Department of Pediatrics, Stanford University, Stanford, CA 94305, USA.
Neoplasia. 2012 Jul;14(7):666-9. doi: 10.1593/neo.12634.
High-resolution image guidance for resection of residual tumor cells would enable more precise and complete excision for more effective treatment of cancers, such as medulloblastoma, the most common pediatric brain cancer. Numerous studies have shown that brain tumor patient outcomes correlate with the precision of resection. To enable guided resection with molecular specificity and cellular resolution, molecular probes that effectively delineate brain tumor boundaries are essential. Therefore, we developed a bioinformatics approach to analyze micro-array datasets for the identification of transcripts that encode candidate cell surface biomarkers that are highly enriched in medulloblastoma. The results identified 380 genes with greater than a two-fold increase in the expression in the medulloblastoma compared with that in the normal cerebellum. To enrich for targets with accessibility for extracellular molecular probes, we further refined this list by filtering it with gene ontology to identify genes with protein localization on, or within, the plasma membrane. To validate this meta-analysis, the top 10 candidates were evaluated with immunohistochemistry. We identified two targets, fibrillin 2 and EphA3, which specifically stain medulloblastoma. These results demonstrate a novel bioinformatics approach that successfully identified cell surface and extracellular candidate markers enriched in medulloblastoma versus adjacent cerebellum. These two proteins are high-value targets for the development of tumor-specific probes in medulloblastoma. This bioinformatics method has broad utility for the identification of accessible molecular targets in a variety of cancers and will enable probe development for guided resection.
高分辨率图像引导切除残余肿瘤细胞将使肿瘤切除更加精确和完整，从而更有效地治疗癌症，如髓母细胞瘤，这是最常见的小儿脑癌。许多研究表明，脑肿瘤患者的预后与切除的精确性相关。为了实现具有分子特异性和细胞分辨率的引导切除，有效描绘脑肿瘤边界的分子探针是必不可少的。因此，我们开发了一种生物信息学方法来分析微阵列数据集，以鉴定编码候选细胞表面生物标志物的转录本，这些标志物在髓母细胞瘤中高度富集。结果确定了 380 个基因，这些基因在髓母细胞瘤中的表达比正常小脑组织中的表达高出两倍以上。为了富集可用于细胞外分子探针的靶标，我们进一步通过基因本体论对该列表进行过滤，以鉴定定位于质膜上或质膜内的蛋白质定位基因。为了验证这种荟萃分析，我们用免疫组织化学法评估了前 10 个候选物。我们鉴定了两种靶标，原纤维蛋白 2 和 EphA3，它们特异性地染色髓母细胞瘤。这些结果表明，一种新的生物信息学方法成功地鉴定了细胞表面和细胞外候选标志物，这些标志物在髓母细胞瘤与相邻小脑组织中富集。这两种蛋白是髓母细胞瘤中肿瘤特异性探针开发的高价值靶标。这种生物信息学方法具有广泛的用途，可以识别各种癌症中可及的分子靶标，并将为引导切除的探针开发提供支持。
