Identification of Genes for Predicting Prognosis in Pediatric Cancers

预测儿童癌症预后的基因鉴定

• 批准号: 8938415
• 负责人: Javed Khan
• 金额: $ 37.62万
• 依托单位: DIVISION OF CLINICAL SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8938415
• 关键词: 1p36; Age; Algorithms; Amino Acids; Biological; Biological Assay; Biological Markers; Biological Neural Networks; Cell Culture Techniques; Cessation of life; Chromosomal Duplication; Clinic; Collaborations; Development; Diagnosis; Diagnostic; Disease-Free Survival; Fingerprint; Gene Expression Profiling; Genes; Genetic; Genomics; Histology; Isotopically-Coded Affinity Tagging; Label; Light; MYCN gene; Malignant Childhood Neoplasm; Malignant Neoplasms; Measurement; Methods; Molecular; Neural Crest; Neuroblastoma; Outcome; Pathway interactions; Patients; Pattern; Pattern Recognition; Phosphorylation; Ploidies; Prognostic Marker; Property; Proteins; Proteomics; Reagent; Research; Sampling; Series; Somatic Mutation; Stable Isotope Labeling; Staging; Techniques; Tissues; Translating; Tumor Biology; base; cDNA Arrays; cancer genomics; cell type; conventional therapy; effective therapy; exome sequencing; genome sequencing; high risk; next generation; next generation sequencing; outcome forecast; prognostic; protein expression; therapeutic target; transcriptome sequencing; tumor

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. 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), 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.

神经母细胞瘤是神经嵴起源的癌症，具有取决于表现时的年龄、分期、组织学、MYCN扩增的存在、染色体倍性和1 p36的缺失状态的可变扩散。很少有人知道的分子机制，赋予良好或不良的预后，在这种和其他恶性肿瘤。我们已经证明，可以使用cDNA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的基础上诊断癌症。肿瘤基因组学部门与治疗适用研究以产生有效治疗（TARGET）小组合作扩展了这项研究，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，我们正在鉴定体细胞突变和肿瘤特异性表达模式或指纹，它们可以唯一地鉴定预后不良的组，以及与特定遗传畸变（包括MYCN扩增）相关的组。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多重PCR检测。同位素编码的亲和标签（ICAT）、细胞培养物中氨基酸的稳定同位素标记（SILAC）和磷酸化蛋白质组学分析允许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和重同位素形式化学标记两种样品来比较来自两种样品的蛋白质。通过这种和其他蛋白质组学方法，我们计划测序并鉴定具有差（死亡）和好（无事件生存3年）结果的肿瘤之间多达3000-4000个差异表达的蛋白质。这些蛋白质及其磷酸化状态表明了高危患者治疗、诊断和预后标志物的潜在靶点，并为这些对常规治疗无反应的肿瘤的生物学提供了重要线索。