Lazova Rossitza, Seeley Erin H, Keenan Megan, Gueorguieva Ralitza, Caprioli Richard M
Department of Dermatology, Yale University School of Medicine and the Yale Cancer Center, New Haven, CT 06520, USA.
Am J Dermatopathol. 2012 Feb;34(1):82-90. doi: 10.1097/DAD.0b013e31823df1e2.
Differentiating Spitz nevus (SN) from Spitzoid malignant melanoma (SMM) is one the most difficult problems in dermatopathology.
To identify differences on proteomic level between SN and SMM.
We performed Imaging Mass Spectrometry analysis on formalin-fixed, paraffin-embedded tissue samples to identify differences on proteomic level between SN and SMM. The diagnosis of SN and SMM was based on histopathologic criteria, clinical features, and follow-up data, which confirmed that none of the lesions diagnosed as SN recurred or metastasized. The melanocytic component (tumor) and tumor microenvironment (dermis) from 114 cases of SN and SMM from the Yale Spitzoid Neoplasm Repository were analyzed. After obtaining mass spectra from each sample, classification models were built using a training set of biopsies from 26 SN and 25 SMM separately for tumor and for dermis. The classification algorithms developed on the training data set were validated on another set of 30 samples from SN and 33 from SMM.
We found proteomic differences between the melanocytic components of SN and SMM and identified 5 peptides that were differentially expressed in the 2 groups. From these data, 29 of 30 SN and 26 of 29 SMM were recognized correctly based on tumor analysis in the validation set. This method correctly classified SN with 97% sensitivity and 90% specificity in the validation cohort.
Imaging Mass Spectrometry analysis can reliably differentiate SN from SMM in formalin-fixed, paraffin-embedded tissue based on proteomic differences.
鉴别Spitz痣（SN）与Spitz样恶性黑色素瘤（SMM）是皮肤病理学中最困难的问题之一。
确定SN和SMM在蛋白质组水平上的差异。
我们对福尔马林固定、石蜡包埋的组织样本进行成像质谱分析，以确定SN和SMM在蛋白质组水平上的差异。SN和SMM的诊断基于组织病理学标准、临床特征和随访数据，这些数据证实，诊断为SN的病变均未复发或转移。对来自耶鲁Spitz样肿瘤库的114例SN和SMM的黑素细胞成分（肿瘤）和肿瘤微环境（真皮）进行了分析。从每个样本获得质谱后，分别使用来自26例SN和25例SMM活检的训练集建立肿瘤和真皮的分类模型。在训练数据集上开发的分类算法在另一组30例SN样本和33例SMM样本上进行了验证。
我们发现SN和SMM的黑素细胞成分之间存在蛋白质组差异，并鉴定出5种在两组中差异表达的肽段。根据这些数据，在验证集中基于肿瘤分析正确识别了30例SN中的29例和29例SMM中的26例。该方法在验证队列中对SN的正确分类敏感性为97%，特异性为90%。
成像质谱分析可基于蛋白质组差异在福尔马林固定、石蜡包埋的组织中可靠地区分SN和SMM。
