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。我们已经证明，癌症的诊断可以基于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。肿瘤基因组学部与治疗应用研究合作扩展了这项研究，以产生有效的治疗(目标)小组，使用下一代全基因组、外显子组和转录组测序对一系列临床注释的神经母细胞瘤样本进行更广泛的基因组分析。通过这些方法，我们正在识别体细胞突变和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群体，以及与包括MYCN扩增在内的特定遗传异常相关的那些。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。同位素编码亲和标记(ICAT)、细胞培养中氨基酸稳定同位素标记(SILAC)和磷酸蛋白质组学分析可以定量测量不同细胞类型和组织中的蛋白质表达水平和磷酸化状态。在这些方法中，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。这些蛋白及其磷酸化状态为高危患者的治疗、诊断和预后标记物提供了潜在的靶点，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 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 study in collaboration with the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) group to perform more extensive genomic analysis using next generation whole genome, exome and transcriptome sequencing on a series of clinically annotated neuroblastoma samples. With these methods we are identifying somatic mutations, and 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. By these techniques, we hope to classify genomic profiles that correlate with prognosis and hence identify the genes that confer these biological properties. Isotope-coded affinity tags (ICAT), stable isotope labeling by amino acids in cell culture (SILAC) and phospho-proteomic analysis allows the quantitative measurement of protein expression levels and the phosphorylation status in different cell types and tissues. In these methods 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 and their phosphorylation status indicates 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.