Identification of Genes for Predicting Prognosis in Pediatric Cancers

神经母细胞瘤是起源于神经嵴的癌症，其预后取决于发病时的年龄、分期、组织学、MYCN扩增的存在、染色体倍性和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知之甚少。我们最近已经证明，癌症可以在基因表达谱的基础上诊断，使用cDNA微阵列和复杂的模式识别算法，如人工神经网络。肿瘤基因组学部分通过对不同阶段和预后的神经母细胞瘤进行分析，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，可以唯一地识别预后不良组，以及与特定遗传畸变相关的基因，包括MYCN扩增。我们现在正在应用下一代测序策略来全面研究癌症基因组。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簇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的检测。同位素编码亲和标签（ICAT）可以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和重同位素形式对两个样品进行化学标记来比较两个样品的蛋白质。通过这种方法和其他蛋白质组学方法，我们计划对不良（死亡）和良好（无事件生存期3年）肿瘤之间多达3000-4000个差异表达蛋白进行测序和鉴定。这些蛋白代表了高风险患者治疗、诊断和预后标记的潜在靶点，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.