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: 8554048
• 负责人: Javed Khan
• 金额: $ 27.19万
• 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/8554048
• 关键词: 1p36; Age; Algorithms; Amino Acids; Base Sequence; 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 Profile; Gene Expression Profiling; Genes; Genetic; Genome; 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; high risk; next generation; outcome forecast; prognostic; protein expression; therapeutic target; 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rest起源的癌症，预后可变，具体取决于演示，阶段，组织学，MYCN扩增的存在，染色体倍性和1P36的缺失状态。对于在这种和其他恶性肿瘤中赋予预后良好或不良的分子机制知之甚少。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）根据基因表达分析来诊断癌症。致癌基因学部分通过与治疗疗法的研究合作扩展了这项研究，以生成有效的治疗方法（目标）组，以使用下一代全基因组，外显子组和转录组测序进行一系列临床注释的神经母细胞瘤样品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，肿瘤特异性表达模式或指纹，这些模式或指纹识别出独特的预后群，以及与包括MyCN扩增在内的特定遗传畸变相关的群体。通过这些技术，我们希望对与预后相关的基因组谱进行分类，从而确定赋予这些生物学特性的基因。一旦我们缩小了将特定癌症或诊断或预后组群集定义为最小数量的基因列表，我们将把发现转化为患者，例如在诊所中开发基于多重PCR的测定法以进行诊断目的。同位素编码的亲和力标签（ICAT），氨基酸在细胞培养（SILAC）和磷酸蛋白质分析中稳定的同位素标记，可以定量测量蛋白质表达水平以及不同细胞类型和组织中的磷酸化状态。 在这些方法中，可以通过分别将两个试剂的光和重量同位素形式化学标记两个样品来比较两个样品的蛋白质。使用这种和其他蛋白质组学方法，我们计划在较差（死亡）和良好（无事件生存> 3年）的肿瘤之间进行测序和鉴定多达3000-4000种差异表达的蛋白质。这些蛋白质及其磷酸化状态表明了对高危患者的治疗，诊断和预后标记的潜在靶标，并为这些肿瘤的生物学提供了重要的线索，这些线索未能对常规治疗反应。