Proteopath GmbH, 54296 Trier, Germany.
Institute of Pathology, University Hospital Heidelberg, 69120 Heidelberg, Germany.
Int J Mol Sci. 2023 Mar 7;24(6):5110. doi: 10.3390/ijms24065110.
Mutations of the oncogenes v-raf murine sarcoma viral oncogene homolog B1 () and neuroblastoma RAS viral oncogene homolog () are the most frequent genetic alterations in melanoma and are mutually exclusive. V600 mutations are predictive for response to the two inhibitors vemurafenib and dabrafenib and the mitogen-activated protein kinase kinase (MEK) inhibitor trametinib. However, inter- and intra-tumoral heterogeneity and the development of acquired resistance to inhibitors have important clinical implications. Here, we investigated and compared the molecular profile of and mutated and wildtype melanoma patients' tissue samples using imaging mass spectrometry-based proteomic technology, to identify specific molecular signatures associated with the respective tumors. SCiLSLab and R-statistical software were used to classify peptide profiles using linear discriminant analysis and support vector machine models optimized with two internal cross-validation methods (leave-one-out, k-fold). Classification models showed molecular differences between and mutated melanoma, and identification of both was possible with an accuracy of 87-89% and 76-79%, depending on the respective classification method applied. In addition, differential expression of some predictive proteins, such as histones or glyceraldehyde-3-phosphate-dehydrogenase, correlated with or mutation status. Overall, these findings provide a new molecular method to classify melanoma patients carrying and mutations and help provide a broader view of the molecular characteristics of these patients that may help understand the signaling pathways and interactions involving the altered genes.
致癌基因 v-raf 鼠肉瘤病毒癌基因同源物 B1 () 和神经母细胞瘤 RAS 病毒癌基因同源物 () 的突变是黑色素瘤中最常见的遗传改变，且相互排斥。V600 突变可预测对两种 抑制剂维莫非尼和达拉非尼以及丝裂原活化蛋白激酶激酶 (MEK) 抑制剂曲美替尼的反应。然而，肿瘤内和肿瘤间的异质性以及对 抑制剂获得性耐药具有重要的临床意义。在这里，我们使用基于成像质谱的蛋白质组学技术研究和比较了 和 突变和野生型黑色素瘤患者组织样本的分子特征，以确定与各自肿瘤相关的特定分子特征。使用 SCiLSLab 和 R 统计软件，使用线性判别分析和支持向量机模型（使用两种内部交叉验证方法（留一法、k 折）进行分类）对肽谱进行分类。分类模型显示了 和 突变黑色素瘤之间的分子差异，并且可以通过两种分类方法（留一法、k 折）分别达到 87-89%和 76-79%的准确率来识别这两种突变。此外，一些预测蛋白（如组蛋白或甘油醛-3-磷酸脱氢酶）的差异表达与 或 突变状态相关。总的来说，这些发现提供了一种新的分子方法来对携带 和 突变的黑色素瘤患者进行分类，并有助于更全面地了解这些患者的分子特征，这可能有助于理解涉及改变基因的信号通路和相互作用。
