Identification of Genes for Predicting Prognosis in Pediatric Cancers

神经母细胞瘤是一种起源于神经脊的癌症，其预后取决于发病年龄、分期、组织学、MYCN扩增的存在、染色体倍体和1p36缺失状态。对于这种和其他恶性肿瘤预后好坏的分子机制，我们知之甚少。我们最近已经证明，可以根据使用基因微阵列和复杂的模式识别算法(如人工神经网络)的基因表达谱来诊断癌症。肿瘤基因组学组通过描绘一系列不同阶段的神经母细胞瘤和预后，进一步扩展了这一概念。通过这些方法，我们正在识别肿瘤特异性表达模式或指纹，这些模式或指纹唯一地识别预后不良的组，以及与包括MYCN扩增在内的特定遗传异常相关的那些。我们现在正在应用下一代测序策略来全面询问癌症基因组。通过这些技术，我们希望对与预后相关的基因组图谱进行分类，从而确定赋予这些生物学特性的基因。一旦我们将定义特定癌症或诊断或预后组群的基因列表缩小到最小数量，我们将把我们的发现转化为患者，例如开发用于临床诊断目的的基于多重PCR的分析方法。同位素编码亲和标签(ICAT)允许定量测量不同细胞类型和组织中的蛋白质表达水平。在这种方法中，可以通过分别用试剂的轻同位素形式和重同位素形式对两个样本进行化学标记来比较两个样本的蛋白质。通过这种和其他蛋白质组学方法，我们计划对预后差(死亡)和好(无事件生存3年)的肿瘤之间多达3000-4000个差异表达的蛋白质进行测序和鉴定。这些蛋白质是高危患者治疗、诊断和预后标记物的潜在靶点，并为这些对常规治疗无效的肿瘤的生物学提供了重要线索。

Neuroblastomas are cancers of neural crest origin with variable prognoses depending on age at presentation, stage, histology, presence of MYCN amplification, chromosomal ploidy, and deletion status of 1p36. Very little is known of the molecular mechanisms that confer good or poor prognosis in this and other malignancies. We have recently demonstrated that cancers can be diagnosed on the basis of gene expression profiling using cDNA microarrays and sophisticated pattern recognition algorithms such as Artificial Neural Networks. The Oncogenomics Section has expanded this concept further by profiling a series on neuroblastoma of different stages and prognosis. With these methods we are identifying tumor-specific expression patterns, or fingerprints, that uniquely identify a poor prognostic group, as well as those associated with specific genetic aberrations including MYCN amplification. We are now applying next generation sequencing strategies to comprehensively interrogate the cancer genome. By these techniques, we hope to classify genomic profiles that correlate with prognosis and hence identify the genes that confer these biological properties. Once we have narrowed down the list of genes that defines a particular cancer or diagnostic or prognostic group cluster to a minimum number, we will translate our findings to the patient for example develop multiplex PCR-based assays for diagnostic purposes in the clinic. Isotope-coded affinity tags (ICAT), allows the quantitative measurement of protein expression levels in different cell types and tissues. In this method proteins from two samples can be compared by chemically labeling both samples with the light and heavy isotopic forms of a reagent respectively. With this and other proteomic method we plan to sequence and identify up to 3000-4000 differentially expressed proteins between tumors with poor (death) and good (event free survival > 3yrs) outcome. These proteins represent potential targets for therapy, diagnostic and prognostic markers for high-risk patients as well as provide important clues on the biology of these tumors that fail to respond to conventional therapy.