肿瘤进行免疫组化标志物表达分析，包括一个训练集（534例黑色素瘤和痣的组织芯片）以及4个独立验证集：痣内发生的黑色素瘤组织切片；发育异常痣；Spitz痣；以及误诊的黑素细胞肿瘤。对每个标志物的表达强度和模式进行评分。基于训练集中痣和黑色素瘤之间这5种标志物的差异表达，获得了一种诊断算法。使用该算法，对验证集中的病变诊断为痣或黑色素瘤，并将结果与已知的组织学诊断进行比较。与痣相比，黑色素瘤中每个标志物的表达强度和模式均有显著差异。利用这些差异的诊断算法在训练集中实现了95%的特异性和91%的敏感性。在验证集中，多标志物检测正确诊断了高比例的痣内发生的黑色素瘤、Spitz痣、发育异常痣和误诊病变。本文所述的多标志物检测方法有助于黑色素瘤的诊断。
